From 688681c4f5b9ed32a7e2da3130c560f5612ab3a5 Mon Sep 17 00:00:00 2001 From: suraj813 Date: Thu, 28 Jan 2021 02:16:52 -0500 Subject: [PATCH 001/120] refactor - till custom datasets --- .../quickstart/dataquickstart_tutorial.py | 174 +++++----- .../quickstart/quickstart_tutorial.py | 298 ++++++++++-------- beginner_source/quickstart/tensor_tutorial.py | 294 ++++++++--------- 3 files changed, 388 insertions(+), 378 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 79d14554de2..d06813e85aa 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -15,25 +15,17 @@ """ ################################################################# -# Getting Started With Data in PyTorch -# ----------------- -# -# Before we start building models with PyTorch, let's first learn how to load and process data. Data can be sourced from local files, cloud datastores and database queries. It comes in all sorts of forms and formats from structured tables to image, audio, text, video files and more. +# Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code +# to be decoupled from our model training code for better readability and modularity. +# PyTorch provides two data primitives: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``, and a number of other abstractions +# that allow you to use pre-loaded as well as your own custom datasets easily. +# ``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around +# the ``Dataset`` to enable easy access to the samples. # - -############################################################### -# .. figure:: /_static/img/quickstart/typesdata.png -# :alt: typesdata -# - -############################################################ -# Different data types require different python libraries to load and process such as `openCV `_ and `PIL `_ for images, `NLTK `_ and `spaCy `_ for text and `Librosa `_ for audio. -# -# If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking and managing collections of data. -# -# A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. They are subclasses of `torch.utils.data.Dataset` that have parameters and functions specific to the type of data and the particular dataset. The actual data samples can be downloaded from the internet. These are useful for benchmarking and testing your models before training on your own custom datasets. -# -# You can find some of these datasets +# A number of pre-loaded datasets such as FashionMNIST that implement this interface are built into PyTorch domain libraries. +# They all subclass ``torch.utils.data.Dataset`` and implement functions specific to the particular dataset. +# These are useful for prototyping and benchmarking your model before training it on your own custom datasets. +# You can find some of these datasets # here: `Image Datasets `_, # `Text Datasets `_, and # `Audio Datasets `_ @@ -42,69 +34,90 @@ ############################################################ # Loading a Dataset # ------------------- -# -# Here is an example of how to load the `Fashion-MNIST `_ dataset from torch vision. -# `Fashion-MNIST `_ is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. -# Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. Read more `here `_. -# -# To load the FashionMNIST Dataset we need to provide the following three parameters: -# - ``root`` is the path where the train/test data is stored. -# - ``train`` includes the training dataset. +# +# Here is an example of how to load the `Fashion-MNIST `_ dataset from TorchVision. +# Fashion-MNIST is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. +# Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. +# +# To load the `FashionMNIST Dataset `_ we need to provide the following three parameters: +# - ``root`` is the path where the train/test data is stored. +# - ``train`` includes the training dataset. # - ``download=True`` downloads the data from the internet if it's not available at root. # -import torch -from torch.utils.data import Dataset -import torchvision.datasets as datasets +import torch +from torch.utils.data import Dataset, DataLoader +from torchvision import datasets import matplotlib.pyplot as plt import numpy as np clothing = datasets.FashionMNIST( - 'data', # specifies data directory to store data - train=True, # specifies training or test dataset to use - transform=None, # specifies transforms to apply to features (images) - target_transform=None, # specifies transforms to apply to labels - download=True) # should the data be downloaded from the Internet + "data", # specifies data directory to store data + train=True, # specifies training or test dataset to use + transform=None, # specifies transforms to apply to features (images) + target_transform=None, # specifies transforms to apply to labels + download=True, # should the data be downloaded from the Internet +) ################################################################# # Iterating and Visualizing the Dataset # ----------------- -# -# Once we have the ``clothing`` dataset, we can index it manually like a list: ``clothing[index]``. Then use ``matplotlib`` to visualize the dataset. - -labels_map = {0 : 'T-Shirt', 1 : 'Trouser', 2 : 'Pullover', 3 : 'Dress', 4 : 'Coat', 5 : 'Sandal', 6 : 'Shirt', 7 : 'Sneaker', 8 : 'Bag', 9 : 'Ankle Boot'} -figure = plt.figure(figsize=(8,8)) +# +# Once we have the ``clothing`` dataset, we can index it manually like a list: ``clothing[index]``. +# Then use ``matplotlib`` to visualize the dataset. + +labels_map = { + 0: "T-Shirt", + 1: "Trouser", + 2: "Pullover", + 3: "Dress", + 4: "Coat", + 5: "Sandal", + 6: "Shirt", + 7: "Sneaker", + 8: "Bag", + 9: "Ankle Boot", +} +figure = plt.figure(figsize=(8, 8)) cols, rows = 3, 3 -for i in range(1, cols*rows +1): - sample_idx = np.random.randint(len(clothing)) - img = clothing[sample_idx][0] +for i in range(1, cols * rows + 1): + sample_idx = torch.random.randint(len(clothing), size=(1,)).item() + img, label = clothing[sample_idx] figure.add_subplot(rows, cols, i) - plt.title(labels_map[clothing[sample_idx][1]]) - plt.axis('off') - plt.imshow(img, cmap='gray') + plt.title(labels_map[label]) + plt.axis("off") + plt.imshow(img, cmap="gray") plt.show() ################################################################# # .. # .. figure:: /_static/img/quickstart/fashion_mnist.png # :alt: fashion_mnist + + +###################################################################### +# -------------- # ################################################################# # Creating a Custom Dataset # ----------------- # -# To work with your own data, we can implement a custom class that inherits from ``Dataset``. This custom class must implement three functions: `__init__`, `__len__`, and `__getitem__`. Let's look at a custom image dataset implementation. In this example, we have a number of images stored in a directory, and their labels stored separately in a CSV file. Here's what it looks like; in the following sections, we will break down what's happening in each function. +# To work with your own data, we can implement a custom class that inherits from ``Dataset``. +# This custom class must implement three functions: `__init__`, `__len__`, and `__getitem__`. +# Let's look at a custom image dataset implementation. In this example, we have a number of images stored +# in a directory, and their labels stored separately in a CSV file. +# in the following sections, we will break down what's happening in each function. # import os -import torch import pandas as pd from torch.utils.data import Dataset from torchvision import transforms, utils from torchvision.io import read_image + class CustomImageDataset(Dataset): def __init__(self, annotations_file, img_dir, transform=None): self.img_labels = pd.read_csv(annotations_file) @@ -118,23 +131,23 @@ def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() - img_path = os.path.join(self.root_dir, - self.img_labels.iloc[idx, 0]) + img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) image = read_image(img_path) label = self.img_labels.iloc[idx, 1:] - sample = {'image': image, 'label': label} + sample = {"image": image, "label": label} if self.transform: sample = self.transform(sample) - return sample - + return sample + + ################################################################# # Import the packages # ------- -# +# # Import ``os`` for file handling, ``torch`` for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and ``Dataset`` to implement the Dataset interface. -# +# # Example: # @@ -149,8 +162,8 @@ def __getitem__(self, idx): # __init__ # ----------------- # -# The __init__ function is run once when instantiating our Dataset object. Here, we use it to load -# the directory containing the images, and their labels (contained in a csv file). While creating the +# The __init__ function is run once when instantiating our Dataset object. Here, we use it to load +# the directory containing the images, and their labels (contained in a csv file). While creating the # Dataset object, we can optionally pass it the transform that should be run on the images. # # The labels.csv file looks like: :: @@ -159,61 +172,66 @@ def __getitem__(self, idx): # tshirt2.jpg, 0 # ...... # ankleboot999.jpg, 9 -# +# # Example: -# +# + def __init__(self, labels_file, img_dir, transform=None): self.img_labels = pd.read_csv(labels_file) self.img_dir = img_dir self.transform = transform + ################################################################# # __len__ # ----------------- # -# The __len__ function returns the number of samples in our dataset. -# +# The __len__ function returns the number of samples in our dataset. +# # Example: + def __len__(self): return len(self.img_labels) + ################################################################# # __getitem__ # ----------------- # # The __getitem__ function is the most important function in the Datasets interface. It takes a tensor or an index as input and returns a loaded sample from your dataset at the given indices. -# +# # If provided a tensor as an index, we convert the tensor to a list first. We then load the file at the given index from our image directory, as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a transform on and return. Transforms will be discussed in more detail in the next section: `Transforms `_ -# +# # Example: # + def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() - img_path = os.path.join(self.root_dir, - self.img_labels.iloc[idx, 0]) + img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) image = read_image(img_path) label = self.img_labels.iloc[idx, 1:] - sample = {'image': image, 'label': label} + sample = {"image": image, "label": label} if self.transform: sample = self.transform(sample) - return sample + return sample + ################################################################# # Preparing your data for training with DataLoaders # ------------------------------------------------- # -# Now we have an organized mechanism for managing data which is great, but there is still a lot of manual work we would have to do to train a model with our Dataset. -# -# For example we would have to manually maintain the code for: +# Now we have an organized mechanism for managing data which is great, but there is still a lot of manual work we would have to do to train a model with our Dataset. +# +# For example we would have to manually maintain the code for: +# +# * Batching +# * Shuffling +# * Parallel batch distribution # -# * Batching -# * Shuffling -# * Parallel batch distribution -# # The PyTorch Dataloader ``torch.utils.data.DataLoader`` is an iterator that handles all of this complexity for us, enabling us to load a dataset and focus on training our model. dataloader = DataLoader(clothing, batch_size=4, shuffle=True, num_workers=0) @@ -229,17 +247,17 @@ def __getitem__(self, idx): # Display image and label. for train_features, train_labels in dataloader.dataset: print(train_labels) - plt.imshow(train_features, cmap='gray') + plt.imshow(train_features, cmap="gray") plt.show() - break; + break # Count the number of occurances for label number 9 which is for the 'Bag' count = 0 for train_features, train_labels in dataloader.dataset: - if(train_labels==9): - count+=1 + if train_labels == 9: + count += 1 print(count) ################################################################# # With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. -# +# diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 68e38ab08e2..7fa6e3c7665 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -11,22 +11,37 @@ Learn the Basics =================== -The basic machine learning concepts in any framework should include: Working with data, -Creating models, Optimizing Parameters, Saving and Loading Models. In this PyTorch tutorial we will -go through these concepts and how to apply them with PyTorch. The dataset we will be using is the -FashionMNIST clothing images dataset that demonstrates these core steps applied to create ML Models. +Authors: +`Suraj Subramanian `_, +`Seth Juarez `_, +`Cassie Breviu `_, +`Dmitry Soshnikov `_, +`Ari Bornstein `_ -You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step. -Using this dataset we will be able to predict if the image is one of the following classes: T-shirt/top, -Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, or Ankle boot. Lets get started! +A basic machine learning workflow involves working with data, creating models, optimizing model +parameters, and saving the trained models. This tutorial introduces you to the complete ML workflow +as implemented in PyTorch, with links to learn more about of these concepts. + +We'll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs +to one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, +Bag, or Ankle boot. + +This tutorial assumes a basic familiarity with Python and Deep Learning concepts. + +Running the Tutorial Code +------------------ +You can run this tutorial in a few ways: + +- **In the cloud**: This is the easiest way to get started! Each section has a Colab link at the top, which opens a notebook with the code in a fully-hosted environment. Pro tip: Use Colab with a GPU runtime to speed up operations *Runtime > Change runtime type > GPU* +- **Locally**: This option requires you to setup PyTorch and TorchVision first on your local machine (`installation instructions `_). Download the notebook or copy the code into your favorite IDE. How to Use this Guide ----------------- -This guide is setup to cover machine learning concepts and how to apply them with PyTorch. This main page is a -highlevel intro to each step with the code examples to build the model. You have the option to jump -into the concepts introduced in each section to get more details and explanations to better understand each concept -and how to apply them with PyTorch. The topics are introduced in a sequenced order as listed below: +This page contains an overview of the code used at each step of the tutorial. If you're familiar with +other deep learning frameworks, this is a quick way to get acquainted with PyTorch's API. + +If this is your first time, head right into our step-by-step guide: .. include:: /beginner_source/quickstart/qs_toc.txt @@ -42,200 +57,220 @@ /beginner/quickstart/saveloadrun_tutorial -Running the Tutorial Code ------------------- -The navigation above allows you to run the Jupyter Notebook on the cloud, download the Jupyter Notebook -or download the python file to run locally. -If you want to run the code locally on your machine you will need some tools you -may or may not have installed already. -Below are some good tool options for configuring local development or for more detailed instructions check out `get started locally `_. -- `Visual Studio Code `_ : You can open run python code in Visual Studio Code or open a Jupyter Notebook in VS Code. +-------------- -- `Anaconda for Package Management `_ : You will need to install the packages using either ``pip`` or ``conda`` to run the code locally. Working with data ----------------- -""" +PyTorch has two data primitives to work with data: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``. +``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around +the ``Dataset``. -###################################################################### -# -# PyTorch has two basic data primitives: ``DataSet`` and ``DataLoader``. -# The `torchvision.datasets` ``DataSet`` object includes a ``transforms`` mechanism to -# modify data in-place. Below is an example of how to load that data from the PyTorch open datasets and transform the data to a normalized tensor. -# This example is using the `torchvision.datasets` which is a subclass from the primitive `torch.utils.data.Dataset`. Note that the primitive dataset doesnt have the built in transforms param like the built in dataset in `torchvision.datasets.` -# For more details on the concepts introduced here check out `Tensors `_, -# `DataSets & DataLoaders `_, -# and `Transforms `_. -# +""" import torch -import torch.nn as nn -import torch.onnx as onnx -import matplotlib.pyplot as plt +from torch import nn from torch.utils.data import DataLoader -from torchvision import datasets, transforms -import torch.nn.functional as F +from torchvision import datasets +from torchvision.transforms import ToTensor, Lambda, Compose +import matplotlib.pyplot as plt -classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] +###################################################################### +# The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like ImageNet, +# CIFAR, COCO (`full list here `_). In this tutorial, we +# use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and +# ``target_transform`` to modify the samples and labels respectively. + +classes = [ + "T-shirt/top", + "Trouser", + "Pullover", + "Dress", + "Coat", + "Sandal", + "Shirt", + "Sneaker", + "Bag", + "Ankle boot", +] # Download training data from open datasets. -training_data = datasets.FashionMNIST('data', train=True, download=True, - transform=transforms.Compose([transforms.ToTensor()]), - target_transform=transforms.Compose([ - transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) - ]) +training_data = datasets.FashionMNIST( + root="data", + train=True, + download=True, + transform=ToTensor(), + target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) # Download test data from open datasets. -test_data = datasets.FashionMNIST('data', train=False, download=True, - transform=transforms.Compose([transforms.ToTensor()]), - target_transform=transforms.Compose([ - transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) - ]) - +test_data = datasets.FashionMNIST( + root="data", + train=False, + download=True, + transform=ToTensor(), + target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) +###################################################################### +# We pass the ``Dataset`` as an argument to ``DataLoader``. This wraps an iterable over our dataset, and supports +# automatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element +# in the dataloader iterable will return a batch of 64 features and labels. + batch_size = 64 # Create data loaders. -train_dataloader = DataLoader(training_data, batch_size=batch_size, num_workers=0, pin_memory=True) -test_dataloader = DataLoader(test_data, batch_size=batch_size, num_workers=0, pin_memory=True) +train_dataloader = DataLoader(training_data, batch_size=batch_size) +test_dataloader = DataLoader(test_data, batch_size=batch_size) + +for X, y in test_dataloader: + print("Shape of X [N, C, H, W]: ", X.shape) + print("Shape of y: ", y.shape) + break + +###################################################################### +# -------------- +# ################################ # Creating Models -# --------------- -# -# There are two ways of creating models: in-line or as a class. -# The most common way to define a neural network is to use a class inherited -# from `nn.Module `_. -# It provides great parameter management across all nested submodules, which gives us more -# flexibility, because we can construct layers of any complexity, including the ones with shared weights. -# For more details checkout `building the model `_. +# ------------------ +# To define a neural network in PyTorch, we create a class that inherits +# from `nn.Module `_. We define the layers of the network +# in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate +# operations in the NN, we move it to the GPU if available. # Get cpu or gpu device for training. -device = 'cuda' if torch.cuda.is_available() else 'cpu' -print('Using {} device'.format(device)) +device = "cuda" if torch.cuda.is_available() else "cpu" +print("Using {} device".format(device)) # Define model class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() self.flatten = nn.Flatten() - self.layer1 = nn.Linear(28*28, 512) - self.layer2 = nn.Linear(512, 512) - self.output = nn.Linear(512, 10) - + self.softmax = nn.Softmax(dim=1) + self.nn_layers = nn.Sequential( + nn.Linear(28 * 28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, 10) + ) def forward(self, x): x = self.flatten(x) - x = F.relu(self.layer1(x)) - x = F.relu(self.layer2(x)) - x = self.output(x) - return F.softmax(x, dim=1) + x = self.nn_layers(x) + return self.softmax(x) + model = NeuralNetwork().to(device) - print(model) ###################################################################### -# Optimizing Parameters and Training -# --------------------- -# -# Optimizing model parameters requires a loss function, optimizer, -# and the optimization loop. -# Training a model is essentially an optimization process similar to the one we described in the -# `Autograd `_ section. We run the optimization process on the whole dataset -# several times, and each run is refered to as an **epoch**. During each run, we present data -# in **minibatches**, and for each minibatch compute gradients and correct parameters of the model -# according to back propagation algorithm. Read more about the `Optimization Loop `_. +# Read more about `building neural networks in PyTorch `_. # -# Cost function used to determine best parameters. -cost = torch.nn.BCELoss() -# This is used to create optimal parameters. -learning_rate = 1e-3 -optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) +###################################################################### +# -------------- +# + +##################################################################### +# Training the Model +# ---------------------------------------- +# To train a model, we need a `loss function `_ +# and an `optimizer `_. -# Create the training function. -def train(dataloader, model, loss, optimizer): +loss_fn = nn.BCELoss() +optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) + +####################################################################### +# In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and +# backpropagates the prediction error to adjust the model's parameters. + +def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) - for batch, (X, Y) in enumerate(dataloader): - X, Y = X.to(device), Y.to(device) - optimizer.zero_grad() + for batch, (X, y) in enumerate(dataloader): + X, y = X.to(device), y.to(device) + + # Compute prediction error pred = model(X) - loss = cost(pred, Y) + loss = loss_fn(pred, y) + + # Backpropagation + optimizer.zero_grad() loss.backward() optimizer.step() - + if batch % 100 == 0: loss, current = loss.item(), batch * len(X) - print(f'loss: {loss:>7f} [{current:>5d}/{size:>5d}]') - + print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") -# Create the validation/test function +############################################################################## +# We also check the model's performance against the test dataset to ensure it is learning. def test(dataloader, model): size = len(dataloader.dataset) model.eval() test_loss, correct = 0, 0 - with torch.no_grad(): - for batch, (X, Y) in enumerate(dataloader): - X, Y = X.to(device), Y.to(device) + for X, y in dataloader: + X, y = X.to(device), y.to(device) pred = model(X) - - test_loss += cost(pred, Y).item() - correct += (pred.argmax(1) == Y.argmax(1)).type(torch.float).sum().item() - + test_loss += loss_fn(pred, y).item() + correct += (pred.argmax(1) == y.argmax(1)).type(torch.float).sum().item() test_loss /= size correct /= size + print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n") - print(f'\nTest Error:\nacc: {(100*correct):>0.1f}%, avg loss: {test_loss:>8f}\n') - - -# Call the train and test function in a training loop with the number of epochs indicated. +############################################################################## +# The training process is conducted over several iterations (*epochs*). During each epoch, the model learns +# parameters to make better predictions. We print the model's accuracy and loss at each epoch; we'd like to see the +# accuracy increase and the loss decrease with every epoch. epochs = 5 - for t in range(epochs): - print(f'Epoch {t+1}\n-------------------------------') - train(train_dataloader, model, cost, optimizer) + print(f"Epoch {t+1}\n-------------------------------") + train(train_dataloader, model, loss_fn, optimizer) test(test_dataloader, model) -print('Done!') +print("Done!") + +###################################################################### +# Read more about `Training your model `_. +# + +###################################################################### +# -------------- +# ###################################################################### # Saving Models # ------------- -# -# PyTorch has different ways you can save your model. One way is to serialize the internal model state to a file. Another would be to use the built-in `ONNX `_ support. -# +# A common way to save a model is to serialize the internal state dictionary (containing the model parameters). + +torch.save(model.state_dict(), "model.pth") +print("Saved PyTorch Model State to model.pth") -torch.save(model.state_dict(), 'model.pth') -print('Saved PyTorch Model to model.pth') -# Save to ONNX, create dummy variable to traverse graph +###################################################################### +# -------------- +# -x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255 -onnx.export(model, x, 'model.onnx') -print('Saved onnx model to model.onnx') ###################################################################### # Loading Models # ---------------------------- -# -# Once a model has been serialized the process for loading the -# parameters includes re-creating the model shape and then loading -# the state dictionary. Once loaded the model can be used for either -# retraining or inference purposes (in this example it is used for -# inference). Check out more details on `saving, loading and running models with PyTorch `_ # +# The process for loading a model includes re-creating the model structure and loading +# the state dictionary into it. -loaded_model = NeuralNetwork() +model = NeuralNetwork() +model.load_state_dict(torch.load("model.pth")) -loaded_model.load_state_dict(torch.load('model.pth')) -loaded_model.eval() +############################################################# +# This model can now be used to make predictions. -# inference +loaded_model.eval() x, y = test_data[0][0], test_data[0][1] with torch.no_grad(): pred = loaded_model(x) @@ -244,10 +279,9 @@ def test(dataloader, model): ############################################################# +# Read more on `saving, loading and running models with PyTorch `_ # -# Looking for more resources? Check out the other tutorials on the `tutorials home page `_. # - -################################################################## +# # -# Authors: `Seth Juarez `_, `Cassie Breviu `_, `Dmitry Soshnikov `_, `Ari Bornstein `_, `Suraj Subramanian `_ + diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 4d8941251d2..e194b949098 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -8,254 +8,212 @@ `Optimization `_ > `Save & Load Model `_ -Tensors and Operations +Tensors ========================== -**Tensor** is the basic computational unit in PyTorch. It is very -similar to **NumPy array**, and supports similar operations. However, -there are two very important features of Torch tensors that make them -especially useful for training large-scale neural networks. First, tensor operations can be performed on -GPUs or other specialized hardware to accelerate computing. Second, tensor operations support -automatic differentiation using `pytorch.autograd engine `__. +Tensors are a specialized data structure that are very similar to arrays and matrices. +In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters. -Lets look at how we can easily convert between Torch tensors and NumPy arrays: +Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators. Tensors +are also optimized for automatic differentiation (we'll see more about that later in the `Autograd `__ +section). If you’re familiar with ndarrays, you’ll be right at home with the Tensor API. If not, follow along! """ import torch import numpy as np -np_array = np.arange(10) -tensor = torch.from_numpy(np_array) - -print(f"Tensor={tensor}, Array={tensor.numpy()}") - ###################################################################### -# .. note:: -# When using CPU for computations, tensors converted from arrays -# share the same memory for data. Thus, changing the underlying array -# will also affect the tensor. +# Tensor Initialization +# ~~~~~~~~~~~~~~~~~~~~~ # - - -###################################################################### -# Creating Tensors -# ~~~~~~~~~~~~~~~~ +# Tensors can be initialized in various ways. Take a look at the following examples: # -# The fastest way to create a tensor is to define an *uninitialized* -# tensor. This means the values of this tensor are not set and depend on the -# data that was there in memory: +# **Directly from data** # +# Tensors can be created directly from data. The data type is automatically inferred. -x = torch.empty(3, 6) - +data = [[1, 2],[3, 4]] +x_data = torch.tensor(data) ###################################################################### -# In practice, we often want to create tensors initialized to some values, -# such as zeros, ones or random values. You can also specify the -# type of elements using ``dtype`` parameter, and chosing one of ``torch`` -# types: +# **From a NumPy array** # +# Tensors can be created from NumPy arrays (and vice versa - see :ref:`bridge-to-np-label`). +np_array = np.array(data) +x_np = torch.from_numpy(np_array) -x = torch.randn(3, 5) -y = torch.zeros(3, 5, dtype=torch.int) -z = torch.ones(3, 5, dtype=torch.double) -###################################################################### -# You can create random tensors with values sampled from different -# distributions, as described `in the -# documentation `__. -# -# Similarly to NumPy, you can use ``eye`` to create a diagonal identity -# matrix: +############################################################### +# **From another tensor:** # +# The new tensor retains the properties (shape, datatype) of the argument tensor, unless explicitly overridden. + +x_ones = torch.ones_like(x_data) # retains the properties of x_data +print(f"Ones Tensor: \n {x_ones} \n") -I = torch.eye(10) +x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides the datatype of x_data +print(f"Random Tensor: \n {x_rand} \n") ###################################################################### -# You can create new tensors with the same properties or size as -# existing tensors: +# **With random or constant values:** # +# ``shape`` is a tuple of tensor dimensions. In the functions below, it determines the dimensionality of the output tensor. -print(z.new_ones(2, 2)) # new_ method allows specifying new size -# _like method supports overriding dtype -print(torch.zeros_like(x, dtype=torch.long)) +shape = (2,3,) +rand_tensor = torch.rand(shape) +ones_tensor = torch.ones(shape) +zeros_tensor = torch.zeros(shape) +print(f"Random Tensor: \n {rand_tensor} \n") +print(f"Ones Tensor: \n {ones_tensor} \n") +print(f"Zeros Tensor: \n {zeros_tensor}") -###################################################################### -# Size of the tensor can be obtained using ``.size()`` method, which -# returns a tuple-like object: -# -print(z.size()) # Prints [3.0] +###################################################################### +# -------------- +# ###################################################################### -# Tensor Operations +# Tensor Attributes # ~~~~~~~~~~~~~~~~~ # -# Tensors support all basic arithmetic operations, which can be specified -# in different ways: -# - Using operators, such as ``+``, ``-``, etc. \* -# - Using functions such as ``add``, ``mult``, etc. Functions can either return values, or store them in the specified ouput variable (using ``out=`` parameter) -# - In-place operations, which modify one of the arguments. Those operations have ``_`` appended to their name, eg. ``add_``. -# Complete reference to all tensor operations can be found `in the -# documentation `__. -# -# Let us see examples of those operations on two tensors, ``x`` and ``y``. -# +# Tensor attributes describe their shape, datatype, and the device on which they are stored. -x = torch.randn(3, 5) -y = torch.randn(3, 5) +tensor = torch.rand(3,4) + +print(f"Shape of tensor: {tensor.shape}") +print(f"Datatype of tensor: {tensor.dtype}") +print(f"Device tensor is stored on: {tensor.device}") ###################################################################### -# Using operator notation -# ^^^^^^^^^^^^^^^^^^^^^^^ -# -# We can use overloaded arithmetic operators, such as ``+`` and ``*``: +# -------------- # -z = x*y - - ###################################################################### -# Note, that ``*`` means elementwise product, and not the matrix product. -# To compute matrix product, we need to use `@` operator or ``matmul`` function, as shown -# below. -# -# Using functions -# ^^^^^^^^^^^^^^^ +# Tensor Operations +# ~~~~~~~~~~~~~~~~~ # -# While only some operations are available as Python operators, `many more -# functions `__ -# can be specified using the full name. In the example below, ``t`` -# transposes the matrix, and ``matmul`` means matrix multiplication: +# Over 100 tensor operations, including arithmetic, linear algebra, matrix manipulation (transposing, +# indexing, slicing), sampling and more are +# comprehensively described `here `__. # +# Each of these operations can be run on the GPU (at typically higher speeds than on a +# CPU). If you’re using Colab, allocate a GPU by going to Runtime > Change runtime type > GPU. +# +# By default, tensors are created on the CPU. We need to explicitly move tensors to the GPU using +# ``.to`` method (after checking for GPU availability). Keep in mind that copying large tensors +# across devices can be expensive in terms of time and memory! -z = torch.matmul(x, y.t()) +# We move our tensor to the GPU if available +if torch.cuda.is_available(): + tensor = tensor.to('cuda') ###################################################################### -# Simple operations (addition, multiplication, etc.) also have -# corresponsing functions, and can be called either as methods, or as -# functions: +# Try out some of the operations from the list. +# If you're familiar with the NumPy API, you'll find the Tensor API a breeze to use. # -z = x.add(y) -z = torch.add(x, y) +############################################################### +# **Standard numpy-like indexing and slicing:** +tensor = torch.ones(4, 4) +print('First row: ',tensor[0]) +print('First column: ', tensor[:, 0]) +print('Last column:', tensor[..., -1]) +tensor[:,1] = 0 +print(tensor) ###################################################################### -# Sometimes it may be more convenient to store the result into specified -# variable, instead of returning it from a function. In this case you can -# use ``out=`` parameter: -# - -torch.add(x, y, out=z) - +# **Joining tensors** You can use ``torch.cat`` to concatenate a sequence of tensors along a given dimension. +# See also `torch.stack `__, +# another tensor joining op that is subtly different from ``torch.cat``. +t1 = torch.cat([tensor, tensor, tensor], dim=1) +print(t1) ###################################################################### -# In-place operations -# ^^^^^^^^^^^^^^^^^^^ -# -# When training neural networks, you often need to **modify** the weights, -# i.e. perform some operation and then store the result into the original -# variable. Those operations are called **in-place operations**, and they -# are marked by the ``_`` symbol at the end of their name: -# +# **Arithmetic operations** -x.add_(y) # x will be modified +# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value +y1 = tensor @ tensor.T +y2 = tensor.matmul(tensor.T) +torch.matmul(tensor, tensor.T, out=y3) -###################################################################### -# .. note:: -# In-place operations save some memory, but can be problematic when -# computing derivatives because of an immediate loss -# of history. Hence, their use is discouraged. + +# This computes the element-wise product. z1, z2, z3 will have the same value +z1 = tensor * tensor +z2 = tensor.mul(tensor) +torch.mul(tensor, tensor, out=z3) ###################################################################### -# Resizing and Indexing -# ~~~~~~~~~~~~~~~~~~~~~ -# -# Often you need to change the shape of the tensor without modifying -# its values, eg. to add an extra dimension. To do that, you can use -# ``view`` method, which provides a **view** to the same in-memory values -# using different dimensions: -# +# **Single-element tensors** If you have a one-element tensor, for example by aggregating all +# values of a tensor into one value, you can convert it to a Python +# numerical value using ``item()``: -print('Original size of x =',x.size()) # original size of x is 3x5 -print('Size after reshaping is',x.view(5, 3, 1).size()) # will give size 5x3x1 -print('Reshaped tensor:\n',x.view(5, -1)) # will result in size 5x3 +agg = tensor.sum() +agg_item = agg.item() +print(agg_item, type(agg_item)) ###################################################################### -# The number of elements in a view should be the same as in the -# original tensor. You can use ``-1`` in one of the dimensions to -# figure out this dimension automatically. -# +# **In-place operations** +# Operations that store the result into the operand are called in-place. They are denoted by a ``_`` suffix. +# For example: ``x.copy_(y)``, ``x.t_()``, will change ``x``. +print(tensor, "\n") +tensor.add_(5) +print(tensor) ###################################################################### -# .. note:: ``view`` is similar to ``reshape`` operation in NumPy. There -# is also a ``reshape`` method available in PyTorch, and it is more -# powerful than ``view``, because it can also reshape non-contiguous -# arrays by copying them to the new shape. However, in vast majority of -# cases you can use ``view`` and make sure that no data copying occurs, -# and the operation is always efficient. -# +# .. note:: +# In-place operations save some memory, but can be problematic when computing derivatives because of an immediate loss +# of history. Hence, their use is discouraged. + ###################################################################### -# Tensors support all slicing operations that exist in NumPy: +# -------------- # -print(x.size()) # original size of x is 3x5 -print('First row: ',x[0]) -print('First column: ', x[:, 0]) -print('Last column:', x[..., -1]) - ###################################################################### -# If you have a one-element tensor, for example, after aggregating all -# values of the tensor into one value, you can convert it to a Python -# numerical value using ``item()``: +# .. _bridge-to-np-label: # +# Bridge with NumPy +# ~~~~~~~~~~~~~~~~~ +# Tensors on the CPU and NumPy arrays can share their underlying memory +# locations, and changing one will change the other. -val = x.sum().item() # will compute the sum of all elements -print(val) ###################################################################### -# Hardware-Accelerated Computations -# ~~~~~~~~~~~~~~~~ -# -# One of the major benefits of using PyTorch is the ability to perform -# tensor operations on GPUs and some other specialized hardware. To do that, -# we need to explicitly **move** tensors to another computing platform using ``.to`` method. -# -# In most of the cases, we check for the availability of GPU in the beginning -# of the script, and define the ``device`` object accordingly. Then we move all -# tensors to that device before performing the computations: -# +# Tensor to NumPy array +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +t = torch.ones(5) +print(f"t: {t}") +n = t.numpy() +print(f"n: {n}") -if torch.cuda.is_available(): - device = torch.device("cuda") -else: - device = torch.device("cpu") +###################################################################### +# A change in the tensor reflects in the NumPy array. -print("Doing computations on {}".format(device)) +t.add_(1) +print(f"t: {t}") +print(f"n: {n}") -x = torch.randn(3, 5, device=device) # create tensor on specified device -y = torch.ones_like(x) # create tensor on CPU -y = y.to(device) # move tensor to another device -z = x+y # this is performed on GPU if it is available -print(z.to("cpu", torch.double)) +###################################################################### +# NumPy array to Tensor +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +n = np.ones(5) +t = torch.from_numpy(n) ###################################################################### -# In the last operation, when we move the tensor back to the CPU, we can -# also change the ``dtype``. This does not result in additional -# computational time, because we need to copy and transform the data when -# moving it from GPU anyway. -# +# Changes in the NumPy array reflects in the tensor. +np.add(n, 1, out=n) +print(f"t: {t}") +print(f"n: {n}") From abbf34bee937032cdb1c2cf36340cff664eb3100 Mon Sep 17 00:00:00 2001 From: suraj813 Date: Thu, 28 Jan 2021 19:17:17 -0500 Subject: [PATCH 002/120] Updated data --- .../quickstart/dataquickstart_tutorial.py | 171 ++++++++---------- .../quickstart/quickstart_tutorial.py | 2 +- 2 files changed, 75 insertions(+), 98 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index d06813e85aa..e033ff99894 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -17,15 +17,14 @@ ################################################################# # Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code # to be decoupled from our model training code for better readability and modularity. -# PyTorch provides two data primitives: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``, and a number of other abstractions -# that allow you to use pre-loaded as well as your own custom datasets easily. +# PyTorch provides two data primitives: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset`` +# that allow you to use pre-loaded datasets as well as your own data. # ``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around # the ``Dataset`` to enable easy access to the samples. # -# A number of pre-loaded datasets such as FashionMNIST that implement this interface are built into PyTorch domain libraries. -# They all subclass ``torch.utils.data.Dataset`` and implement functions specific to the particular dataset. -# These are useful for prototyping and benchmarking your model before training it on your own custom datasets. -# You can find some of these datasets +# PyTorch domain libraries provide a number of pre-loaded datasets (such as FashionMNIST) that +# subclass ``torch.utils.data.Dataset`` and implement functions specific to the particular data. +# They can be used to prototype and benchmark your model. You can find them # here: `Image Datasets `_, # `Text Datasets `_, and # `Audio Datasets `_ @@ -46,18 +45,19 @@ # import torch -from torch.utils.data import Dataset, DataLoader +from torch.utils.data import Dataset from torchvision import datasets +from torchvision.transforms import ToTensor, Lambda import matplotlib.pyplot as plt -import numpy as np + clothing = datasets.FashionMNIST( - "data", # specifies data directory to store data - train=True, # specifies training or test dataset to use - transform=None, # specifies transforms to apply to features (images) - target_transform=None, # specifies transforms to apply to labels - download=True, # should the data be downloaded from the Internet -) + root="data", + train=True, + download=True, + transform=ToTensor(), + target_transform=None +) ################################################################# @@ -82,12 +82,12 @@ figure = plt.figure(figsize=(8, 8)) cols, rows = 3, 3 for i in range(1, cols * rows + 1): - sample_idx = torch.random.randint(len(clothing), size=(1,)).item() + sample_idx = torch.randint(len(clothing), size=(1,)).item() img, label = clothing[sample_idx] figure.add_subplot(rows, cols, i) plt.title(labels_map[label]) plt.axis("off") - plt.imshow(img, cmap="gray") + plt.imshow(img.squeeze(), cmap="gray") plt.show() ################################################################# @@ -104,67 +104,46 @@ # Creating a Custom Dataset # ----------------- # -# To work with your own data, we can implement a custom class that inherits from ``Dataset``. +# To work with our own data, we can implement a custom class that inherits from ``Dataset``. # This custom class must implement three functions: `__init__`, `__len__`, and `__getitem__`. -# Let's look at a custom image dataset implementation. In this example, we have a number of images stored -# in a directory, and their labels stored separately in a CSV file. -# in the following sections, we will break down what's happening in each function. +# Let's look at a custom image dataset implementation. In this example, we have a number of FashionMNIST images stored +# in a directory (``img_dir``), and their labels stored separately in a CSV file (``annotations_file``). # +# In the next sections, we'll break down what's happening in each of these functions. + import os import pandas as pd -from torch.utils.data import Dataset -from torchvision import transforms, utils from torchvision.io import read_image - class CustomImageDataset(Dataset): - def __init__(self, annotations_file, img_dir, transform=None): + def __init__(self, annotations_file, img_dir, transform=None, target_transform=None): self.img_labels = pd.read_csv(annotations_file) self.img_dir = img_dir self.transform = transform + self.target_transform = target_transform def __len__(self): return len(self.img_labels) def __getitem__(self, idx): - if torch.is_tensor(idx): - idx = idx.tolist() - - img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) + img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0]) image = read_image(img_path) - label = self.img_labels.iloc[idx, 1:] - sample = {"image": image, "label": label} - + label = self.img_labels.iloc[idx, 1] if self.transform: - sample = self.transform(sample) - + image = self.transform(image) + if self.target_transform: + label = self.target_transform(label) + sample = {"image": image, "label": label} return sample -################################################################# -# Import the packages -# ------- -# -# Import ``os`` for file handling, ``torch`` for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and ``Dataset`` to implement the Dataset interface. -# -# Example: -# - -import os -import torch -import pandas as pd -from torchvision.io import read_image -from torch.utils.data import Dataset -from torch.utils.data import DataLoader - ################################################################# # __init__ # ----------------- # -# The __init__ function is run once when instantiating our Dataset object. Here, we use it to load -# the directory containing the images, and their labels (contained in a csv file). While creating the -# Dataset object, we can optionally pass it the transform that should be run on the images. +# The __init__ function is run once when instantiating the Dataset object. We initialize +# the directory containing the images, the annotations file, and both transforms (if). # # The labels.csv file looks like: :: # @@ -172,15 +151,13 @@ def __getitem__(self, idx): # tshirt2.jpg, 0 # ...... # ankleboot999.jpg, 9 -# -# Example: -# -def __init__(self, labels_file, img_dir, transform=None): - self.img_labels = pd.read_csv(labels_file) +def __init__(self, annotations_file, img_dir, transform=None, target_transform=None): + self.img_labels = pd.read_csv(annotations_file) self.img_dir = img_dir self.transform = transform + self.target_transform = target_transform ################################################################# @@ -200,64 +177,64 @@ def __len__(self): # __getitem__ # ----------------- # -# The __getitem__ function is the most important function in the Datasets interface. It takes a tensor or an index as input and returns a loaded sample from your dataset at the given indices. -# -# If provided a tensor as an index, we convert the tensor to a list first. We then load the file at the given index from our image directory, as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a transform on and return. Transforms will be discussed in more detail in the next section: `Transforms `_ -# -# Example: -# - +# The __getitem__ function loads and returns a sample from the dataset at the given index ``idx``. +# Based on the index, it identifies the image's location on disk, converts that to a tensor using ``read_image``, retrieves the +# corresponding label from the csv data in ``self.img_labels``, calls the transform functions on them (if applicable), and returns the +# tensor image and corresponding label in a Python dict. def __getitem__(self, idx): - if torch.is_tensor(idx): - idx = idx.tolist() - img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) + img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0]) image = read_image(img_path) - label = self.img_labels.iloc[idx, 1:] - sample = {"image": image, "label": label} + label = self.img_labels.iloc[idx, 1] if self.transform: - sample = self.transform(sample) + image = self.transform(image) + if self.target_transform: + label = self.target_transform(label) + sample = {"image": image, "label": label} return sample +###################################################################### +# -------------- +# + + ################################################################# # Preparing your data for training with DataLoaders # ------------------------------------------------- +# The ``Dataset`` retrieves our dataset's features and labels one sample at a time. While training a model, we typically want to +# pass samples in "minibatches", reshuffle the data at every epoch to reduce model overfitting, and use Python's ``multiprocessing`` to +# speed up data retrieval. # -# Now we have an organized mechanism for managing data which is great, but there is still a lot of manual work we would have to do to train a model with our Dataset. -# -# For example we would have to manually maintain the code for: -# -# * Batching -# * Shuffling -# * Parallel batch distribution -# -# The PyTorch Dataloader ``torch.utils.data.DataLoader`` is an iterator that handles all of this complexity for us, enabling us to load a dataset and focus on training our model. +# ``DataLoader`` is an iterable that abstracts this complexity for us in an easy API. + +from torch.utils.data import DataLoader -dataloader = DataLoader(clothing, batch_size=4, shuffle=True, num_workers=0) +dataloader = DataLoader(clothing, batch_size=64, shuffle=True) ########################### # Iterate through the Dataset # -------------------------- # -# We have loaded that dataset into the ``dataloader`` and can iterate through the dataset as needed. -# Below is a simple example of how to iterate and display an image or return a label count: - +# We have loaded that dataset into the ``Dataloader`` and can iterate through the dataset as needed. +# Each iteration below returns a batch of ``train_features`` and ``train_labels``(containing ``batch_size=64`` features and labels respectively). +# Because we specified ``shuffle=True``, after we iterate over all batches the data is shuffled (for finer-grained control over +# the data loading order, take a look at `Samplers `_). # Display image and label. -for train_features, train_labels in dataloader.dataset: - print(train_labels) - plt.imshow(train_features, cmap="gray") - plt.show() - break - -# Count the number of occurances for label number 9 which is for the 'Bag' -count = 0 -for train_features, train_labels in dataloader.dataset: - if train_labels == 9: - count += 1 -print(count) +train_features, train_labels = next(iter(dataloader)) +print(f"Feature batch shape: {train_features.size()}") +print(f"Labels batch shape: {train_labels.size()}") +img = train_features[0].squeeze() +label = train_labels[0] +plt.imshow(img, cmap="gray") +plt.show() +print(f"Label: {label}") + ################################################################# -# With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. -# +# Further Reading +# ~~~~~~~~~~~~~~~~~ +# - `torch.utils.data API `_ + + diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 7fa6e3c7665..4acb4a71628 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -77,7 +77,7 @@ import matplotlib.pyplot as plt ###################################################################### -# The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like ImageNet, +# The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like # CIFAR, COCO (`full list here `_). In this tutorial, we # use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and # ``target_transform`` to modify the samples and labels respectively. From 95d6cdba0f7809b2e5777c20ced611ea32bed9fc Mon Sep 17 00:00:00 2001 From: suraj813 Date: Sat, 30 Jan 2021 16:21:21 -0500 Subject: [PATCH 003/120] updated buildmodel --- .../quickstart/buildmodel_tutorial.py | 199 ++++++++++-------- .../quickstart/dataquickstart_tutorial.py | 22 +- .../quickstart/quickstart_tutorial.py | 13 +- .../quickstart/transforms_tutorial.py | 160 +++----------- 4 files changed, 158 insertions(+), 236 deletions(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 283453aa1ef..fc157fe6bd3 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -1,5 +1,4 @@ """ - `Learn the Basics `_ > `Tensors `_ > `Datasets & DataLoaders `_ > @@ -9,36 +8,22 @@ `Optimization `_ > `Save & Load Model `_ -Build the Neural Network Model +Build the Neural Network =================== -""" +Neural networks comprise of layers/modules that perform operations on data. +The `torch.nn `_ namespace provides all the building blocks you need to +build your own neural network. Every module in PyTorch subclasses the `nn.Module `_. +A neural network is a module itself that consists of other modules (layers). This nested structure allows for +building and managing complex architectures easily. -################################################################# -# -# Now that we have loaded and transformed the data, we can build the neural network model. -# Neural network consists of a number of layers and PyTorch `torch.nn `_ namespace provides predefined layers -# that helps us build the model. -# -# The most common way to define a neural network is to use a class inherited -# from `nn.Module `_. -# It provides great parameter management across all nested submodules, which gives us more -# flexibility, because we can construct layers of any complexity, including ones with shared weights. -# -# In the below example, for our FashionMNIST image dataset, we will create a dense multi-layer network. -# Lets break down the steps to build this model below. -# +In the following sections, we'll build a neural network to classify images in the FashionMNIST dataset. -############################################# -# Import the Packages -# -------------------------- -# +""" import os import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.onnx as onnx +from torch import nn from torch.utils.data import DataLoader from torchvision import datasets, transforms @@ -46,13 +31,10 @@ ############################################# # Get Device for Training # ----------------------- -# We want to be able to train our model on both CPU and GPU, if it is available. It is common practice to -# define a variable ``device`` which will designate the device we will be training on. -# We check to see if `torch.cuda `_ -# is available to use the GPU, else we will use the CPU. -# -# Example: -# +# We want to be able to train our model on a hardware accelerator like the GPU, +# if it is available. Let's check to see if +# `torch.cuda `_ is available, else we +# continue to use the CPU. device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) @@ -60,60 +42,74 @@ ############################################## # Define the Class # ------------------------- -# -# First we define the `NeuralNetwork` class which inherits from ``nn.Module``, the base class for -# building all neural network modules in PyTorch. We use the ``__init__`` function to define and initialize the NN layers that will be then called in the module's ``forward`` function. -# Then we call the ``NeuralNetwork`` class and assign the device. When training -# the model we will call ``model`` and pass the data (x) into the forward function and -# through each layer of our network. -# -# +# We define our neural network by subclassing ``nn.Module``, and +# initialize the neural network layers in ``__init__``. Every ``nn.Module`` subclass implements +# the operations on input data in the ``forward`` method. class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() - self.flatten = nn.Flatten() - self.layer1 = nn.Linear(28*28, 512) - self.layer2 = nn.Linear(512, 512) - self.output = nn.Linear(512, 10) + self.flatten = nn.Flatten(start_dim=1, end_dim=2) + self.linear_relu_stack = nn.Sequential( + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, 10), + nn.ReLU() + ) def forward(self, x): x = self.flatten(x) - x = F.relu(self.layer1(x)) - x = F.relu(self.layer2(x)) - x = self.output(x) - return F.softmax(x, dim=1) + logits = self.linear_relu_stack(x) + return logits + +############################################## +# We create an instance of ``NeuralNetwork``, and move it to the ``device``, and print +# it's structure. model = NeuralNetwork().to(device) print(model) -input = torch.rand(5, 28, 28) -# equivalent to model.forward(input) -model(input) +############################################## +# To use the model, we pass it the input data. This executes the model's ``forward``, +# along with some `background operations `_. +# Do not call ``model.forward()`` directly! +# +# Calling the model on the input returns a 10-dimensional tensor with raw predicted values for each class. +# We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module. + +X = torch.rand(1, 28, 28) +logits = model(X) +pred_probab = nn.Softmax(dim=1)(logits) +y_pred = pred_probab.argmax(1) +print(f"Predicted class: {y_pred}") + + +###################################################################### +# -------------- +# + ############################################## # Model Layers # ------------------------- # -# Lets break down each layer in the FashionMNIST model. To illustrate it, we -# will take a sample minibatch of 100 images of size 28x28 and see what happens to it as -# we pass it through the network. The code in the sections below would essentially explain -# what happens inside the ``forward`` method of our ``NeuralNetwork`` class. -# +# Lets break down the layers in the FashionMNIST model. To illustrate it, we +# will take a sample minibatch of 3 images of size 28x28 and see what happens to it as +# we pass it through the network. -input_image = torch.rand(100,28,28) +input_image = torch.rand(3,28,28) print(input_image.size()) ################################################## # nn.Flatten # ----------------------------------------------- -# -# First we call `nn.Flatten `_ to reduce tensor dimensions to one. -# -# In our case, flatten keeps the minibatch dimension, but two image dimensions are -# reduced to one: - +# We initialize the `nn.Flatten `_ +# layer to convert each 2D 28x28 image into a contiguous array of 784 pixel values ( +# the minibatch dimension (at dim=0) is maintained). + flatten = nn.Flatten(start_dim=1, end_dim=2) flat_image = flatten(input_image) print(flat_image.size()) @@ -121,44 +117,67 @@ def forward(self, x): ############################################## # nn.Linear # ------------------------------- +# The `linear layer `_ +# is a module that applies a linear transformation on the input using it's stored weights and biases. # -# Now that we have flattened our tensor dimension we will pass our data through a `linear layer `_. The linear layer is -# a module that applies a linear transformation on the input using it's stored weights and biases. -# - -layer1 = nn.Linear(in_features=28*28, out_features=512) +layer1 = nn.Linear(in_features=28*28, out_features=20) hidden1 = layer1(flat_image) print(hidden1.size()) + ################################################# -# Activation Functions +# nn.ReLU() # ------------------------- +# Non-linear activations are what create the complex mappings between the model's inputs and outputs. +# They are applied after linear transformations to introduce *nonlinearity*, helping neural networks +# learn a wide variety of phenomena. # -# In between layers of a neural network, we need to put non-linear activation functions, -# such as `nn.ReLU `_ (which is often -# used in between hidden layers) or `nn.Softmax `_, -# which turns the output of the network into probabilities by rescaling values between 0 and 1, and all sum to one. -# +# In this model, we use `nn.ReLU `_ between our +# linear layers, but there's other activations to introduce non-linearity in your model. + +print(f"Before ReLU: {hidden1}\n\n") +hidden1 = nn.ReLU()(hidden1) +print(f"After ReLU: {hidden1}") + +# nn.Sequential +# ------------------------------- +# `nn.Sequential `_ is an ordered +# container of modules. The data is passed through all the modules in the same order as defined. You can use +# sequential containers to put together a quick network like ``seq_modules``. + +seq_modules = nn.Sequential( + flatten, + layer1, + nn.ReLU(), + nn.Linear(20, 10) +) +input_image = torch.rand(3,28,28) +logits = seq_modules(input_image) + +################################################################ +# nn.Softmax() +# ------------------------- +# The last linear layer of the neural network returns `logits` - raw values in [-\infty, \infty] - which are passed to the +# `nn.Softmax `_ module. The logits are scaled to values +# [0, 1] representing the model's predicted probabilities for each class. ``dim`` parameter indicates the dimension along +# which the values must sum to 1. + +softmax = nn.Softmax(dim=1) +pred_probab = softmax(logits) -layer2 = nn.Linear(512,512) -output = nn.Linear(512,10) -hidden2 = layer2(F.relu(hidden1)) -print('Hidden 2 output size =',hidden2.size()) -z = output(F.relu(hidden2)) -out = F.softmax(z) -print('Output size =',out.size()) ################################################# -# Parameter Tracking +# Model Parameters # ------------------------- +# Many layers inside a neural network are *parameterized*, i.e. have associated weights +# and biases that are optimized during training. Subclassing ``nn.Module`` automatically +# tracks all fields defined inside your model object, and makes all parameters +# accessible using your model's ``parameters()`` or ``named_parameters()`` methods. +# +# In this example, we iterate over each parameter, and print its size and a preview of its values. # -# The main reason to put all code inside a class inherited from ``nn.Module`` is to -# utilize **parameter tracking**. Most of the layers inside a neural network, -# in our case all linear layers, have associated weights and biases that need to -# be adjusted during training. ``nn.Module`` automatically tracks all fields defined -# inside the class, and makes all parameters accessible using ``parameters()`` -# or ``named_parameters()`` methods. Let's have a look at the first two parameters of -# our neural network that were defined in the beginning of this section: -# -print(list(model.named_parameters())[0:2]) +print("Model structure: ", model, "\n\n") + +for name, param in model.named_parameters(): + print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n") \ No newline at end of file diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index e033ff99894..06060392d68 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -38,11 +38,13 @@ # Fashion-MNIST is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. # Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. # -# To load the `FashionMNIST Dataset `_ we need to provide the following three parameters: -# - ``root`` is the path where the train/test data is stored. -# - ``train`` includes the training dataset. -# - ``download=True`` downloads the data from the internet if it's not available at root. -# +# To load the `FashionMNIST Dataset `_ +# we need to provide the following three parameters: +# - ``root`` is the path where the train/test data is stored, +# - ``train`` specifies training or test dataset, +# - ``download=True`` downloads the data from the internet if it's not available at ``root``. +# - ``transform`` and ``target_transform`` specify the feature and label transformations (more on this in the next section) + import torch from torch.utils.data import Dataset @@ -101,8 +103,8 @@ # ################################################################# -# Creating a Custom Dataset -# ----------------- +# Creating a Custom Dataset for your files +# --------------------------------------------------- # # To work with our own data, we can implement a custom class that inherits from ``Dataset``. # This custom class must implement three functions: `__init__`, `__len__`, and `__getitem__`. @@ -143,7 +145,8 @@ def __getitem__(self, idx): # ----------------- # # The __init__ function is run once when instantiating the Dataset object. We initialize -# the directory containing the images, the annotations file, and both transforms (if). +# the directory containing the images, the annotations file, and both transforms (covered +# in more detail in the next section). # # The labels.csv file looks like: :: # @@ -231,6 +234,9 @@ def __getitem__(self, idx): plt.show() print(f"Label: {label}") +###################################################################### +# -------------- +# ################################################################# # Further Reading diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 4acb4a71628..b7d41a4ca78 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -270,18 +270,9 @@ def test(dataloader, model): ############################################################# # This model can now be used to make predictions. -loaded_model.eval() +model.eval() x, y = test_data[0][0], test_data[0][1] with torch.no_grad(): - pred = loaded_model(x) + pred = model(x) predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] print(f'Predicted: "{predicted}", Actual: "{actual}"') - - -############################################################# -# Read more on `saving, loading and running models with PyTorch `_ -# -# -# -# - diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 9eef4202d1f..7676ac085e8 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -11,152 +11,58 @@ Transforms =================== -In most cases data does not come in its final processed form that is required for training machine learning algorithms. We need to do different data manipulations or **transformations** to prepare it for training. There are many types of transformations, and it depends on the type of model you are building and the state of your data as to which ones you should use. - -In the example below, let's take FashionMNIST image dataset, which is available from ``torchvision.datasets`` using the following function: +Data does not always come in its final processed form that is required for +training machine learning algorithms. We use **transforms** to perform some +manipulation of the data and make it suitable for training. + +All TorchVision datasets have two parameters -``transform`` to modify the features and +``target_transform`` to modify the labels - that accept callables containing the transformation logic. +The `torchvision.transforms `_ module offers +several commonly-used transforms out of the box. + +The FashionMNIST features are in PIL Image format, and the labels are integers. +For training, we need the features as normalized tensors, and the labels as one-hot encoded tensors. +To make these transformations, we use ``ToTensor`` and ``Lambda``. """ -import torchvision - -ds = torchvision.datasets.FashionMNIST( - 'data', # specifies data directory to store data - train=True, # specifies training or test dataset to use - transform=None, # specifies transforms to apply to features (images) - target_transform=None, # specifies transforms to apply to labels - download=False) # should the data be downloaded from the Internet -################################ -#To prepare data for training we need to take our image (also called features, x), turn it into a tensor and normalize it. Then we need to convert labels (y) into one-hot encoding. -# -#We will break down each of these steps below. +from torchvision import datasets +from torchvision.transforms import ToTensor, Lambda -############################################## -# PyTorch Datasets -# -------------------------- -# -# We are using the built-in FashionMNIST dataset from the PyTorch library. -# For more info on the Datasets and Loaders check out `this `_ section of the tutorial. -# The ``train=True`` argument indicates we want the training split of the dataset (``train=False`` downloads the test split instead). -# This way we have data partitioned out for training and testing within the provided PyTorch datasets. -# We will apply the same transforms to both the training and testing datasets. - -# import packages -import os -import torch -import torch.nn as nn -import torch.onnx as onnx -import matplotlib.pyplot as plt -from torch.utils.data import DataLoader -from torchvision import datasets, transforms - -# Here we define the image classes. -classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", - "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] - -############################################## -# Feature Transforms and Label Transforms -# --------------------------------------- -# -# Below is the code to load the FashionMNIST dataset and apply the transforms: - -training_data = datasets.FashionMNIST( - "data", +ds = datasets.FashionMNIST( + root="data", train=True, download=True, - transform=transforms.ToTensor(), - target_transform=transforms.Lambda( - lambda y: torch.zeros(10, dtype=torch.float) - .scatter_(0, torch.tensor(y), value=1) - ) + transform=ToTensor(), + target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) -######################################## -# Here we define two transformations: -# -# * ``transform`` is the transformation we apply to features, in our case - to images. The dataset contains images in PIL format so we need to convert them to tensors using the ``ToTensor()`` transform. -# * ``target_transform`` defines a transformation that is applied to labels in the dataset. Here, the label is a class number from 0 to 9, and we need to convert it to one-hot encoding. - ################################################# # ToTensor() # ------------------------------- # -# `transforms.ToTensor `_ transform is required to prepare an image for training. It takes the PIL image, converts it into a `tensor `_, and normalizes our data by scaling the image pixel intensity values to be between 0 and 1. -# +# `ToTensor `_ +# converts a PIL image or NumPy ``ndarray`` into a ``FloatTensor``. and scales +# the image's pixel intensity values in the range [0., 1.] # ############################################## # Lambda Transforms # ------------------------------- # -# We use a **lambda transform** to turn the class number into one-hot encoding. This function takes y as an input and creates a zero tensor of size 10. Then it calls scatter `torch.Tensor.scatter_ class `_ to take a value 1 and store it into the correct position of the zero vector defined by the class number. +# Lambda transforms apply any user-defined lambda function. Here, we define a function +# to turn the integer into a one-hot encoded tensor. +# It first creates a zero tensor of size 10 (the number of labels in our dataset) and calls +# `scatter_ `_ which assigns a +# ``value=1`` on the index as given by the label ``y``. -target_transform = transforms.Lambda(lambda y: torch.zeros( +target_transform = Lambda(lambda y: torch.zeros( 10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)) -############################################### -# Check out more `torchvision transforms `_ -# - -##################################################### -# Compose -# ------------------------ +###################################################################### +# -------------- # -# In many cases, we need to perform several transformations on the data sequentially. `transforms.Compose `_ allows us to string together different steps of transformations in a sequential order. We will see an example of using composition transform in the next section. - -############################################## -# Using your own data -# -------------------------------------- -# -# Below is an example for processing image data using a dataset from a local directory. It assumes that we have ``train`` and ``val`` subdirectories with training and validation dataset. In this example we want to apply different sets of transforms for training and validation dataset: -# -# * For training data, we want to perform some **data augmentation**, and do random croping/resizing of the original image. We also introduce random horizontal flips. -# * For testing, we typically want to be consistent and always use the same images - thus we do not do any augmentation, just resizing. -# -# We also normalize values by subtracting the mean, which was computed along the whole dataset. -# -# To be able to unify the code for train and validation datasets, we use a special trick and create a dictionary of transforms for the train and validation dataset: -# -# .. code-block:: Python -# -# data_transforms = { -# 'train': -# transforms.Compose([ -# transforms.RandomResizedCrop(224), -# transforms.RandomHorizontalFlip(), -# transforms.ToTensor(), -# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) -# ]), -# 'val': -# transforms.Compose([ -# transforms.Resize(256), -# transforms.CenterCrop(224), -# transforms.ToTensor(), -# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) -# ]), -# } -# -# Next, we define a similar dictionary of train and validation datasets by using ``datasets.ImageFolder`` class. This class allows us to create a dataset from all files in a folder, and apply any transformations to them: -# -# .. code-block:: Python -# -# data_dir = 'data' -# -# image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), -# data_transforms[x]) -# for x in ['train', 'val']} -# -# Similarly we define a dictionary of dataloaders to prepare our datasets for training. They allow us to shuffle data and group them into batches of a specified size: -# -# .. code-block:: Python -# -# batch_size = 4 -# -# dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], -# batch_size=batch_size, -# shuffle=True, num_workers=4) -# for x in ['train', 'val']} -# -# dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} -# -# class_names = image_datasets['train'].classes -# +################################################################# +# Further Reading +# ~~~~~~~~~~~~~~~~~ +# - `torchvision.tranasforms API `_ From c0459834d55f907d97ff49efe2c952ba38b08609 Mon Sep 17 00:00:00 2001 From: suraj813 Date: Mon, 1 Feb 2021 13:47:17 -0500 Subject: [PATCH 004/120] update the other sections --- .../quickstart/autograd_tutorial.py | 9 + .../quickstart/buildmodel_tutorial.py | 28 +- .../quickstart/dataquickstart_tutorial.py | 38 +- .../quickstart/optimization_tutorial.py | 346 ++++++++++-------- .../quickstart/quickstart_tutorial.py | 84 ++--- .../quickstart/saveloadrun_tutorial.py | 10 +- .../quickstart/transforms_tutorial.py | 2 +- model.pth | Bin 0 -> 2681451 bytes 8 files changed, 285 insertions(+), 232 deletions(-) create mode 100644 model.pth diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 023dd138879..93ae25ad8e3 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -231,3 +231,12 @@ # gradients in case of a scalar-valued function, such as loss during # neural network training. # + +###################################################################### +# -------------- +# + +################################################################# +# Further Reading +# ~~~~~~~~~~~~~~~~~ +# - `Autograd Mechanics `_ \ No newline at end of file diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index fc157fe6bd3..b25fc757222 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -49,7 +49,7 @@ class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() - self.flatten = nn.Flatten(start_dim=1, end_dim=2) + self.flatten = nn.Flatten() self.linear_relu_stack = nn.Sequential( nn.Linear(28*28, 512), nn.ReLU(), @@ -105,18 +105,18 @@ def forward(self, x): ################################################## # nn.Flatten -# ----------------------------------------------- +# ^^^^^^^^^^^^^^^^^^^^^^ # We initialize the `nn.Flatten `_ # layer to convert each 2D 28x28 image into a contiguous array of 784 pixel values ( # the minibatch dimension (at dim=0) is maintained). -flatten = nn.Flatten(start_dim=1, end_dim=2) +flatten = nn.Flatten() flat_image = flatten(input_image) print(flat_image.size()) ############################################## # nn.Linear -# ------------------------------- +# ^^^^^^^^^^^^^^^^^^^^^^ # The `linear layer `_ # is a module that applies a linear transformation on the input using it's stored weights and biases. # @@ -126,8 +126,8 @@ def forward(self, x): ################################################# -# nn.ReLU() -# ------------------------- +# nn.ReLU +# ^^^^^^^^^^^^^^^^^^^^^^ # Non-linear activations are what create the complex mappings between the model's inputs and outputs. # They are applied after linear transformations to introduce *nonlinearity*, helping neural networks # learn a wide variety of phenomena. @@ -139,8 +139,10 @@ def forward(self, x): hidden1 = nn.ReLU()(hidden1) print(f"After ReLU: {hidden1}") + +################################################# # nn.Sequential -# ------------------------------- +# ^^^^^^^^^^^^^^^^^^^^^^ # `nn.Sequential `_ is an ordered # container of modules. The data is passed through all the modules in the same order as defined. You can use # sequential containers to put together a quick network like ``seq_modules``. @@ -155,8 +157,8 @@ def forward(self, x): logits = seq_modules(input_image) ################################################################ -# nn.Softmax() -# ------------------------- +# nn.Softmax +# ^^^^^^^^^^^^^^^^^^^^^^ # The last linear layer of the neural network returns `logits` - raw values in [-\infty, \infty] - which are passed to the # `nn.Softmax `_ module. The logits are scaled to values # [0, 1] representing the model's predicted probabilities for each class. ``dim`` parameter indicates the dimension along @@ -180,4 +182,10 @@ def forward(self, x): print("Model structure: ", model, "\n\n") for name, param in model.named_parameters(): - print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n") \ No newline at end of file + print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n") + + +################################################################# +# Further Reading +# ~~~~~~~~~~~~~~~~~ +# - `torch.nn API `_ \ No newline at end of file diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 06060392d68..4fd8be201e1 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -38,8 +38,7 @@ # Fashion-MNIST is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. # Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. # -# To load the `FashionMNIST Dataset `_ -# we need to provide the following three parameters: +# We load the `FashionMNIST Dataset `_ with the following parameters: # - ``root`` is the path where the train/test data is stored, # - ``train`` specifies training or test dataset, # - ``download=True`` downloads the data from the internet if it's not available at ``root``. @@ -53,12 +52,18 @@ import matplotlib.pyplot as plt -clothing = datasets.FashionMNIST( +training_data = datasets.FashionMNIST( root="data", train=True, download=True, - transform=ToTensor(), - target_transform=None + transform=ToTensor() +) + +test_data = datasets.FashionMNIST( + root="data", + train=False, + download=True, + transform=ToTensor() ) @@ -66,8 +71,8 @@ # Iterating and Visualizing the Dataset # ----------------- # -# Once we have the ``clothing`` dataset, we can index it manually like a list: ``clothing[index]``. -# Then use ``matplotlib`` to visualize the dataset. +# We can index ``Datasets`` manually like a list: ``training_data[index]``. +# We use ``matplotlib`` to visualize some samples in our training data. labels_map = { 0: "T-Shirt", @@ -84,8 +89,8 @@ figure = plt.figure(figsize=(8, 8)) cols, rows = 3, 3 for i in range(1, cols * rows + 1): - sample_idx = torch.randint(len(clothing), size=(1,)).item() - img, label = clothing[sample_idx] + sample_idx = torch.randint(len(training_data), size=(1,)).item() + img, label = training_data[sample_idx] figure.add_subplot(rows, cols, i) plt.title(labels_map[label]) plt.axis("off") @@ -142,7 +147,7 @@ def __getitem__(self, idx): ################################################################# # __init__ -# ----------------- +# ^^^^^^^^^^^^^^^^^^^^ # # The __init__ function is run once when instantiating the Dataset object. We initialize # the directory containing the images, the annotations file, and both transforms (covered @@ -165,7 +170,7 @@ def __init__(self, annotations_file, img_dir, transform=None, target_transform=N ################################################################# # __len__ -# ----------------- +# ^^^^^^^^^^^^^^^^^^^^ # # The __len__ function returns the number of samples in our dataset. # @@ -178,7 +183,7 @@ def __len__(self): ################################################################# # __getitem__ -# ----------------- +# ^^^^^^^^^^^^^^^^^^^^ # # The __getitem__ function loads and returns a sample from the dataset at the given index ``idx``. # Based on the index, it identifies the image's location on disk, converts that to a tensor using ``read_image``, retrieves the @@ -213,10 +218,11 @@ def __getitem__(self, idx): from torch.utils.data import DataLoader -dataloader = DataLoader(clothing, batch_size=64, shuffle=True) +train_dataloader = DataLoader(training_data, batch_size=64, shuffle=True) +test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True) ########################### -# Iterate through the Dataset +# Iterate through the DataLoader # -------------------------- # # We have loaded that dataset into the ``Dataloader`` and can iterate through the dataset as needed. @@ -225,7 +231,7 @@ def __getitem__(self, idx): # the data loading order, take a look at `Samplers `_). # Display image and label. -train_features, train_labels = next(iter(dataloader)) +train_features, train_labels = next(iter(train_dataloader)) print(f"Feature batch shape: {train_features.size()}") print(f"Labels batch shape: {train_labels.size()}") img = train_features[0].squeeze() @@ -240,7 +246,7 @@ def __getitem__(self, idx): ################################################################# # Further Reading -# ~~~~~~~~~~~~~~~~~ +# -------------- # - `torch.utils.data API `_ diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 6e8982866e0..bb7332910e0 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -12,166 +12,194 @@ =========================== Now that we have a model and data it's time to train, validate and test our model by optimizing it's parameters on -our data. Training a model is an iterative process; in each iteration (called an *epoch*) the model makes a guess about the output, calculates the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in the `previous section `_), and **optimizes** these parameters using gradient descent. For a more detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown `__. +our data. Training a model is an iterative process; in each iteration (called an *epoch*) the model makes a guess about the output, calculates +the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in +the `previous section `_), and **optimizes** these parameters using gradient descent. For a more +detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown `__. -Hyperparameters +Pre-requisite Code ----------------- +We load the code from the previous sections on `Datasets & DataLoaders `_ +and `Build Model `_. +""" -Hyperparameters are adjustable parameters that let you control the model optimization process. -Different hyperparameter values can impact model training and convergence rates (`read more `__ about hyperparameter tuning) - -In our case, we need to define the following hyperparameters: - - - **Number of Epochs**- the number times to iterate over the dataset - - **Batch Size** - the number of data samples seen by the model in each epoch - - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training. - -.. code-block:: Python - - learning_rate = 1e-3 - batch_size = 64 - epochs = 5 - -We also need to create the model class instance (defined in the previous section): - -.. code-block:: Python - - model = NeuralNetwork() - -Optimization Loop ------------------ - -.. figure:: /_static/img/quickstart/optimizationloops.png - :alt: - -Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. Each -iteration of the optimization loop is called an **epoch**. Each epoch consists of two main parts: - - 1. **The Train Loop** - main loop that iterates over all dataset and performs training - 2. **The Validation/Test Loop** - goes through the validation / test dataset to evaluate model performance on the test data. - -Here is a high-level view of optimization loop: - -.. code-block:: Python - - for epoch in range(num_epochs): # Iterate over all epochs - - # Training loop: - for train_features, train_labels in train_dataloader: # Go over all minibatches - out = model(train_features) # Compute network output - loss = loss_function(out,train_labels) # Compute loss function - # optimize weights to minimize loss - ... - - # Evaluation loop - model.eval() # set to evaluation mode not to compute gradients - for test_features, test_labels: - out = model(test_features) - loss = loss_function(out,train_labels) - # store / display the loss and/or other metrics - ... - -Complete code for optimization loop will be presented at the end of this section. - -Loss Function -------------- - -When presented with some training data, our untrained network is likely not to give the correct -answer. **Loss function** measures the degree of dissimilarity of obtained result to the target value, -and it is the loss function that we want to minimize during training. To calculate the loss we make a -prediction using the inputs of our given data sample and compare it against the true data label value. - -Common loss functions include `Mean Square Error `_ (for regression tasks), `Negative Log Likelihood `_, and `CrossEntropyLoss `_ (for classification tasks). - -In our example, we will use the built-in Cross Entropy Loss function: - -.. code-block:: Python - - # Initialize the loss function - loss_function = nn.CrossEntropyLoss() - -Optimizer ---------- - -Optimization is the process of adjusting model parameters to reduce model error in each training step. **Optimization algorithms** define how this process is performed (in this example we use Stochastic Gradient Descent). -All optimization logic is encapsulated in the``optimizer`` object. In this case, we use the SGD optimizer: - -.. code-block:: Python - - optimizer = optim.SGD(model.parameters(), lr=learning_rate) - -In addition to SGD there are many `different optimizers `_ available in PyTorch such as ADAM and RMSProp, that work better for different kinds of models. - -Inside the training loop, optimization happens in three steps: - - * Call ``optimizer.zero_grad()`` function to zero the gradients. As you have seen in the previous section on automatic differentiation, gradients by default add up, so we need to explicitly zero them on each step. - * Calculate the loss using loss function. This builds a computation graph, which PyTorch uses to automatically update parameters with respect to our model's loss during training. This is done with one call to ``loss.backwards()``. - * Once we have our gradients, we call ``optimizer.step()`` to adjust the parameters by the gradients collected in the backward pass. - -.. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png - :alt: tensor graph - -Putting it all together ------------------------ - -Below is the complete code for the optimization loop. If you want a complete runnable example of training the model, refer to the `main page `_. The code below is commented to explain what goes on, but essentially it is put together from concepts that we have described above. - -.. code-block:: Python - - for epoch in range(num_epochs): # Do training for each epoch - - # Training loop over all data in minibatches - for train_batch, (train_inputs, train_labels) in enumerate(train_dataloader): - model.train() # Set model to train mode - # we need to move the data to the devices used for training - train_inputs, train_labels = - train_inputs.to(device), train_labels.to(device) - optimizer.zero_grad() # zero out gradients - pred = model(train_inputs) # make a prediction on the current batch - loss = cost_function(pred, train_labels) # compute loss function - loss.backward() # compute gradients of loss function - optimizer.step() # update parameters - - # Test loop: go over test dataset - for test_batch, (test_inputs, test_labels) in enumerate(test_dataloader): - # move data to the device we use for computations - test_inputs, test_labels = - test_inputs.to(device), test_labels.to(device) - pred = model(test_inputs) # evaluate model on test minibatch - test_loss += cost_function(pred, test_labels).item() # compute loss - # compute the metrics for classification: - # how many classes were guessed correctly - correct += - (pred.argmax(1) == test_labels.argmax(1)) - .type(torch.float).sum().item() - - test_loss /= len(test_dataloader.dataset) - correct /= len(test_dataloader.dataset) - print('Epoch {} test Error:'.format(epoch)) - print('acc: {:>0.1f}%, avg loss: {:>8f}'.format(100*correct, test_loss)) - -Creating Custom Cost Functions ------------------------------- - -In addition to the included PyTorch cost functions you can create your own custom cost functions as long as they are differentiable. Here is an example of custom Cross Entropy Loss implementation from the `Stanford CS230 `_ course: - -.. code-block:: Python - - def myCrossEntropyLoss(outputs, labels): - batch_size = outputs.size()[0] - # compute the log of softmax values - outputs = F.log_softmax(outputs, dim=1) - # pick the values corresponding to the labels - outputs = outputs[range(batch_size), labels] - return -torch.sum(outputs)/num_examples - -It can be called just like the out of the box implementation above. - -.. code-block:: Python - - loss = myCrossEntropyLoss(model_prediction, true_value) - -A more in depth explanation of PyTorch cost functions is outside the scope of the tutorial but you can learn more -about the different common cost functions for deep learning in the PyTorch `documentation `_. +import torch +from torch import nn +from torch.utils.data import DataLoader +from torchvision import datasets +from torchvision.transforms import ToTensor, Lambda + +training_data = datasets.FashionMNIST( + root="data", + train=True, + download=True, + transform=ToTensor() +) + +test_data = datasets.FashionMNIST( + root="data", + train=False, + download=True, + transform=ToTensor() +) + +train_dataloader = DataLoader(training_data, batch_size=64) +test_dataloader = DataLoader(test_data, batch_size=64) + +class NeuralNetwork(nn.Module): + def __init__(self): + super(NeuralNetwork, self).__init__() + self.flatten = nn.Flatten() + self.linear_relu_stack = nn.Sequential( + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, 10), + nn.ReLU() + ) + + def forward(self, x): + x = self.flatten(x) + logits = self.linear_relu_stack(x) + return logits + +model = NeuralNetwork() + + +############################################## +# Hyperparameters +# ----------------- +# +# Hyperparameters are adjustable parameters that let you control the model optimization process. +# Different hyperparameter values can impact model training and convergence rates +# (`read more `__ about hyperparameter tuning) +# +# We define the following hyperparameters for training: +# - **Number of Epochs** - the number times to iterate over the dataset +# - **Batch Size** - the number of data samples seen by the model in each epoch +# - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training. +# + +learning_rate = 1e-3 +batch_size = 64 +epochs = 5 + + + +##################################### +# Optimization Loop +# ----------------- +# +# Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. Each +# iteration of the optimization loop is called an **epoch**. +# +# Each epoch consists of two main parts: +# - **The Train Loop** - iterate over the training dataset and try to converge to optimal parameters. +# - **The Validation/Test Loop** - iterate over the test dataset to check if model performance is improving. +# +# Let's briefly familiarize ourselves with some of the concepts used in the training loop. Jump ahead to +# see the :ref:`full-impl-label` of the optimization loop. +# +# Loss Function +# ~~~~~~~~~~~~~~~~~ +# +# When presented with some training data, our untrained network is likely not to give the correct +# answer. **Loss function** measures the degree of dissimilarity of obtained result to the target value, +# and it is the loss function that we want to minimize during training. To calculate the loss we make a +# prediction using the inputs of our given data sample and compare it against the true data label value. +# +# Common loss functions include `nn.MSELoss `_ (Mean Square Error) for regression tasks, and +# `nn.NLLLoss `_ (Negative Log Likelihood) for classification. +# `nn.CrossEntropyLoss `_ combines ``nn.LogSoftmax`` and ``nn.NLLLoss``. +# +# We pass our model's output logits to ``nn.CrossEntropyLoss``, which will normalize the logits and compute the prediction error. + +# Initialize the loss function +loss_fn = nn.CrossEntropyLoss() + + + +##################################### +# Optimizer +# ~~~~~~~~~~~~~~~~~ +# +# Optimization is the process of adjusting model parameters to reduce model error in each training step. **Optimization algorithms** define how this process is performed (in this example we use Stochastic Gradient Descent). +# All optimization logic is encapsulated in the ``optimizer`` object. Here, we use the SGD optimizer; additionally, there are many `different optimizers `_ +# available in PyTorch such as ADAM and RMSProp, that work better for different kinds of models and data. +# +# We initialize the optimizer by registering the model's parameters that need to be trained, and passing in the learning rate hyperparameter. + +optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) + +##################################### +# Inside the training loop, optimization happens in three steps: +# * Call ``optimizer.zero_grad()`` to reset the gradients of model parameters. Gradients by default add up; to prevent double-counting, we explicitly zero them at each iteration. +# * Backpropagate the prediction loss with a call to ``loss.backwards()``. PyTorch deposits the gradients of the loss w.r.t. each parameter. +# * Once we have our gradients, we call ``optimizer.step()`` to adjust the parameters by the gradients collected in the backward pass. + + +######################################## +# .. _full-impl-label: +# +# Full Implementation +# ----------------------- +# We define ``train_loop`` that loops over our optimization code, and ``test_loop`` that +# evaluates the model's performance against our test data. + +def train_loop(dataloader, model, loss_fn, optimizer): + size = len(dataloader.dataset) + for batch, (X, y) in enumerate(dataloader): + # Compute prediction and loss + pred = model(X) + loss = loss_fn(pred, y) + + # Backpropagation + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if batch % 100 == 0: + loss, current = loss.item(), batch * len(X) + print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") + + +def test_loop(dataloader, model, loss_fn): + size = len(dataloader.dataset) + test_loss, correct = 0, 0 + + with torch.no_grad(): + for X, y in dataloader: + pred = model(X) + test_loss += loss_fn(pred, y).item() + correct += (pred.argmax(1) == y).type(torch.float).sum().item() + + test_loss /= size + correct /= size + print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n") + + +######################################## +# We initialize the loss function and optimizer, and pass it to ``train_loop`` and ``test_loop``. +# Feel free to increase the number of epochs to track the model's improving performance. + +loss_fn = nn.CrossEntropyLoss() +optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) + +epochs = 10 +for t in range(epochs): + print(f"Epoch {t+1}\n-------------------------------") + train_loop(train_dataloader, model, loss_fn, optimizer) + test_loop(test_dataloader, model, loss_fn) +print("Done!") + + + +################################################################# +# Further Reading +# ----------------------- +# - `Loss Functions `_ +# - `torch.optim `_ +# - `Warmstart Training a Model `_ +# -""" diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index b7d41a4ca78..248361719a6 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -18,9 +18,9 @@ `Dmitry Soshnikov `_, `Ari Bornstein `_ -A basic machine learning workflow involves working with data, creating models, optimizing model -parameters, and saving the trained models. This tutorial introduces you to the complete ML workflow -as implemented in PyTorch, with links to learn more about of these concepts. +Most machine learning workflows involve working with data, creating models, optimizing model +parameters, and saving the trained models. This tutorial introduces you to a complete ML workflow +implemented in PyTorch, with links to learn more about each of these concepts. We'll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs to one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, @@ -30,7 +30,7 @@ Running the Tutorial Code ------------------ -You can run this tutorial in a few ways: +You can run this tutorial in a couple of ways: - **In the cloud**: This is the easiest way to get started! Each section has a Colab link at the top, which opens a notebook with the code in a fully-hosted environment. Pro tip: Use Colab with a GPU runtime to speed up operations *Runtime > Change runtime type > GPU* - **Locally**: This option requires you to setup PyTorch and TorchVision first on your local machine (`installation instructions `_). Download the notebook or copy the code into your favorite IDE. @@ -38,10 +38,10 @@ How to Use this Guide ----------------- -This page contains an overview of the code used at each step of the tutorial. If you're familiar with -other deep learning frameworks, this is a quick way to get acquainted with PyTorch's API. +The rest of this page contains an *overview* of the code used in the complete ML workflow. +If you're familiar with other deep learning frameworks, this is a quick way to get acquainted with PyTorch's API. -If this is your first time, head right into our step-by-step guide: +If this is your first time working with deep learning frameworks, head right into our step-by-step guide: .. include:: /beginner_source/quickstart/qs_toc.txt @@ -56,14 +56,13 @@ /beginner/quickstart/optimization_tutorial /beginner/quickstart/saveloadrun_tutorial - - -------------- Working with data ----------------- -PyTorch has two data primitives to work with data: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``. +PyTorch has two `primitives to work with data `_: +``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``. ``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around the ``Dataset``. @@ -82,26 +81,12 @@ # use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and # ``target_transform`` to modify the samples and labels respectively. -classes = [ - "T-shirt/top", - "Trouser", - "Pullover", - "Dress", - "Coat", - "Sandal", - "Shirt", - "Sneaker", - "Bag", - "Ankle boot", -] - # Download training data from open datasets. training_data = datasets.FashionMNIST( root="data", train=True, download=True, transform=ToTensor(), - target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) # Download test data from open datasets. @@ -110,7 +95,6 @@ train=False, download=True, transform=ToTensor(), - target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) ###################################################################### @@ -126,9 +110,13 @@ for X, y in test_dataloader: print("Shape of X [N, C, H, W]: ", X.shape) - print("Shape of y: ", y.shape) + print("Shape of y: ", y.shape, y.dtype) break +###################################################################### +# Read more about `loading data in PyTorch `_. +# + ###################################################################### # -------------- # @@ -139,7 +127,7 @@ # To define a neural network in PyTorch, we create a class that inherits # from `nn.Module `_. We define the layers of the network # in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate -# operations in the NN, we move it to the GPU if available. +# operations in the neural network, we move it to the GPU if available. # Get cpu or gpu device for training. device = "cuda" if torch.cuda.is_available() else "cpu" @@ -150,18 +138,19 @@ class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() self.flatten = nn.Flatten() - self.softmax = nn.Softmax(dim=1) - self.nn_layers = nn.Sequential( - nn.Linear(28 * 28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, 10) - ) + self.linear_relu_stack = nn.Sequential( + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, 10), + nn.ReLU() + ) + def forward(self, x): x = self.flatten(x) - x = self.nn_layers(x) - return self.softmax(x) + logits = self.linear_relu_stack(x) + return logits model = NeuralNetwork().to(device) print(model) @@ -181,8 +170,8 @@ def forward(self, x): # To train a model, we need a `loss function `_ # and an `optimizer `_. -loss_fn = nn.BCELoss() -optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) +loss_fn = nn.CrossEntropyLoss() +optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) ####################################################################### # In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and @@ -218,7 +207,7 @@ def test(dataloader, model): X, y = X.to(device), y.to(device) pred = model(X) test_loss += loss_fn(pred, y).item() - correct += (pred.argmax(1) == y.argmax(1)).type(torch.float).sum().item() + correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= size correct /= size print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n") @@ -270,9 +259,22 @@ def test(dataloader, model): ############################################################# # This model can now be used to make predictions. +classes = [ + "T-shirt/top", + "Trouser", + "Pullover", + "Dress", + "Coat", + "Sandal", + "Shirt", + "Sneaker", + "Bag", + "Ankle boot", +] + model.eval() x, y = test_data[0][0], test_data[0][1] with torch.no_grad(): pred = model(x) - predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] + predicted, actual = classes[pred[0].argmax(0)], classes[y] print(f'Predicted: "{predicted}", Actual: "{actual}"') diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 2f16a09f5b5..5642d372b10 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -74,9 +74,9 @@ # and in different programming languages. For more details, we recommend # visiting `ONNX tutorial `_. # -# Congratulations! You have completed the PyTorch beginner tutorial! You can -# now `return to the first page `_ and go over the sample code -# again and we hope you have a better understanding of how to do deep learning with PyTorch. -# Good luck on your deep learning journey! -# +# Congratulations! You have completed the PyTorch beginner tutorial! Try +# `revisting the first page `_ to see the tutorial in its entirety +# again. We hope this tutorial has helped you get started with deep learning on PyTorch. +# Good luck! # + diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 7676ac085e8..36d680b117b 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -65,4 +65,4 @@ ################################################################# # Further Reading # ~~~~~~~~~~~~~~~~~ -# - `torchvision.tranasforms API `_ +# - `torchvision.transforms API `_ diff --git a/model.pth b/model.pth new file mode 100644 index 0000000000000000000000000000000000000000..74e7c186ca9cba73be4e4fd72745736105428b3d GIT binary patch literal 2681451 zcmbTdXH=BIvNlSRoO6znbB1}VRYXO_h&hXhf}#=yGeJaxVjzkliU9>h5DD{Eqlg40 zhyfH85m6B_pr`}|?(DtKS@)j%oxOj2v)0U7^fX=l)Ku47UG>xgPeCC*K2cG=|Klag zC(q{{?BnOZ)7N^Vcc`~j;MRZ{Q>FP<{*PCrfRE3PfB;{gQ2!mw7kfuXd#51b<$}CXpTMvnk=cB@!hE4Yq6vekl+7Do`bWqgPo(TtDQ}d 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zHOFD(fDb6RQr#c}d6FO4%dhl`h3F5%_`o|D(*q09?w%Lg+lWhqi8~7loSPuZaVYL&LM`MU=Sf3fsN=0938F_^3WZ;J0fB#+ZzuyxT`~98-{rP?V-_QLoFZHUo literal 0 HcmV?d00001 From 4e92afc959c87bc3efc44971fc92689910f98ac2 Mon Sep 17 00:00:00 2001 From: suraj813 Date: Mon, 1 Feb 2021 15:09:46 -0500 Subject: [PATCH 005/120] final edits --- beginner_source/quickstart/dataquickstart_tutorial.py | 4 ++-- beginner_source/quickstart/quickstart_tutorial.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 4fd8be201e1..b953f3567d9 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -36,13 +36,13 @@ # # Here is an example of how to load the `Fashion-MNIST `_ dataset from TorchVision. # Fashion-MNIST is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. -# Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. +# Each example comprises a 28×28 grayscale image and an associated label from one of 10 classes. # # We load the `FashionMNIST Dataset `_ with the following parameters: # - ``root`` is the path where the train/test data is stored, # - ``train`` specifies training or test dataset, # - ``download=True`` downloads the data from the internet if it's not available at ``root``. -# - ``transform`` and ``target_transform`` specify the feature and label transformations (more on this in the next section) +# - ``transform`` and ``target_transform`` specify the feature and label transformations import torch diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 248361719a6..b4aa5f4f908 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -165,7 +165,7 @@ def forward(self, x): # ##################################################################### -# Training the Model +# Optimizing the Model Parameters # ---------------------------------------- # To train a model, we need a `loss function `_ # and an `optimizer `_. From c7e9d6d4cd0781c33d5730e13530ae38223b77b9 Mon Sep 17 00:00:00 2001 From: Jinlin Zhang Date: Thu, 24 Sep 2020 10:47:47 -0700 Subject: [PATCH 006/120] A fix for one line comment when removing runnable code. (#1165) Co-authored-by: v-jizhang <66389669+buck-bot@users.noreply.github.com> --- .jenkins/remove_runnable_code.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/.jenkins/remove_runnable_code.py b/.jenkins/remove_runnable_code.py index 6a61cb656bc..bd62f0c5156 100644 --- a/.jenkins/remove_runnable_code.py +++ b/.jenkins/remove_runnable_code.py @@ -16,9 +16,17 @@ if line.startswith('#'): ret_lines.append(line) state = STATE_NORMAL + elif ((line.startswith('"""') or line.startswith('r"""')) and + line.endswith('"""')): + ret_lines.append(line) + state = STATE_NORMAL elif line.startswith('"""') or line.startswith('r"""'): ret_lines.append(line) state = STATE_IN_MULTILINE_COMMENT_BLOCK_DOUBLE_QUOTE + elif ((line.startswith("'''") or line.startswith("r'''")) and + line.endswith("'''")): + ret_lines.append(line) + state = STATE_NORMAL elif line.startswith("'''") or line.startswith("r'''"): ret_lines.append(line) state = STATE_IN_MULTILINE_COMMENT_BLOCK_SINGLE_QUOTE From 67bb706438bc278ac22e73818d56bf3fafb65922 Mon Sep 17 00:00:00 2001 From: Pritam Damania <9958665+pritamdamania87@users.noreply.github.com> Date: Thu, 24 Sep 2020 19:58:10 -0700 Subject: [PATCH 007/120] Update tutorials to use TensorPipeRpcBackendOptions. (#1164) * Update tutorials to use TensorPipeRpcBackendOptions. * Commit Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * Commit2 Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: Co-authored-by: pritam --- advanced_source/rpc_ddp_tutorial/main.py | 4 ++-- intermediate_source/dist_pipeline_parallel_tutorial.rst | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/advanced_source/rpc_ddp_tutorial/main.py b/advanced_source/rpc_ddp_tutorial/main.py index f83384d0a8d..3d0d6ba2219 100644 --- a/advanced_source/rpc_ddp_tutorial/main.py +++ b/advanced_source/rpc_ddp_tutorial/main.py @@ -6,7 +6,7 @@ import torch.distributed as dist import torch.distributed.autograd as dist_autograd import torch.distributed.rpc as rpc -from torch.distributed.rpc import ProcessGroupRpcBackendOptions +from torch.distributed.rpc import TensorPipeRpcBackendOptions import torch.multiprocessing as mp import torch.optim as optim from torch.distributed.optim import DistributedOptimizer @@ -128,7 +128,7 @@ def run_worker(rank, world_size): os.environ['MASTER_PORT'] = '29500' - rpc_backend_options = ProcessGroupRpcBackendOptions() + rpc_backend_options = TensorPipeRpcBackendOptions() rpc_backend_options.init_method='tcp://localhost:29501' # Rank 2 is master, 3 is ps and 0 and 1 are trainers. diff --git a/intermediate_source/dist_pipeline_parallel_tutorial.rst b/intermediate_source/dist_pipeline_parallel_tutorial.rst index 693043478fb..2c8cc730258 100644 --- a/intermediate_source/dist_pipeline_parallel_tutorial.rst +++ b/intermediate_source/dist_pipeline_parallel_tutorial.rst @@ -316,7 +316,7 @@ where the ``shutdown`` by default will block until all RPC participants finish. def run_worker(rank, world_size, num_split): os.environ['MASTER_ADDR'] = 'localhost' os.environ['MASTER_PORT'] = '29500' - options = rpc.ProcessGroupRpcBackendOptions(num_send_recv_threads=128) + options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=128) if rank == 0: rpc.init_rpc( From 0b1416e786bd88a97d9d831164651b6df0b139a7 Mon Sep 17 00:00:00 2001 From: supriyar Date: Fri, 25 Sep 2020 13:38:34 -0700 Subject: [PATCH 008/120] Update dynamic quant tutorial for saving quantized model (#1167) Summary: Addresses https://github.com/pytorch/pytorch/issues/43016 Test Plan: Reviewers: Subscribers: Tasks: Tags: --- .../dynamic_quantization_bert_tutorial.rst | 20 +++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/intermediate_source/dynamic_quantization_bert_tutorial.rst b/intermediate_source/dynamic_quantization_bert_tutorial.rst index 6642f6768c8..2bca7117f51 100644 --- a/intermediate_source/dynamic_quantization_bert_tutorial.rst +++ b/intermediate_source/dynamic_quantization_bert_tutorial.rst @@ -492,7 +492,7 @@ follows: | Prec | F1 score | Model Size | 1 thread | 4 threads | | FP32 | 0.9019 | 438 MB | 160 sec | 85 sec | - | INT8 | 0.8953 | 181 MB | 90 sec | 46 sec | + | INT8 | 0.902 | 181 MB | 90 sec | 46 sec | We have 0.6% F1 score accuracy after applying the post-training dynamic quantization on the fine-tuned BERT model on the MRPC task. As a @@ -520,15 +520,23 @@ processing the evaluation of MRPC dataset. 3.3 Serialize the quantized model ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -We can serialize and save the quantized model for the future use. +We can serialize and save the quantized model for the future use using +`torch.jit.save` after tracing the model. .. code:: python - quantized_output_dir = configs.output_dir + "quantized/" - if not os.path.exists(quantized_output_dir): - os.makedirs(quantized_output_dir) - quantized_model.save_pretrained(quantized_output_dir) + input_ids = ids_tensor([8, 128], 2) + token_type_ids = ids_tensor([8, 128], 2) + attention_mask = ids_tensor([8, 128], vocab_size=2) + dummy_input = (input_ids, attention_mask, token_type_ids) + traced_model = torch.jit.trace(quantized_model, dummy_input) + torch.jit.save(traced_model, "bert_traced_eager_quant.pt") +To load the quantized model, we can use `torch.jit.load` + +.. code:: python + + loaded_quantized_model = torch.jit.load("bert_traced_eager_quant.pt") Conclusion ---------- From afd8d15817b702b9c30a57056a2def817a6632d4 Mon Sep 17 00:00:00 2001 From: Basil Hosmer Date: Mon, 28 Sep 2020 14:08:51 -0700 Subject: [PATCH 009/120] retitle dispatcher tutorial (#1168) --- advanced_source/dispatcher.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/advanced_source/dispatcher.rst b/advanced_source/dispatcher.rst index 4f3b52fea32..0d474e90dce 100644 --- a/advanced_source/dispatcher.rst +++ b/advanced_source/dispatcher.rst @@ -1,5 +1,5 @@ -Dispatcher in C++ -================= +Registering a Dispatched Operator in C++ +======================================== The dispatcher is an internal component of PyTorch which is responsible for figuring out what code should actually get run when you call a function like From ba29dd6b76e16971e647b2d9141a30ca3e55a4d1 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Tue, 29 Sep 2020 13:17:53 -0700 Subject: [PATCH 010/120] minor typo fixes (#1160) Co-authored-by: Brian Johnson --- recipes_source/mobile_perf.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/recipes_source/mobile_perf.rst b/recipes_source/mobile_perf.rst index e4d432b4297..2e7e7c17f73 100644 --- a/recipes_source/mobile_perf.rst +++ b/recipes_source/mobile_perf.rst @@ -72,7 +72,7 @@ Code your model: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Do not be confused that fuse_modules is in the quantization package. -It works for all ``torcn.nn.Module``. +It works for all ``torch.nn.Module``. ``torch.quantization.fuse_modules`` fuses a list of modules into a single module. It fuses only the following sequence of modules: @@ -237,7 +237,7 @@ Now we are ready to benchmark your model: :: - adb shell "/data/local/tmp/speed_benchmark_torch --model="/data/local/tmp/model.pt" --input_dims="1,3,224,224" --input_type="float" + adb shell "/data/local/tmp/speed_benchmark_torch --model=/data/local/tmp/model.pt" --input_dims="1,3,224,224" --input_type="float" ----- output ----- Starting benchmark. Running warmup runs. @@ -250,7 +250,7 @@ iOS - Benchmarking Setup For iOS, we'll be using our `TestApp `_ as the benchmarking tool. -To begin with, let's apply the ``optimize_for_mobile`` method to our python script located at `TestApp/benchmark/trace_mode.py `_. Simply modify the code as below. +To begin with, let's apply the ``optimize_for_mobile`` method to our python script located at `TestApp/benchmark/trace_model.py `_. Simply modify the code as below. :: From fba61d95e727946ea6b306c4fdedfb8c239ff39b Mon Sep 17 00:00:00 2001 From: Yuxin Wu Date: Fri, 2 Oct 2020 09:01:42 -0700 Subject: [PATCH 011/120] Update saving_loading_models.py (#1173) --- beginner_source/saving_loading_models.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/beginner_source/saving_loading_models.py b/beginner_source/saving_loading_models.py index 74a8edc892d..c94d2d8ce36 100644 --- a/beginner_source/saving_loading_models.py +++ b/beginner_source/saving_loading_models.py @@ -262,7 +262,8 @@ # as this contains buffers and parameters that are updated as the model # trains. Other items that you may want to save are the epoch you left off # on, the latest recorded training loss, external ``torch.nn.Embedding`` -# layers, etc. +# layers, etc. As a result, such a checkpoint is often 2~3 times larger +# than the model alone. # # To save multiple components, organize them in a dictionary and use # ``torch.save()`` to serialize the dictionary. A common PyTorch From 37f747b196843df505e10c9bd0a2789208074c80 Mon Sep 17 00:00:00 2001 From: Brian Johnson Date: Fri, 9 Oct 2020 12:45:02 -0700 Subject: [PATCH 012/120] Removes Outdated Trainer Tutorial (#1181) * Update index.rst * Delete aws_distributed_training_tutorial.py --- .../aws_distributed_training_tutorial.py | 691 ------------------ index.rst | 8 - 2 files changed, 699 deletions(-) delete mode 100644 beginner_source/aws_distributed_training_tutorial.py diff --git a/beginner_source/aws_distributed_training_tutorial.py b/beginner_source/aws_distributed_training_tutorial.py deleted file mode 100644 index 1789516c2b0..00000000000 --- a/beginner_source/aws_distributed_training_tutorial.py +++ /dev/null @@ -1,691 +0,0 @@ -""" -(advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS -============================================================= - -**Author**: `Nathan Inkawhich `_ - -**Edited by**: `Teng Li `_ - -""" - - -###################################################################### -# In this tutorial we will show how to setup, code, and run a PyTorch 1.0 -# distributed trainer across two multi-gpu Amazon AWS nodes. We will start -# with describing the AWS setup, then the PyTorch environment -# configuration, and finally the code for the distributed trainer. -# Hopefully you will find that there is actually very little code change -# required to extend your current training code to a distributed -# application, and most of the work is in the one-time environment setup. -# - - -###################################################################### -# Amazon AWS Setup -# ---------------- -# -# In this tutorial we will run distributed training across two multi-gpu -# nodes. In this section we will first cover how to create the nodes, then -# how to setup the security group so the nodes can communicate with -# eachother. -# -# Creating the Nodes -# ~~~~~~~~~~~~~~~~~~ -# -# In Amazon AWS, there are seven steps to creating an instance. To get -# started, login and select **Launch Instance**. -# -# **Step 1: Choose an Amazon Machine Image (AMI)** - Here we will select -# the ``Deep Learning AMI (Ubuntu) Version 14.0``. As described, this -# instance comes with many of the most popular deep learning frameworks -# installed and is preconfigured with CUDA, cuDNN, and NCCL. It is a very -# good starting point for this tutorial. -# -# **Step 2: Choose an Instance Type** - Now, select the GPU compute unit -# called ``p2.8xlarge``. Notice, each of these instances has a different -# cost but this instance provides 8 NVIDIA Tesla K80 GPUs per node, and -# provides a good architecture for multi-gpu distributed training. -# -# **Step 3: Configure Instance Details** - The only setting to change here -# is increasing the *Number of instances* to 2. All other configurations -# may be left at default. -# -# **Step 4: Add Storage** - Notice, by default these nodes do not come -# with a lot of storage (only 75 GB). For this tutorial, since we are only -# using the STL-10 dataset, this is plenty of storage. But, if you want to -# train on a larger dataset such as ImageNet, you will have to add much -# more storage just to fit the dataset and any trained models you wish to -# save. -# -# **Step 5: Add Tags** - Nothing to be done here, just move on. -# -# **Step 6: Configure Security Group** - This is a critical step in the -# configuration process. By default two nodes in the same security group -# would not be able to communicate in the distributed training setting. -# Here, we want to create a **new** security group for the two nodes to be -# in. However, we cannot finish configuring in this step. For now, just -# remember your new security group name (e.g. launch-wizard-12) then move -# on to Step 7. -# -# **Step 7: Review Instance Launch** - Here, review the instance then -# launch it. By default, this will automatically start initializing the -# two instances. You can monitor the initialization progress from the -# dashboard. -# -# Configure Security Group -# ~~~~~~~~~~~~~~~~~~~~~~~~ -# -# Recall that we were not able to properly configure the security group -# when creating the instances. Once you have launched the instance, select -# the *Network & Security > Security Groups* tab in the EC2 dashboard. -# This will bring up a list of security groups you have access to. Select -# the new security group you created in Step 6 (i.e. launch-wizard-12), -# which will bring up tabs called *Description, Inbound, Outbound, and -# Tags*. First, select the *Inbound* tab and *Edit* to add a rule to allow -# "All Traffic" from "Sources" in the launch-wizard-12 security group. -# Then select the *Outbound* tab and do the exact same thing. Now, we have -# effectively allowed all Inbound and Outbound traffic of all types -# between nodes in the launch-wizard-12 security group. -# -# Necessary Information -# ~~~~~~~~~~~~~~~~~~~~~ -# -# Before continuing, we must find and remember the IP addresses of both -# nodes. In the EC2 dashboard find your running instances. For both -# instances, write down the *IPv4 Public IP* and the *Private IPs*. For -# the remainder of the document, we will refer to these as the -# **node0-publicIP**, **node0-privateIP**, **node1-publicIP**, and -# **node1-privateIP**. The public IPs are the addresses we will use to SSH -# in, and the private IPs will be used for inter-node communication. -# - - -###################################################################### -# Environment Setup -# ----------------- -# -# The next critical step is the setup of each node. Unfortunately, we -# cannot configure both nodes at the same time, so this process must be -# done on each node separately. However, this is a one time setup, so once -# you have the nodes configured properly you will not have to reconfigure -# for future distributed training projects. -# -# The first step, once logged onto the node, is to create a new conda -# environment with python 3.6 and numpy. Once created activate the -# environment. -# -# :: -# -# $ conda create -n nightly_pt python=3.6 numpy -# $ source activate nightly_pt -# -# Next, we will install a nightly build of Cuda 9.0 enabled PyTorch with -# pip in the conda environment. -# -# :: -# -# $ pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu90/torch_nightly.html -# -# We must also install torchvision so we can use the torchvision model and -# dataset. At this time, we must build torchvision from source as the pip -# installation will by default install an old version of PyTorch on top of -# the nightly build we just installed. -# -# :: -# -# $ cd -# $ git clone https://github.com/pytorch/vision.git -# $ cd vision -# $ python setup.py install -# -# And finally, **VERY IMPORTANT** step is to set the network interface -# name for the NCCL socket. This is set with the environment variable -# ``NCCL_SOCKET_IFNAME``. To get the correct name, run the ``ifconfig`` -# command on the node and look at the interface name that corresponds to -# the node's *privateIP* (e.g. ens3). Then set the environment variable as -# -# :: -# -# $ export NCCL_SOCKET_IFNAME=ens3 -# -# Remember, do this on both nodes. You may also consider adding the -# NCCL\_SOCKET\_IFNAME setting to your *.bashrc*. An important observation -# is that we did not setup a shared filesystem between the nodes. -# Therefore, each node will have to have a copy of the code and a copy of -# the datasets. For more information about setting up a shared network -# filesystem between nodes, see -# `here `__. -# - - -###################################################################### -# Distributed Training Code -# ------------------------- -# -# With the instances running and the environments setup we can now get -# into the training code. Most of the code here has been taken from the -# `PyTorch ImageNet -# Example `__ -# which also supports distributed training. This code provides a good -# starting point for a custom trainer as it has much of the boilerplate -# training loop, validation loop, and accuracy tracking functionality. -# However, you will notice that the argument parsing and other -# non-essential functions have been stripped out for simplicity. -# -# In this example we will use -# `torchvision.models.resnet18 `__ -# model and will train it on the -# `torchvision.datasets.STL10 `__ -# dataset. To accomodate for the dimensionality mismatch of STL-10 with -# Resnet18, we will resize each image to 224x224 with a transform. Notice, -# the choice of model and dataset are orthogonal to the distributed -# training code, you may use any dataset and model you wish and the -# process is the same. Lets get started by first handling the imports and -# talking about some helper functions. Then we will define the train and -# test functions, which have been largely taken from the ImageNet Example. -# At the end, we will build the main part of the code which handles the -# distributed training setup. And finally, we will discuss how to actually -# run the code. -# - - -###################################################################### -# Imports -# ~~~~~~~ -# -# The important distributed training specific imports here are -# `torch.nn.parallel `__, -# `torch.distributed `__, -# `torch.utils.data.distributed `__, -# and -# `torch.multiprocessing `__. -# It is also important to set the multiprocessing start method to *spawn* -# or *forkserver* (only supported in Python 3), -# as the default is *fork* which may cause deadlocks when using multiple -# worker processes for dataloading. -# - -import time -import sys -import torch - -import torch.nn as nn -import torch.nn.parallel -import torch.distributed as dist -import torch.optim -import torch.utils.data -import torch.utils.data.distributed -import torchvision.transforms as transforms -import torchvision.datasets as datasets -import torchvision.models as models - -from torch.multiprocessing import Pool, Process - - -###################################################################### -# Helper Functions -# ~~~~~~~~~~~~~~~~ -# -# We must also define some helper functions and classes that will make -# training easier. The ``AverageMeter`` class tracks training statistics -# like accuracy and iteration count. The ``accuracy`` function computes -# and returns the top-k accuracy of the model so we can track learning -# progress. Both are provided for training convenience but neither are -# distributed training specific. -# - -class AverageMeter(object): - """Computes and stores the average and current value""" - def __init__(self): - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - -def accuracy(output, target, topk=(1,)): - """Computes the precision@k for the specified values of k""" - with torch.no_grad(): - maxk = max(topk) - batch_size = target.size(0) - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) - res.append(correct_k.mul_(100.0 / batch_size)) - return res - - -###################################################################### -# Train Functions -# ~~~~~~~~~~~~~~~ -# -# To simplify the main loop, it is best to separate a training epoch step -# into a function called ``train``. This function trains the input model -# for one epoch of the *train\_loader*. The only distributed training -# artifact in this function is setting the -# `non\_blocking `__ -# attributes of the data and label tensors to ``True`` before the forward -# pass. This allows asynchronous GPU copies of the data meaning transfers -# can be overlapped with computation. This function also outputs training -# statistics along the way so we can track progress throughout the epoch. -# -# The other function to define here is ``adjust_learning_rate``, which -# decays the initial learning rate at a fixed schedule. This is another -# boilerplate trainer function that is useful to train accurate models. -# - -def train(train_loader, model, criterion, optimizer, epoch): - - batch_time = AverageMeter() - data_time = AverageMeter() - losses = AverageMeter() - top1 = AverageMeter() - top5 = AverageMeter() - - # switch to train mode - model.train() - - end = time.time() - for i, (input, target) in enumerate(train_loader): - - # measure data loading time - data_time.update(time.time() - end) - - # Create non_blocking tensors for distributed training - input = input.cuda(non_blocking=True) - target = target.cuda(non_blocking=True) - - # compute output - output = model(input) - loss = criterion(output, target) - - # measure accuracy and record loss - prec1, prec5 = accuracy(output, target, topk=(1, 5)) - losses.update(loss.item(), input.size(0)) - top1.update(prec1[0], input.size(0)) - top5.update(prec5[0], input.size(0)) - - # compute gradients in a backward pass - optimizer.zero_grad() - loss.backward() - - # Call step of optimizer to update model params - optimizer.step() - - # measure elapsed time - batch_time.update(time.time() - end) - end = time.time() - - if i % 10 == 0: - print('Epoch: [{0}][{1}/{2}]\t' - 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' - 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' - 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' - 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' - 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( - epoch, i, len(train_loader), batch_time=batch_time, - data_time=data_time, loss=losses, top1=top1, top5=top5)) - -def adjust_learning_rate(initial_lr, optimizer, epoch): - """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" - lr = initial_lr * (0.1 ** (epoch // 30)) - for param_group in optimizer.param_groups: - param_group['lr'] = lr - - - -###################################################################### -# Validation Function -# ~~~~~~~~~~~~~~~~~~~ -# -# To track generalization performance and simplify the main loop further -# we can also extract the validation step into a function called -# ``validate``. This function runs a full validation step of the input -# model on the input validation dataloader and returns the top-1 accuracy -# of the model on the validation set. Again, you will notice the only -# distributed training feature here is setting ``non_blocking=True`` for -# the training data and labels before they are passed to the model. -# - -def validate(val_loader, model, criterion): - - batch_time = AverageMeter() - losses = AverageMeter() - top1 = AverageMeter() - top5 = AverageMeter() - - # switch to evaluate mode - model.eval() - - with torch.no_grad(): - end = time.time() - for i, (input, target) in enumerate(val_loader): - - input = input.cuda(non_blocking=True) - target = target.cuda(non_blocking=True) - - # compute output - output = model(input) - loss = criterion(output, target) - - # measure accuracy and record loss - prec1, prec5 = accuracy(output, target, topk=(1, 5)) - losses.update(loss.item(), input.size(0)) - top1.update(prec1[0], input.size(0)) - top5.update(prec5[0], input.size(0)) - - # measure elapsed time - batch_time.update(time.time() - end) - end = time.time() - - if i % 100 == 0: - print('Test: [{0}/{1}]\t' - 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' - 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' - 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' - 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( - i, len(val_loader), batch_time=batch_time, loss=losses, - top1=top1, top5=top5)) - - print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}' - .format(top1=top1, top5=top5)) - - return top1.avg - - -###################################################################### -# Inputs -# ~~~~~~ -# -# With the helper functions out of the way, now we have reached the -# interesting part. Here is where we will define the inputs for the run. -# Some of the inputs are standard model training inputs such as batch size -# and number of training epochs, and some are specific to our distributed -# training task. The required inputs are: -# -# - **batch\_size** - batch size for *each* process in the distributed -# training group. Total batch size across distributed model is -# batch\_size\*world\_size -# -# - **workers** - number of worker processes used with the dataloaders in -# each process -# -# - **num\_epochs** - total number of epochs to train for -# -# - **starting\_lr** - starting learning rate for training -# -# - **world\_size** - number of processes in the distributed training -# environment -# -# - **dist\_backend** - backend to use for distributed training -# communication (i.e. NCCL, Gloo, MPI, etc.). In this tutorial, since -# we are using several multi-gpu nodes, NCCL is suggested. -# -# - **dist\_url** - URL to specify the initialization method of the -# process group. This may contain the IP address and port of the rank0 -# process or be a non-existant file on a shared file system. Here, -# since we do not have a shared file system this will incorporate the -# **node0-privateIP** and the port on node0 to use. -# - -print("Collect Inputs...") - -# Batch Size for training and testing -batch_size = 32 - -# Number of additional worker processes for dataloading -workers = 2 - -# Number of epochs to train for -num_epochs = 2 - -# Starting Learning Rate -starting_lr = 0.1 - -# Number of distributed processes -world_size = 4 - -# Distributed backend type -dist_backend = 'nccl' - -# Url used to setup distributed training -dist_url = "tcp://172.31.22.234:23456" - - -###################################################################### -# Initialize process group -# ~~~~~~~~~~~~~~~~~~~~~~~~ -# -# One of the most important parts of distributed training in PyTorch is to -# properly setup the process group, which is the **first** step in -# initializing the ``torch.distributed`` package. To do this, we will use -# the ``torch.distributed.init_process_group`` function which takes -# several inputs. First, a *backend* input which specifies the backend to -# use (i.e. NCCL, Gloo, MPI, etc.). An *init\_method* input which is -# either a url containing the address and port of the rank0 machine or a -# path to a non-existant file on the shared file system. Note, to use the -# file init\_method, all machines must have access to the file, similarly -# for the url method, all machines must be able to communicate on the -# network so make sure to configure any firewalls and network settings to -# accomodate. The *init\_process\_group* function also takes *rank* and -# *world\_size* arguments which specify the rank of this process when run -# and the number of processes in the collective, respectively. -# The *init\_method* input can also be "env://". In this case, the address -# and port of the rank0 machine will be read from the following two -# environment variables respectively: MASTER_ADDR, MASTER_PORT. If *rank* -# and *world\_size* arguments are not specified in the *init\_process\_group* -# function, they both can be read from the following two environment -# variables respectively as well: RANK, WORLD_SIZE. -# -# Another important step, especially when each node has multiple gpus is -# to set the *local\_rank* of this process. For example, if you have two -# nodes, each with 8 GPUs and you wish to train with all of them then -# :math:`world\_size=16` and each node will have a process with local rank -# 0-7. This local\_rank is used to set the device (i.e. which GPU to use) -# for the process and later used to set the device when creating a -# distributed data parallel model. It is also recommended to use NCCL -# backend in this hypothetical environment as NCCL is preferred for -# multi-gpu nodes. -# - -print("Initialize Process Group...") -# Initialize Process Group -# v1 - init with url -dist.init_process_group(backend=dist_backend, init_method=dist_url, rank=int(sys.argv[1]), world_size=world_size) -# v2 - init with file -# dist.init_process_group(backend="nccl", init_method="file:///home/ubuntu/pt-distributed-tutorial/trainfile", rank=int(sys.argv[1]), world_size=world_size) -# v3 - init with environment variables -# dist.init_process_group(backend="nccl", init_method="env://", rank=int(sys.argv[1]), world_size=world_size) - - -# Establish Local Rank and set device on this node -local_rank = int(sys.argv[2]) -dp_device_ids = [local_rank] -torch.cuda.set_device(local_rank) - - -###################################################################### -# Initialize Model -# ~~~~~~~~~~~~~~~~ -# -# The next major step is to initialize the model to be trained. Here, we -# will use a resnet18 model from ``torchvision.models`` but any model may -# be used. First, we initialize the model and place it in GPU memory. -# Next, we make the model ``DistributedDataParallel``, which handles the -# distribution of the data to and from the model and is critical for -# distributed training. The ``DistributedDataParallel`` module also -# handles the averaging of gradients across the world, so we do not have -# to explicitly average the gradients in the training step. -# -# It is important to note that this is a blocking function, meaning -# program execution will wait at this function until *world\_size* -# processes have joined the process group. Also, notice we pass our device -# ids list as a parameter which contains the local rank (i.e. GPU) we are -# using. Finally, we specify the loss function and optimizer to train the -# model with. -# - -print("Initialize Model...") -# Construct Model -model = models.resnet18(pretrained=False).cuda() -# Make model DistributedDataParallel -model = torch.nn.parallel.DistributedDataParallel(model, device_ids=dp_device_ids, output_device=local_rank) - -# define loss function (criterion) and optimizer -criterion = nn.CrossEntropyLoss().cuda() -optimizer = torch.optim.SGD(model.parameters(), starting_lr, momentum=0.9, weight_decay=1e-4) - - -###################################################################### -# Initialize Dataloaders -# ~~~~~~~~~~~~~~~~~~~~~~ -# -# The last step in preparation for the training is to specify which -# dataset to use. Here we use the `STL-10 -# dataset `__ from -# `torchvision.datasets.STL10 `__. -# The STL10 dataset is a 10 class dataset of 96x96px color images. For use -# with our model, we resize the images to 224x224px in the transform. One -# distributed training specific item in this section is the use of the -# ``DistributedSampler`` for the training set, which is designed to be -# used in conjunction with ``DistributedDataParallel`` models. This object -# handles the partitioning of the dataset across the distributed -# environment so that not all models are training on the same subset of -# data, which would be counterproductive. Finally, we create the -# ``DataLoader``'s which are responsible for feeding the data to the -# processes. -# -# The STL-10 dataset will automatically download on the nodes if they are -# not present. If you wish to use your own dataset you should download the -# data, write your own dataset handler, and construct a dataloader for -# your dataset here. -# - -print("Initialize Dataloaders...") -# Define the transform for the data. Notice, we must resize to 224x224 with this dataset and model. -transform = transforms.Compose( - [transforms.Resize(224), - transforms.ToTensor(), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) - -# Initialize Datasets. STL10 will automatically download if not present -trainset = datasets.STL10(root='./data', split='train', download=True, transform=transform) -valset = datasets.STL10(root='./data', split='test', download=True, transform=transform) - -# Create DistributedSampler to handle distributing the dataset across nodes when training -# This can only be called after torch.distributed.init_process_group is called -train_sampler = torch.utils.data.distributed.DistributedSampler(trainset) - -# Create the Dataloaders to feed data to the training and validation steps -train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=(train_sampler is None), num_workers=workers, pin_memory=False, sampler=train_sampler) -val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, num_workers=workers, pin_memory=False) - - -###################################################################### -# Training Loop -# ~~~~~~~~~~~~~ -# -# The last step is to define the training loop. We have already done most -# of the work for setting up the distributed training so this is not -# distributed training specific. The only detail is setting the current -# epoch count in the ``DistributedSampler``, as the sampler shuffles the -# data going to each process deterministically based on epoch. After -# updating the sampler, the loop runs a full training epoch, runs a full -# validation step then prints the performance of the current model against -# the best performing model so far. After training for num\_epochs, the -# loop exits and the tutorial is complete. Notice, since this is an -# exercise we are not saving models but one may wish to keep track of the -# best performing model then save it at the end of training (see -# `here `__). -# - -best_prec1 = 0 - -for epoch in range(num_epochs): - # Set epoch count for DistributedSampler - train_sampler.set_epoch(epoch) - - # Adjust learning rate according to schedule - adjust_learning_rate(starting_lr, optimizer, epoch) - - # train for one epoch - print("\nBegin Training Epoch {}".format(epoch+1)) - train(train_loader, model, criterion, optimizer, epoch) - - # evaluate on validation set - print("Begin Validation @ Epoch {}".format(epoch+1)) - prec1 = validate(val_loader, model, criterion) - - # remember best prec@1 and save checkpoint if desired - # is_best = prec1 > best_prec1 - best_prec1 = max(prec1, best_prec1) - - print("Epoch Summary: ") - print("\tEpoch Accuracy: {}".format(prec1)) - print("\tBest Accuracy: {}".format(best_prec1)) - - -###################################################################### -# Running the Code -# ---------------- -# -# Unlike most of the other PyTorch tutorials, this code may not be run -# directly out of this notebook. To run, download the .py version of this -# file (or convert it using -# `this `__) -# and upload a copy to both nodes. The astute reader would have noticed -# that we hardcoded the **node0-privateIP** and :math:`world\_size=4` but -# input the *rank* and *local\_rank* inputs as arg[1] and arg[2] command -# line arguments, respectively. Once uploaded, open two ssh terminals into -# each node. -# -# - On the first terminal for node0, run ``$ python main.py 0 0`` -# -# - On the second terminal for node0 run ``$ python main.py 1 1`` -# -# - On the first terminal for node1, run ``$ python main.py 2 0`` -# -# - On the second terminal for node1 run ``$ python main.py 3 1`` -# -# The programs will start and wait after printing "Initialize Model..." -# for all four processes to join the process group. Notice the first -# argument is not repeated as this is the unique global rank of the -# process. The second argument is repeated as that is the local rank of -# the process running on the node. If you run ``nvidia-smi`` on each node, -# you will see two processes on each node, one running on GPU0 and one on -# GPU1. -# -# We have now completed the distributed training example! Hopefully you -# can see how you would use this tutorial to help train your own models on -# your own datasets, even if you are not using the exact same distributed -# envrionment. If you are using AWS, don't forget to **SHUT DOWN YOUR -# NODES** if you are not using them or you may find an uncomfortably large -# bill at the end of the month. -# -# **Where to go next** -# -# - Check out the `launcher -# utility `__ -# for a different way of kicking off the run -# -# - Check out the `torch.multiprocessing.spawn -# utility `__ -# for another easy way of kicking off multiple distributed processes. -# `PyTorch ImageNet Example `__ -# has it implemented and can demonstrate how to use it. -# -# - If possible, setup a NFS so you only need one copy of the dataset -# diff --git a/index.rst b/index.rst index 06a24e8c76d..c17172771e4 100644 --- a/index.rst +++ b/index.rst @@ -325,13 +325,6 @@ Welcome to PyTorch Tutorials :link: intermediate/ddp_tutorial.html :tags: Parallel-and-Distributed-Training -.. customcarditem:: - :header: (advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS - :card_description: Set up the distributed package of PyTorch, use the different communication strategies, and go over some the internals of the package. - :image: _static/img/thumbnails/cropped/advanced-PyTorch-1point0-Distributed-Trainer-with-Amazon-AWS.png - :link: beginner/aws_distributed_training_tutorial.html - :tags: Parallel-and-Distributed-Training - .. customcarditem:: :header: Writing Distributed Applications with PyTorch :card_description: Set up the distributed package of PyTorch, use the different communication strategies, and go over some the internals of the package. @@ -541,7 +534,6 @@ Additional Resources intermediate/ddp_tutorial intermediate/dist_tuto intermediate/rpc_tutorial - beginner/aws_distributed_training_tutorial intermediate/rpc_param_server_tutorial intermediate/dist_pipeline_parallel_tutorial intermediate/rpc_async_execution From 2407a51896b7dea077a1ff9eae9664570d16a3e4 Mon Sep 17 00:00:00 2001 From: sethjuarez Date: Mon, 2 Nov 2020 12:14:51 -0800 Subject: [PATCH 013/120] complete PyTorch quickstart proposal --- .devcontainer/Dockerfile | 7 + .devcontainer/devcontainer.json | 21 ++ .devcontainer/requirements.txt | 33 +++ .github/workflows/staging.yml | 44 ++++ .gitignore | 3 + beginner_source/quickstart/README.txt | 7 + .../autograd_quickstart_tutorial.py | 16 ++ .../quickstart/build_model_tutorial.py | 145 +++++++++++++ .../quickstart/data_quickstart_tutorial.py | 183 +++++++++++++++++ .../quickstart/images/fashion_mnist.png | Bin 0 -> 33424 bytes .../quickstart/images/optimization_loops.PNG | Bin 0 -> 60630 bytes .../quickstart/images/typesofdata.PNG | Bin 0 -> 14723 bytes .../quickstart/optimization_tutorial.py | 113 +++++++++++ .../quickstart/save_load_run_tutorial.py | 21 ++ .../quickstart/tensor_quickstart_tutorial.py | 122 +++++++++++ .../quickstart/transforms_tutorial.py | 135 ++++++++++++ beginner_source/quickstart_tutorial.py | 192 ++++++++++++++++++ index.rst | 1 + preview.sh | 10 + 19 files changed, 1053 insertions(+) create mode 100644 .devcontainer/Dockerfile create mode 100644 .devcontainer/devcontainer.json create mode 100644 .devcontainer/requirements.txt create mode 100644 .github/workflows/staging.yml create mode 100644 beginner_source/quickstart/README.txt create mode 100644 beginner_source/quickstart/autograd_quickstart_tutorial.py create mode 100644 beginner_source/quickstart/build_model_tutorial.py create mode 100644 beginner_source/quickstart/data_quickstart_tutorial.py create mode 100644 beginner_source/quickstart/images/fashion_mnist.png create mode 100644 beginner_source/quickstart/images/optimization_loops.PNG create mode 100644 beginner_source/quickstart/images/typesofdata.PNG create mode 100644 beginner_source/quickstart/optimization_tutorial.py create mode 100644 beginner_source/quickstart/save_load_run_tutorial.py create mode 100644 beginner_source/quickstart/tensor_quickstart_tutorial.py create mode 100644 beginner_source/quickstart/transforms_tutorial.py create mode 100644 beginner_source/quickstart_tutorial.py create mode 100755 preview.sh diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile new file mode 100644 index 00000000000..3ca67455049 --- /dev/null +++ b/.devcontainer/Dockerfile @@ -0,0 +1,7 @@ +FROM python:3.6-slim + +COPY requirements.txt requirements.txt + +RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \ + && apt-get install git gcc unzip make -y \ + && pip install --no-cache-dir -r requirements.txt \ No newline at end of file diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json new file mode 100644 index 00000000000..a0212d506ff --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,21 @@ +{ + "name": "PyTorch", + "build": { + "context": "..", + "dockerfile": "Dockerfile", + "args": { } + }, + "settings": { + "terminal.integrated.shell.linux": "/bin/bash", + "workbench.startupEditor": "none", + "files.autoSave": "afterDelay", + "python.dataScience.enabled": true, + "python.dataScience.alwaysTrustNotebooks": true, + "python.insidersChannel": "weekly", + "python.showStartPage": false + }, + "extensions": [ + "ms-python.python", + "lextudio.restructuredtext" + ] +} \ No newline at end of file diff --git a/.devcontainer/requirements.txt b/.devcontainer/requirements.txt new file mode 100644 index 00000000000..993febbb4c6 --- /dev/null +++ b/.devcontainer/requirements.txt @@ -0,0 +1,33 @@ +# Refer to ./jenkins/build.sh for tutorial build instructions + +sphinx==1.8.2 +sphinx-gallery==0.3.1 +tqdm +numpy +matplotlib +torch +torchvision +torchtext +torchaudio +PyHamcrest +bs4 +awscli==1.16.35 +flask +spacy +ray[tune] + +# PyTorch Theme +-e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme + +ipython + +# to run examples +pandas +scikit-image +# pillow >= 4.2 will throw error when trying to write mode RGBA as JPEG, +# this is a workaround to the issue. +pillow==4.1.1 +wget + +# for codespaces env +pylint diff --git a/.github/workflows/staging.yml b/.github/workflows/staging.yml new file mode 100644 index 00000000000..5ba350be56b --- /dev/null +++ b/.github/workflows/staging.yml @@ -0,0 +1,44 @@ +name: PyTorch Tutorial Staging + +on: + push: + branches: + - seth-blitz + pull_request: + branches: + - seth-blitz + +jobs: + tutorial-staging: + runs-on: ubuntu-latest + env: + WEB_PATH: $web + ACCOUNT: ${{ secrets.stagingaccount }} + KEY: ${{ secrets.stagingkey }} + SOURCEDIR: . + BUILDDIR: _build/html + steps: + - uses: actions/checkout@v2 + - uses: actions/setup-python@v2 + with: + python-version: 3.6 + - + # install requirements + name: install requirements + run: | + pip install --no-cache-dir -r requirements.txt + - + # build site + name: sphinx build + run: | + sphinx-build -D plot_gallery=0 -b html "$SOURCEDIR" "$BUILDDIR" + - + # clear old site + name: clear old site + run : | + az storage blob delete-batch --source $WEB_PATH --account-name $ACCOUNT --account-key $KEY + - + # push to azure storage + name: push to azure storage + run : | + az storage blob upload-batch -s $BUILDDIR -d $WEB_PATH --account-name $ACCOUNT --account-key $KEY diff --git a/.gitignore b/.gitignore index 27c61631029..2834a874f07 100644 --- a/.gitignore +++ b/.gitignore @@ -121,3 +121,6 @@ cleanup.sh # PyTorch things *.pt + +# vscode things +.vscode/ diff --git a/beginner_source/quickstart/README.txt b/beginner_source/quickstart/README.txt new file mode 100644 index 00000000000..0dcf2df4681 --- /dev/null +++ b/beginner_source/quickstart/README.txt @@ -0,0 +1,7 @@ +PyTorch Quickstart +---------------------------------- + +1. data_tutorial.py + Data Tutorial + https://pytorch.org/tutorials/beginner/quickstart/data_tutorial.html + diff --git a/beginner_source/quickstart/autograd_quickstart_tutorial.py b/beginner_source/quickstart/autograd_quickstart_tutorial.py new file mode 100644 index 00000000000..49713300413 --- /dev/null +++ b/beginner_source/quickstart/autograd_quickstart_tutorial.py @@ -0,0 +1,16 @@ +""" +Autograd +=================== +""" + +################################################################## +# More help with the FashionMNIST Pytorch Blitz +# ---------------------- +# `Tensors `_ +# `DataSets and DataLoaders `_ +# `Transformations `_ +# `Build Model `_ +# `Optimization Loop `_ +# `AutoGrad `_ +# `Back to FashionMNIST main code base <>`_ +# \ No newline at end of file diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py new file mode 100644 index 00000000000..d0e012640cc --- /dev/null +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -0,0 +1,145 @@ +""" +Build Model Tutorial +=================== + +The data has been loaded and transformed we can now build the model. We will leverage `torch.nn `_ predefined layers that Pytorch has that can both simplify our code, and make it faster. + +In the below example, for our FashionMNIT image dataset, we are using a `Sequential` container from class `torch.nn.Sequential `_ that allows us to define the model layers inline. The neural network modules layers will be added to it in the order they are passed in. + +Another way this model could be bulid is with a class using `nn.Module `_. We will break down each of these step of the model below. + +Inline nn.Sequential Example: +""" +import os +import torch +import torch.nn as nn +import torch.onnx as onnx +from torch.utils.data import DataLoader +from torchvision import datasets, transforms + + +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('Using {} device'.format(device)) + +# model +model = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, len(classes)), + nn.Softmax(dim=1) + ).to(device) + +print(model) + +""" +Class nn.Module Example: +""" +class Model(nn.Module): + def __init__(self, x): + super(Model, self).__init__() + self.layer1 = nn.Linear(28*28, 512) + self.layer2 = nn.Linear(512, 512) + self.output = nn.Linear(512, 10) + + def forward(self, x): + x = F.relu(self.layer1(x)) + x = F.relu(self.layer2(x)) + x = self.output(x) + return F.softmax(x, dim=1) + +############################################# +# Get Device for Training +# ----------------------- +# Here we check to see if `torch.cuda `_ is available to use the GPU, else we will use the CPU. +# +# Example: + +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('Using {} device'.format(device)) + +############################################## +# The Model Module Layers +# ------------------------- +# +# +# Lets break down each model layer in the FashionMNIST model. +# +################################################## +# [nn.Flatten](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html) to reduce tensor dimensions to one. +# +# From the docs: +# ``` +# torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1) +# ``` +# +#Here is an example using one of the training_data set items: +tensor = training_data[0][0] +print(tensor.size()) + +# Output: torch.Size([1, 28, 28]) + +model = nn.Sequential( + nn.Flatten() +) +flattened_tensor = model(tensor) +flattened_tensor.size() + +#vOutput: torch.Size([1, 784]) + +############################################## +# [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) to add a linear layer to the model. +# +# Now that we have flattened our tensor dimension we will apply a linear layer transform that will calculate/learn the weights and the bias. +# +# From the docs: +# ``` +# torch.nn.Linear(in_features: int, out_features: int, bias: bool = True) +# +# in_features – size of each input sample +# +# out_features – size of each output sample +# +# bias – If set to False, the layer will not learn an additive bias. Default: True +# +# Lets take a look at the resulting data example with the flatten layer and linear layer added: + +input = training_data[0][0] +print(input.size()) +model = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 512), +) +output = model(input) +output.size() + + +# Output: +# torch.Size([1, 28, 28]) +# torch.Size([1, 512]) + +################################################# +# Activation Functions +# +# - [nn.ReLU](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html) Activation: +# "Applies the rectified linear unit function element-wise" +# - [nn.Softmax]() Activation: +# "Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0,1] and sum to 1." + +###################################################### +# Resources +# +# `torch.nn `_ + +################################################################## +# More help with the FashionMNIST Pytorch Blitz +# ------------------------- +# `Tensors `_ +# `DataSets and DataLoaders `_ +# `Transformations `_ +# `Build Model `_ +# `Optimization Loop `_ +# `AutoGrad `_ +# `Back to FashionMNIST main code base <>`_ diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py new file mode 100644 index 00000000000..d622f63ca7a --- /dev/null +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -0,0 +1,183 @@ +""" +Datasets & Dataloaders +=================== +""" +################################################################# +# Getting Started With Data in PyTorch +# ----------------- +# +# Before we can even think about building a model with PyTorch, we need to first learn how to load and process data. Data can be sourced from local files, cloud datastores and database queries. It comes in all sorts of forms and formats from structured tables to image, audio, text, video files and more. +# +# .. figure:: /images/typesofdata.PNG +# :alt: +# +# Different data types require different python libraries to load and process such as `openCV `_ and `PIL `_ for images, `NLTK `_ and `spaCy `_ for text and `Librosa `_ for audio. +# +# If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking managing collections of data. +# +# A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. These are useful for benchmarking and testing your models before training on your own custom datasets. +# +# You can find some of them below. +# * `Image Datasets _` +# * `Text Datasets `_ +# * `Audio Datasets `_ +# +################################################################# +# Iterating through a Dataset +# ----------------- +# +# Once we have a Dataset we can index it manually like a list *clothing[index]*. +# +# Here is an example of how to load the fashion MNIST dataset from torch vision. +# +# + +import torch +from torch.utils.data import Dataset +import torchvision.datasets as datasets +import matplotlib.pyplot as plt +import numpy as np + +clothing = datasets.FashionMNIST('data', train=True, download=True) +labels_map = {0 : 'T-Shirt', 1 : 'Trouser', 2 : 'Pullover', 3 : 'Dress', 4 : 'Coat', 5 : 'Sandal', 6 : 'Shirt', 7 : 'Sneaker', 8 : 'Bag', 9 : 'Ankle Boot'} +figure = plt.figure(figsize=(8,8)) +cols, rows = 3, 3 +for i in range(1, cols*rows +1): + sample_idx = np.random.randint(len(clothing)) + img = clothing[sample_idx][0][0,:,:] + figure.add_subplot(rows, cols, i) + plt.title(labels_map[clothing[sample_idx][1]]) + plt.axis('off') + plt.imshow(img, cmap='gray') +plt.show() + +################################################################# +# .. figure:: /images/fashion_mnist.PNG +# :alt: +# +################################################################# +# Creating a Custom Dataset +# ----------------- +# +# To work with your own data lets look at the a simple custom image Dataset implementation: + +import os +import torch +import pandas as pd +from torch.utils.data import Dataset +from torchvision import transforms, utils +from torchvision.io import read_image + +class CustomImageDataset(Dataset): + def __init__(self, annotations_file, img_dir, transform=None): + self.img_labels = pd.read_csv(annotations_file) + self.img_dir = img_dir + self.transform = transform + + def __len__(self): + return len(self.img_labels) + + def __getitem__(self, idx): + if torch.is_tensor(idx): + idx = idx.tolist() + + img_name = os.path.join(self.root_dir, + self.img_labels.iloc[idx, 0]) + image = read_image('path_to_image.jpeg') + label = self.img_labels.iloc[idx, 1:] + sample = {'image': image, 'label': label} + + if self.transform: + sample = self.transform(sample) + + return sample + +################################################################# +# Imports +# ----------------- +# +# Import os for file handling, torch for PyTorch, [pandas](https://pandas.pydata.org/) for loading labels, [torch vision](https://pytorch.org/blog/pytorch-1.7-released/) to read image files, and Dataset to implement the Dataset interface. +# +# Example: +import os +import torch +import pandas as pd +from torchvision.io import read_image +from torch.utils.data import Dataset +from torch.utils.data import DataLoader + +################################################################# +# Init +# ----------------- +## +# The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and the keep track of directory of our image file. Note that different types of data can take different init inputs you are not limited to just an annotations file, directory_path and transforms but for images this is a standard practice. +# +# Example: +# + +def __init__(self, annotations_file, img_dir, transform=None): + self.img_labels = pd.read_csv(annotations_file) + self.img_dir = img_dir + self.transform = transform + +################################################################# +# __len__ +# ----------------- +# +# The __len__ function is very simple here we just need to return the number of samples in our dataset. +# +# Example: + +def __len__(self): + return len(self.img_labels) + +################################################################# +# __getitem__ +# ----------------- +# +# The __getitem__ function is the most important function in the Datasets interface this. It takes a tensor or an index as input and returns a loaded sample from you dataset at from the given indecies. +# +# In this sample if provided a tensor we convert the tensor to a list containing our index. We then load the file at the given index from our image directory as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a Transform on and return. To learn more about Transforms see the next section of the Blitz. +# +# Example: +def __getitem__(self, idx): + if torch.is_tensor(idx): + idx = idx.tolist() + img_name = os.path.join(self.root_dir, + self.img_labels.iloc[idx, 0]) + image = read_image('path_to_image.jpeg') + label = self.img_labels.iloc[idx, 1:] + sample = {'image': image, 'label': label} + if self.transform: + sample = self.transform(sample) + return sample + +################################################################# +# Preparing your data for training with DataLoaders +# ----------------- +# +# Now we have a organized mechansim for managing data which is great, but there is still a lot of manual work we would have to do train a model with our Dataset. +# +# For example we would have to manually maintain the code for: +# * Batching +# * Suffling +# * Parallel batch distribution +# +# The PyTorch Dataloader *torch.utils.data.DataLoader* is an iterator that handles all of this complexity for us enabling us to load a dataset and focusing on train our model. + +dataloader = DataLoader(clothing, batch_size=4, shuffle=True, num_workers=0) + +################################################################# +# With this we have all we need to know to load an process data of any kind in PyTorch to train deep learning models. +# +################################################################## +# More help with the FashionMNIST Pytorch Blitz +# ----------------- +# +#| `Tensors `_ +#| `DataSets and DataLoaders `_ +#| `Transformations `_ +#| `Build Model `_ +#| `Optimization Loop `_ +#| `AutoGrad `_ +#| `Back to FashionMNIST main code base <>`_ diff --git a/beginner_source/quickstart/images/fashion_mnist.png b/beginner_source/quickstart/images/fashion_mnist.png new file mode 100644 index 0000000000000000000000000000000000000000..213b1e1f17b764182a251c3ed3b700978864b46c GIT binary patch literal 33424 zcmeFZcT`h(yEcsDsAI(rh=2_h5CH|GV*yd>Py&b$#E1x?XhIJG9Z{qR7K&0837sH9 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a how to handle 5 core deep learning concepts in PyTorch +1. Hyperparameters (learning rates, batch sizes, epochs etc) +2. Optimization Loops +3. Loss +4. AutoGrad +5. Optimizers + +Let's dissect these concepts one by one and look at some code at the end we'll see how it all fits together. + + +""" + +###################################################### +# Hyperparameters +# ----------------- +# +#Hyperparameters are adjustable parameters that let you control the model optimization process. For example, with neural networks, you can configure: +# +# - **Number of Epochs**- the number times iterate over the dataset to update model parameters +# - **Batch Size** - the number of samples in the dataset to evaluate before you update model parameters +# - **Cost Function** - the method used to decide how to evaluate the model on a data sample to update the model parameters +# - **Learning Rate** - how much to update models parameters at each batch/epoch set this to large and you won't update optimally if you set it to small you will learn really slowly + +learning_rate = 1e-3 +batch_size = 64 +epochs = 5 + +###################################################### +# Optimizaton Loops +# ----------------- +# Once we set our hyperparameters we can then optimize our our model with optimization loops. +# +# The optimziation loop is comprized of three main subloops in PyTorch. +# +# .. figure:: /images/optimization_loops.PNG +# :alt: +# add +# 1. The Train Loop - Core loop iterates over all the epochs +# 2. The Validation Loop - Validate loss after each weight parameter update and can be used to gauge hyper parameter performance and update them for the next batch. +# 3. The Test Loop - is used to evaluate our models performance after each epoch on traditional metrics to show how much our model is generalizing from the train and validation dataset to the test dataset it's never seen before. +# + +for epoch in range(num_epochs): # Optimization Loop + # Train loop over batches + model.train() # set model to train + # Model Update Code + model.eval() # After exiting batch loop set model to eval to speed up evaluation and not track gradients (this is explained below) + # Validation Loop + # - Put sample validation metric logging and hyperparameter update code here + # After exiting train loop set model to eval to speed up evaluation and not track gradients (this is explained below) + # Test Loop + # - Put sample test metric logging and hyperparameter update code here + +###################################################### +# Loss +# ----------------- +#The loss is the value used to update our parameters. To calculate the loss we make a prediction using the inputs of our given data sample. +# + +preds = model(inputs) +loss = cost_function(preds, labels) + +###################################################### +# AutoGrad and Optimizer (We might want to split this when we go more in depth on autograd ) +# ----------------- +# By default each tensor maintains a graph of every operation applied on it unless otherwise specified using the torch.no_grad() command. +# +# `Autograd graph `_ +# +# PyTorch uses this graph to automatically update parameters with respect to our models loss during training. This is done with one line loss.backwards(). Once we have our gradients the optimizer is used to propgate the gradients from the backwards command to update all the parameters in our model. + +optimizer.zero_grad() # make sure previous gradients are cleared +loss.backward() # calculates gradients with respect to loss +optimizer.step() + +###################################################### +# The standard method for optimization is called Stochastic Gradient Descent, to learn more check out this awesome video by `3blue1brown `_. There are many different optimizers and variations of this method in PyTorch such as ADAM and RMSProp that work better for different kinds of models, they are out side the scope of this Blitz, but can check out the full list of optimizers[here](https://pytorch.org/docs/stable/optim.html) + +###################################################### +# Putting it all together lets look at a basic optimization loop +# ----------------- +# +# +# +# #initilize optimizer and example cost function +# +# # For loop to iterate over epoch +# # Train loop over batches +# # Set model to train mode +# # Calculate loss using +# # clear optimizer gradient +# # loss.backword +# # optimizer step +# # Set model to evaluate mode and start validation loop +# #calculate validation loss and update optimizer hyper parameters +# # Set model to evaluate test loop + + +################################################################## +# More help with the FashionMNIST Pytorch Blitz +# ----------------- +# `Tensors `_ +# `DataSets and DataLoaders `_ +# `Transformations `_ +# `Build Model `_ +# `Optimization Loop `_ +# `AutoGrad `_ +# `Back to FashionMNIST main code base <>`_ diff --git a/beginner_source/quickstart/save_load_run_tutorial.py b/beginner_source/quickstart/save_load_run_tutorial.py new file mode 100644 index 00000000000..7abcd11a6e3 --- /dev/null +++ b/beginner_source/quickstart/save_load_run_tutorial.py @@ -0,0 +1,21 @@ +""" +Save Load Run Tutorial +=================== + +More to come + +""" + +x = 5 + + +################################################################## +# More help with the FashionMNIST Pytorch Blitz +################################################################## +# `Tensors `_ +# `DataSets and DataLoaders `_ +# `Transformations `_ +# `Build Model `_ +# `Optimization Loop `_ +# `AutoGrad `_ +# `Back to FashionMNIST main code base <>`_ diff --git a/beginner_source/quickstart/tensor_quickstart_tutorial.py b/beginner_source/quickstart/tensor_quickstart_tutorial.py new file mode 100644 index 00000000000..c37dd9eacd6 --- /dev/null +++ b/beginner_source/quickstart/tensor_quickstart_tutorial.py @@ -0,0 +1,122 @@ +""" +Tensors and Operations +=================== + +Tensors and Operations +When training neural network models for real world tasks, we need to be able to effectively represent different types of input data: sets of numerical features, images, videos, sounds, etc. All those different input types can be represented as multi-dimensional arrays of numbers that are called tensors. + +Tensor is the basic computational unit in PyTorch. It is very similar to NumPy array, and supports similar operations. However, there are two very important features of Torch tensors that make the especially useful for training large-scale neural networks: + + - Tensor operations can be performed on GPU using CUDA + - Tensor operations support automatic differentiation using `AutoGrad `_ + +Conversion between Torch tensors and NumPy arrays can be done easily: +""" + +import torch +import numpy as np + +np_array = np.arange(10) +tensor = torch.from_numpy(np_array) + +print(f"Tensor={tensor}, Array={tensor.numpy()}") + +################################################################# +# .. code:: python +# Output: Tensor=tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32), Array=[0 1 2 3 4 5 6 7 8 9] +# +# .. note:: When using CPU for computations, tensors converted from arrays share the same memory for data. Thus, changing the underlying array will also affect the tensor. +# +# +# Creating Tensors +# ------------- +# The fastest way to create a tensor is to define an uninitialized tensor - the values of this tensor are not set, and depend on the whatever data was there in memory: +# + +x = torch.empty(3,6) +print(x) + +############################################################################ +# .. code:: python +# Output: tensor([[-1.3822e-06, 6.5301e-43, -1.3822e-06, 6.5301e-43, -1.4041e-06, +# 6.5301e-43], +# [-1.3855e-06, 6.5301e-43, -2.9163e-07, 6.5301e-43, -2.9163e-07, +# 6.5301e-43], +# [-1.4066e-06, 6.5301e-43, -1.3788e-06, 6.5301e-43, -2.9163e-07, +# 6.5301e-43]]) +# +# +# In practice, we ofter want to create tensors initialized to some values, such as zeros, ones or random values. Note that you can also specify the type of elements using dtype parameter, and chosing one of torch types: + + +x = torch.randn(3,5) +print(x) +y = torch.zeros(3,5,dtype=torch.int) +print(y) +z = torch.ones(3,5,dtype=torch.double) +print(z) + +###################################################################### +# Output: +# tensor([[-1.0166, -0.6828, 1.8886, -1.2115, 0.0202], +# [-1.1278, 0.7447, 0.4260, -2.1909, 0.5653], +# [ 0.0562, -0.1393, 0.6145, -0.6181, 0.1879]]) +# tensor([[0, 0, 0, 0, 0], +# [0, 0, 0, 0, 0], +# [0, 0, 0, 0, 0]], dtype=torch.int32) +# tensor([[1., 1., 1., 1., 1.], +# [1., 1., 1., 1., 1.], +# [1., 1., 1., 1., 1.]], dtype=torch.float64) +# +# +# You can also create random tensors with values sampled from different distributions, as described `in documentation. `_ +# +#Similarly to NumPy, you can use eye to create a diagonal identity matrix: + +print(torch.eye(10)) + +################################################################ +# Output: +# tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.], +# [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], +# [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.], +# [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], +# [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.], +# [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], +# [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.], +# [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], +# [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.], +# [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]]) +# +# +# You can also create new tensors with the same properties or size as existing tensors: +# + +print(z.new_ones(2,2)) +print(torch.zeros_like(x,dtype=torch.long)) + +############################################################################ +# Tensor Operations +# ------------- +# Tensors support all basic arithmetic operations, which can be specified in different ways: +# +# - Using operators, such as +, -, etc. +# - Using functions such as add, mult, etc. Functions can either return values, or store them in the specified ouput variable (using out= parameter) +# - In-place operations, which modify one of the arguments. Those operations have _ appended to their name, eg. add_. +# +# Complete reference to all tensor operations can be found in documentation. +# +# Let us see examples of those operations on two tensors, x and y. +# +# +# +################################################################## +# More help with the FashionMNIST Pytorch Blitz +# ---------------------------------- +# `Tensors `_ +# `DataSets and DataLoaders `_ +# `Transformations `_ +# `Build Model `_ +# `Optimization Loop `_ +# `AutoGrad `_ +# `Back to FashionMNIST main code base <>`_ diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py new file mode 100644 index 00000000000..2dbfad4abf6 --- /dev/null +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -0,0 +1,135 @@ +""" +Transforms +=================== + +Data does not come ready to be processed in the machine learning algorithm. We need to do different data manipulations or transforms to prepare it for training. There are many types of transformations and it depends on the type of model you are building and the state of your data as to which ones you should use. + +In the below example, for our FashionMNIT image dataset, we are taking our image features (x), turning it into a tensor and normalizing it. Then taking the labels (y) padding with zeros to get a consistent shape. We will break down each of these steps and the why below. + +Full Section Example: +""" +import os +import torch +import torch.nn as nn +import torch.onnx as onnx +import matplotlib.pyplot as plt +from torch.utils.data import DataLoader +from torchvision import datasets, transforms + +# image classes +classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] + +# data used for training +training_data = datasets.FashionMNIST('data', train=True, download=True, + transform=transforms.Compose([transforms.ToTensor()]), + target_transform=transforms.Compose([ + transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) + ]) + ) + +# data used for testing +test_data = datasets.FashionMNIST('data', train=False, download=True, + transform=transforms.Compose([transforms.ToTensor()]), + target_transform=transforms.Compose([ + transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) + ]) + ) + +############################################## +# Pytorch Datasets +# -------------------------- +# +# We are using the built-in open FashionMNIST datasets from the PyTorch library. For more info on the Datasets and Loaders check out [this]() resource. The `Train=True`indicates we want to download the training dataset from the built-in datasets, `Train=False` indicates to download the testing dataset. This way we have data partitioned out for training and testing within the provided PyTorch datasets. We will apply the same transfoms to both the training and testing datasets. +# +# From the docs: +# +# ```torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=False)``` + +############################################## +# Transform: Features +# --------------------------- +# Example: +# + +transform=transforms.Compose([transforms.ToTensor()]) + +# *Compose* +# The `transforms.compose` allows us to string together different steps of transformations in a sequential order. This allows us to add an array of transforms for both the features and labels when preparing our data for training. +# +# *ToTensor()* +#For the feature transforms we have an array of transforms to process our image data for training. The first transform in the array is `transforms.ToTensor()` this is from class [torchvision.transforms.ToTensor](https://pytorch.org/docs/stable/torchvision/transforms.html#torchvision.transforms.ToTensor). We need to take our images and turn them into a tensor. (To learn more about Tensors check out [this]() resource.) The ToTensor() transformation is doing more than converting our image into a tensor. Its also normalizing our data for us by scaling the images to be between 0 and 1. +# +# +# ..note: ToTensor only normalized image data that is in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling. +# + +############################################## +# Target_Transform: Labels +# ------------------------------- +# +#Example: + +target_transform= transforms.Lambda(lambda y: torch.zeros(10, dtype=torchfloat).scatter_(dim=0, index=torchtensor(y), value=1)) + +# This function is taking the y input and creating a tensor of size 10 with a float datatype. Then its calling scatter ([torch.Tensor.scatter_ class](https://pytorch.org/docs/stable/tensors.html#torch.Tensor.scatter_)) to send each item to torch.zeros, according to the row, index and current item value. +# * *Dim=0* is row wise index +# * *index* = torchtensor(y)` is the index of the element toscatter +# * *value* = 1` is the source elemnt + +############################################## +# Using your own data +# -------------------------------------- +# Below is an example for processing image data using a dataset from a local directory. +# +#Example: + +data_dir='data' +batch_size=4 + +data_transforms = { + 'train': transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + ]), + 'val': transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + ]), +} +image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), + data_transforms[x]) + for x in ['train', 'val']} + +dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], + batch_size=batch_size, + shuffle=True, num_workers=4) + for x in ['train', 'val']} + +dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} + +class_names = image_datasets['train'].classes + + +################################################## +# Resources +#------------------------------------------- +#Check out the other TorchVision Transforms available: https://pytorch.org/docs/stable/torchvision/transforms.html +# +# +################################################################## +# More help with the FashionMNIST Pytorch Blitz +# ---------------------------------------- +# `Tensors `_ +# `DataSets and DataLoaders `_ +# `Transformations `_ +# `Build Model `_ +# `Optimization Loop `_ +# `AutoGrad `_ +# `Back to FashionMNIST main code base <>`_ + + + diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart_tutorial.py new file mode 100644 index 00000000000..05a0a0a265b --- /dev/null +++ b/beginner_source/quickstart_tutorial.py @@ -0,0 +1,192 @@ +""" +PyTorch Quickstart +=================== + +The basic machine learning concepts in any framework should include: Working with data, Creating models, Optimizing Parameters, Saving and Loading Models + +""" + +import torch +import torch.nn as nn +import torch.onnx as onnx +import matplotlib.pyplot as plt +from torch.utils.data import DataLoader +from torchvision import datasets, transforms + +###################################################################### +# Working with data +# ----------------- +# +# PyTorch has two basic data primitives: ``DataSet`` and ``DataLoader``. +# These ``DataSet`` objects include a ``transforms`` mechanism to +# modify data in-place. + +classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] + +training_data = datasets.FashionMNIST('data', train=True, download=True, + transform=transforms.Compose([transforms.ToTensor()]), + target_transform=transforms.Compose([ + transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) + ]) +) + +test_data = datasets.FashionMNIST('data', train=False, download=True, + transform=transforms.Compose([transforms.ToTensor()]), + target_transform=transforms.Compose([ + transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) + ]) +) + +###################################################################### +# DataLoader + +# batch size +batch_size = 64 + +# loader +train_dataloader = DataLoader(training_data, batch_size=batch_size, num_workers=0, pin_memory=True) +test_dataloader = DataLoader(test_data, batch_size=batch_size, num_workers=0, pin_memory=True) + + +###################################################################### +# More details `DataSet and DataLoader `_ +# More details `Tensors `_ +# More details `Transformations `_ +# +# +# Creating Models +# --------------- +# +# There are two ways of creating models: in-line or as a class. This +# quickstart will consider an in-line definition. + +# where to run +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('Using {} device'.format(device)) + +# model +model = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, len(classes)), + nn.Softmax(dim=1) + ).to(device) + +print(model) + +###################################################################### +# More details `on building the model `_ +# +# Optimizing Parameters +# --------------------- +# +# Optimizing model parameters requires a loss function, and optimizer, +# and the optimization loop. + +# cost function used to determine best parameters +cost = torch.nn.BCELoss() + +# used to create optimal parameters +learning_rate = 1e-3 +optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) + +###################################################################### +# training function +def train(dataloader, model, loss, optimizer): + size = len(dataloader.dataset) + for batch, (X, Y) in enumerate(dataloader): + X, Y = X.to(device), Y.to(device) + optimizer.zero_grad() + pred = model(X) + loss = cost(pred, Y) + loss.backward() + optimizer.step() + + if batch % 100 == 0: + loss, current = loss.item(), batch * len(X) + print(f'loss: {loss:>7f} [{current:>5d}/{size:>5d}]') + +###################################################################### +# validation/test function +def test(dataloader, model): + size = len(dataloader.dataset) + model.eval() + test_loss, correct = 0, 0 + + with torch.no_grad(): + for batch, (X, Y) in enumerate(dataloader): + X, Y = X.to(device), Y.to(device) + pred = model(X) + + test_loss += cost(pred, Y).item() + correct += (pred.argmax(1) == Y.argmax(1)).type(torch.float).sum().item() + + test_loss /= size + correct /= size + + print(f'\nTest Error:\nacc: {(100*correct):>0.1f}%, avg loss: {test_loss:>8f}\n') + +###################################################################### +# training loop +epochs = 5 + +for t in range(epochs): + print(f'Epoch {t+1}\n-------------------------------') + train(train_dataloader, model, cost, optimizer) + test(test_dataloader, model) +print('Done!') + +###################################################################### +# More details `optimization and training loops `_ +# More deatils `AutoGrad `_ +# +# Saving Models +# ------------- +# +# PyTorch has can serialize the internal model state to a file. It also +# has built-in ONNX support. + +# saving PyTorch Model Dictionary +torch.save(model.state_dict(), 'model.pth') +print('Saved PyTorch Model to model.pth') + +# create dummy variable to traverse graph +x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255 +onnx.export(model, x, 'model.onnx') +print('Saved onnx model to model.onnx') + +###################################################################### +# More details `Saving loading and running `_ +# +# Loading Models +# ---------------------------- +# +# Once a model has been serialized the process for loading the +# parameters includes re-creating the model shape and then loading +# the state dictionary. Once loaded the model can be used for either +# retraining or inference purposes (in this example it is used for +# inference) + +loaded_model = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, len(classes)), + nn.Softmax(dim=1) + ) + +loaded_model.load_state_dict(torch.load('model.pth')) +loaded_model.eval() + +# inference +x, y = test_data[0][0], test_data[0][1] +with torch.no_grad(): + pred = loaded_model(x) + predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] + print(f'Predicted: "{predicted}", Actual: "{actual}"') + diff --git a/index.rst b/index.rst index c17172771e4..3f616b7800a 100644 --- a/index.rst +++ b/index.rst @@ -437,6 +437,7 @@ Additional Resources :includehidden: :caption: Learning PyTorch + beginner/quickstart_tutorial beginner/deep_learning_60min_blitz beginner/pytorch_with_examples beginner/nn_tutorial diff --git a/preview.sh b/preview.sh new file mode 100755 index 00000000000..2bb8f143681 --- /dev/null +++ b/preview.sh @@ -0,0 +1,10 @@ +pip install sphinx-autobuild +sphinx-autobuild --ignore "*.png" \ + --ignore "advanced/*" \ + --ignore "beginner/*" \ + --ignore "intermediate/*" \ + --ignore "prototype/*" \ + --ignore "recipes/*" \ + --ignore "*.zip" \ + --ignore "*.ipynb" \ + -D plot_gallery=0 -b html "." "_build/html" \ No newline at end of file From acf287d735d1f13d0574cb21c826cba77aaf542f Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Mon, 7 Dec 2020 10:11:54 -0600 Subject: [PATCH 014/120] Formatting and wording updates (#27) --- .github/workflows/staging.yml | 4 - _static/img/quickstart/comp-graph.png | Bin 0 -> 14368 bytes _static/img/quickstart/fashion_mnist.png | Bin 0 -> 33424 bytes _static/img/quickstart/optimizationloops.png | Bin 0 -> 60630 bytes _static/img/quickstart/typesdata.png | Bin 0 -> 14723 bytes .../quickstart/autograd_tutorial.py | 251 +++++++++++++++++ .../quickstart/build_model_tutorial.py | 103 ++++--- .../quickstart/data_quickstart_tutorial.py | 55 ++-- .../quickstart/optimization_tutorial.py | 106 ++++---- .../quickstart/save_load_run_tutorial.py | 106 +++++++- beginner_source/quickstart/tensor_tutorial.py | 257 ++++++++++++++++++ .../quickstart/transforms_tutorial.py | 64 +++-- beginner_source/quickstart_tutorial.py | 97 ++++--- 13 files changed, 848 insertions(+), 195 deletions(-) create mode 100644 _static/img/quickstart/comp-graph.png create mode 100644 _static/img/quickstart/fashion_mnist.png create mode 100644 _static/img/quickstart/optimizationloops.png create mode 100644 _static/img/quickstart/typesdata.png create mode 100644 beginner_source/quickstart/autograd_tutorial.py create mode 100644 beginner_source/quickstart/tensor_tutorial.py diff --git a/.github/workflows/staging.yml b/.github/workflows/staging.yml index 5ba350be56b..1e5eb5a2544 100644 --- a/.github/workflows/staging.yml +++ b/.github/workflows/staging.yml @@ -4,10 +4,6 @@ on: push: branches: - seth-blitz - pull_request: - branches: - - seth-blitz - jobs: tutorial-staging: runs-on: ubuntu-latest diff --git a/_static/img/quickstart/comp-graph.png b/_static/img/quickstart/comp-graph.png new file mode 100644 index 0000000000000000000000000000000000000000..cfa6163d58a85baea0c9d90ff583ec956be11ec1 GIT binary patch literal 14368 zcmeHuWmMbE)-O(r7T4kq#fwWR?nO#*DOMbcy9amo0s%^Kx1d3ayHg;z6bJ>1!wu~@ z=RWVZ^M1YSW+m&NjBS}cGkgE`B;uWlEG8N$8XO!Pro5b#IvgA#6b=qv?-?SjM7l3U z0QP`)R+oJXS2;$$5Bq^+DWNO@2Uin^erJLV2Zu2CPDxW5R{sBA|6c?Dk7?jqNCsx% zaHlTnDjIO`2#ClisL!6GV_;%ozrex8!zUmjCLtvwe@Q_}MNLCXN6)~>#LU9V_KKZ@ zlZ%^&_cb5CfRM0=sF?U0iMNu{GO}{=3W~}qs_)d)H8kIAY3u0f=^Gdt8Jm1GH8Z!c z{A6uwXYb(X^x4_P)y>_*%iG7-&p#kAC^#e(5EdR085JE97oU)rl#-g3o{^cIlbiP? zzn~CUR9y14^jleZMP*fWO>JF$Lt|5OOKTgby`!_My9eCc*FW%maA#Um(|GpS1BxS@wcTFO8J3If&IyHXaqMwQK1UR!Q=jp zmSfk)7Vb$u$9|4w|5&{D^9$I`{t?gNyd!ufhNr%jsAnO1HWynQEbDY`JRRGVo8dZ4 ze=g5HW%qq+DATS@7b{P|so8wKF>`28vUO6nB}}7RAsbx)|K^vnJBQy9w)by;myKT8 z+sivhL!bNFx%o}F)xc})N>RXXa=qL2*XJ?%1H+lb?BC?#Jz-L2SEjy)ro!mUEE2zO-la# z_Ne7CrSSPL4g%LB-SeU48*_RaL%6J}aJXE(Lo!5N_(G)hXTVwUTx8WxrjlXX z4b5uYT?Da{d~9if!xNU`L#%yqyY{Q8Fl_p32lKoF)=sXou+D{F{%x1{M@EdQ4~Gu7 zZu556eb|JDAE)j$QHkB!l zeB;??`Oy4vZYfSwk8<&1NtKd?^0k@q>Gh9sfx_GCKE$wS7XQ7Hsw1Hd-@DT#oevXn z!10yw)BTmYeVW&nf!qWd(TveC3Q^tJ zU^qL1g+8(N8{GutHy-5s#(yV~$N_1UT!LsQ+5>MPy}q52+|(#p65ZeTIJU!umf82j z83pDkgf6Po2elTeZ&!lmvnz^t|n~K2i5A;@uz+W zIk6d&!7q1{HB4-M>3IeHi!PK?Nq?in*5L|7mH;9Z+6u502OI$qam&hBO#amlqG}Qb zR!LBb01*^Bz^GUd`Rg)AAZ7d*;bxK3qy|k8bgfqDVZfuWKvv=pZ z*g(M-npov0(n7?4bwuLD7u*zz({W1yJ&l{ro#(z9phP_JO{<*QYr@G>rl}YQj#t-E z?!O^k3h@QY+wk~MW&#qi5dPAJ%8-wJJu_Z0;Vr%wA4|>%3lJYaQ21aq={lDeD9N+0 z5SV~Ekv@oYO&ue^ndkziWZQ=lnm;$TR|j8#C%-)K$0Y6yj5Pp-U?K;E^GG?36X=9=j5&Yin4k+ck))cARa8yV z2>Ca$LV1LDNK6XLhx^~A%!B7Tk@xVk2VspLUk=vmgv}WrF z9k=|ZP||&+?3i46<`RcO8Wxm>*xNI?Uj@?5enKm782oFug*AyfGx+v&BpCCyh`GG2 z)>Q*ZU5&|hXS>zQy$(k;WaA0-LteI2DL7=+xyi~;+STo%m#q#r>u2jpz)h6kB)XcO zmnYji^hZ!1@%2T@`-J)1edvAOI!+>?C+RKso|<*xcn6$J^pjw^UAvueMDnET(jb2^ z`9l{yM7HC%L@4{UNZLuZ(WfeJ8XPzXnh@L*>-%lHCawk@Z|i#gk}R?7&!UTgGVqn~ zT(V(ST6g7h0Wo=YUXTiL^vI_BaME;YAAh5?thOv7uNFRA{v#X+4O1rV@Z7eGCDHAA z$I13cbxGj9Y2%7to^h&Zu{T~Onf_DbJ4LHPOM!Nhyq7$bCRt@|f9(eyIzB8)YgG@EP_U-GrcBFfdXOW%@i>|oTS{6 zaex3AVwCu~icGI_F2;p7!pCr&&r06Oy?|O>7wXVBL!-0J71K!3Z`28+l^fuyM|wtEkt_qBo?U0 zH0JSE3lc+Yf92@YmLC#ewR1tittw2_gqzuLRj1%zr(&)n=M~qcA&F`SM%NPY@$ovt z4i_8vOO*QGC9{ySfC?=i0WH z2h0N5s+Y86+dsWRYF1TZ;^l7qF?}JxPHUAq>?l%r?-b!+d5*E%fI| zSmS9#03zD=c>^)(W>==fUK94!SCxFkW1hC{(NssturE8+urc}h{g2c>qFC{@J@ERf zLrl&SGu;u{e1h$z6+0HJC)z`zWOg8FI*poyWWm1*6_2vXCM`h|r!`MRH?BZ0*62rY zK{_W2;J6tQxDVin=au2_no`T#g)eSma{F_lQuch1(}A!GNDa}c763E@W~3|#R-)H# zdk{LqPBqL2jM15q6#_`;2o?~g(UTg2X2Cvx3@{p7PGJz5d@Fb4_ozk}O|g=-y4t&2(+uk&2%`_zjGG zY8k-XDVd)$#V^HA{_flecWPO)A>mp0PIU->sQbWF>}i5J1DECL&3kGs;2Pd1<7}Bs zsU=blxV2rsHr{<0n6ZEBg)!j2MR;ymc!BWeezny-$}7NH=jBZ8_jLE*&m|y%&K~pm%!c63zA_mHau%Q?O2+7 zS2*C^oS94wST|5-W+E7`t!&1k|K(b#UFAOy%|#vRE)L#*ve3IV7G;l4k*bOdJNA=j z@AYt~`^YvC@A#QDW*Ut^bFo4pqm%~sb`BG^xPIG4x{wcYk6&7`GRi^o+NB=l5Q~|J z`5UCxl*sp-jx(wl2i?Vie}?rb>ybd?2^nTaO@0+cYS?09S+O4>>Kizt5id1jm2;P! 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In this algorithm, parameters (model weights) are +adjusted according to the **gradient** of the loss function with respect +to the given parameter. + +To compute those gradients, PyTorch has a built-in mechanism called +**AutoGrad**. It supports automatic computation of gradient for any +computational graph. + +Consider the simplest one-layer neural network, with input ``x``, +parameters ``w`` and ``b``, and some loss function. It can be defined in +PyTorch in the following manner: +""" + +import torch +x = torch.ones(5) # input tensor +y = torch.zeros(3) # expected output +w = torch.randn(5,3,requires_grad=True) +b = torch.randn(3,requires_grad=True) +z = torch.matmul(x,w)+b +loss = torch.nn.functional.binary_cross_entropy_with_logits(z,y) + + +###################################################################### +# Tensors, Functions and Computational graph +# ------------------------------------------ +# +# This code defines the following **computational graph**: +# +# .. figure:: /_static/img/quickstart/comp-graph.png +# :alt: +# +# In this network, ``w`` and ``b`` are **parameters**, which we need to +# optimize. Thus, we need to be able to compute the gradients of loss +# function with respect to those variables. In orded to do that, we set +# the ``requires_grad`` property of those tensors. + +####################################################################### +# .. note:: You can set the value of ``requires_grad`` when creating a +# tensor, or later by using ``x.requires_grad_(True)`` method. + +####################################################################### +# A function that we apply to tensors to construct computational graph is +# in fact an object of class ``Function``. This object knows how to +# compute the function in the *forward* direction, and also how to compute +# it's derivative during the *backward propagation* step. A reference to +# the backward propagation function is stored in ``grad_fn`` property of a +# tensor. You can find more information of ``Function`` `in +# documentation `__. +# + +print(z.grad_fn,loss.grad_fn,sep='\n') + +###################################################################### +# Computing Gradients +# ------------------- +# +# To optimize weights of parameters in the neural network, we need to +# compute the derivatives of our loss function with respect to parameters, +# namely, we need :math:`\frac{\partial loss}{\partial w}` and +# :math:`\frac{\partial loss}{\partial b}` under some fixed values of +# ``x`` and ``y``. To compute those derivatives, we call +# ``loss.backward()``, and then retrieve the values from ``w.grad`` and +# ``b.grad``: +# + +loss.backward() +print(w.grad) +print(b.grad) + + +###################################################################### +# .. note:: +# - We can only obtain the ``grad`` properties for the leaf +# nodes of the computational graph, which have ``requires_grad`` property +# set to ``True``. For all other nodes in our graph gradients will not be +# available. +# - We can only perform gradient calculations using +# ``backward`` once on a given graph, for performance reasons. If we need +# to do several ``backward`` calls on the same graph, we need to pass +# ``retain_graph=True`` to the ``backward`` call. +# + + +###################################################################### +# Tensor Gradients and Jacobian Products +# -------------------------------------- +# +# In many cases, we have a scalar loss function, and we need to compute +# the gradient with respect to some parameters. However, there are cases +# when the output function is an arbitrary tensor. In this case, PyTorch +# allows you to compute so-called **Jacobian product**, and not the actual +# gradient. +# +# For a vector function :math:`\vec{y}=f(\vec{x})`, where +# :math:`\vec{x}=\langle x_1,\dots,x_n\rangle` and +# :math:`\vec{y}=\langle y_1,\dots,y_m\rangle`, a gradient of +# :math:`\vec{y}` with respect to :math:`\vec{x}` is given by **Jacobian +# matrix**: +# +# .. math:: +# +# +# \begin{align}J=\left(\begin{array}{ccc} +# \frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{1}}{\partial x_{n}}\\ +# \vdots & \ddots & \vdots\\ +# \frac{\partial y_{m}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}} +# \end{array}\right)\end{align} +# +# Instead of computing the Jacobian matrix itself, PyTorch allows you to +# compute **Jacobian Product** :math:`v^T\cdot J` for a given input vector +# :math:`v=(v_1 \dots v_m)`. This is achieved by calling ``backward`` with +# :math:`v` as an argument. The size of :math:`v` should be the same as +# the size of the original tensor, with respect to which we want to +# compute the product: +# + +inp = torch.eye(5,requires_grad=True) +out = (inp+1).pow(2) +out.backward(torch.ones_like(inp),retain_graph=True) +print("First call\n",inp.grad) +out.backward(torch.ones_like(inp),retain_graph=True) +print("\nSecond call\n",inp.grad) +inp.grad.zero_() +out.backward(torch.ones_like(inp),retain_graph=True) +print("\nCall after zeroing gradients\n",inp.grad) + + +###################################################################### +# Notice that when we call ``backward`` for the second time with the same +# argument, the value of the gradient is different. This happens because +# when doing ``backward`` propagation, PyTorch **accumulates the +# gradients**, i.e. the value of computed gradients is added to the +# ``grad`` property of all leaf nodes of computational graph. If you want +# to compute the proper gradients, you need to zero out the ``grad`` +# property before. In real-life training an *optimizer* helps us to do +# this. + +###################################################################### +# .. note:: Previously we were calling ``backward()`` function without +# parameters. This is essentially equivalent to calling +# ``backward(torch.tensor(1.0))``, which is a useful way to compute the +# gradients in case of a scalar-valued function, such as loss during +# neural network training. +# + + +###################################################################### +# Disabling Gradient Tracking +# --------------------------- +# +# By default, all tensors with ``requires_grad=True`` are tracking their +# computational history and support gradient computation. However, there +# are some cases when we do not need to do that, for example, when we have +# trained the model and just want to apply it to some input data, i.e. we +# only want to do *forward* computations through the network. We can stop +# tracking computations by surrounding our computation code with +# ``with torch.no_grad()`` block: +# + +z = torch.matmul(x,w)+b +print(z.requires_grad) + +with torch.no_grad(): + z = torch.matmul(x,w)+b +print(z.requires_grad) + + +###################################################################### +# Another way to achieve the same result is to use the ``detach()`` method +# on the tensor: +# + +z = torch.matmul(x,w)+b +z_det = z.detach() +print(z_det.requires_grad) + + +###################################################################### +# All forward-pass computations on tensors that do not track gradients +# would be more efficient. +# + + +###################################################################### +# Example of Gradient Descent +# --------------------------- +# +# Let's use the AutoGrad functionality to minimize a simple function of +# two variables :math:`f(x_1,x_2)=(x_1-3)^2+(x_2+2)^2`. We will use the +# ``x`` tensor to represent the coordinates of a point. To do the gradient +# descent, we start with some initial value :math:`x^{(0)}=(0,0)`, and +# compute each consecutive step using: +# +# .. math:: +# +# +# x^{(n+1)} = x^{(n)} - \eta\nabla f +# +# Here :math:`\eta` is so-called **learning rate** (we will call it ``lr`` +# in our code), and +# :math:`\nabla f = (\frac{\partial f}{\partial x_1},\frac{\partial f}{\partial x_2})` +# is the gradient of :math:`f`. +# +# We will start by defining the initial value of ``x`` and the function +# ``f``: +# + +x = torch.zeros(2,requires_grad=True) +f = lambda x : (x-torch.tensor([3,-2])).pow(2).sum() +lr = 0.1 + + +###################################################################### +# For the gradient descent, let's do 15 iterations. On each iteration, we +# will update the coordinate tensor ``x`` and print its coordinates to +# make sure that we are approaching the minimum: +# + +for i in range(15): + y = f(x) + y.backward() + gr = x.grad + x.data.add_(-lr*gr) + x.grad.zero_() + print("Step {}: x[0]={}, x[1]={}".format(i,x[0],x[1])) + + +###################################################################### +# As you can see, we have obtained the values close to the optimal point +# :math:`(3,-2)`. Training a neural network is in fact a very similar +# process, we will need to do a number of iterations to minimize the value +# of **loss function**. +# +# Next: Learn more about `how to use AutoGrad to train a neural network model `_. +# + +################################################################## +# Pytorch Quickstart Topics +# ----------------- +#| `Tensors `_ +#| `DataSets and DataLoaders `_ +#| `Transforms `_ +#| `Build Model `_ +#| `Optimization Loop `_ +#| `AutoGrad `_ +#| `Save, Load and Run Model `_ diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index d0e012640cc..01e97febf14 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -1,15 +1,30 @@ """ Build Model Tutorial -=================== - -The data has been loaded and transformed we can now build the model. We will leverage `torch.nn `_ predefined layers that Pytorch has that can both simplify our code, and make it faster. +======================================= +""" -In the below example, for our FashionMNIT image dataset, we are using a `Sequential` container from class `torch.nn.Sequential `_ that allows us to define the model layers inline. The neural network modules layers will be added to it in the order they are passed in. +############################################### +# The data has been loaded and transformed we can now build the model. +# We will leverage `torch.nn `_ +# predefined layers that Pytorch has that can both simplify our code, and make it faster. +# +# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` +# container from class `torch.nn. Sequential `_ +# that allows us to define the model layers inline. +# The neural network modules layers will be added to it in the order they are passed in. +# +# Another way to bulid this model is with a class +# using `nn.Module `_ This gives us more flexibility, because +# we can construct layers of any complexity, including the ones with shared weights. +# +# Lets break down the steps to build this model below +# -Another way this model could be bulid is with a class using `nn.Module `_. We will break down each of these step of the model below. +########################################## +# Inline nn.Sequential Example: +# ---------------------------- +# -Inline nn.Sequential Example: -""" import os import torch import torch.nn as nn @@ -17,7 +32,6 @@ from torch.utils.data import DataLoader from torchvision import datasets, transforms - device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) @@ -34,9 +48,15 @@ print(model) -""" -Class nn.Module Example: -""" +############## +# Class nn.Module Example: +# -------------------------- +# + +class NeuralNework(nn.Module): + def __init__(self, x): + super(NeuralNework, self).__init__() + class Model(nn.Module): def __init__(self, x): super(Model, self).__init__() @@ -56,6 +76,7 @@ def forward(self, x): # Here we check to see if `torch.cuda `_ is available to use the GPU, else we will use the CPU. # # Example: +# device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) @@ -64,6 +85,19 @@ def forward(self, x): # The Model Module Layers # ------------------------- # +# Lets break down each model layer in the FashionMNIST model. +# + +################################################## +# `nn.Flatten `_ to reduce tensor dimensions to one. +# ----------------------------------------------- +# +# From the docs: +# ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)`` +# + +# Here is an example using one of the training_data set items: +======= # # Lets break down each model layer in the FashionMNIST model. # @@ -75,7 +109,8 @@ def forward(self, x): # torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1) # ``` # -#Here is an example using one of the training_data set items: +# Here is an example using one of the training_data set items: + tensor = training_data[0][0] print(tensor.size()) @@ -87,23 +122,11 @@ def forward(self, x): flattened_tensor = model(tensor) flattened_tensor.size() -#vOutput: torch.Size([1, 784]) - ############################################## # [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) to add a linear layer to the model. # # Now that we have flattened our tensor dimension we will apply a linear layer transform that will calculate/learn the weights and the bias. # -# From the docs: -# ``` -# torch.nn.Linear(in_features: int, out_features: int, bias: bool = True) -# -# in_features – size of each input sample -# -# out_features – size of each output sample -# -# bias – If set to False, the layer will not learn an additive bias. Default: True -# # Lets take a look at the resulting data example with the flatten layer and linear layer added: input = training_data[0][0] @@ -122,24 +145,22 @@ def forward(self, x): ################################################# # Activation Functions +# ------------------------- # -# - [nn.ReLU](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html) Activation: -# "Applies the rectified linear unit function element-wise" -# - [nn.Softmax]() Activation: -# "Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0,1] and sum to 1." - -###################################################### -# Resources +# - `nn.ReLU `_ Activation +# - `nn.Softmax `_ Activation +# +# Next: Learn more about how the `optimzation loop works with this example `_. # -# `torch.nn `_ ################################################################## -# More help with the FashionMNIST Pytorch Blitz -# ------------------------- -# `Tensors `_ -# `DataSets and DataLoaders `_ -# `Transformations `_ -# `Build Model `_ -# `Optimization Loop `_ -# `AutoGrad `_ -# `Back to FashionMNIST main code base <>`_ +# Pytorch Quickstart Topics +# ----------------- +#| `Tensors `_ +#| `DataSets and DataLoaders `_ +#| `Transforms `_ +#| `Build Model `_ +#| `Optimization Loop `_ +#| `AutoGrad `_ +#| `Save, Load and Run Model `_ + diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py index d622f63ca7a..c3070530c01 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -2,14 +2,17 @@ Datasets & Dataloaders =================== """ + ################################################################# # Getting Started With Data in PyTorch # ----------------- # # Before we can even think about building a model with PyTorch, we need to first learn how to load and process data. Data can be sourced from local files, cloud datastores and database queries. It comes in all sorts of forms and formats from structured tables to image, audio, text, video files and more. -# -# .. figure:: /images/typesofdata.PNG -# :alt: +# + +############################################################### +# .. figure:: /_static/img/quickstart/typesdata.png +# :alt: typesdata # # Different data types require different python libraries to load and process such as `openCV `_ and `PIL `_ for images, `NLTK `_ and `spaCy `_ for text and `Librosa `_ for audio. # @@ -17,20 +20,21 @@ # # A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. These are useful for benchmarking and testing your models before training on your own custom datasets. # -# You can find some of them below. -# * `Image Datasets _` -# * `Text Datasets `_ -# * `Audio Datasets `_ +# You can find some of them below. +# +# - `Image Datasets `_ +# - `Text Datasets `_ +# - `Audio Datasets `_ # + ################################################################# # Iterating through a Dataset # ----------------- -# -# Once we have a Dataset we can index it manually like a list *clothing[index]*. +# +# Once we have a Dataset we can index it manually like a list `clothing[index]`. # # Here is an example of how to load the fashion MNIST dataset from torch vision. -# -# +# import torch from torch.utils.data import Dataset @@ -52,14 +56,17 @@ plt.show() ################################################################# -# .. figure:: /images/fashion_mnist.PNG -# :alt: + +# .. figure:: /_static/img/quickstart/fashion_mnist.png +# :alt: fashion_mnist # + ################################################################# # Creating a Custom Dataset # ----------------- # # To work with your own data lets look at the a simple custom image Dataset implementation: +# import os import torch @@ -95,10 +102,11 @@ def __getitem__(self, idx): ################################################################# # Imports # ----------------- -# -# Import os for file handling, torch for PyTorch, [pandas](https://pandas.pydata.org/) for loading labels, [torch vision](https://pytorch.org/blog/pytorch-1.7-released/) to read image files, and Dataset to implement the Dataset interface. +# +# Import os for file handling, torch for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and Dataset to implement the Dataset interface. # # Example: + import os import torch import pandas as pd @@ -109,7 +117,7 @@ def __getitem__(self, idx): ################################################################# # Init # ----------------- -## +# # The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and the keep track of directory of our image file. Note that different types of data can take different init inputs you are not limited to just an annotations file, directory_path and transforms but for images this is a standard practice. # # Example: @@ -170,14 +178,17 @@ def __getitem__(self, idx): ################################################################# # With this we have all we need to know to load an process data of any kind in PyTorch to train deep learning models. # +# Next: Learn more about how to `transform that data for training `_. +# + ################################################################## -# More help with the FashionMNIST Pytorch Blitz +# Pytorch Quickstart Topics # ----------------- -# -#| `Tensors `_ +#| `Tensors `_ #| `DataSets and DataLoaders `_ -#| `Transformations `_ +#| `Transforms `_ #| `Build Model `_ #| `Optimization Loop `_ -#| `AutoGrad `_ -#| `Back to FashionMNIST main code base <>`_ +#| `AutoGrad `_ +#| `Save, Load and Run Model `_ + diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 12cbfacdb19..f00b84e4a8c 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -4,23 +4,20 @@ Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! To do this we need to understand a how to handle 5 core deep learning concepts in PyTorch -1. Hyperparameters (learning rates, batch sizes, epochs etc) -2. Optimization Loops -3. Loss -4. AutoGrad -5. Optimizers - -Let's dissect these concepts one by one and look at some code at the end we'll see how it all fits together. + 1. Hyperparameters (learning rates, batch sizes, epochs etc) + 2. Optimization Loops + 3. Loss + 4. AutoGrad + 5. Optimizers -""" +Let's dissect these concepts one by one and look at some code at the end we'll see how it all fits together. ###################################################### # Hyperparameters # ----------------- # -#Hyperparameters are adjustable parameters that let you control the model optimization process. For example, with neural networks, you can configure: -# +# Hyperparameters are adjustable parameters that let you control the model optimization process. For example, with neural networks, you can configure: # - **Number of Epochs**- the number times iterate over the dataset to update model parameters # - **Batch Size** - the number of samples in the dataset to evaluate before you update model parameters # - **Cost Function** - the method used to decide how to evaluate the model on a data sample to update the model parameters @@ -33,38 +30,56 @@ ###################################################### # Optimizaton Loops # ----------------- +# # Once we set our hyperparameters we can then optimize our our model with optimization loops. # # The optimziation loop is comprized of three main subloops in PyTorch. # -# .. figure:: /images/optimization_loops.PNG + + +############################################################ +# .. figure:: /_static/img/quickstart/optimizationloops.png # :alt: -# add +# + +############################################################# # 1. The Train Loop - Core loop iterates over all the epochs # 2. The Validation Loop - Validate loss after each weight parameter update and can be used to gauge hyper parameter performance and update them for the next batch. # 3. The Test Loop - is used to evaluate our models performance after each epoch on traditional metrics to show how much our model is generalizing from the train and validation dataset to the test dataset it's never seen before. # -for epoch in range(num_epochs): # Optimization Loop +for epoch in range(num_epochs): +# Optimization Loop # Train loop over batches - model.train() # set model to train - # Model Update Code - model.eval() # After exiting batch loop set model to eval to speed up evaluation and not track gradients (this is explained below) - # Validation Loop - # - Put sample validation metric logging and hyperparameter update code here + model.train() # set model to train + # Model Update Code + model.eval() # After exiting batch loop set model to eval to speed up evaluation and not track gradients (this is explained below) + # Validation Loop + # - Put sample validation metric logging and hyperparameter update code here # After exiting train loop set model to eval to speed up evaluation and not track gradients (this is explained below) # Test Loop - # - Put sample test metric logging and hyperparameter update code here + # - Put sample test metric logging and hyperparameter update code here ###################################################### # Loss # ----------------- -#The loss is the value used to update our parameters. To calculate the loss we make a prediction using the inputs of our given data sample. +# +# The loss is the value used to update our parameters. To calculate the loss we make a prediction using the inputs of our given data sample. # preds = model(inputs) loss = cost_function(preds, labels) +# Make sure previous gradients are cleared +optimizer.zero_grad() +# Calculates gradients with respect to loss +loss.backward() +optimizer.step() + +###################################################### +# The standard method for optimization is called Stochastic Gradient Descent, to learn more check out this awesome video by `3blue1brown `_. There are many different optimizers and variations of this method in PyTorch such as ADAM and RMSProp that work better for different kinds of models, they are out side the scope of this Blitz, but can check out the full list of optimizers `here `_ +======= + ###################################################### # AutoGrad and Optimizer (We might want to split this when we go more in depth on autograd ) # ----------------- @@ -74,40 +89,35 @@ # # PyTorch uses this graph to automatically update parameters with respect to our models loss during training. This is done with one line loss.backwards(). Once we have our gradients the optimizer is used to propgate the gradients from the backwards command to update all the parameters in our model. -optimizer.zero_grad() # make sure previous gradients are cleared -loss.backward() # calculates gradients with respect to loss -optimizer.step() - -###################################################### -# The standard method for optimization is called Stochastic Gradient Descent, to learn more check out this awesome video by `3blue1brown `_. There are many different optimizers and variations of this method in PyTorch such as ADAM and RMSProp that work better for different kinds of models, they are out side the scope of this Blitz, but can check out the full list of optimizers[here](https://pytorch.org/docs/stable/optim.html) - ###################################################### # Putting it all together lets look at a basic optimization loop # ----------------- # -# -# -# #initilize optimizer and example cost function +# Initilize optimizer and example cost function # -# # For loop to iterate over epoch -# # Train loop over batches -# # Set model to train mode -# # Calculate loss using -# # clear optimizer gradient -# # loss.backword -# # optimizer step -# # Set model to evaluate mode and start validation loop -# #calculate validation loss and update optimizer hyper parameters -# # Set model to evaluate test loop +# For loop to iterate over epoch +# - Train loop over batches +# - Set model to train mode +# - Calculate loss using +# - clear optimizer gradient +# - loss.backword +# - optimizer step +# - Set model to evaluate mode and start validation loop +# - calculate validation loss and update optimizer hyper parameters +# - Set model to evaluate test loop +# +# Next: Learn more about `AutoGrad `_. +# ################################################################## -# More help with the FashionMNIST Pytorch Blitz +# Pytorch Quickstart Topics # ----------------- -# `Tensors `_ -# `DataSets and DataLoaders `_ -# `Transformations `_ -# `Build Model `_ -# `Optimization Loop `_ -# `AutoGrad `_ -# `Back to FashionMNIST main code base <>`_ +#| `Tensors `_ +#| `DataSets and DataLoaders `_ +#| `Transforms `_ +#| `Build Model `_ +#| `Optimization Loop `_ +#| `AutoGrad `_ +#| `Save, Load and Run Model `_ + diff --git a/beginner_source/quickstart/save_load_run_tutorial.py b/beginner_source/quickstart/save_load_run_tutorial.py index 7abcd11a6e3..542d76c4b61 100644 --- a/beginner_source/quickstart/save_load_run_tutorial.py +++ b/beginner_source/quickstart/save_load_run_tutorial.py @@ -1,21 +1,103 @@ """ -Save Load Run Tutorial +Save, Load and Use the Model =================== -More to come +We have trained the model! Now lets take a look at how to save, load and use the model created. +Full Section Example: """ -x = 5 +import os +import torch +import torch.nn as nn +import torch.onnx as onnx +# create dummy variable to traverse graph +x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255 +onnx.export(model, x, 'model.onnx') +print('Saved onnx model to model.onnx') + +# saving PyTorch Model Dictionary +torch.save(model.state_dict(), 'model.pth') +print('Saved PyTorch Model to model.pth') + +draw_clothes(test_data) + +#rehydrate model +loaded_model = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, len(classes)), + nn.Softmax(dim=1) + ) + +#load graph +loaded_model.load_state_dict(torch.load('model.pth')) +loaded_model.eval() + +x, y = test_data[0][0], test_data[0][1] +with torch.no_grad(): + pred = loaded_model(x) + predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] + print(f'Predicted: "{predicted}", Actual: "{actual}"') + + +###################################################### +# Save the Model +# ----------------------- +# Example: + +# create dummy variable to traverse graph +x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255 +onnx.export(model, x, 'model.onnx') +print('Saved onnx model to model.onnx') + +# saving PyTorch Model Dictionary +torch.save(model.state_dict(), 'model.pth') +print('Saved PyTorch Model to model.pth') + + +####################################################################### +# Load the Model +# --------------------------- +# Example: + +draw_clothes(test_data) + +loaded_model = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, len(classes)), + nn.Softmax(dim=1) + ) +loaded_model.load_state_dict(torch.load('model.pth')) +loaded_model.eval() + +###################################################################### +# Test the Model +# ---------------------------------- +# Example: + +x, y = test_data[0][0], test_data[0][1] +with torch.no_grad(): + pred = loaded_model(x) + predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] + print(f'Predicted: "{predicted}", Actual: "{actual}"') ################################################################## -# More help with the FashionMNIST Pytorch Blitz -################################################################## -# `Tensors `_ -# `DataSets and DataLoaders `_ -# `Transformations `_ -# `Build Model `_ -# `Optimization Loop `_ -# `AutoGrad `_ -# `Back to FashionMNIST main code base <>`_ +# Pytorch Quickstart Topics +# ---------------------------------------- +# | `Tensors `_ +# | `DataSets and DataLoaders `_ +# | `Transforms `_ +# | `Build Model `_ +# | `Optimization Loop `_ +# | `AutoGrad `_ +# | `Save, Load and Run Model `_ + diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py new file mode 100644 index 00000000000..5a0630f1a7a --- /dev/null +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -0,0 +1,257 @@ +""" +Tensors and Operations +---------------------- + +**Tensor** is the basic computational unit in PyTorch. It is very +similar to **NumPy array**, and supports similar operations. However, +there are two very important features of Torch tensors that make them +especially useful for training large-scale neural networks: + +- Tensor operations can be performed on GPU using CUDA +- Tensor operations support automatic differentiation using + `AutoGrad `__ + +Conversion between Torch tensors and NumPy arrays can be done easily: + +""" + +import torch +import numpy as np + +np_array = np.arange(10) +tensor = torch.from_numpy(np_array) + +print(f"Tensor={tensor}, Array={tensor.numpy()}") + + +###################################################################### +# .. note:: When using CPU for computations, tensors converted from arrays share the same memory for data. Thus, changing the underlying array will also affect the tensor. +# + + +###################################################################### +# Creating Tensors +# ~~~~~~~~~~~~~~~~ +# +# The fastest way to create a tensor is to define an *uninitialized* +# tensor - the values of this tensor are not set, and depend on the +# whatever data was there in memory: +# + +x = torch.empty(3,6) + + +###################################################################### +# In practice, we often want to create tensors initialized to some values, +# such as zeros, ones or random values. Note that you can also specify the +# type of elements using ``dtype`` parameter, and chosing one of ``torch`` +# types: +# + +x = torch.randn(3,5) +y = torch.zeros(3,5,dtype=torch.int) +z = torch.ones(3,5,dtype=torch.double) + +###################################################################### +# You can also create random tensors with values sampled from different +# distributions, as described `in the +# documentation `__. +# +# Similarly to NumPy, you can use ``eye`` to create a diagonal identity +# matrix: +# + +I = torch.eye(10) + + +###################################################################### +# You can also create new tensors with the same properties or size as +# existing tensors: +# + +print(z.new_ones(2,2)) # new_ method allows specifying new size +print(torch.zeros_like(x,dtype=torch.long)) # _like method supports overriding dtype + + +###################################################################### +# Size of the tensor can be obtained using ``.size()`` method, which +# returns a tuple-like object: +# + +print(z.size()) # Prints [3.0] + + +###################################################################### +# Tensor Operations +# ~~~~~~~~~~~~~~~~~ +# +# Tensors support all basic arithmetic operations, which can be specified +# in different ways: +# - Using operators, such as ``+``, ``-``, etc. \* +# - Using functions such as ``add``, ``mult``, etc. Functions can either return values, or store them in the specified ouput variable (using ``out=`` parameter) +# - In-place operations, which modify one of the arguments. Those operations have ``_`` appended to their name, eg. ``add_``. +# Complete reference to all tensor operations can be found `in the +# documentation `__. +# +# Let us see examples of those operations on two tensors, ``x`` and ``y``. +# + +x = torch.randn(3,5) +y = torch.randn(3,5) + + +###################################################################### +# Using operator notation +# ^^^^^^^^^^^^^^^^^^^^^^^ +# +# We can use overloaded arithmetic operators, such as ``+`` and ``*``: +# + +z = x*y + + +###################################################################### +# Note, that ``*`` means elementwise product, and not the matrix product. +# To compute matrix product, we need to use ``matmul`` function, as shown +# below. +# +# Using functions +# ^^^^^^^^^^^^^^^ +# +# While only some operations are available as Python operators, `many more +# functions `__ +# can be specified using the full name. In the example below, ``t`` +# transposes the matrix, and ``matmul`` means matrix multiplication: +# + +z = torch.matmul(x,y.t()) + + +###################################################################### +# Simple operations (addition, multiplication, etc.) also have +# corresponsing functions, and can be called either as methods, or as +# functions: +# + +z = x.add(y) +z = torch.add(x,y) + + +###################################################################### +# Sometimes it may be more convenient to store the result into specified +# variable, instead of returning it from a function. In this case you can +# use ``out=`` parameter: +# + +torch.add(x,y,out=z) + + +###################################################################### +# In-place operations +# ^^^^^^^^^^^^^^^^^^^ +# +# When training neural networks, you often need to **modify** the weights, +# i.e. perform some operation and then store the result into the original +# variable. Those operations are called **in-place operations**, and they +# are marked by the ``_`` symbol at the end of their name: +# + +x.add_(y) # x will be modified + + +###################################################################### +# Resizing and Indexing +# ~~~~~~~~~~~~~~~~~~~~~ +# +# Often you need to change the shape of the tensor without modifying +# its values, eg. to add an extra dimension. To do that, you can use +# ``view`` method, which provides a **view** to the same in-memory values +# using different dimensions: +# + +print(x.size()) # original size of x is 3x5 +print(x.view(5,3,1).size()) # will give size 5x3x1 +print(x.view(5,-1)) # will result in size 5x3 + + +###################################################################### +# The number of elements in a view should be the same as in the +# original tensor, and that you can use ``-1`` in one of the dimensions to +# figure out this dimension automatically. +# + + +###################################################################### +# .. note:: ``view`` is similar to ``reshape`` operation in NumPy. There +# is also a ``reshape`` method available in PyTorch, and it is more +# powerful than ``view``, because it can also reshape non-contiguous +# arrays by copying them to the new shape. However, in vast majority of +# cases you can use ``view`` and make sure that no data copying occurs, +# and the operation is always efficient. +# + + +###################################################################### +# Tensors support all slicing operations that exist in NymPy: +# + +print(x.size()) # original size of x is 3x5 +print(x[0].size(), x[:,0].size(), x[...,1].size()) # will give 5, 3, 3 + + +###################################################################### +# If you have a one-element tensor, for example, after aggregating all +# values of the tensor into one value, you can convert it to a Python +# numerical value using ``item()``: +# + +print(x.sum().item()) # will print + + +###################################################################### +# GPU Computations +# ~~~~~~~~~~~~~~~~ +# +# One of the major benefits of using PyTorch is the ability to perform +# tensor operations on GPU. To do that, we need to explicitly **move** +# tensors to GPU using ``.to`` method. +# +# In most of the cases, we check for the availability of GPU on our +# machine, and define the ``device`` object accordingly. Then we move all +# tensors to that device before performing the computations: +# + +if torch.cuda.is_available(): + device = torch.device("cuda") +else: + device = torch.device("cpu") + +print("Doing computations on {}".format(device)) + +x = torch.randn(3,5,device=device) +y = torch.ones_like(x) +y = y.to(device) +z = x+y # this is performed on GPU if it is available +print(z) +print(z.to("cpu",torch.double)) + + +###################################################################### +# In the last operation, when we move the tensor back to the CPU, we can +# also change the ``dtype``. This does not result in additional +# computational time, because we need to copy and transform the data when +# moving it from GPU anyway. +# +# Next learn how to load built in and custom `datasets with dataloaders `_ +# + +################################################################## +# Pytorch Quickstart Topics +# ----------------- +#| `Tensors `_ +#| `DataSets and DataLoaders `_ +#| `Transforms `_ +#| `Build Model `_ +#| `Optimization Loop `_ +#| `AutoGrad `_ +#| `Save, Load and Run Model `_ diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 2dbfad4abf6..aaca92e07a8 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -39,11 +39,11 @@ # Pytorch Datasets # -------------------------- # -# We are using the built-in open FashionMNIST datasets from the PyTorch library. For more info on the Datasets and Loaders check out [this]() resource. The `Train=True`indicates we want to download the training dataset from the built-in datasets, `Train=False` indicates to download the testing dataset. This way we have data partitioned out for training and testing within the provided PyTorch datasets. We will apply the same transfoms to both the training and testing datasets. +# We are using the built-in open FashionMNIST datasets from the PyTorch library. For more info on the Datasets and Loaders check out `this `_ resource. The ``Train=True`` indicates we want to download the training dataset from the built-in datasets, ``Train=False`` indicates to download the testing dataset. This way we have data partitioned out for training and testing within the provided PyTorch datasets. We will apply the same transfoms to both the training and testing datasets. # # From the docs: # -# ```torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=False)``` +# ``torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=False)`` ############################################## # Transform: Features @@ -53,35 +53,49 @@ transform=transforms.Compose([transforms.ToTensor()]) -# *Compose* -# The `transforms.compose` allows us to string together different steps of transformations in a sequential order. This allows us to add an array of transforms for both the features and labels when preparing our data for training. +##################################################### +# Compose +# ------------------------ # -# *ToTensor()* -#For the feature transforms we have an array of transforms to process our image data for training. The first transform in the array is `transforms.ToTensor()` this is from class [torchvision.transforms.ToTensor](https://pytorch.org/docs/stable/torchvision/transforms.html#torchvision.transforms.ToTensor). We need to take our images and turn them into a tensor. (To learn more about Tensors check out [this]() resource.) The ToTensor() transformation is doing more than converting our image into a tensor. Its also normalizing our data for us by scaling the images to be between 0 and 1. +# The `transforms.compose`` allows us to string together different steps of transformations in a sequential order. This allows us to add an array of transforms for both the features and labels when preparing our data for training. +# + +################################################# +# ToTensor() +# ------------------------------- +# +# For the feature transforms we have an array of transforms to process our image data for training. The first transform in the array is ``transforms.ToTensor()`` this is from class `torchvision.transforms.ToTensor `_. We need to take our images and turn them into a tensor. (To learn more about Tensors check out `this `_ resource.) The ``ToTensor()`` transformation is doing more than converting our image into a tensor. Its also normalizing our data for us by scaling the images to be between 0 and 1. # # -# ..note: ToTensor only normalized image data that is in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling. +# .. note:: ToTensor only normalized image data that is in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling. +# +# +# Check out the other `TorchVision Transforms `_ # ############################################## # Target_Transform: Labels # ------------------------------- # -#Example: +# Example: +# target_transform= transforms.Lambda(lambda y: torch.zeros(10, dtype=torchfloat).scatter_(dim=0, index=torchtensor(y), value=1)) -# This function is taking the y input and creating a tensor of size 10 with a float datatype. Then its calling scatter ([torch.Tensor.scatter_ class](https://pytorch.org/docs/stable/tensors.html#torch.Tensor.scatter_)) to send each item to torch.zeros, according to the row, index and current item value. -# * *Dim=0* is row wise index -# * *index* = torchtensor(y)` is the index of the element toscatter -# * *value* = 1` is the source elemnt +################################################# +# This function is taking the y input and creating a tensor of size 10 with a float datatype. Then its calling scatter `torch.Tensor.scatter_ class `_ to send each item to torch.zeros, according to the row, index and current item value. +# - Dim=0 is row wise index +# - index = torchtensor(y) is the index of the element toscatter +# - value = 1 is the source elemnt ############################################## # Using your own data # -------------------------------------- +# # Below is an example for processing image data using a dataset from a local directory. # -#Example: +# Example: +# data_dir='data' batch_size=4 @@ -114,22 +128,20 @@ class_names = image_datasets['train'].classes -################################################## -# Resources -#------------------------------------------- -#Check out the other TorchVision Transforms available: https://pytorch.org/docs/stable/torchvision/transforms.html -# +################################################################## +# Next learn how to `build the model `_ # + ################################################################## -# More help with the FashionMNIST Pytorch Blitz +# Pytorch Quickstart Topics # ---------------------------------------- -# `Tensors `_ -# `DataSets and DataLoaders `_ -# `Transformations `_ -# `Build Model `_ -# `Optimization Loop `_ -# `AutoGrad `_ -# `Back to FashionMNIST main code base <>`_ +# | `Tensors `_ +# | `DataSets and DataLoaders `_ +# | `Transforms ` +# | `Build Model `_ +# | `Optimization Loop `_ +# | `AutoGrad `_ +# | `Save, Load and Run Model `_ diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart_tutorial.py index 05a0a0a265b..ef2c3c6b7f1 100644 --- a/beginner_source/quickstart_tutorial.py +++ b/beginner_source/quickstart_tutorial.py @@ -2,10 +2,25 @@ PyTorch Quickstart =================== -The basic machine learning concepts in any framework should include: Working with data, Creating models, Optimizing Parameters, Saving and Loading Models +The basic machine learning concepts in any framework should include: Working with data, Creating models, Optimizing Parameters, Saving and Loading Models. In this quickstart we will go through an example of an applied machine learning model using the FashionMNIST dataset that demonstrates these core steps using Pytorch. + +Working with data +----------------- """ +###################################################################### +# +# PyTorch has two basic data primitives: ``DataSet`` and ``DataLoader``. +# These ``DataSet`` objects include a ``transforms`` mechanism to +# modify data in-place. Below is an example of how to load that data from the Pytorch open datasets and transform the data to a normalized tensor. +# +# To see more examples and details of how to work with Tensors, Datasets, DataLoaders and Transforms in Pytoch with this example checkout these resources: +# +# - `Tensors `_ +# - `DataSet and DataLoader `_ +# - `Transforms `_ + import torch import torch.nn as nn import torch.onnx as onnx @@ -13,14 +28,6 @@ from torch.utils.data import DataLoader from torchvision import datasets, transforms -###################################################################### -# Working with data -# ----------------- -# -# PyTorch has two basic data primitives: ``DataSet`` and ``DataLoader``. -# These ``DataSet`` objects include a ``transforms`` mechanism to -# modify data in-place. - classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] training_data = datasets.FashionMNIST('data', train=True, download=True, @@ -37,34 +44,22 @@ ]) ) -###################################################################### -# DataLoader - -# batch size batch_size = 64 -# loader train_dataloader = DataLoader(training_data, batch_size=batch_size, num_workers=0, pin_memory=True) test_dataloader = DataLoader(test_data, batch_size=batch_size, num_workers=0, pin_memory=True) - -###################################################################### -# More details `DataSet and DataLoader `_ -# More details `Tensors `_ -# More details `Transformations `_ -# -# # Creating Models # --------------- # # There are two ways of creating models: in-line or as a class. This -# quickstart will consider an in-line definition. +# quickstart will consider an in-line definition. For more examples checkout `building the model `_. -# where to run device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) -# model +# in-line model + model = nn.Sequential( nn.Flatten(), nn.Linear(28*28, 512), @@ -78,13 +73,16 @@ print(model) ###################################################################### -# More details `on building the model `_ -# # Optimizing Parameters # --------------------- # -# Optimizing model parameters requires a loss function, and optimizer, +# Optimizing model parameters requires a loss function, optimizer, # and the optimization loop. +# +# To see more examples and details of how to work with Optimization and Training loops in Pytoch with this example checkout these resources: +# - `Optimization and training loops `_ +# - `Automatic differentiation and AutoGrad `_ +# # cost function used to determine best parameters cost = torch.nn.BCELoss() @@ -93,8 +91,8 @@ learning_rate = 1e-3 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) -###################################################################### -# training function +# Create the training function + def train(dataloader, model, loss, optimizer): size = len(dataloader.dataset) for batch, (X, Y) in enumerate(dataloader): @@ -109,8 +107,9 @@ def train(dataloader, model, loss, optimizer): loss, current = loss.item(), batch * len(X) print(f'loss: {loss:>7f} [{current:>5d}/{size:>5d}]') -###################################################################### -# validation/test function + +# Create the validation/test function + def test(dataloader, model): size = len(dataloader.dataset) model.eval() @@ -130,7 +129,12 @@ def test(dataloader, model): print(f'\nTest Error:\nacc: {(100*correct):>0.1f}%, avg loss: {test_loss:>8f}\n') ###################################################################### -# training loop +# Training Models +# ------------- +# +# Call the train and test function in a training loop with the number of epochs indicated +# + epochs = 5 for t in range(epochs): @@ -140,27 +144,22 @@ def test(dataloader, model): print('Done!') ###################################################################### -# More details `optimization and training loops `_ -# More deatils `AutoGrad `_ -# # Saving Models # ------------- # -# PyTorch has can serialize the internal model state to a file. It also -# has built-in ONNX support. +# PyTorch has different ways you can save your model. One way is to serialize the internal model state to a file. Another would be to use the built-in `ONNX `_ support. +# Saving PyTorch Model Dictionary -# saving PyTorch Model Dictionary torch.save(model.state_dict(), 'model.pth') print('Saved PyTorch Model to model.pth') -# create dummy variable to traverse graph +# Save to ONNX, create dummy variable to traverse graph + x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255 onnx.export(model, x, 'model.onnx') print('Saved onnx model to model.onnx') ###################################################################### -# More details `Saving loading and running `_ -# # Loading Models # ---------------------------- # @@ -168,7 +167,8 @@ def test(dataloader, model): # parameters includes re-creating the model shape and then loading # the state dictionary. Once loaded the model can be used for either # retraining or inference purposes (in this example it is used for -# inference) +# inference). Check out more details on `saving, loading and running models with Pytorch `_ +# loaded_model = nn.Sequential( nn.Flatten(), @@ -190,3 +190,16 @@ def test(dataloader, model): predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] print(f'Predicted: "{predicted}", Actual: "{actual}"') +################################################################## +# Pytorch Quickstart Topics +# ---------------------------------------- +# | `Tensors `_ +# | `DataSets and DataLoaders `_ +# | `Transforms `_ +# | `Build Model `_ +# | `Optimization Loop `_ +# | `AutoGrad `_ +# | `Save, Load and Run Model `_ +# +# *Authors: Seth Juarez, Ari Bornstein, Cassie Breviu, Dmitry Soshnikov* + From 15a5cbcfe288e39ec264e80970734f5906eaca3d Mon Sep 17 00:00:00 2001 From: sethjuarez Date: Mon, 7 Dec 2020 08:44:45 -0800 Subject: [PATCH 015/120] corrected rebase errors --- .../autograd_quickstart_tutorial.py | 16 -- .../quickstart/autograd_tutorial.py | 253 ++++++++++-------- .../quickstart/build_model_tutorial.py | 216 ++++++--------- .../quickstart/data_quickstart_tutorial.py | 104 ++++--- .../quickstart/optimization_tutorial.py | 210 ++++++++++----- beginner_source/quickstart/qs_toc.txt | 10 + .../quickstart/save_load_run_tutorial.py | 120 ++++----- .../quickstart/tensor_quickstart_tutorial.py | 122 --------- beginner_source/quickstart/tensor_tutorial.py | 155 ++++++----- .../quickstart/transforms_tutorial.py | 68 +++-- 10 files changed, 571 insertions(+), 703 deletions(-) delete mode 100644 beginner_source/quickstart/autograd_quickstart_tutorial.py create mode 100644 beginner_source/quickstart/qs_toc.txt delete mode 100644 beginner_source/quickstart/tensor_quickstart_tutorial.py diff --git a/beginner_source/quickstart/autograd_quickstart_tutorial.py b/beginner_source/quickstart/autograd_quickstart_tutorial.py deleted file mode 100644 index 49713300413..00000000000 --- a/beginner_source/quickstart/autograd_quickstart_tutorial.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -Autograd -=================== -""" - -################################################################## -# More help with the FashionMNIST Pytorch Blitz -# ---------------------- -# `Tensors `_ -# `DataSets and DataLoaders `_ -# `Transformations `_ -# `Build Model `_ -# `Optimization Loop `_ -# `AutoGrad `_ -# `Back to FashionMNIST main code base <>`_ -# \ No newline at end of file diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index e56a68af1b4..07c4c74cd02 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -1,5 +1,5 @@ """ -Automatic Differentiation with AutoGrad +Automatic Differentiation with ``torch.autograd`` ======================================= When training neural networks, the most frequently used algorithm is @@ -7,8 +7,8 @@ adjusted according to the **gradient** of the loss function with respect to the given parameter. -To compute those gradients, PyTorch has a built-in mechanism called -**AutoGrad**. It supports automatic computation of gradient for any +To compute those gradients, PyTorch has a built-in differentiation engine +called ``torch.autograd``. It supports automatic computation of gradient for any computational graph. Consider the simplest one-layer neural network, with input ``x``, @@ -17,12 +17,12 @@ """ import torch -x = torch.ones(5) # input tensor -y = torch.zeros(3) # expected output -w = torch.randn(5,3,requires_grad=True) -b = torch.randn(3,requires_grad=True) -z = torch.matmul(x,w)+b -loss = torch.nn.functional.binary_cross_entropy_with_logits(z,y) +x = torch.ones(5) # input tensor +y = torch.zeros(3) # expected output +w = torch.randn(5, 3, requires_grad=True) +b = torch.randn(3, requires_grad=True) +z = torch.matmul(x, w)+b +loss = torch.nn.functional.binary_cross_entropy_with_logits(z, y) ###################################################################### @@ -51,14 +51,14 @@ # the backward propagation function is stored in ``grad_fn`` property of a # tensor. You can find more information of ``Function`` `in # documentation `__. -# +# -print(z.grad_fn,loss.grad_fn,sep='\n') +print(z.grad_fn, loss.grad_fn, sep='\n') ###################################################################### # Computing Gradients # ------------------- -# +# # To optimize weights of parameters in the neural network, we need to # compute the derivatives of our loss function with respect to parameters, # namely, we need :math:`\frac{\partial loss}{\partial w}` and @@ -66,7 +66,7 @@ # ``x`` and ``y``. To compute those derivatives, we call # ``loss.backward()``, and then retrieve the values from ``w.grad`` and # ``b.grad``: -# +# loss.backward() print(w.grad) @@ -83,76 +83,13 @@ # ``backward`` once on a given graph, for performance reasons. If we need # to do several ``backward`` calls on the same graph, we need to pass # ``retain_graph=True`` to the ``backward`` call. -# - - -###################################################################### -# Tensor Gradients and Jacobian Products -# -------------------------------------- -# -# In many cases, we have a scalar loss function, and we need to compute -# the gradient with respect to some parameters. However, there are cases -# when the output function is an arbitrary tensor. In this case, PyTorch -# allows you to compute so-called **Jacobian product**, and not the actual -# gradient. -# -# For a vector function :math:`\vec{y}=f(\vec{x})`, where -# :math:`\vec{x}=\langle x_1,\dots,x_n\rangle` and -# :math:`\vec{y}=\langle y_1,\dots,y_m\rangle`, a gradient of -# :math:`\vec{y}` with respect to :math:`\vec{x}` is given by **Jacobian -# matrix**: -# -# .. math:: -# -# -# \begin{align}J=\left(\begin{array}{ccc} -# \frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{1}}{\partial x_{n}}\\ -# \vdots & \ddots & \vdots\\ -# \frac{\partial y_{m}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}} -# \end{array}\right)\end{align} -# -# Instead of computing the Jacobian matrix itself, PyTorch allows you to -# compute **Jacobian Product** :math:`v^T\cdot J` for a given input vector -# :math:`v=(v_1 \dots v_m)`. This is achieved by calling ``backward`` with -# :math:`v` as an argument. The size of :math:`v` should be the same as -# the size of the original tensor, with respect to which we want to -# compute the product: -# - -inp = torch.eye(5,requires_grad=True) -out = (inp+1).pow(2) -out.backward(torch.ones_like(inp),retain_graph=True) -print("First call\n",inp.grad) -out.backward(torch.ones_like(inp),retain_graph=True) -print("\nSecond call\n",inp.grad) -inp.grad.zero_() -out.backward(torch.ones_like(inp),retain_graph=True) -print("\nCall after zeroing gradients\n",inp.grad) - - -###################################################################### -# Notice that when we call ``backward`` for the second time with the same -# argument, the value of the gradient is different. This happens because -# when doing ``backward`` propagation, PyTorch **accumulates the -# gradients**, i.e. the value of computed gradients is added to the -# ``grad`` property of all leaf nodes of computational graph. If you want -# to compute the proper gradients, you need to zero out the ``grad`` -# property before. In real-life training an *optimizer* helps us to do -# this. - -###################################################################### -# .. note:: Previously we were calling ``backward()`` function without -# parameters. This is essentially equivalent to calling -# ``backward(torch.tensor(1.0))``, which is a useful way to compute the -# gradients in case of a scalar-valued function, such as loss during -# neural network training. -# +# ###################################################################### # Disabling Gradient Tracking # --------------------------- -# +# # By default, all tensors with ``requires_grad=True`` are tracking their # computational history and support gradient computation. However, there # are some cases when we do not need to do that, for example, when we have @@ -160,58 +97,62 @@ # only want to do *forward* computations through the network. We can stop # tracking computations by surrounding our computation code with # ``with torch.no_grad()`` block: -# +# -z = torch.matmul(x,w)+b +z = torch.matmul(x, w)+b print(z.requires_grad) with torch.no_grad(): - z = torch.matmul(x,w)+b + z = torch.matmul(x, w)+b print(z.requires_grad) ###################################################################### # Another way to achieve the same result is to use the ``detach()`` method # on the tensor: -# +# -z = torch.matmul(x,w)+b +z = torch.matmul(x, w)+b z_det = z.detach() print(z_det.requires_grad) - ###################################################################### -# All forward-pass computations on tensors that do not track gradients -# would be more efficient. -# +# There are several reasons you might want to disable gradient tracking: +# - To mark some parameters in your neural network at **frozen parameters**. This is +# a very common scenario for +# `finetuning a pretrained network `__ +# - To **speed up computations** when you are only doing forward pass, because computations on tensors that do +# not track gradients would be more efficient. ###################################################################### # Example of Gradient Descent # --------------------------- -# +# # Let's use the AutoGrad functionality to minimize a simple function of # two variables :math:`f(x_1,x_2)=(x_1-3)^2+(x_2+2)^2`. We will use the # ``x`` tensor to represent the coordinates of a point. To do the gradient # descent, we start with some initial value :math:`x^{(0)}=(0,0)`, and # compute each consecutive step using: -# +# # .. math:: -# -# +# +# # x^{(n+1)} = x^{(n)} - \eta\nabla f -# +# # Here :math:`\eta` is so-called **learning rate** (we will call it ``lr`` # in our code), and # :math:`\nabla f = (\frac{\partial f}{\partial x_1},\frac{\partial f}{\partial x_2})` # is the gradient of :math:`f`. -# +# # We will start by defining the initial value of ``x`` and the function # ``f``: -# +# + +x = torch.zeros(2, requires_grad=True) +def f(x): return (x-torch.tensor([3, -2])).pow(2).sum() + -x = torch.zeros(2,requires_grad=True) -f = lambda x : (x-torch.tensor([3,-2])).pow(2).sum() lr = 0.1 @@ -219,7 +160,7 @@ # For the gradient descent, let's do 15 iterations. On each iteration, we # will update the coordinate tensor ``x`` and print its coordinates to # make sure that we are approaching the minimum: -# +# for i in range(15): y = f(x) @@ -227,25 +168,111 @@ gr = x.grad x.data.add_(-lr*gr) x.grad.zero_() - print("Step {}: x[0]={}, x[1]={}".format(i,x[0],x[1])) + print("Step {}: x[0]={}, x[1]={}".format(i, x[0], x[1])) ###################################################################### # As you can see, we have obtained the values close to the optimal point -# :math:`(3,-2)`. Training a neural network is in fact a very similar +# :math:`(3,-2)`. `Training a neural network `_ is in fact a very similar # process, we will need to do a number of iterations to minimize the value # of **loss function**. -# -# Next: Learn more about `how to use AutoGrad to train a neural network model `_. -# - -################################################################## -# Pytorch Quickstart Topics -# ----------------- -#| `Tensors `_ -#| `DataSets and DataLoaders `_ -#| `Transforms `_ -#| `Build Model `_ -#| `Optimization Loop `_ -#| `AutoGrad `_ -#| `Save, Load and Run Model `_ + +###################################################################### +# More on Computational Graphs +# ---------------------------- +# Conceptually, autograd keeps a record of data (tensors) & all executed +# operations (along with the resulting new tensors) in a directed acyclic +# graph (DAG) consisting of +# `Function `__ +# objects. In this DAG, leaves are the input tensors, roots are the output +# tensors. By tracing this graph from roots to leaves, you can +# automatically compute the gradients using the chain rule. +# +# In a forward pass, autograd does two things simultaneously: +# +# - run the requested operation to compute a resulting tensor, and +# - maintain the operation’s *gradient function* in the DAG. +# +# The backward pass kicks off when ``.backward()`` is called on the DAG +# root. ``autograd`` then: +# +# - computes the gradients from each ``.grad_fn``, +# - accumulates them in the respective tensor’s ``.grad`` attribute, and +# - using the chain rule, propagates all the way to the leaf tensors. +# +# .. note:: +# **DAGs are dynamic in PyTorch** +# An important thing to note is that the graph is recreated from scratch; after each +# ``.backward()`` call, autograd starts populating a new graph. This is +# exactly what allows you to use control flow statements in your model; +# you can change the shape, size and operations at every iteration if +# needed. + +###################################################################### +# Optional Reading: Tensor Gradients and Jacobian Products +# -------------------------------------- +# +# In many cases, we have a scalar loss function, and we need to compute +# the gradient with respect to some parameters. However, there are cases +# when the output function is an arbitrary tensor. In this case, PyTorch +# allows you to compute so-called **Jacobian product**, and not the actual +# gradient. +# +# For a vector function :math:`\vec{y}=f(\vec{x})`, where +# :math:`\vec{x}=\langle x_1,\dots,x_n\rangle` and +# :math:`\vec{y}=\langle y_1,\dots,y_m\rangle`, a gradient of +# :math:`\vec{y}` with respect to :math:`\vec{x}` is given by **Jacobian +# matrix**: +# +# .. math:: +# +# +# \begin{align}J=\left(\begin{array}{ccc} +# \frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{1}}{\partial x_{n}}\\ +# \vdots & \ddots & \vdots\\ +# \frac{\partial y_{m}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}} +# \end{array}\right)\end{align} +# +# Instead of computing the Jacobian matrix itself, PyTorch allows you to +# compute **Jacobian Product** :math:`v^T\cdot J` for a given input vector +# :math:`v=(v_1 \dots v_m)`. This is achieved by calling ``backward`` with +# :math:`v` as an argument. The size of :math:`v` should be the same as +# the size of the original tensor, with respect to which we want to +# compute the product: +# + +inp = torch.eye(5, requires_grad=True) +out = (inp+1).pow(2) +out.backward(torch.ones_like(inp), retain_graph=True) +print("First call\n", inp.grad) +out.backward(torch.ones_like(inp), retain_graph=True) +print("\nSecond call\n", inp.grad) +inp.grad.zero_() +out.backward(torch.ones_like(inp), retain_graph=True) +print("\nCall after zeroing gradients\n", inp.grad) + + +###################################################################### +# Notice that when we call ``backward`` for the second time with the same +# argument, the value of the gradient is different. This happens because +# when doing ``backward`` propagation, PyTorch **accumulates the +# gradients**, i.e. the value of computed gradients is added to the +# ``grad`` property of all leaf nodes of computational graph. If you want +# to compute the proper gradients, you need to zero out the ``grad`` +# property before. In real-life training an *optimizer* helps us to do +# this. + +###################################################################### +# .. note:: Previously we were calling ``backward()`` function without +# parameters. This is essentially equivalent to calling +# ``backward(torch.tensor(1.0))``, which is a useful way to compute the +# gradients in case of a scalar-valued function, such as loss during +# neural network training. +# + + +###################################################################### +# Next: Learn more about `how to use automatic differentiation to train a neural network model `_. +# +# .. include:: /beginner_source/quickstart/qs_toc.txt +# diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index 01e97febf14..cc7442e4863 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -1,166 +1,100 @@ """ -Build Model Tutorial -======================================= +Optimizing Model Parameters +=================== +Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! +To do this we need to understand a how to handle 5 core deep learning concepts in PyTorch + 1. Hyperparameters (learning rates, batch sizes, epochs etc) + 2. Optimization Loops + 3. Loss + 4. AutoGrad + 5. Optimizers +Let's dissect these concepts one by one and look at some code at the end we'll see how it all fits together. +Hyperparameters +----------------- """ -############################################### -# The data has been loaded and transformed we can now build the model. -# We will leverage `torch.nn `_ -# predefined layers that Pytorch has that can both simplify our code, and make it faster. -# -# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` -# container from class `torch.nn. Sequential `_ -# that allows us to define the model layers inline. -# The neural network modules layers will be added to it in the order they are passed in. -# -# Another way to bulid this model is with a class -# using `nn.Module `_ This gives us more flexibility, because -# we can construct layers of any complexity, including the ones with shared weights. +###################################################### +# Hyperparameters are adjustable parameters that let you control the model optimization process. For example, with neural networks, you can configure: # -# Lets break down the steps to build this model below -# - -########################################## -# Inline nn.Sequential Example: -# ---------------------------- -# - -import os -import torch -import torch.nn as nn -import torch.onnx as onnx -from torch.utils.data import DataLoader -from torchvision import datasets, transforms +# - **Number of Epochs**- the number times iterate over the dataset to update model parameters +# - **Batch Size** - the number of samples in the dataset to evaluate before you update model parameters +# - **Cost Function** - the method used to decide how to evaluate the model on a data sample to update the model parameters +# - **Learning Rate** - how much to update models parameters at each batch/epoch set this to large and you won't update optimally if you set it to small you will learn really slowly -device = 'cuda' if torch.cuda.is_available() else 'cpu' -print('Using {} device'.format(device)) - -# model -model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, len(classes)), - nn.Softmax(dim=1) - ).to(device) - -print(model) - -############## -# Class nn.Module Example: -# -------------------------- -# +learning_rate = 1e-3 +batch_size = 64 +epochs = 5 -class NeuralNework(nn.Module): - def __init__(self, x): - super(NeuralNework, self).__init__() - -class Model(nn.Module): - def __init__(self, x): - super(Model, self).__init__() - self.layer1 = nn.Linear(28*28, 512) - self.layer2 = nn.Linear(512, 512) - self.output = nn.Linear(512, 10) - - def forward(self, x): - x = F.relu(self.layer1(x)) - x = F.relu(self.layer2(x)) - x = self.output(x) - return F.softmax(x, dim=1) - -############################################# -# Get Device for Training -# ----------------------- -# Here we check to see if `torch.cuda `_ is available to use the GPU, else we will use the CPU. -# -# Example: +###################################################### +# Optimizaton Loops +# ----------------- # - -device = 'cuda' if torch.cuda.is_available() else 'cpu' -print('Using {} device'.format(device)) - -############################################## -# The Model Module Layers -# ------------------------- +# Once we set our hyperparameters we can then optimize our our model with optimization loops. # -# Lets break down each model layer in the FashionMNIST model. +# The optimziation loop is comprized of three main subloops in PyTorch. # -################################################## -# `nn.Flatten `_ to reduce tensor dimensions to one. -# ----------------------------------------------- -# -# From the docs: -# ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)`` +############################################################ +# .. figure:: /_static/img/quickstart/optimizationloops.png +# :alt: # -# Here is an example using one of the training_data set items: -======= -# -# Lets break down each model layer in the FashionMNIST model. -# -################################################## -# [nn.Flatten](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html) to reduce tensor dimensions to one. -# -# From the docs: -# ``` -# torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1) -# ``` +############################################################# +# 1. The Train Loop - Core loop iterates over all the epochs +# 2. The Validation Loop - Validate loss after each weight parameter update and can be used to gauge hyper parameter performance and update them for the next batch. +# 3. The Test Loop - is used to evaluate our models performance after each epoch on traditional metrics to show how much our model is generalizing from the train and validation dataset to the test dataset it's never seen before. # -# Here is an example using one of the training_data set items: -tensor = training_data[0][0] -print(tensor.size()) +for epoch in range(num_epochs): + # Optimization Loop + # Train loop over batches + model.train() # set model to train + # Model Update Code + model.eval() # After exiting batch loop set model to eval to speed up evaluation and not track gradients (this is explained below) + # Validation Loop + # - Put sample validation metric logging and hyperparameter update code here + # After exiting train loop set model to eval to speed up evaluation and not track gradients (this is explained below) + # Test Loop + # - Put sample test metric logging and hyperparameter update code here -# Output: torch.Size([1, 28, 28]) - -model = nn.Sequential( - nn.Flatten() -) -flattened_tensor = model(tensor) -flattened_tensor.size() - -############################################## -# [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) to add a linear layer to the model. +###################################################### +# Loss +# ----------------- # -# Now that we have flattened our tensor dimension we will apply a linear layer transform that will calculate/learn the weights and the bias. +# The loss is the value used to update our parameters. To calculate the loss we make a prediction using the inputs of our given data sample. # -# Lets take a look at the resulting data example with the flatten layer and linear layer added: -input = training_data[0][0] -print(input.size()) -model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), -) -output = model(input) -output.size() +preds = model(inputs) +loss = cost_function(preds, labels) +# Make sure previous gradients are cleared +optimizer.zero_grad() +# Calculates gradients with respect to loss +loss.backward() +optimizer.step() +###################################################### +# The standard method for optimization is called Stochastic Gradient Descent, to learn more check out this awesome video by `3blue1brown `_. There are many different optimizers and variations of this method in PyTorch such as ADAM and RMSProp that work better for different kinds of models, they are out side the scope of this Blitz, but can check out the full list of optimizers `here `_ -# Output: -# torch.Size([1, 28, 28]) -# torch.Size([1, 512]) - -################################################# -# Activation Functions -# ------------------------- +###################################################### +# Putting it all together lets look at a basic optimization loop +# ----------------- +# +# Initilize optimizer and example cost function # -# - `nn.ReLU `_ Activation -# - `nn.Softmax `_ Activation +# For loop to iterate over epoch +# - Train loop over batches +# - Set model to train mode +# - Calculate loss using +# - clear optimizer gradient +# - loss.backword +# - optimizer step +# - Set model to evaluate mode and start validation loop +# - calculate validation loss and update optimizer hyper parameters +# - Set model to evaluate test loop # -# Next: Learn more about how the `optimzation loop works with this example `_. +# Next: Learn more about `AutoGrad `_. # ################################################################## -# Pytorch Quickstart Topics -# ----------------- -#| `Tensors `_ -#| `DataSets and DataLoaders `_ -#| `Transforms `_ -#| `Build Model `_ -#| `Optimization Loop `_ -#| `AutoGrad `_ -#| `Save, Load and Run Model `_ - +# .. include:: /beginner_source/quickstart/qs_toc.txt +# diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py index c3070530c01..8805a6fd4cc 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -7,19 +7,19 @@ # Getting Started With Data in PyTorch # ----------------- # -# Before we can even think about building a model with PyTorch, we need to first learn how to load and process data. Data can be sourced from local files, cloud datastores and database queries. It comes in all sorts of forms and formats from structured tables to image, audio, text, video files and more. +# Before we can even think about building a model with PyTorch, we need to first learn how to load and process data. Data can be sourced from local files, cloud datastores and database queries. It comes in all sorts of forms and formats from structured tables to image, audio, text, video files and more. # ############################################################### # .. figure:: /_static/img/quickstart/typesdata.png # :alt: typesdata -# -# Different data types require different python libraries to load and process such as `openCV `_ and `PIL `_ for images, `NLTK `_ and `spaCy `_ for text and `Librosa `_ for audio. -# -# If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking managing collections of data. -# +# +# Different data types require different python libraries to load and process such as `openCV `_ and `PIL `_ for images, `NLTK `_ and `spaCy `_ for text and `Librosa `_ for audio. +# +# If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking managing collections of data. +# # A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. These are useful for benchmarking and testing your models before training on your own custom datasets. -# +# # You can find some of them below. # # - `Image Datasets `_ @@ -31,24 +31,31 @@ # Iterating through a Dataset # ----------------- # -# Once we have a Dataset we can index it manually like a list `clothing[index]`. -# +# Once we have a Dataset we can index it manually like a list `clothing[index]`. +# # Here is an example of how to load the fashion MNIST dataset from torch vision. # -import torch +from torch.utils.data import DataLoader +from torchvision.io import read_image +from torchvision import transforms, utils +import pandas as pd +import torch +import os +import torch from torch.utils.data import Dataset import torchvision.datasets as datasets import matplotlib.pyplot as plt import numpy as np clothing = datasets.FashionMNIST('data', train=True, download=True) -labels_map = {0 : 'T-Shirt', 1 : 'Trouser', 2 : 'Pullover', 3 : 'Dress', 4 : 'Coat', 5 : 'Sandal', 6 : 'Shirt', 7 : 'Sneaker', 8 : 'Bag', 9 : 'Ankle Boot'} -figure = plt.figure(figsize=(8,8)) +labels_map = {0: 'T-Shirt', 1: 'Trouser', 2: 'Pullover', 3: 'Dress', + 4: 'Coat', 5: 'Sandal', 6: 'Shirt', 7: 'Sneaker', 8: 'Bag', 9: 'Ankle Boot'} +figure = plt.figure(figsize=(8, 8)) cols, rows = 3, 3 -for i in range(1, cols*rows +1): +for i in range(1, cols*rows + 1): sample_idx = np.random.randint(len(clothing)) - img = clothing[sample_idx][0][0,:,:] + img = clothing[sample_idx][0][0, :, :] figure.add_subplot(rows, cols, i) plt.title(labels_map[clothing[sample_idx][1]]) plt.axis('off') @@ -68,12 +75,6 @@ # To work with your own data lets look at the a simple custom image Dataset implementation: # -import os -import torch -import pandas as pd -from torch.utils.data import Dataset -from torchvision import transforms, utils -from torchvision.io import read_image class CustomImageDataset(Dataset): def __init__(self, annotations_file, img_dir, transform=None): @@ -97,31 +98,26 @@ def __getitem__(self, idx): if self.transform: sample = self.transform(sample) - return sample - + return sample + ################################################################# -# Imports +# Imports # ----------------- # # Import os for file handling, torch for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and Dataset to implement the Dataset interface. -# +# # Example: -import os -import torch -import pandas as pd -from torchvision.io import read_image -from torch.utils.data import Dataset -from torch.utils.data import DataLoader ################################################################# # Init # ----------------- # # The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and the keep track of directory of our image file. Note that different types of data can take different init inputs you are not limited to just an annotations file, directory_path and transforms but for images this is a standard practice. -# +# # Example: -# +# + def __init__(self, annotations_file, img_dir, transform=None): self.img_labels = pd.read_csv(annotations_file) @@ -132,10 +128,11 @@ def __init__(self, annotations_file, img_dir, transform=None): # __len__ # ----------------- # -# The __len__ function is very simple here we just need to return the number of samples in our dataset. -# +# The __len__ function is very simple here we just need to return the number of samples in our dataset. +# # Example: + def __len__(self): return len(self.img_labels) @@ -144,10 +141,12 @@ def __len__(self): # ----------------- # # The __getitem__ function is the most important function in the Datasets interface this. It takes a tensor or an index as input and returns a loaded sample from you dataset at from the given indecies. -# -# In this sample if provided a tensor we convert the tensor to a list containing our index. We then load the file at the given index from our image directory as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a Transform on and return. To learn more about Transforms see the next section of the Blitz. -# +# +# In this sample if provided a tensor we convert the tensor to a list containing our index. We then load the file at the given index from our image directory as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a Transform on and return. To learn more about Transforms see the next section of the Blitz. +# # Example: + + def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() @@ -158,37 +157,30 @@ def __getitem__(self, idx): sample = {'image': image, 'label': label} if self.transform: sample = self.transform(sample) - return sample + return sample ################################################################# # Preparing your data for training with DataLoaders # ----------------- # -# Now we have a organized mechansim for managing data which is great, but there is still a lot of manual work we would have to do train a model with our Dataset. -# -# For example we would have to manually maintain the code for: -# * Batching -# * Suffling -# * Parallel batch distribution -# +# Now we have a organized mechansim for managing data which is great, but there is still a lot of manual work we would have to do train a model with our Dataset. +# +# For example we would have to manually maintain the code for: +# * Batching +# * Suffling +# * Parallel batch distribution +# # The PyTorch Dataloader *torch.utils.data.DataLoader* is an iterator that handles all of this complexity for us enabling us to load a dataset and focusing on train our model. + dataloader = DataLoader(clothing, batch_size=4, shuffle=True, num_workers=0) ################################################################# # With this we have all we need to know to load an process data of any kind in PyTorch to train deep learning models. -# +# # Next: Learn more about how to `transform that data for training `_. # ################################################################## -# Pytorch Quickstart Topics -# ----------------- -#| `Tensors `_ -#| `DataSets and DataLoaders `_ -#| `Transforms `_ -#| `Build Model `_ -#| `Optimization Loop `_ -#| `AutoGrad `_ -#| `Save, Load and Run Model `_ - +# .. include:: /beginner_source/quickstart/qs_toc.txt +# diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index f00b84e4a8c..2418df1bb64 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -1,28 +1,81 @@ """ Optimizing Model Parameters -=================== +=========================== Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! +To get started lets take a look at some example model optimization code: +""" -To do this we need to understand a how to handle 5 core deep learning concepts in PyTorch +# Initilize hyper parameters +learning_rate = 0.01 +num_epochs = 100 - 1. Hyperparameters (learning rates, batch sizes, epochs etc) - 2. Optimization Loops - 3. Loss - 4. AutoGrad - 5. Optimizers +# Initilize model, optimizer and example cost function +model = NeuralNework() # From Previous Model Section +optimizer = optim.SGD(model.parameters(), lr=learning_rate) # optimizer +cost_function = nn.CrossEntropyLoss() -Let's dissect these concepts one by one and look at some code at the end we'll see how it all fits together. +# For loop to iterate over epoch +for epoch in range(num_epochs): + # Train loop over batches + for train_batch, (train_inputs, train_labels) in enumerate(train_dataloader): + model.train() # Set model to train mode + train_inputs, train_labels = train_inputs.to( + device), train_labels.to(device) + optimizer.zero_grad() # zero out gradient + pred = model(train_inputs) # make a prediction on this batch! + loss = cost_function(pred, train_labels) # how bad is it? + loss.backward() # compute gradients + optimizer.step() # update parameters + + # validation loop + model.eval() # Set model to evaluate mode and start validation loop + for val_batch, (val_inputs, val_labels) in enumerate(val_dataloader): + val_inputs, val_labels = val_inputs.to( + device), val_labels.to(device) + pred = model(val_inputs) + test_loss += cost_function(pred, val_labels).item() + correct += (pred.argmax(1) == val_labels.argmax(1) + ).type(torch.float).sum().item() + val_loss /= len(val_dataloader.dataset) + correct /= len(val_dataloader.dataset) + print('\nValidation Error:') + print('acc: {:>0.1f}%, avg loss: {:>8f}'.format(100*correct, val_loss)) + # Make any additonal hyperparameter modifications here + + # Test loop + for test_batch, (test_inputs, test_labels) in enumerate(test_dataloader): + test_inputs, test_labels = test_inputs.to( + device), test_labels.to(device) + pred = model(test_inputs) + test_loss += cost_function(pred, test_labels).item() + correct += (pred.argmax(1) == test_labels.argmax(1) + ).type(torch.float).sum().item() + test_loss /= len(test_dataloader.dataset) + correct /= len(test_dataloader.dataset) + print('\nTest Error:') + print('acc: {:>0.1f}%, avg loss: {:>8f}'.format(100*correct, test_loss)) ###################################################### -# Hyperparameters -# ----------------- +# To understand this code need to understand a how to handle 4 core deep learning concepts in PyTorch: +# +# 1. Hyperparameters (learning rates, batch sizes, epochs etc) +# 2. Optimization Loops +# 3. Loss +# 4. Optimizers +# +# Let's dissect these core concepts one by one by the time we end every line the code above will make sense. # +# Hyperparameters +# ----------------- + + +###################################################### # Hyperparameters are adjustable parameters that let you control the model optimization process. For example, with neural networks, you can configure: +# # - **Number of Epochs**- the number times iterate over the dataset to update model parameters # - **Batch Size** - the number of samples in the dataset to evaluate before you update model parameters -# - **Cost Function** - the method used to decide how to evaluate the model on a data sample to update the model parameters -# - **Learning Rate** - how much to update models parameters at each batch/epoch set this to large and you won't update optimally if you set it to small you will learn really slowly - +# - **Learning Rate** - how much to update models parameters at each batch/epoch. Set this value too large and your model won't learn optimally if you set it too small and it will learn really slowly. + learning_rate = 1e-3 batch_size = 64 epochs = 5 @@ -31,11 +84,10 @@ # Optimizaton Loops # ----------------- # -# Once we set our hyperparameters we can then optimize our our model with optimization loops. -# -# The optimziation loop is comprized of three main subloops in PyTorch. +# Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. +# +# Each iteration of the optimiziation loop is called an Epoch. Each epoch is comprized of three main subloops in PyTorch. # - ############################################################ # .. figure:: /_static/img/quickstart/optimizationloops.png @@ -43,81 +95,103 @@ # ############################################################# -# 1. The Train Loop - Core loop iterates over all the epochs -# 2. The Validation Loop - Validate loss after each weight parameter update and can be used to gauge hyper parameter performance and update them for the next batch. -# 3. The Test Loop - is used to evaluate our models performance after each epoch on traditional metrics to show how much our model is generalizing from the train and validation dataset to the test dataset it's never seen before. -# +# 1. **The Train Loop** - Core loop iterates over all the epochs +# 2. **The Validation Loop** - Validate loss after each weight parameter update and can be used to gauge hyper parameter performance and update them for the next batch. +# 3. **The Test Loop** - is used to evaluate our models performance after each epoch on traditional metrics to show how much our model is generalizing from the train and validation dataset to the test dataset it's never seen before. +# -for epoch in range(num_epochs): -# Optimization Loop +for epoch in range(num_epochs): # Optimization Loop # Train loop over batches - model.train() # set model to train + model.train() # set model to train # Model Update Code - model.eval() # After exiting batch loop set model to eval to speed up evaluation and not track gradients (this is explained below) + model.eval() # After exiting batch loop set model to eval to speed up evaluation and not track gradients (this is explained below) # Validation Loop - # - Put sample validation metric logging and hyperparameter update code here - # After exiting train loop set model to eval to speed up evaluation and not track gradients (this is explained below) + # - Validation metric logging and hyperparameter update happens here + # After exiting train loop set model to eval to speed up evaluation and not track gradients (this is explained below) # Test Loop - # - Put sample test metric logging and hyperparameter update code here + # - Test preformance happens here ###################################################### -# Loss -# ----------------- +# Loss and Cost Function +# ---------------------- # -# The loss is the value used to update our parameters. To calculate the loss we make a prediction using the inputs of our given data sample. +# The loss is the value used to update our parameters. To calculate the loss we make a prediction using the inputs of our given data sample and compare it with a cost function against the true data label value. # preds = model(inputs) loss = cost_function(preds, labels) -# Make sure previous gradients are cleared -optimizer.zero_grad() -# Calculates gradients with respect to loss -loss.backward() -optimizer.step() - ###################################################### -# The standard method for optimization is called Stochastic Gradient Descent, to learn more check out this awesome video by `3blue1brown `_. There are many different optimizers and variations of this method in PyTorch such as ADAM and RMSProp that work better for different kinds of models, they are out side the scope of this Blitz, but can check out the full list of optimizers `here `_ -======= +# Common loss functions include `Mean Square Error `_, +# `Negative Log Likelihood `_, +# and `CrossEntropyLoss `_. +# Here is an example built in Cross Entropy Loss cost function call from the PyTorch nn module. +# + +cost_function = nn.CrossEntropyLoss() +loss = cost_function(model_prediction, true_value) ###################################################### -# AutoGrad and Optimizer (We might want to split this when we go more in depth on autograd ) -# ----------------- -# By default each tensor maintains a graph of every operation applied on it unless otherwise specified using the torch.no_grad() command. +# In addition to the included PyTorch cost functions you can create your own custom cost functions as long as they are differentiable. # -# `Autograd graph `_ +# See this example custom Cross Entropy Loss implementation from the `Stanford CS230 `_ course below. # -# PyTorch uses this graph to automatically update parameters with respect to our models loss during training. This is done with one line loss.backwards(). Once we have our gradients the optimizer is used to propgate the gradients from the backwards command to update all the parameters in our model. + + +def myCrossEntropyLoss(outputs, labels): + batch_size = outputs.size()[0] # batch_size + # compute the log of softmax values + outputs = F.log_softmax(outputs, dim=1) + # pick the values corresponding to the labels + outputs = outputs[range(batch_size), labels] + return -torch.sum(outputs)/num_examples ###################################################### -# Putting it all together lets look at a basic optimization loop -# ----------------- +# It can be called just like the out of the box implementation above. # -# Initilize optimizer and example cost function -# -# For loop to iterate over epoch -# - Train loop over batches -# - Set model to train mode -# - Calculate loss using -# - clear optimizer gradient -# - loss.backword -# - optimizer step -# - Set model to evaluate mode and start validation loop -# - calculate validation loss and update optimizer hyper parameters -# - Set model to evaluate test loop + + +loss = myCrossEntropyLoss(model_prediction, true_value) + +###################################################### +# A more in depth explanation of PyTorch cost functions is outside the scope of the blitz but you can learn more +# about the different common cost functions for deep learning in the PyTorch `documentation `_. +# +# Optimizer +# --------- +# Using the loss, we can then optimize our models parameters. By default, each tensor maintains +# a graph of every operation applied on it unless otherwise specified using the torch.no_grad() command. + +############################################################ +# .. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png +# :alt: tensor graph # -# Next: Learn more about `AutoGrad `_. +# PyTorch uses this graph to automatically update parameters with respect to our model's loss during training. This is done with one +# line ``loss.backwards()``. Once we have our gradients the optimizer is used to propgate the gradients from the backwards command +# to update all the parameters in our model. + +optimizer.zero_grad() # make sure previous gradients are cleared +loss.backward() # calculates gradients with respect to loss +optimizer.step() + +###################################################### +# The standard method for optimization is called Stochastic Gradient Descent, to learn more check out this awesome +# video by `3blue1brown `_. # +# An Optimizer can be initalized with the Pytorch optim module, as an example lets initialize an SGD optimizer. +# The PyTorch SGD optimizer takes our model and our learning rate hyperparameter as input. +optimizer = optim.SGD(model.parameters(), lr=learning_rate) + +###################################################### +# In addition to SGD there are many different optimizers and variations of this method in PyTorch such +# as ADAM and RMSProp, that work better for different kinds of models. They are outside the scope +# of this Blitz, but can check out the full list of optimizers `here `_. +# +# With this we have all we need to know to train, validate and test PyTorch deep learning models. ################################################################## -# Pytorch Quickstart Topics -# ----------------- -#| `Tensors `_ -#| `DataSets and DataLoaders `_ -#| `Transforms `_ -#| `Build Model `_ -#| `Optimization Loop `_ -#| `AutoGrad `_ -#| `Save, Load and Run Model `_ +# Next: Learn more about `Automatic Differentiation with AutoGrad `_. +# .. include:: /beginner_source/quickstart/qs_toc.txt +# diff --git a/beginner_source/quickstart/qs_toc.txt b/beginner_source/quickstart/qs_toc.txt new file mode 100644 index 00000000000..86fbdc05364 --- /dev/null +++ b/beginner_source/quickstart/qs_toc.txt @@ -0,0 +1,10 @@ + +Pytorch Quickstart Topics +------------------------- +| `Tensors `_ +| `DataSets and DataLoaders `_ +| `Transforms `_ +| `Build Model `_ +| `Automatic Differentiation `_ +| `Optimization Loop `_ +| `Save, Load and Use Model `_ \ No newline at end of file diff --git a/beginner_source/quickstart/save_load_run_tutorial.py b/beginner_source/quickstart/save_load_run_tutorial.py index 542d76c4b61..3573e87b763 100644 --- a/beginner_source/quickstart/save_load_run_tutorial.py +++ b/beginner_source/quickstart/save_load_run_tutorial.py @@ -1,89 +1,73 @@ """ Save, Load and Use the Model -=================== - -We have trained the model! Now lets take a look at how to save, load and use the model created. - -Full Section Example: +============================ +In this section we will look at how to save, load and use persisted model state +to run predictions. """ -import os import torch import torch.nn as nn import torch.onnx as onnx -# create dummy variable to traverse graph -x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255 -onnx.export(model, x, 'model.onnx') -print('Saved onnx model to model.onnx') +####################################################################### +# Save the Model +# -------------- +# PyTorch stores the learned parameters in the model's internal +# state dictionary. These are persisted via the `torch.save` +# method: # saving PyTorch Model Dictionary torch.save(model.state_dict(), 'model.pth') -print('Saved PyTorch Model to model.pth') - -draw_clothes(test_data) - -#rehydrate model -loaded_model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, len(classes)), - nn.Softmax(dim=1) - ) - -#load graph -loaded_model.load_state_dict(torch.load('model.pth')) -loaded_model.eval() - -x, y = test_data[0][0], test_data[0][1] -with torch.no_grad(): - pred = loaded_model(x) - predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] - print(f'Predicted: "{predicted}", Actual: "{actual}"') - -###################################################### -# Save the Model -# ----------------------- -# Example: +####################################################################### +# PyTorch also has native ONNX export support. Given the dynamic nature of the +# PyTorch execution graph however, the export process must +# traverse the execution graph to produce a persisted onnx model. As such, a +# test variable of the appropriate size should be passed in to the +# export routine: -# create dummy variable to traverse graph +# create test variable to traverse graph x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255 onnx.export(model, x, 'model.onnx') -print('Saved onnx model to model.onnx') - -# saving PyTorch Model Dictionary -torch.save(model.state_dict(), 'model.pth') -print('Saved PyTorch Model to model.pth') ####################################################################### # Load the Model -# --------------------------- -# Example: - -draw_clothes(test_data) - +# -------------- +# Loading a persisted PyTorch model consists of two primary steps: +# +# 1. Recreating the appropriate model shape, and +# 2. Rehydrating the parameters into the newly created model's state dictionary +# +# These two steps are illustrated here: + +# recreate model loaded_model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, len(classes)), - nn.Softmax(dim=1) - ) + nn.Flatten(), + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, len(classes)), + nn.Softmax(dim=1) +) + +# hydrate state dictionary loaded_model.load_state_dict(torch.load('model.pth')) -loaded_model.eval() + ###################################################################### -# Test the Model -# ---------------------------------- -# Example: +# Use the Model +# ------------- +# Once the model is loaded it can be used for both training as well +# as inference. The model's ``eval()`` method is called in this case +# to indicate the model will be used for inference. This method +# only affects internal modules like Dropout and BatchNorm which +# are not necessary for inference. Using ``torch.no_grad()`` turns off +# `automatic differentiation `_ since it +# is also unnecessary: +loaded_model.eval() x, y = test_data[0][0], test_data[0][1] with torch.no_grad(): pred = loaded_model(x) @@ -91,13 +75,5 @@ print(f'Predicted: "{predicted}", Actual: "{actual}"') ################################################################## -# Pytorch Quickstart Topics -# ---------------------------------------- -# | `Tensors `_ -# | `DataSets and DataLoaders `_ -# | `Transforms `_ -# | `Build Model `_ -# | `Optimization Loop `_ -# | `AutoGrad `_ -# | `Save, Load and Run Model `_ - +# .. include:: /beginner_source/quickstart/qs_toc.txt +# diff --git a/beginner_source/quickstart/tensor_quickstart_tutorial.py b/beginner_source/quickstart/tensor_quickstart_tutorial.py deleted file mode 100644 index c37dd9eacd6..00000000000 --- a/beginner_source/quickstart/tensor_quickstart_tutorial.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -Tensors and Operations -=================== - -Tensors and Operations -When training neural network models for real world tasks, we need to be able to effectively represent different types of input data: sets of numerical features, images, videos, sounds, etc. All those different input types can be represented as multi-dimensional arrays of numbers that are called tensors. - -Tensor is the basic computational unit in PyTorch. It is very similar to NumPy array, and supports similar operations. However, there are two very important features of Torch tensors that make the especially useful for training large-scale neural networks: - - - Tensor operations can be performed on GPU using CUDA - - Tensor operations support automatic differentiation using `AutoGrad `_ - -Conversion between Torch tensors and NumPy arrays can be done easily: -""" - -import torch -import numpy as np - -np_array = np.arange(10) -tensor = torch.from_numpy(np_array) - -print(f"Tensor={tensor}, Array={tensor.numpy()}") - -################################################################# -# .. code:: python -# Output: Tensor=tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32), Array=[0 1 2 3 4 5 6 7 8 9] -# -# .. note:: When using CPU for computations, tensors converted from arrays share the same memory for data. Thus, changing the underlying array will also affect the tensor. -# -# -# Creating Tensors -# ------------- -# The fastest way to create a tensor is to define an uninitialized tensor - the values of this tensor are not set, and depend on the whatever data was there in memory: -# - -x = torch.empty(3,6) -print(x) - -############################################################################ -# .. code:: python -# Output: tensor([[-1.3822e-06, 6.5301e-43, -1.3822e-06, 6.5301e-43, -1.4041e-06, -# 6.5301e-43], -# [-1.3855e-06, 6.5301e-43, -2.9163e-07, 6.5301e-43, -2.9163e-07, -# 6.5301e-43], -# [-1.4066e-06, 6.5301e-43, -1.3788e-06, 6.5301e-43, -2.9163e-07, -# 6.5301e-43]]) -# -# -# In practice, we ofter want to create tensors initialized to some values, such as zeros, ones or random values. Note that you can also specify the type of elements using dtype parameter, and chosing one of torch types: - - -x = torch.randn(3,5) -print(x) -y = torch.zeros(3,5,dtype=torch.int) -print(y) -z = torch.ones(3,5,dtype=torch.double) -print(z) - -###################################################################### -# Output: -# tensor([[-1.0166, -0.6828, 1.8886, -1.2115, 0.0202], -# [-1.1278, 0.7447, 0.4260, -2.1909, 0.5653], -# [ 0.0562, -0.1393, 0.6145, -0.6181, 0.1879]]) -# tensor([[0, 0, 0, 0, 0], -# [0, 0, 0, 0, 0], -# [0, 0, 0, 0, 0]], dtype=torch.int32) -# tensor([[1., 1., 1., 1., 1.], -# [1., 1., 1., 1., 1.], -# [1., 1., 1., 1., 1.]], dtype=torch.float64) -# -# -# You can also create random tensors with values sampled from different distributions, as described `in documentation. `_ -# -#Similarly to NumPy, you can use eye to create a diagonal identity matrix: - -print(torch.eye(10)) - -################################################################ -# Output: -# tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.], -# [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], -# [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.], -# [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], -# [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.], -# [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], -# [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.], -# [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], -# [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.], -# [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]]) -# -# -# You can also create new tensors with the same properties or size as existing tensors: -# - -print(z.new_ones(2,2)) -print(torch.zeros_like(x,dtype=torch.long)) - -############################################################################ -# Tensor Operations -# ------------- -# Tensors support all basic arithmetic operations, which can be specified in different ways: -# -# - Using operators, such as +, -, etc. -# - Using functions such as add, mult, etc. Functions can either return values, or store them in the specified ouput variable (using out= parameter) -# - In-place operations, which modify one of the arguments. Those operations have _ appended to their name, eg. add_. -# -# Complete reference to all tensor operations can be found in documentation. -# -# Let us see examples of those operations on two tensors, x and y. -# -# -# -################################################################## -# More help with the FashionMNIST Pytorch Blitz -# ---------------------------------- -# `Tensors `_ -# `DataSets and DataLoaders `_ -# `Transformations `_ -# `Build Model `_ -# `Optimization Loop `_ -# `AutoGrad `_ -# `Back to FashionMNIST main code base <>`_ diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 5a0630f1a7a..54afaa08d86 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -1,18 +1,14 @@ """ Tensors and Operations ---------------------- - **Tensor** is the basic computational unit in PyTorch. It is very similar to **NumPy array**, and supports similar operations. However, there are two very important features of Torch tensors that make them especially useful for training large-scale neural networks: - -- Tensor operations can be performed on GPU using CUDA +- Tensor operations can be performed on GPUs or other specialized hardware to accelerate computing - Tensor operations support automatic differentiation using - `AutoGrad `__ - + `pytorch.autograd engine `__ Conversion between Torch tensors and NumPy arrays can be done easily: - """ import torch @@ -25,20 +21,23 @@ ###################################################################### -# .. note:: When using CPU for computations, tensors converted from arrays share the same memory for data. Thus, changing the underlying array will also affect the tensor. -# +# .. note:: +# When using CPU for computations, tensors converted from arrays +# share the same memory for data. Thus, changing the underlying array +# will also affect the tensor. +# ###################################################################### # Creating Tensors # ~~~~~~~~~~~~~~~~ -# +# # The fastest way to create a tensor is to define an *uninitialized* # tensor - the values of this tensor are not set, and depend on the # whatever data was there in memory: -# +# -x = torch.empty(3,6) +x = torch.empty(3, 6) ###################################################################### @@ -46,20 +45,20 @@ # such as zeros, ones or random values. Note that you can also specify the # type of elements using ``dtype`` parameter, and chosing one of ``torch`` # types: -# +# -x = torch.randn(3,5) -y = torch.zeros(3,5,dtype=torch.int) -z = torch.ones(3,5,dtype=torch.double) +x = torch.randn(3, 5) +y = torch.zeros(3, 5, dtype=torch.int) +z = torch.ones(3, 5, dtype=torch.double) ###################################################################### # You can also create random tensors with values sampled from different # distributions, as described `in the # documentation `__. -# +# # Similarly to NumPy, you can use ``eye`` to create a diagonal identity # matrix: -# +# I = torch.eye(10) @@ -67,24 +66,25 @@ ###################################################################### # You can also create new tensors with the same properties or size as # existing tensors: -# +# -print(z.new_ones(2,2)) # new_ method allows specifying new size -print(torch.zeros_like(x,dtype=torch.long)) # _like method supports overriding dtype +print(z.new_ones(2, 2)) # new_ method allows specifying new size +# _like method supports overriding dtype +print(torch.zeros_like(x, dtype=torch.long)) ###################################################################### # Size of the tensor can be obtained using ``.size()`` method, which # returns a tuple-like object: -# +# -print(z.size()) # Prints [3.0] +print(z.size()) # Prints [3.0] ###################################################################### # Tensor Operations # ~~~~~~~~~~~~~~~~~ -# +# # Tensors support all basic arithmetic operations, which can be specified # in different ways: # - Using operators, such as ``+``, ``-``, etc. \* @@ -92,93 +92,99 @@ # - In-place operations, which modify one of the arguments. Those operations have ``_`` appended to their name, eg. ``add_``. # Complete reference to all tensor operations can be found `in the # documentation `__. -# +# # Let us see examples of those operations on two tensors, ``x`` and ``y``. -# +# -x = torch.randn(3,5) -y = torch.randn(3,5) +x = torch.randn(3, 5) +y = torch.randn(3, 5) ###################################################################### # Using operator notation # ^^^^^^^^^^^^^^^^^^^^^^^ -# +# # We can use overloaded arithmetic operators, such as ``+`` and ``*``: -# +# z = x*y ###################################################################### # Note, that ``*`` means elementwise product, and not the matrix product. -# To compute matrix product, we need to use ``matmul`` function, as shown +# To compute matrix product, we need to use `@` operator or ``matmul`` function, as shown # below. -# +# # Using functions # ^^^^^^^^^^^^^^^ -# +# # While only some operations are available as Python operators, `many more # functions `__ # can be specified using the full name. In the example below, ``t`` # transposes the matrix, and ``matmul`` means matrix multiplication: -# +# -z = torch.matmul(x,y.t()) +z = torch.matmul(x, y.t()) ###################################################################### # Simple operations (addition, multiplication, etc.) also have # corresponsing functions, and can be called either as methods, or as # functions: -# +# z = x.add(y) -z = torch.add(x,y) +z = torch.add(x, y) ###################################################################### # Sometimes it may be more convenient to store the result into specified # variable, instead of returning it from a function. In this case you can # use ``out=`` parameter: -# +# -torch.add(x,y,out=z) +torch.add(x, y, out=z) ###################################################################### # In-place operations # ^^^^^^^^^^^^^^^^^^^ -# +# # When training neural networks, you often need to **modify** the weights, # i.e. perform some operation and then store the result into the original # variable. Those operations are called **in-place operations**, and they # are marked by the ``_`` symbol at the end of their name: -# +# -x.add_(y) # x will be modified +x.add_(y) # x will be modified + +###################################################################### +# .. note:: +# In-place operations save some memory, but can be problematic when +# computing derivatives because of an immediate loss +# of history. Hence, their use is discouraged. ###################################################################### # Resizing and Indexing # ~~~~~~~~~~~~~~~~~~~~~ -# +# # Often you need to change the shape of the tensor without modifying # its values, eg. to add an extra dimension. To do that, you can use # ``view`` method, which provides a **view** to the same in-memory values # using different dimensions: -# +# -print(x.size()) # original size of x is 3x5 -print(x.view(5,3,1).size()) # will give size 5x3x1 -print(x.view(5,-1)) # will result in size 5x3 +print(x.size()) # original size of x is 3x5 +print(x.view(5, 3, 1).size()) # will give size 5x3x1 +print(x.view(5, -1)) # will result in size 5x3 ###################################################################### # The number of elements in a view should be the same as in the -# original tensor, and that you can use ``-1`` in one of the dimensions to +# original tensor. You can use ``-1`` in one of the dimensions to # figure out this dimension automatically. -# +# ###################################################################### @@ -188,38 +194,38 @@ # arrays by copying them to the new shape. However, in vast majority of # cases you can use ``view`` and make sure that no data copying occurs, # and the operation is always efficient. -# +# ###################################################################### # Tensors support all slicing operations that exist in NymPy: -# +# -print(x.size()) # original size of x is 3x5 -print(x[0].size(), x[:,0].size(), x[...,1].size()) # will give 5, 3, 3 +print(x.size()) # original size of x is 3x5 +print(x[0].size(), x[:, 0].size(), x[..., 1].size()) # will give 5, 3, 3 ###################################################################### # If you have a one-element tensor, for example, after aggregating all # values of the tensor into one value, you can convert it to a Python # numerical value using ``item()``: -# +# -print(x.sum().item()) # will print +val = x.sum().item() # will compute the sum of all elements ###################################################################### -# GPU Computations +# Hardware-Accelerated Computations # ~~~~~~~~~~~~~~~~ -# +# # One of the major benefits of using PyTorch is the ability to perform -# tensor operations on GPU. To do that, we need to explicitly **move** -# tensors to GPU using ``.to`` method. -# -# In most of the cases, we check for the availability of GPU on our -# machine, and define the ``device`` object accordingly. Then we move all +# tensor operations on GPUs and some other specialized hardware. To do that, +# we need to explicitly **move** tensors to another computing platform using ``.to`` method. +# +# In most of the cases, we check for the availability of GPU in the beginning +# of the script, and define the ``device`` object accordingly. Then we move all # tensors to that device before performing the computations: -# +# if torch.cuda.is_available(): device = torch.device("cuda") @@ -228,12 +234,12 @@ print("Doing computations on {}".format(device)) -x = torch.randn(3,5,device=device) -y = torch.ones_like(x) -y = y.to(device) -z = x+y # this is performed on GPU if it is available +x = torch.randn(3, 5, device=device) # create tensor on specified device +y = torch.ones_like(x) # create tensor on CPU +y = y.to(device) # move tensor to another device +z = x+y # this is performed on GPU if it is available print(z) -print(z.to("cpu",torch.double)) +print(z.to("cpu", torch.double)) ###################################################################### @@ -241,17 +247,8 @@ # also change the ``dtype``. This does not result in additional # computational time, because we need to copy and transform the data when # moving it from GPU anyway. -# +# # Next learn how to load built in and custom `datasets with dataloaders `_ # - -################################################################## -# Pytorch Quickstart Topics -# ----------------- -#| `Tensors `_ -#| `DataSets and DataLoaders `_ -#| `Transforms `_ -#| `Build Model `_ -#| `Optimization Loop `_ -#| `AutoGrad `_ -#| `Save, Load and Run Model `_ +# .. include:: /beginner_source/quickstart/qs_toc.txt +# diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index aaca92e07a8..56d0e9167d5 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -17,32 +17,37 @@ from torchvision import datasets, transforms # image classes -classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] +classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", + "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] # data used for training training_data = datasets.FashionMNIST('data', train=True, download=True, - transform=transforms.Compose([transforms.ToTensor()]), - target_transform=transforms.Compose([ - transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) - ]) - ) + transform=transforms.Compose( + [transforms.ToTensor()]), + target_transform=transforms.Compose([ + transforms.Lambda(lambda y: torch.zeros( + 10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) + ]) + ) # data used for testing test_data = datasets.FashionMNIST('data', train=False, download=True, - transform=transforms.Compose([transforms.ToTensor()]), - target_transform=transforms.Compose([ - transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) - ]) - ) + transform=transforms.Compose( + [transforms.ToTensor()]), + target_transform=transforms.Compose([ + transforms.Lambda(lambda y: torch.zeros( + 10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) + ]) + ) ############################################## # Pytorch Datasets # -------------------------- -# +# # We are using the built-in open FashionMNIST datasets from the PyTorch library. For more info on the Datasets and Loaders check out `this `_ resource. The ``Train=True`` indicates we want to download the training dataset from the built-in datasets, ``Train=False`` indicates to download the testing dataset. This way we have data partitioned out for training and testing within the provided PyTorch datasets. We will apply the same transfoms to both the training and testing datasets. -# +# # From the docs: -# +# # ``torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=False)`` ############################################## @@ -51,7 +56,7 @@ # Example: # -transform=transforms.Compose([transforms.ToTensor()]) +transform = transforms.Compose([transforms.ToTensor()]) ##################################################### # Compose @@ -80,7 +85,8 @@ # Example: # -target_transform= transforms.Lambda(lambda y: torch.zeros(10, dtype=torchfloat).scatter_(dim=0, index=torchtensor(y), value=1)) +target_transform = transforms.Lambda(lambda y: torch.zeros( + 10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)) ################################################# # This function is taking the y input and creating a tensor of size 10 with a float datatype. Then its calling scatter `torch.Tensor.scatter_ class `_ to send each item to torch.zeros, according to the row, index and current item value. @@ -89,7 +95,7 @@ # - value = 1 is the source elemnt ############################################## -# Using your own data +# Using your own data # -------------------------------------- # # Below is an example for processing image data using a dataset from a local directory. @@ -97,8 +103,8 @@ # Example: # -data_dir='data' -batch_size=4 +data_dir = 'data' +batch_size = 4 data_transforms = { 'train': transforms.Compose([ @@ -116,12 +122,12 @@ } image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) - for x in ['train', 'val']} + for x in ['train', 'val']} -dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], - batch_size=batch_size, - shuffle=True, num_workers=4) - for x in ['train', 'val']} +dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], + batch_size=batch_size, + shuffle=True, num_workers=4) + for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} @@ -133,15 +139,5 @@ # ################################################################## -# Pytorch Quickstart Topics -# ---------------------------------------- -# | `Tensors `_ -# | `DataSets and DataLoaders `_ -# | `Transforms ` -# | `Build Model `_ -# | `Optimization Loop `_ -# | `AutoGrad `_ -# | `Save, Load and Run Model `_ - - - +# .. include:: /beginner_source/quickstart/qs_toc.txt +# From 74be97c414ae87ddb06d8d6517a0771192b48677 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 8 Dec 2020 12:52:33 -0600 Subject: [PATCH 016/120] Some updates based on feedback to quickstart and build model (#29) * Added more detail to the intro of the quickstart * primitive dataset text update * fix build model --- .../quickstart/build_model_tutorial.py | 196 +++++++++++------- beginner_source/quickstart_tutorial.py | 6 +- 2 files changed, 124 insertions(+), 78 deletions(-) diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index cc7442e4863..f139fe40882 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -1,100 +1,144 @@ """ -Optimizing Model Parameters -=================== -Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! -To do this we need to understand a how to handle 5 core deep learning concepts in PyTorch - 1. Hyperparameters (learning rates, batch sizes, epochs etc) - 2. Optimization Loops - 3. Loss - 4. AutoGrad - 5. Optimizers -Let's dissect these concepts one by one and look at some code at the end we'll see how it all fits together. -Hyperparameters ------------------ +Build Model Tutorial +======================================= """ -###################################################### -# Hyperparameters are adjustable parameters that let you control the model optimization process. For example, with neural networks, you can configure: +############################################### +# The data has been loaded and transformed we can now build the model. +# We will leverage `torch.nn `_ +# predefined layers that Pytorch has that can simplify our code. +# +# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` +# container from class `torch.nn. Sequential `_ +# that allows us to define the model layers inline. +# The neural network modules layers will be added to it in the order they are passed in. +# +# Another way to bulid this model is with a class +# using `nn.Module `_ This gives us more flexibility, because +# we can construct layers of any complexity, including the ones with shared weights. # -# - **Number of Epochs**- the number times iterate over the dataset to update model parameters -# - **Batch Size** - the number of samples in the dataset to evaluate before you update model parameters -# - **Cost Function** - the method used to decide how to evaluate the model on a data sample to update the model parameters -# - **Learning Rate** - how much to update models parameters at each batch/epoch set this to large and you won't update optimally if you set it to small you will learn really slowly +# Lets break down the steps to build this model below +# + +########################################## +# Inline nn.Sequential Example: +# ---------------------------- +# + +import os +import torch +import torch.nn as nn +import torch.onnx as onnx +from torch.utils.data import DataLoader +from torchvision import datasets, transforms + +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('Using {} device'.format(device)) -learning_rate = 1e-3 -batch_size = 64 -epochs = 5 +# model +model = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, len(classes)), + nn.Softmax(dim=1) + ).to(device) + +print(model) -###################################################### -# Optimizaton Loops -# ----------------- +############################################# +# Class nn.Module Example: +# -------------------------- # -# Once we set our hyperparameters we can then optimize our our model with optimization loops. + +class NeuralNework(nn.Module): + def __init__(self, x): + super(NeuralNework, self).__init__() + self.layer1 = nn.Linear(28*28, 512) + self.layer2 = nn.Linear(512, 512) + self.output = nn.Linear(512, 10) + + def forward(self, x): + x = F.relu(self.layer1(x)) + x = F.relu(self.layer2(x)) + x = self.output(x) + return F.softmax(x, dim=1) + +############################################# +# Get Device for Training +# ----------------------- +# Here we check to see if `torch.cuda `_ is available to use the GPU, else we will use the CPU. # -# The optimziation loop is comprized of three main subloops in PyTorch. +# Example: # -############################################################ -# .. figure:: /_static/img/quickstart/optimizationloops.png -# :alt: +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('Using {} device'.format(device)) + +############################################## +# The Model Module Layers +# ------------------------- +# +# Lets break down each model layer in the FashionMNIST model. # -############################################################# -# 1. The Train Loop - Core loop iterates over all the epochs -# 2. The Validation Loop - Validate loss after each weight parameter update and can be used to gauge hyper parameter performance and update them for the next batch. -# 3. The Test Loop - is used to evaluate our models performance after each epoch on traditional metrics to show how much our model is generalizing from the train and validation dataset to the test dataset it's never seen before. +################################################## +# `nn.Flatten `_ to reduce tensor dimensions to one. +# ----------------------------------------------- # +# From the docs: +# ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)`` +# + +# Here is an example using one of the training_data set items: +tensor = training_data[0][0] +print(tensor.size()) + +# Output: torch.Size([1, 28, 28]) -for epoch in range(num_epochs): - # Optimization Loop - # Train loop over batches - model.train() # set model to train - # Model Update Code - model.eval() # After exiting batch loop set model to eval to speed up evaluation and not track gradients (this is explained below) - # Validation Loop - # - Put sample validation metric logging and hyperparameter update code here - # After exiting train loop set model to eval to speed up evaluation and not track gradients (this is explained below) - # Test Loop - # - Put sample test metric logging and hyperparameter update code here +model = nn.Sequential( + nn.Flatten() +) +flattened_tensor = model(tensor) +flattened_tensor.size() -###################################################### -# Loss -# ----------------- +# Output: torch.Size([1, 784]) + +############################################## +# `nn.Linear `_ to add a linear layer to the model. +# ------------------------------- +# +# Now that we have flattened our tensor dimension we will apply a linear layer transform that will calculate/learn the weights and the bias. # -# The loss is the value used to update our parameters. To calculate the loss we make a prediction using the inputs of our given data sample. +# From the docs: +# +# ``torch.nn.Linear(in_features: int, out_features: int, bias: bool = True)`` # -preds = model(inputs) -loss = cost_function(preds, labels) -# Make sure previous gradients are cleared -optimizer.zero_grad() -# Calculates gradients with respect to loss -loss.backward() -optimizer.step() +input = training_data[0][0] +print(input.size()) +model = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 512), +) +output = model(input) +output.size() -###################################################### -# The standard method for optimization is called Stochastic Gradient Descent, to learn more check out this awesome video by `3blue1brown `_. There are many different optimizers and variations of this method in PyTorch such as ADAM and RMSProp that work better for different kinds of models, they are out side the scope of this Blitz, but can check out the full list of optimizers `here `_ -###################################################### -# Putting it all together lets look at a basic optimization loop -# ----------------- -# -# Initilize optimizer and example cost function +# Output: +# torch.Size([1, 28, 28]) +# torch.Size([1, 512]) + +################################################# +# Activation Functions +# ------------------------- # -# For loop to iterate over epoch -# - Train loop over batches -# - Set model to train mode -# - Calculate loss using -# - clear optimizer gradient -# - loss.backword -# - optimizer step -# - Set model to evaluate mode and start validation loop -# - calculate validation loss and update optimizer hyper parameters -# - Set model to evaluate test loop +# - `nn.ReLU `_ Activation +# - `nn.Softmax `_ Activation # -# Next: Learn more about `AutoGrad `_. +# Next: Learn more about how the `optimzation loop works with this example `_. # - -################################################################## # .. include:: /beginner_source/quickstart/qs_toc.txt -# +# \ No newline at end of file diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart_tutorial.py index ef2c3c6b7f1..71ea6d38b79 100644 --- a/beginner_source/quickstart_tutorial.py +++ b/beginner_source/quickstart_tutorial.py @@ -3,7 +3,7 @@ =================== -The basic machine learning concepts in any framework should include: Working with data, Creating models, Optimizing Parameters, Saving and Loading Models. In this quickstart we will go through an example of an applied machine learning model using the FashionMNIST dataset that demonstrates these core steps using Pytorch. +The basic machine learning concepts in any framework should include: Working with data, Creating models, Optimizing Parameters, Saving and Loading Models. In this PyTorch Quickstart we will go through these concepts and how to apply them with PyTorch. That dataset we will be using is the FashionMNIST clothing images dataset that demonstrates these core steps applied to create ML Models. You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step. Using this dataset we will be able to predict if the image is one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, or Ankle boot. Lets get started! Working with data ----------------- @@ -12,8 +12,10 @@ ###################################################################### # # PyTorch has two basic data primitives: ``DataSet`` and ``DataLoader``. -# These ``DataSet`` objects include a ``transforms`` mechanism to +# The `torchvision.datasets` ``DataSet`` object includes a ``transforms`` mechanism to # modify data in-place. Below is an example of how to load that data from the Pytorch open datasets and transform the data to a normalized tensor. + +# This example is using the `torchvision.datasets` which is a subclass from the primitive `torch.utils.data.Dataset`. Note that the primitive dataset doesnt have the built in transforms param like the built in dataset in `torchvision.datasets.` # # To see more examples and details of how to work with Tensors, Datasets, DataLoaders and Transforms in Pytoch with this example checkout these resources: # From a024120e31ec81a8b837957c61a231c0f1903b57 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 9 Dec 2020 10:53:23 -0600 Subject: [PATCH 017/120] updated quickstart to class model (#30) * fix build model * updated quickstart to class model --- .../quickstart/build_model_tutorial.py | 20 +++++++--- beginner_source/quickstart_tutorial.py | 38 +++++++++++-------- 2 files changed, 36 insertions(+), 22 deletions(-) diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index f139fe40882..728bb610ca6 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -6,7 +6,8 @@ ############################################### # The data has been loaded and transformed we can now build the model. # We will leverage `torch.nn `_ -# predefined layers that Pytorch has that can simplify our code. + +# predefined layers that PyTorch has that can simplify our code. # # In the below example, for our FashionMNIT image dataset, we are using a `Sequential` # container from class `torch.nn. Sequential `_ @@ -53,18 +54,26 @@ # -------------------------- # -class NeuralNework(nn.Module): - def __init__(self, x): - super(NeuralNework, self).__init__() + +class NeuralNetwork(nn.Module): + def __init__(self): + super(NeuralNetwork, self).__init__() + self.flatten = nn.Flatten() self.layer1 = nn.Linear(28*28, 512) self.layer2 = nn.Linear(512, 512) self.output = nn.Linear(512, 10) def forward(self, x): + + x = self.flatten(x) x = F.relu(self.layer1(x)) x = F.relu(self.layer2(x)) x = self.output(x) return F.softmax(x, dim=1) +model = NeuralNetwork().to(device) + +print(model) + ############################################# # Get Device for Training @@ -90,8 +99,6 @@ def forward(self, x): # # From the docs: # ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)`` -# - # Here is an example using one of the training_data set items: tensor = training_data[0][0] print(tensor.size()) @@ -112,6 +119,7 @@ def forward(self, x): # # Now that we have flattened our tensor dimension we will apply a linear layer transform that will calculate/learn the weights and the bias. # + # From the docs: # # ``torch.nn.Linear(in_features: int, out_features: int, bias: bool = True)`` diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart_tutorial.py index 71ea6d38b79..eaa06edc2c4 100644 --- a/beginner_source/quickstart_tutorial.py +++ b/beginner_source/quickstart_tutorial.py @@ -13,11 +13,10 @@ # # PyTorch has two basic data primitives: ``DataSet`` and ``DataLoader``. # The `torchvision.datasets` ``DataSet`` object includes a ``transforms`` mechanism to -# modify data in-place. Below is an example of how to load that data from the Pytorch open datasets and transform the data to a normalized tensor. - +# modify data in-place. Below is an example of how to load that data from the PyTorch open datasets and transform the data to a normalized tensor. # This example is using the `torchvision.datasets` which is a subclass from the primitive `torch.utils.data.Dataset`. Note that the primitive dataset doesnt have the built in transforms param like the built in dataset in `torchvision.datasets.` # -# To see more examples and details of how to work with Tensors, Datasets, DataLoaders and Transforms in Pytoch with this example checkout these resources: +# To see more examples and details of how to work with Tensors, Datasets, DataLoaders and Transforms in PyTorch with this example checkout these resources: # # - `Tensors `_ # - `DataSet and DataLoader `_ @@ -29,6 +28,7 @@ import matplotlib.pyplot as plt from torch.utils.data import DataLoader from torchvision import datasets, transforms +import torch.nn.functional as F classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] @@ -51,26 +51,32 @@ train_dataloader = DataLoader(training_data, batch_size=batch_size, num_workers=0, pin_memory=True) test_dataloader = DataLoader(test_data, batch_size=batch_size, num_workers=0, pin_memory=True) +################################ # Creating Models # --------------- # # There are two ways of creating models: in-line or as a class. This -# quickstart will consider an in-line definition. For more examples checkout `building the model `_. +# quickstart will consider a class definition. For more examples checkout `building the model `_. device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) -# in-line model - -model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, len(classes)), - nn.Softmax(dim=1) - ).to(device) +# Define model +class NeuralNetwork(nn.Module): + def __init__(self): + super(NeuralNetwork, self).__init__() + self.flatten = nn.Flatten() + self.layer1 = nn.Linear(28*28, 512) + self.layer2 = nn.Linear(512, 512) + self.output = nn.Linear(512, 10) + + def forward(self, x): + x = self.flatten(x) + x = F.relu(self.layer1(x)) + x = F.relu(self.layer2(x)) + x = self.output(x) + return F.softmax(x, dim=1) +model = NeuralNetwork().to(device) print(model) @@ -193,7 +199,7 @@ def test(dataloader, model): print(f'Predicted: "{predicted}", Actual: "{actual}"') ################################################################## -# Pytorch Quickstart Topics +# PyTorch Quickstart Topics # ---------------------------------------- # | `Tensors `_ # | `DataSets and DataLoaders `_ From 317bec86f0a0ae2c94238034f9899fe7c6a6613e Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 10 Dec 2020 17:19:13 -0600 Subject: [PATCH 018/120] Updates to quickstart main page, build model page based on feedback (#31) * updates to load class model, build model page work * more work on build model --- .../quickstart/build_model_tutorial.py | 96 +++++++++++++------ .../quickstart/save_load_run_tutorial.py | 11 +-- beginner_source/quickstart_tutorial.py | 10 +- 3 files changed, 68 insertions(+), 49 deletions(-) diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index 728bb610ca6..3f10e798457 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -1,25 +1,22 @@ """ Build Model Tutorial ======================================= -""" -############################################### -# The data has been loaded and transformed we can now build the model. -# We will leverage `torch.nn `_ +The data has been loaded and transformed we can now build the model. +We will leverage `torch.nn `_ predefined layers that PyTorch has that can simplify our code. -# predefined layers that PyTorch has that can simplify our code. -# -# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` -# container from class `torch.nn. Sequential `_ -# that allows us to define the model layers inline. -# The neural network modules layers will be added to it in the order they are passed in. -# -# Another way to bulid this model is with a class -# using `nn.Module `_ This gives us more flexibility, because -# we can construct layers of any complexity, including the ones with shared weights. -# -# Lets break down the steps to build this model below -# +In the below example, for our FashionMNIT image dataset, we are using a `Sequential` +container from class `torch.nn. Sequential `_ +that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` +method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined. + +Another way to bulid this model is with a class +using `nn.Module `_ +A big plus with using a class that inherits ``nn.Module`` is better parameter management across all nested submodules. +This gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. + +Lets break down the steps to build this model below +""" ########################################## # Inline nn.Sequential Example: @@ -86,6 +83,15 @@ def forward(self, x): device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) +############################################## +# __init__ +# ------------------------- +# +# The ``init`` function inherits from ``nn.Module`` which is the base class for +# building neural network modules. This function defines the layers in your neural network +# then it initializes the modules to be called in the ``forward`` function. +# + ############################################## # The Model Module Layers # ------------------------- @@ -94,30 +100,29 @@ def forward(self, x): # ################################################## -# `nn.Flatten `_ to reduce tensor dimensions to one. +# `nn.Flatten `_ # ----------------------------------------------- # +# First we call nn.Flatten to reduce tensor dimensions to one. +# # From the docs: # ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)`` # Here is an example using one of the training_data set items: tensor = training_data[0][0] print(tensor.size()) -# Output: torch.Size([1, 28, 28]) - model = nn.Sequential( nn.Flatten() ) flattened_tensor = model(tensor) flattened_tensor.size() -# Output: torch.Size([1, 784]) - ############################################## # `nn.Linear `_ to add a linear layer to the model. # ------------------------------- # -# Now that we have flattened our tensor dimension we will apply a linear layer transform that will calculate/learn the weights and the bias. +# Now that we have flattened our tensor dimension we will apply a linear layer +# transform that will calculate/learn the weights and the bias. # # From the docs: @@ -134,17 +139,48 @@ def forward(self, x): output = model(input) output.size() - -# Output: -# torch.Size([1, 28, 28]) -# torch.Size([1, 512]) - ################################################# # Activation Functions # ------------------------- # -# - `nn.ReLU `_ Activation -# - `nn.Softmax `_ Activation +# After the first two linear layer we will call the `nn.ReLU `_ +# activation function. Then after the third linear layer we call the `nn.Softmax `_ +# activation to rescale between 0 and 1 and sum to one. +# + +model = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, len(classes)), + nn.Softmax(dim=1) + ).to(device) + +print(model) + + +################################################### +# Forward Function +# -------------------------------- +# +# In the class implementation of the neural network we define a ``forward`` function. +# Then call the ``NeuralNetwork``class and assign the device. When training the model we will call ``model`` +# and pass the data (x) into the forward function and through each layer of our network. +# +# + def forward(self, x): + x = self.flatten(x) + x = F.relu(self.layer1(x)) + x = F.relu(self.layer2(x)) + x = self.output(x) + return F.softmax(x, dim=1) + model = NeuralNetwork().to(device) + + +################################################ +# In the next section you will learn about how to train the model and the optimization loop for this example. # # Next: Learn more about how the `optimzation loop works with this example `_. # diff --git a/beginner_source/quickstart/save_load_run_tutorial.py b/beginner_source/quickstart/save_load_run_tutorial.py index 3573e87b763..4f4d671ec04 100644 --- a/beginner_source/quickstart/save_load_run_tutorial.py +++ b/beginner_source/quickstart/save_load_run_tutorial.py @@ -42,16 +42,7 @@ # These two steps are illustrated here: # recreate model -loaded_model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, len(classes)), - nn.Softmax(dim=1) -) - +loaded_model = NeuralNetwork() # hydrate state dictionary loaded_model.load_state_dict(torch.load('model.pth')) diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart_tutorial.py index eaa06edc2c4..436340930b2 100644 --- a/beginner_source/quickstart_tutorial.py +++ b/beginner_source/quickstart_tutorial.py @@ -178,15 +178,7 @@ def test(dataloader, model): # inference). Check out more details on `saving, loading and running models with Pytorch `_ # -loaded_model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, len(classes)), - nn.Softmax(dim=1) - ) +loaded_model = NeuralNetwork() loaded_model.load_state_dict(torch.load('model.pth')) loaded_model.eval() From 438903fd7c6232bc5a5fcf35300d381e3c5ce9b6 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Fri, 11 Dec 2020 16:35:08 -0600 Subject: [PATCH 019/120] small formatting fix on build model page (#32) * fix formating on build model page --- .../quickstart/build_model_tutorial.py | 34 ++++++++++--------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index 3f10e798457..cd51ce3778e 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -1,23 +1,25 @@ """ Build Model Tutorial -======================================= - -The data has been loaded and transformed we can now build the model. -We will leverage `torch.nn `_ predefined layers that PyTorch has that can simplify our code. - -In the below example, for our FashionMNIT image dataset, we are using a `Sequential` -container from class `torch.nn. Sequential `_ -that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` -method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined. - -Another way to bulid this model is with a class -using `nn.Module `_ -A big plus with using a class that inherits ``nn.Module`` is better parameter management across all nested submodules. -This gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. - -Lets break down the steps to build this model below +============================ """ +########################################## +# The data has been loaded and transformed we can now build the model. +# We will leverage `torch.nn `_ predefined layers that PyTorch has that can simplify our code. +# +# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` +# container from class `torch.nn. Sequential `_ +# that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` +# method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined. +# +# Another way to bulid this model is with a class +# using `nn.Module `_ +# A big plus with using a class that inherits ``nn.Module`` is better parameter management across all nested submodules. +# This gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. +# +# Lets break down the steps to build this model below +# + ########################################## # Inline nn.Sequential Example: # ---------------------------- From f59a54e4921995a3eaa979a1d9159d609e47024b Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Fri, 11 Dec 2020 17:03:07 -0600 Subject: [PATCH 020/120] build model formatting fixes (#33) * build model format fix --- .../quickstart/build_model_tutorial.py | 27 ++++++++++++++++--- 1 file changed, 24 insertions(+), 3 deletions(-) diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index cd51ce3778e..7f187da1354 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -1,8 +1,28 @@ """ -Build Model Tutorial -============================ +Build the Neural Netowrk +=================== """ +################################################################# +# Get Started Building the Model +# ----------------- +# +# The data has been loaded and transformed we can now build the model. +# We will leverage `torch.nn `_ predefined layers that PyTorch has that can simplify our code. +# +# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` +# container from class `torch.nn. Sequential `_ +# that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` +# method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined. +# +# Another way to bulid this model is with a class +# using `nn.Module `_ +# A big plus with using a class that inherits ``nn.Module`` is better parameter management across all nested submodules. +# This gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. +# +# Lets break down the steps to build this model below +# + ########################################## # The data has been loaded and transformed we can now build the model. # We will leverage `torch.nn `_ predefined layers that PyTorch has that can simplify our code. @@ -109,6 +129,7 @@ def forward(self, x): # # From the docs: # ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)`` +# # Here is an example using one of the training_data set items: tensor = training_data[0][0] print(tensor.size()) @@ -168,7 +189,7 @@ def forward(self, x): # -------------------------------- # # In the class implementation of the neural network we define a ``forward`` function. -# Then call the ``NeuralNetwork``class and assign the device. When training the model we will call ``model`` +# Then call the ``NeuralNetwork`` class and assign the device. When training the model we will call ``model`` # and pass the data (x) into the forward function and through each layer of our network. # # From 3da5c66130fefd2ecac07cfeafcfe22a2222cafc Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Fri, 11 Dec 2020 17:15:10 -0600 Subject: [PATCH 021/120] Update build_model_tutorial.py --- .../quickstart/build_model_tutorial.py | 21 ++----------------- 1 file changed, 2 insertions(+), 19 deletions(-) diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index 7f187da1354..ee4eca92da0 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -1,5 +1,5 @@ """ -Build the Neural Netowrk +Build the Neural Network =================== """ @@ -23,23 +23,6 @@ # Lets break down the steps to build this model below # -########################################## -# The data has been loaded and transformed we can now build the model. -# We will leverage `torch.nn `_ predefined layers that PyTorch has that can simplify our code. -# -# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` -# container from class `torch.nn. Sequential `_ -# that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` -# method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined. -# -# Another way to bulid this model is with a class -# using `nn.Module `_ -# A big plus with using a class that inherits ``nn.Module`` is better parameter management across all nested submodules. -# This gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. -# -# Lets break down the steps to build this model below -# - ########################################## # Inline nn.Sequential Example: # ---------------------------- @@ -208,4 +191,4 @@ def forward(self, x): # Next: Learn more about how the `optimzation loop works with this example `_. # # .. include:: /beginner_source/quickstart/qs_toc.txt -# \ No newline at end of file +# From ce752367b77d76b1a174d1e44203f8230d7bdd6d Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Fri, 11 Dec 2020 17:38:54 -0600 Subject: [PATCH 022/120] Update build_model_tutorial.py --- beginner_source/quickstart/build_model_tutorial.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index ee4eca92da0..a256703a512 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -176,13 +176,13 @@ def forward(self, x): # and pass the data (x) into the forward function and through each layer of our network. # # - def forward(self, x): - x = self.flatten(x) - x = F.relu(self.layer1(x)) - x = F.relu(self.layer2(x)) - x = self.output(x) - return F.softmax(x, dim=1) - model = NeuralNetwork().to(device) +def forward(self, x): + x = self.flatten(x) + x = F.relu(self.layer1(x)) + x = F.relu(self.layer2(x)) + x = self.output(x) + return F.softmax(x, dim=1) +model = NeuralNetwork().to(device) ################################################ From 69a8cacb52f2ff35a2a5e6237b09a249bdc383ca Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Mon, 14 Dec 2020 10:28:45 -0600 Subject: [PATCH 023/120] Update optimization_tutorial.py fixed footer --- beginner_source/quickstart/optimization_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 2418df1bb64..27889415954 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -192,6 +192,6 @@ def myCrossEntropyLoss(outputs, labels): ################################################################## # Next: Learn more about `Automatic Differentiation with AutoGrad `_. - +# # .. include:: /beginner_source/quickstart/qs_toc.txt # From dc665964430c603fab7df3a9302c512569537f0c Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Mon, 14 Dec 2020 11:02:06 -0600 Subject: [PATCH 024/120] Update data_quickstart_tutorial.py fix image on data --- beginner_source/quickstart/data_quickstart_tutorial.py | 1 - 1 file changed, 1 deletion(-) diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py index 8805a6fd4cc..04785e47bd4 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -63,7 +63,6 @@ plt.show() ################################################################# - # .. figure:: /_static/img/quickstart/fashion_mnist.png # :alt: fashion_mnist # From 0f56d123224cdb4add4d8965ae237e1e67c100fb Mon Sep 17 00:00:00 2001 From: PythicCoder Date: Tue, 15 Dec 2020 17:49:58 +0200 Subject: [PATCH 025/120] Update data_quickstart_tutorial.py (#34) Addressed open comments --- .../quickstart/data_quickstart_tutorial.py | 135 ++++++++++-------- 1 file changed, 73 insertions(+), 62 deletions(-) diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py index 04785e47bd4..0c90eb82db9 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -7,20 +7,22 @@ # Getting Started With Data in PyTorch # ----------------- # -# Before we can even think about building a model with PyTorch, we need to first learn how to load and process data. Data can be sourced from local files, cloud datastores and database queries. It comes in all sorts of forms and formats from structured tables to image, audio, text, video files and more. +# Before we start building models with PyTorch, let's first learn how to load and process data. Data can be sourced from local files, cloud datastores and database queries. It comes in all sorts of forms and formats from structured tables to image, audio, text, video files and more. # ############################################################### # .. figure:: /_static/img/quickstart/typesdata.png # :alt: typesdata -# -# Different data types require different python libraries to load and process such as `openCV `_ and `PIL `_ for images, `NLTK `_ and `spaCy `_ for text and `Librosa `_ for audio. -# -# If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking managing collections of data. -# -# A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. These are useful for benchmarking and testing your models before training on your own custom datasets. -# -# You can find some of them below. +# + +############################################################ +# Different data types require different python libraries to load and process such as `openCV `_ and `PIL `_ for images, `NLTK `_ and `spaCy `_ for text and `Librosa `_ for audio. +# +# If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking managing collections of data. +# +# A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. They are subclasses of torch.utils.data.Dataset that have parameters and functions specific to the type of data and the particular dataset. The actual data samples can be downloaded from the internet.These are useful for benchmarking and testing your models before training on your own custom datasets. +# +# You can find some of them below. # # - `Image Datasets `_ # - `Text Datasets `_ @@ -30,32 +32,29 @@ ################################################################# # Iterating through a Dataset # ----------------- -# -# Once we have a Dataset we can index it manually like a list `clothing[index]`. -# -# Here is an example of how to load the fashion MNIST dataset from torch vision. -# +# +# Once we have a Dataset we can index it manually like a list `clothing[index]`. +# +# Here is an example of how to load the [Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) dataset from torch vision. "[Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. Read more [here](https://pytorch.org/docs/stable/torchvision/datasets.html#fashion-mnist). +# To load the FashionMNIST Dataset we need to provide the following three parameters: +# - root is the path where the train/test data is stored. +# - train includes the training dataset. +# - setting download to true downloads the data from the internet if it's not available at root. -from torch.utils.data import DataLoader -from torchvision.io import read_image -from torchvision import transforms, utils -import pandas as pd -import torch -import os -import torch + +import torch from torch.utils.data import Dataset import torchvision.datasets as datasets import matplotlib.pyplot as plt import numpy as np -clothing = datasets.FashionMNIST('data', train=True, download=True) -labels_map = {0: 'T-Shirt', 1: 'Trouser', 2: 'Pullover', 3: 'Dress', - 4: 'Coat', 5: 'Sandal', 6: 'Shirt', 7: 'Sneaker', 8: 'Bag', 9: 'Ankle Boot'} -figure = plt.figure(figsize=(8, 8)) +clothing = datasets.FashionMNIST(root='data', train=True, download=True) +labels_map = {0 : 'T-Shirt', 1 : 'Trouser', 2 : 'Pullover', 3 : 'Dress', 4 : 'Coat', 5 : 'Sandal', 6 : 'Shirt', 7 : 'Sneaker', 8 : 'Bag', 9 : 'Ankle Boot'} +figure = plt.figure(figsize=(8,8)) cols, rows = 3, 3 -for i in range(1, cols*rows + 1): +for i in range(1, cols*rows +1): sample_idx = np.random.randint(len(clothing)) - img = clothing[sample_idx][0][0, :, :] + img = clothing[sample_idx][0][0,:,:] figure.add_subplot(rows, cols, i) plt.title(labels_map[clothing[sample_idx][1]]) plt.axis('off') @@ -74,6 +73,12 @@ # To work with your own data lets look at the a simple custom image Dataset implementation: # +import os +import torch +import pandas as pd +from torch.utils.data import Dataset +from torchvision import transforms, utils +from torchvision.io import read_image class CustomImageDataset(Dataset): def __init__(self, annotations_file, img_dir, transform=None): @@ -88,35 +93,46 @@ def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() - img_name = os.path.join(self.root_dir, + img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) - image = read_image('path_to_image.jpeg') + image = read_image(img_path) label = self.img_labels.iloc[idx, 1:] sample = {'image': image, 'label': label} if self.transform: sample = self.transform(sample) - return sample - + return sample + ################################################################# -# Imports -# ----------------- -# +# Imports +# ------- +# # Import os for file handling, torch for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and Dataset to implement the Dataset interface. -# +# # Example: +# +import os +import torch +import pandas as pd +from torchvision.io import read_image +from torch.utils.data import Dataset +from torch.utils.data import DataLoader ################################################################# # Init # ----------------- # # The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and the keep track of directory of our image file. Note that different types of data can take different init inputs you are not limited to just an annotations file, directory_path and transforms but for images this is a standard practice. -# +# A sample csv annotations file may look as follows: +# tshirt1.jpg, 0 +# tshirt2.jpg, 0 +# ...... +# ankleboot999.jpg, 9 +# # Example: -# - +# def __init__(self, annotations_file, img_dir, transform=None): self.img_labels = pd.read_csv(annotations_file) @@ -127,11 +143,10 @@ def __init__(self, annotations_file, img_dir, transform=None): # __len__ # ----------------- # -# The __len__ function is very simple here we just need to return the number of samples in our dataset. -# +# The __len__ function is very simple here we just need to return the number of samples in our dataset. +# # Example: - def __len__(self): return len(self.img_labels) @@ -140,46 +155,42 @@ def __len__(self): # ----------------- # # The __getitem__ function is the most important function in the Datasets interface this. It takes a tensor or an index as input and returns a loaded sample from you dataset at from the given indecies. -# -# In this sample if provided a tensor we convert the tensor to a list containing our index. We then load the file at the given index from our image directory as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a Transform on and return. To learn more about Transforms see the next section of the Blitz. -# +# +# In this sample if provided a tensor we convert the tensor to a list containing our index. We then load the file at the given index from our image directory as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a Transform on and return. To learn more about Transforms see the next section of the Blitz. +# # Example: - +# def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() - img_name = os.path.join(self.root_dir, + img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) - image = read_image('path_to_image.jpeg') + image = read_image(img_path) label = self.img_labels.iloc[idx, 1:] sample = {'image': image, 'label': label} if self.transform: sample = self.transform(sample) - return sample + return sample ################################################################# # Preparing your data for training with DataLoaders -# ----------------- -# -# Now we have a organized mechansim for managing data which is great, but there is still a lot of manual work we would have to do train a model with our Dataset. -# -# For example we would have to manually maintain the code for: -# * Batching -# * Suffling -# * Parallel batch distribution -# +# ------------------------------------------------- +# +# Now we have a organized mechansim for managing data which is great, but there is still a lot of manual work we would have to do train a model with our Dataset. +# +# For example we would have to manually maintain the code for: +# * Batching +# * Suffling +# * Parallel batch distribution +# # The PyTorch Dataloader *torch.utils.data.DataLoader* is an iterator that handles all of this complexity for us enabling us to load a dataset and focusing on train our model. - dataloader = DataLoader(clothing, batch_size=4, shuffle=True, num_workers=0) ################################################################# # With this we have all we need to know to load an process data of any kind in PyTorch to train deep learning models. -# +# # Next: Learn more about how to `transform that data for training `_. # - -################################################################## # .. include:: /beginner_source/quickstart/qs_toc.txt -# From bf0e386c63322734b6a15826bc1c6d42a62c40a2 Mon Sep 17 00:00:00 2001 From: PythicCoder Date: Tue, 15 Dec 2020 20:29:09 +0200 Subject: [PATCH 026/120] Update data_quickstart_tutorial.py (#35) Fixed links and formatting --- .../quickstart/data_quickstart_tutorial.py | 52 ++++++++++++++++++- 1 file changed, 51 insertions(+), 1 deletion(-) diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py index 0c90eb82db9..803daeac811 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -1,3 +1,38 @@ +Skip to content +Search or jump to… + +Pull requests +Issues +Codespaces +Marketplace +Explore + +@aribornstein +aribornstein +/ +tutorials +forked from sethjuarez/tutorials +0 +0 +2.5k +Code +Pull requests +Actions +Projects +Wiki +Security +Insights +Settings +tutorials/beginner_source/quickstart/data_quickstart_tutorial.py / +@aribornstein +aribornstein Update data_quickstart_tutorial.py +… +Latest commit 4d522bb 35 seconds ago + History + 3 contributors +@sethjuarez@aribornstein@cassieview +199 lines (172 sloc) 8.64 KB + """ Datasets & Dataloaders =================== @@ -35,7 +70,10 @@ # # Once we have a Dataset we can index it manually like a list `clothing[index]`. # -# Here is an example of how to load the [Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) dataset from torch vision. "[Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. Read more [here](https://pytorch.org/docs/stable/torchvision/datasets.html#fashion-mnist). +# Here is an example of how to load the `Fashion-MNIST`_ dataset from torch vision. +# `Fashion-MNIST `_ is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. +# Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. Read more `here `_. +# # To load the FashionMNIST Dataset we need to provide the following three parameters: # - root is the path where the train/test data is stored. # - train includes the training dataset. @@ -194,3 +232,15 @@ def __getitem__(self, idx): # Next: Learn more about how to `transform that data for training `_. # # .. include:: /beginner_source/quickstart/qs_toc.txt +© 2020 GitHub, Inc. +Terms +Privacy +Security +Status +Help +Contact GitHub +Pricing +API +Training +Blog +About From b468223efc3378555f261b5425787168d5989a67 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 15 Dec 2020 12:59:47 -0600 Subject: [PATCH 027/120] Update data_quickstart_tutorial.py removed github markup --- .../quickstart/data_quickstart_tutorial.py | 48 +------------------ 1 file changed, 1 insertion(+), 47 deletions(-) diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py index 803daeac811..1daa8ce667f 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -1,38 +1,3 @@ -Skip to content -Search or jump to… - -Pull requests -Issues -Codespaces -Marketplace -Explore - -@aribornstein -aribornstein -/ -tutorials -forked from sethjuarez/tutorials -0 -0 -2.5k -Code -Pull requests -Actions -Projects -Wiki -Security -Insights -Settings -tutorials/beginner_source/quickstart/data_quickstart_tutorial.py / -@aribornstein -aribornstein Update data_quickstart_tutorial.py -… -Latest commit 4d522bb 35 seconds ago - History - 3 contributors -@sethjuarez@aribornstein@cassieview -199 lines (172 sloc) 8.64 KB - """ Datasets & Dataloaders =================== @@ -232,15 +197,4 @@ def __getitem__(self, idx): # Next: Learn more about how to `transform that data for training `_. # # .. include:: /beginner_source/quickstart/qs_toc.txt -© 2020 GitHub, Inc. -Terms -Privacy -Security -Status -Help -Contact GitHub -Pricing -API -Training -Blog -About +# From 8d19897cd5e5b8f18125a42988478feb57b0245e Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 15 Dec 2020 13:14:17 -0600 Subject: [PATCH 028/120] Update data_quickstart_tutorial.py --- beginner_source/quickstart/data_quickstart_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py index 1daa8ce667f..726f64f553b 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -35,7 +35,7 @@ # # Once we have a Dataset we can index it manually like a list `clothing[index]`. # -# Here is an example of how to load the `Fashion-MNIST`_ dataset from torch vision. +# Here is an example of how to load the `Fashion-MNIST `_ dataset from torch vision. # `Fashion-MNIST `_ is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. # Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. Read more `here `_. # From 2c565e0e6ac5f83cf96ae7ea9c4e65b0a3c0f28f Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 15 Dec 2020 13:35:05 -0600 Subject: [PATCH 029/120] Update build_model_tutorial.py --- beginner_source/quickstart/build_model_tutorial.py | 1 - 1 file changed, 1 deletion(-) diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index a256703a512..a2e6316c8b0 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -130,7 +130,6 @@ def forward(self, x): # Now that we have flattened our tensor dimension we will apply a linear layer # transform that will calculate/learn the weights and the bias. # - # From the docs: # # ``torch.nn.Linear(in_features: int, out_features: int, bias: bool = True)`` From f15987652762cd25d89727421589f8e6c1437c76 Mon Sep 17 00:00:00 2001 From: PythicCoder Date: Wed, 16 Dec 2020 00:11:15 +0200 Subject: [PATCH 030/120] Update data_quickstart_tutorial.py (#36) --- beginner_source/quickstart/data_quickstart_tutorial.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py index 726f64f553b..3fdacfec77a 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -129,9 +129,13 @@ def __getitem__(self, idx): # # The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and the keep track of directory of our image file. Note that different types of data can take different init inputs you are not limited to just an annotations file, directory_path and transforms but for images this is a standard practice. # A sample csv annotations file may look as follows: +# # tshirt1.jpg, 0 +# # tshirt2.jpg, 0 +# # ...... +# # ankleboot999.jpg, 9 # # Example: From a337307a4c9d57ab86349b66f975e39880a2b98b Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 12 Jan 2021 13:51:27 -0600 Subject: [PATCH 031/120] build model section updates, breadcrumb test, linear wording fix (#37) * removed full top ex, moved sequential to layer ex * fixed linear layer wording * breadcrumb test --- .../quickstart/build_model_tutorial.py | 101 ++++++------------ .../quickstart/data_quickstart_tutorial.py | 9 ++ beginner_source/quickstart/tensor_tutorial.py | 9 ++ 3 files changed, 49 insertions(+), 70 deletions(-) diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/build_model_tutorial.py index a2e6316c8b0..2756223ecc8 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/build_model_tutorial.py @@ -10,22 +10,16 @@ # The data has been loaded and transformed we can now build the model. # We will leverage `torch.nn `_ predefined layers that PyTorch has that can simplify our code. # -# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` -# container from class `torch.nn. Sequential `_ -# that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` -# method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined. -# -# Another way to bulid this model is with a class -# using `nn.Module `_ +# In the below example, for our FashionMNIT image dataset, we are using `nn.Module `_ # A big plus with using a class that inherits ``nn.Module`` is better parameter management across all nested submodules. # This gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. # # Lets break down the steps to build this model below # -########################################## -# Inline nn.Sequential Example: -# ---------------------------- +############################################# +# Import the Packages +# -------------------------- # import os @@ -35,27 +29,31 @@ from torch.utils.data import DataLoader from torchvision import datasets, transforms -device = 'cuda' if torch.cuda.is_available() else 'cpu' -print('Using {} device'.format(device)) - -# model -model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, len(classes)), - nn.Softmax(dim=1) - ).to(device) - -print(model) ############################################# -# Class nn.Module Example: -# -------------------------- +# Get Device for Training +# ----------------------- +# Here we check to see if `torch.cuda `_ +# is available to use the GPU, else we will use the CPU. +# +# Example: # +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('Using {} device'.format(device)) + +############################################## +# Define the Class +# ------------------------- +# +# Here we define the `NeuralNetwork` class which inherits from ``nn.Module`` which is the base class for +# building neural network modules. The ``init`` function defines the layers in the neural network +# then it initializes the modules to be called in the ``forward`` function. +# Then we call the ``NeuralNetwork`` class and assign the device. When training +# the model we will call ``model`` and pass the data (x) into the forward function and +# through each layer of our network. +# +# class NeuralNetwork(nn.Module): def __init__(self): @@ -66,7 +64,6 @@ def __init__(self): self.output = nn.Linear(512, 10) def forward(self, x): - x = self.flatten(x) x = F.relu(self.layer1(x)) x = F.relu(self.layer2(x)) @@ -76,32 +73,14 @@ def forward(self, x): print(model) - -############################################# -# Get Device for Training -# ----------------------- -# Here we check to see if `torch.cuda `_ is available to use the GPU, else we will use the CPU. -# -# Example: -# - -device = 'cuda' if torch.cuda.is_available() else 'cpu' -print('Using {} device'.format(device)) - -############################################## -# __init__ -# ------------------------- -# -# The ``init`` function inherits from ``nn.Module`` which is the base class for -# building neural network modules. This function defines the layers in your neural network -# then it initializes the modules to be called in the ``forward`` function. -# - ############################################## # The Model Module Layers # ------------------------- # -# Lets break down each model layer in the FashionMNIST model. +# Lets break down each model layer in the FashionMNIST model. In the below example we are using a `Sequential` +# container from class `torch.nn. Sequential `_ +# that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` +# method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined. # ################################################## @@ -127,8 +106,8 @@ def forward(self, x): # `nn.Linear `_ to add a linear layer to the model. # ------------------------------- # -# Now that we have flattened our tensor dimension we will apply a linear layer -# transform that will calculate/learn the weights and the bias. +# Now that we have flattened our tensor dimension we will apply a linear layer. The linear layer is +# a module that applies a linear transformation on the input using it's stored weights and biases. # # From the docs: # @@ -166,24 +145,6 @@ def forward(self, x): print(model) -################################################### -# Forward Function -# -------------------------------- -# -# In the class implementation of the neural network we define a ``forward`` function. -# Then call the ``NeuralNetwork`` class and assign the device. When training the model we will call ``model`` -# and pass the data (x) into the forward function and through each layer of our network. -# -# -def forward(self, x): - x = self.flatten(x) - x = F.relu(self.layer1(x)) - x = F.relu(self.layer2(x)) - x = self.output(x) - return F.softmax(x, dim=1) -model = NeuralNetwork().to(device) - - ################################################ # In the next section you will learn about how to train the model and the optimization loop for this example. # diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/data_quickstart_tutorial.py index 3fdacfec77a..cdfec8f0a54 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/data_quickstart_tutorial.py @@ -1,4 +1,13 @@ """ +.. raw:: html + +

+ +.. raw:: html + Datasets & Dataloaders =================== """ diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 54afaa08d86..24f248efc6e 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -1,4 +1,13 @@ """ +.. raw:: html + +
+ Tensors +
+ +.. raw:: html + + Tensors and Operations ---------------------- **Tensor** is the basic computational unit in PyTorch. It is very From 788021f7727f4ca7e8c89d0927f386c70d8b908c Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 12 Jan 2021 15:19:17 -0600 Subject: [PATCH 032/120] move TOC (#39) * removed full top ex, moved sequential to layer ex * fixed linear layer wording * breadcrumb test * rename for auto breadcrumb for tutorials * move table of contents to top --- beginner_source/quickstart/autograd_tutorial.py | 7 +++++-- ..._model_tutorial.py => buildmodel_tutorial.py} | 6 ++++-- ...rt_tutorial.py => dataquickstart_tutorial.py} | 16 +++++----------- .../quickstart/optimization_tutorial.py | 7 +++++-- beginner_source/quickstart/qs_toc.txt | 16 +++++++++------- ...d_run_tutorial.py => saveloadrun_tutorial.py} | 8 +++++--- beginner_source/quickstart/tensor_tutorial.py | 8 +++++--- .../quickstart/transforms_tutorial.py | 14 +++++++------- beginner_source/quickstart_tutorial.py | 12 ++++++------ 9 files changed, 51 insertions(+), 43 deletions(-) rename beginner_source/quickstart/{build_model_tutorial.py => buildmodel_tutorial.py} (99%) rename beginner_source/quickstart/{data_quickstart_tutorial.py => dataquickstart_tutorial.py} (96%) rename beginner_source/quickstart/{save_load_run_tutorial.py => saveloadrun_tutorial.py} (99%) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 07c4c74cd02..d400d5ff08e 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -1,3 +1,7 @@ +###################################################### +# .. include:: /beginner_source/quickstart/qs_toc.txt +# + """ Automatic Differentiation with ``torch.autograd`` ======================================= @@ -274,5 +278,4 @@ def f(x): return (x-torch.tensor([3, -2])).pow(2).sum() ###################################################################### # Next: Learn more about `how to use automatic differentiation to train a neural network model `_. # -# .. include:: /beginner_source/quickstart/qs_toc.txt -# + diff --git a/beginner_source/quickstart/build_model_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py similarity index 99% rename from beginner_source/quickstart/build_model_tutorial.py rename to beginner_source/quickstart/buildmodel_tutorial.py index 2756223ecc8..d1366bebb6a 100644 --- a/beginner_source/quickstart/build_model_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -1,3 +1,7 @@ +#################################################### +# .. include:: /beginner_source/quickstart/qs_toc.txt +# + """ Build the Neural Network =================== @@ -150,5 +154,3 @@ def forward(self, x): # # Next: Learn more about how the `optimzation loop works with this example `_. # -# .. include:: /beginner_source/quickstart/qs_toc.txt -# diff --git a/beginner_source/quickstart/data_quickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py similarity index 96% rename from beginner_source/quickstart/data_quickstart_tutorial.py rename to beginner_source/quickstart/dataquickstart_tutorial.py index cdfec8f0a54..68e18835107 100644 --- a/beginner_source/quickstart/data_quickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -1,13 +1,8 @@ -""" -.. raw:: html - - - -.. raw:: html +################################################### +# .. include:: /beginner_source/quickstart/qs_toc.txt +# +""" Datasets & Dataloaders =================== """ @@ -209,5 +204,4 @@ def __getitem__(self, idx): # # Next: Learn more about how to `transform that data for training `_. # -# .. include:: /beginner_source/quickstart/qs_toc.txt -# + diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 27889415954..cda13d49dc3 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -1,3 +1,7 @@ +#################################################### +# .. include:: /beginner_source/quickstart/qs_toc.txt +# + """ Optimizing Model Parameters =========================== @@ -193,5 +197,4 @@ def myCrossEntropyLoss(outputs, labels): ################################################################## # Next: Learn more about `Automatic Differentiation with AutoGrad `_. # -# .. include:: /beginner_source/quickstart/qs_toc.txt -# + diff --git a/beginner_source/quickstart/qs_toc.txt b/beginner_source/quickstart/qs_toc.txt index 86fbdc05364..6a9ad57fd04 100644 --- a/beginner_source/quickstart/qs_toc.txt +++ b/beginner_source/quickstart/qs_toc.txt @@ -1,10 +1,12 @@ Pytorch Quickstart Topics ------------------------- -| `Tensors `_ -| `DataSets and DataLoaders `_ -| `Transforms `_ -| `Build Model `_ -| `Automatic Differentiation `_ -| `Optimization Loop `_ -| `Save, Load and Use Model `_ \ No newline at end of file +| 1. `Tensors `_ +| 2. `DataSets and DataLoaders `_ +| 3. `Transforms `_ +| 4. `Build Model `_ +| 5. `Automatic Differentiation `_ +| 6. `Optimization Loop `_ +| 7. `Save, Load and Use Model `_ +| +| Back to `Pytorch Quickstart `_ \ No newline at end of file diff --git a/beginner_source/quickstart/save_load_run_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py similarity index 99% rename from beginner_source/quickstart/save_load_run_tutorial.py rename to beginner_source/quickstart/saveloadrun_tutorial.py index 4f4d671ec04..1285dd6b38a 100644 --- a/beginner_source/quickstart/save_load_run_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -1,3 +1,7 @@ +################################################################## +# .. include:: /beginner_source/quickstart/qs_toc.txt +# + """ Save, Load and Use the Model ============================ @@ -65,6 +69,4 @@ predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] print(f'Predicted: "{predicted}", Actual: "{actual}"') -################################################################## -# .. include:: /beginner_source/quickstart/qs_toc.txt -# + diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 24f248efc6e..62b747d208d 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -1,3 +1,7 @@ +################################################################## +# .. include:: /beginner_source/quickstart/qs_toc.txt +# + """ .. raw:: html @@ -257,7 +261,5 @@ # computational time, because we need to copy and transform the data when # moving it from GPU anyway. # -# Next learn how to load built in and custom `datasets with dataloaders `_ -# -# .. include:: /beginner_source/quickstart/qs_toc.txt +# Next learn how to load built in and custom `datasets with dataloaders `_ # diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 56d0e9167d5..dc184d705ef 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -1,3 +1,7 @@ +################################################################## +# .. include:: /beginner_source/quickstart/qs_toc.txt +# + """ Transforms =================== @@ -44,7 +48,7 @@ # Pytorch Datasets # -------------------------- # -# We are using the built-in open FashionMNIST datasets from the PyTorch library. For more info on the Datasets and Loaders check out `this `_ resource. The ``Train=True`` indicates we want to download the training dataset from the built-in datasets, ``Train=False`` indicates to download the testing dataset. This way we have data partitioned out for training and testing within the provided PyTorch datasets. We will apply the same transfoms to both the training and testing datasets. +# We are using the built-in open FashionMNIST datasets from the PyTorch library. For more info on the Datasets and Loaders check out `this `_ resource. The ``Train=True`` indicates we want to download the training dataset from the built-in datasets, ``Train=False`` indicates to download the testing dataset. This way we have data partitioned out for training and testing within the provided PyTorch datasets. We will apply the same transfoms to both the training and testing datasets. # # From the docs: # @@ -135,9 +139,5 @@ ################################################################## -# Next learn how to `build the model `_ -# - -################################################################## -# .. include:: /beginner_source/quickstart/qs_toc.txt -# +# Next learn how to `build the model `_ +# \ No newline at end of file diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart_tutorial.py index 436340930b2..f7d16694e1b 100644 --- a/beginner_source/quickstart_tutorial.py +++ b/beginner_source/quickstart_tutorial.py @@ -19,7 +19,7 @@ # To see more examples and details of how to work with Tensors, Datasets, DataLoaders and Transforms in PyTorch with this example checkout these resources: # # - `Tensors `_ -# - `DataSet and DataLoader `_ +# - `DataSet and DataLoader `_ # - `Transforms `_ import torch @@ -56,7 +56,7 @@ # --------------- # # There are two ways of creating models: in-line or as a class. This -# quickstart will consider a class definition. For more examples checkout `building the model `_. +# quickstart will consider a class definition. For more examples checkout `building the model `_. device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) @@ -175,7 +175,7 @@ def test(dataloader, model): # parameters includes re-creating the model shape and then loading # the state dictionary. Once loaded the model can be used for either # retraining or inference purposes (in this example it is used for -# inference). Check out more details on `saving, loading and running models with Pytorch `_ +# inference). Check out more details on `saving, loading and running models with Pytorch `_ # loaded_model = NeuralNetwork() @@ -194,12 +194,12 @@ def test(dataloader, model): # PyTorch Quickstart Topics # ---------------------------------------- # | `Tensors `_ -# | `DataSets and DataLoaders `_ +# | `DataSets and DataLoaders `_ # | `Transforms `_ -# | `Build Model `_ +# | `Build Model `_ # | `Optimization Loop `_ # | `AutoGrad `_ -# | `Save, Load and Run Model `_ +# | `Save, Load and Run Model `_ # # *Authors: Seth Juarez, Ari Bornstein, Cassie Breviu, Dmitry Soshnikov* From 310a6dbc61302a95cffe92c34f54e9d010ea528a Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 12 Jan 2021 15:36:33 -0600 Subject: [PATCH 033/120] toc fix probably (#40) --- beginner_source/quickstart/autograd_tutorial.py | 3 +++ beginner_source/quickstart/buildmodel_tutorial.py | 3 +++ beginner_source/quickstart/dataquickstart_tutorial.py | 7 +++---- beginner_source/quickstart/optimization_tutorial.py | 2 ++ beginner_source/quickstart/saveloadrun_tutorial.py | 2 ++ beginner_source/quickstart/tensor_tutorial.py | 1 + beginner_source/quickstart/transforms_tutorial.py | 3 +++ beginner_source/quickstart_tutorial.py | 2 ++ 8 files changed, 19 insertions(+), 4 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index d400d5ff08e..abe9c98bf66 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -3,6 +3,9 @@ # """ +.. include:: /beginner_source/quickstart/qs_toc.txt + + Automatic Differentiation with ``torch.autograd`` ======================================= diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index d1366bebb6a..fd397155866 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -3,6 +3,9 @@ # """ +.. include:: /beginner_source/quickstart/qs_toc.txt + + Build the Neural Network =================== """ diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 68e18835107..ddbcd507a7b 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -1,8 +1,7 @@ -################################################### -# .. include:: /beginner_source/quickstart/qs_toc.txt -# - """ +.. include:: /beginner_source/quickstart/qs_toc.txt + + Datasets & Dataloaders =================== """ diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index cda13d49dc3..8b6e4e4223b 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -3,6 +3,8 @@ # """ +.. include:: /beginner_source/quickstart/qs_toc.txt + Optimizing Model Parameters =========================== Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 1285dd6b38a..ad645b0fc80 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -3,6 +3,8 @@ # """ +.. include:: /beginner_source/quickstart/qs_toc.txt + Save, Load and Use the Model ============================ In this section we will look at how to save, load and use persisted model state diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 62b747d208d..565fb26f251 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -11,6 +11,7 @@ .. raw:: html +.. include:: /beginner_source/quickstart/qs_toc.txt Tensors and Operations ---------------------- diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index dc184d705ef..95f95e07897 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -3,6 +3,9 @@ # """ +.. include:: /beginner_source/quickstart/qs_toc.txt + + Transforms =================== diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart_tutorial.py index f7d16694e1b..5e3454a201c 100644 --- a/beginner_source/quickstart_tutorial.py +++ b/beginner_source/quickstart_tutorial.py @@ -2,6 +2,8 @@ PyTorch Quickstart =================== +.. include:: /beginner_source/quickstart/qs_toc.txt + The basic machine learning concepts in any framework should include: Working with data, Creating models, Optimizing Parameters, Saving and Loading Models. In this PyTorch Quickstart we will go through these concepts and how to apply them with PyTorch. That dataset we will be using is the FashionMNIST clothing images dataset that demonstrates these core steps applied to create ML Models. You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step. Using this dataset we will be able to predict if the image is one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, or Ankle boot. Lets get started! From f63f2f899bc2b5356a6f829388c04bbbced6073a Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 12 Jan 2021 17:00:05 -0600 Subject: [PATCH 034/120] how to use this guide text (#41) --- .../quickstart/autograd_tutorial.py | 7 +---- .../quickstart/buildmodel_tutorial.py | 7 +---- .../quickstart/dataquickstart_tutorial.py | 3 +- .../quickstart/optimization_tutorial.py | 6 +--- beginner_source/quickstart/qs_toc.txt | 2 -- .../quickstart/saveloadrun_tutorial.py | 6 +--- beginner_source/quickstart/tensor_tutorial.py | 14 +-------- .../quickstart/transforms_tutorial.py | 7 +---- beginner_source/quickstart_tutorial.py | 30 ++++++++++++------- 9 files changed, 26 insertions(+), 56 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index abe9c98bf66..808249911b9 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -1,12 +1,7 @@ -###################################################### -# .. include:: /beginner_source/quickstart/qs_toc.txt -# - """ .. include:: /beginner_source/quickstart/qs_toc.txt - -Automatic Differentiation with ``torch.autograd`` +5. Automatic Differentiation with ``torch.autograd`` ======================================= When training neural networks, the most frequently used algorithm is diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index fd397155866..ad7b5131cf4 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -1,12 +1,7 @@ -#################################################### -# .. include:: /beginner_source/quickstart/qs_toc.txt -# - """ .. include:: /beginner_source/quickstart/qs_toc.txt - -Build the Neural Network +4. Build the Neural Network =================== """ diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index ddbcd507a7b..ac1cad7a0af 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -1,8 +1,7 @@ """ .. include:: /beginner_source/quickstart/qs_toc.txt - -Datasets & Dataloaders +2. Datasets & Dataloaders =================== """ diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 8b6e4e4223b..629ddade9b8 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -1,11 +1,7 @@ -#################################################### -# .. include:: /beginner_source/quickstart/qs_toc.txt -# - """ .. include:: /beginner_source/quickstart/qs_toc.txt -Optimizing Model Parameters +6. Optimizing Model Parameters =========================== Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! To get started lets take a look at some example model optimization code: diff --git a/beginner_source/quickstart/qs_toc.txt b/beginner_source/quickstart/qs_toc.txt index 6a9ad57fd04..ae71517323b 100644 --- a/beginner_source/quickstart/qs_toc.txt +++ b/beginner_source/quickstart/qs_toc.txt @@ -8,5 +8,3 @@ Pytorch Quickstart Topics | 5. `Automatic Differentiation `_ | 6. `Optimization Loop `_ | 7. `Save, Load and Use Model `_ -| -| Back to `Pytorch Quickstart `_ \ No newline at end of file diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index ad645b0fc80..0e2cb99f25a 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -1,11 +1,7 @@ -################################################################## -# .. include:: /beginner_source/quickstart/qs_toc.txt -# - """ .. include:: /beginner_source/quickstart/qs_toc.txt -Save, Load and Use the Model +7. Save, Load and Use the Model ============================ In this section we will look at how to save, load and use persisted model state to run predictions. diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 565fb26f251..d9d1c13e4cc 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -1,19 +1,7 @@ -################################################################## -# .. include:: /beginner_source/quickstart/qs_toc.txt -# - """ -.. raw:: html - -
- Tensors -
- -.. raw:: html - .. include:: /beginner_source/quickstart/qs_toc.txt -Tensors and Operations +1. Tensors and Operations ---------------------- **Tensor** is the basic computational unit in PyTorch. It is very similar to **NumPy array**, and supports similar operations. However, diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 95f95e07897..8ba5354bb81 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -1,12 +1,7 @@ -################################################################## -# .. include:: /beginner_source/quickstart/qs_toc.txt -# - """ .. include:: /beginner_source/quickstart/qs_toc.txt - -Transforms +3. Transforms =================== Data does not come ready to be processed in the machine learning algorithm. We need to do different data manipulations or transforms to prepare it for training. There are many types of transformations and it depends on the type of model you are building and the state of your data as to which ones you should use. diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart_tutorial.py index 5e3454a201c..40da146bb09 100644 --- a/beginner_source/quickstart_tutorial.py +++ b/beginner_source/quickstart_tutorial.py @@ -2,10 +2,25 @@ PyTorch Quickstart =================== -.. include:: /beginner_source/quickstart/qs_toc.txt +The basic machine learning concepts in any framework should include: Working with data, +Creating models, Optimizing Parameters, Saving and Loading Models. In this PyTorch Quickstart we will +go through these concepts and how to apply them with PyTorch. That dataset we will be using is the +FashionMNIST clothing images dataset that demonstrates these core steps applied to create ML Models. + +You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step. +Using this dataset we will be able to predict if the image is one of the following classes: T-shirt/top, +Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, or Ankle boot. Lets get started! -The basic machine learning concepts in any framework should include: Working with data, Creating models, Optimizing Parameters, Saving and Loading Models. In this PyTorch Quickstart we will go through these concepts and how to apply them with PyTorch. That dataset we will be using is the FashionMNIST clothing images dataset that demonstrates these core steps applied to create ML Models. You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step. Using this dataset we will be able to predict if the image is one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, or Ankle boot. Lets get started! +How to Use this Guide +----------------- +This guide is setup to cover machine learning concepts and how to apply them with PyTorch. This main page is a +highlevel intro to each step with the code examples to build the model. You have the option to jump +into the concepts introduced in each section to get more details and explanations to better understand each concept +and how to apply them with PyTorch. The topics are introduceds in a sequenced order as listed below: + + +.. include:: /beginner_source/quickstart/qs_toc.txt Working with data ----------------- @@ -193,15 +208,8 @@ def test(dataloader, model): print(f'Predicted: "{predicted}", Actual: "{actual}"') ################################################################## -# PyTorch Quickstart Topics -# ---------------------------------------- -# | `Tensors `_ -# | `DataSets and DataLoaders `_ -# | `Transforms `_ -# | `Build Model `_ -# | `Optimization Loop `_ -# | `AutoGrad `_ -# | `Save, Load and Run Model `_ +# +# .. include:: /beginner_source/quickstart/qs_toc.txt # # *Authors: Seth Juarez, Ari Bornstein, Cassie Breviu, Dmitry Soshnikov* From 6cb5653149b8e51f647f4f1a751c1051e4cd03a9 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 12 Jan 2021 18:19:41 -0600 Subject: [PATCH 035/120] removed code blocks moved toc (#42) --- .../quickstart/autograd_tutorial.py | 4 +- .../quickstart/buildmodel_tutorial.py | 4 +- .../quickstart/dataquickstart_tutorial.py | 5 +- .../quickstart/optimization_tutorial.py | 5 +- .../quickstart/saveloadrun_tutorial.py | 5 +- beginner_source/quickstart/tensor_tutorial.py | 5 +- .../quickstart/transforms_tutorial.py | 81 ++++++++++--------- beginner_source/quickstart_tutorial.py | 23 +++--- 8 files changed, 69 insertions(+), 63 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 808249911b9..8f927f82c27 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -1,9 +1,9 @@ """ -.. include:: /beginner_source/quickstart/qs_toc.txt - 5. Automatic Differentiation with ``torch.autograd`` ======================================= +.. include:: /beginner_source/quickstart/qs_toc.txt + When training neural networks, the most frequently used algorithm is **back propagation**. In this algorithm, parameters (model weights) are adjusted according to the **gradient** of the loss function with respect diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index ad7b5131cf4..b42e8629dc0 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -1,8 +1,8 @@ """ -.. include:: /beginner_source/quickstart/qs_toc.txt - 4. Build the Neural Network =================== + +.. include:: /beginner_source/quickstart/qs_toc.txt """ ################################################################# diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index ac1cad7a0af..409887ae55d 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -1,8 +1,9 @@ """ -.. include:: /beginner_source/quickstart/qs_toc.txt - 2. Datasets & Dataloaders =================== + +.. include:: /beginner_source/quickstart/qs_toc.txt + """ ################################################################# diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 629ddade9b8..797b7bd8e8a 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -1,8 +1,9 @@ """ -.. include:: /beginner_source/quickstart/qs_toc.txt - 6. Optimizing Model Parameters =========================== + +.. include:: /beginner_source/quickstart/qs_toc.txt + Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! To get started lets take a look at some example model optimization code: """ diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 0e2cb99f25a..ec7a8a99d52 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -1,8 +1,9 @@ """ -.. include:: /beginner_source/quickstart/qs_toc.txt - 7. Save, Load and Use the Model ============================ + +.. include:: /beginner_source/quickstart/qs_toc.txt + In this section we will look at how to save, load and use persisted model state to run predictions. """ diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index d9d1c13e4cc..3f6658032d9 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -1,8 +1,9 @@ """ +1. Tensors and Operations +========================== + .. include:: /beginner_source/quickstart/qs_toc.txt -1. Tensors and Operations ----------------------- **Tensor** is the basic computational unit in PyTorch. It is very similar to **NumPy array**, and supports similar operations. However, there are two very important features of Torch tensors that make them diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 8ba5354bb81..d52f429607d 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -1,15 +1,31 @@ """ -.. include:: /beginner_source/quickstart/qs_toc.txt - 3. Transforms =================== +.. include:: /beginner_source/quickstart/qs_toc.txt + Data does not come ready to be processed in the machine learning algorithm. We need to do different data manipulations or transforms to prepare it for training. There are many types of transformations and it depends on the type of model you are building and the state of your data as to which ones you should use. In the below example, for our FashionMNIT image dataset, we are taking our image features (x), turning it into a tensor and normalizing it. Then taking the labels (y) padding with zeros to get a consistent shape. We will break down each of these steps and the why below. -Full Section Example: """ + +############################################## +# Pytorch Datasets +# -------------------------- +# +# We are using the built-in open FashionMNIST datasets from the PyTorch library. +# For more info on the Datasets and Loaders check out `this `_ resource. +# The ``Train=True`` indicates we want to download the training dataset from the +# built-in datasets, ``Train=False`` indicates to download the testing dataset. +# This way we have data partitioned out for training and testing within the provided PyTorch datasets. +# We will apply the same transforms to both the training and testing datasets. +# +# From the docs: +# +# ``torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=False)`` + +# import packages import os import torch import torch.nn as nn @@ -18,11 +34,16 @@ from torch.utils.data import DataLoader from torchvision import datasets, transforms -# image classes +# Here we define the image classes. classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] -# data used for training +############################################## +# Transform: Features +# --------------------------- +# + +# Create training data from the built in PyTorch dataset. training_data = datasets.FashionMNIST('data', train=True, download=True, transform=transforms.Compose( [transforms.ToTensor()]), @@ -32,40 +53,15 @@ ]) ) -# data used for testing -test_data = datasets.FashionMNIST('data', train=False, download=True, - transform=transforms.Compose( - [transforms.ToTensor()]), - target_transform=transforms.Compose([ - transforms.Lambda(lambda y: torch.zeros( - 10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) - ]) - ) - -############################################## -# Pytorch Datasets -# -------------------------- -# -# We are using the built-in open FashionMNIST datasets from the PyTorch library. For more info on the Datasets and Loaders check out `this `_ resource. The ``Train=True`` indicates we want to download the training dataset from the built-in datasets, ``Train=False`` indicates to download the testing dataset. This way we have data partitioned out for training and testing within the provided PyTorch datasets. We will apply the same transfoms to both the training and testing datasets. -# -# From the docs: -# -# ``torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=False)`` - -############################################## -# Transform: Features -# --------------------------- -# Example: -# - -transform = transforms.Compose([transforms.ToTensor()]) - ##################################################### # Compose # ------------------------ # -# The `transforms.compose`` allows us to string together different steps of transformations in a sequential order. This allows us to add an array of transforms for both the features and labels when preparing our data for training. +# The ``transforms.compose`` allows us to string together different steps of transformations in a +# sequential order. This allows us to add an array of transforms for both the features and labels +# when preparing our data for training. # +transform = transforms.Compose([transforms.ToTensor()]) ################################################# # ToTensor() @@ -84,11 +80,17 @@ # Target_Transform: Labels # ------------------------------- # -# Example: -# -target_transform = transforms.Lambda(lambda y: torch.zeros( - 10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)) +# Create testing data from the built in PyTorch dataset. +test_data = datasets.FashionMNIST('data', train=False, download=True, + transform=transforms.Compose( + [transforms.ToTensor()]), + target_transform=transforms.Compose([ + transforms.Lambda(lambda y: torch.zeros( + 10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) + ]) + ) + ################################################# # This function is taking the y input and creating a tensor of size 10 with a float datatype. Then its calling scatter `torch.Tensor.scatter_ class `_ to send each item to torch.zeros, according to the row, index and current item value. @@ -96,6 +98,9 @@ # - index = torchtensor(y) is the index of the element toscatter # - value = 1 is the source elemnt +target_transform = transforms.Lambda(lambda y: torch.zeros( + 10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)) + ############################################## # Using your own data # -------------------------------------- diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart_tutorial.py index 40da146bb09..d5ebc537b25 100644 --- a/beginner_source/quickstart_tutorial.py +++ b/beginner_source/quickstart_tutorial.py @@ -17,10 +17,18 @@ This guide is setup to cover machine learning concepts and how to apply them with PyTorch. This main page is a highlevel intro to each step with the code examples to build the model. You have the option to jump into the concepts introduced in each section to get more details and explanations to better understand each concept -and how to apply them with PyTorch. The topics are introduceds in a sequenced order as listed below: +and how to apply them with PyTorch. The topics are introduced in a sequenced order as listed below: -.. include:: /beginner_source/quickstart/qs_toc.txt +Pytorch Quickstart Topics +------------------------- +| 1. `Tensors `_ +| 2. `DataSets and DataLoaders `_ +| 3. `Transforms `_ +| 4. `Build Model `_ +| 5. `Automatic Differentiation `_ +| 6. `Optimization Loop `_ +| 7. `Save, Load and Use Model `_ Working with data ----------------- @@ -33,11 +41,6 @@ # modify data in-place. Below is an example of how to load that data from the PyTorch open datasets and transform the data to a normalized tensor. # This example is using the `torchvision.datasets` which is a subclass from the primitive `torch.utils.data.Dataset`. Note that the primitive dataset doesnt have the built in transforms param like the built in dataset in `torchvision.datasets.` # -# To see more examples and details of how to work with Tensors, Datasets, DataLoaders and Transforms in PyTorch with this example checkout these resources: -# -# - `Tensors `_ -# - `DataSet and DataLoader `_ -# - `Transforms `_ import torch import torch.nn as nn @@ -104,10 +107,6 @@ def forward(self, x): # Optimizing model parameters requires a loss function, optimizer, # and the optimization loop. # -# To see more examples and details of how to work with Optimization and Training loops in Pytoch with this example checkout these resources: -# - `Optimization and training loops `_ -# - `Automatic differentiation and AutoGrad `_ -# # cost function used to determine best parameters cost = torch.nn.BCELoss() @@ -208,8 +207,6 @@ def test(dataloader, model): print(f'Predicted: "{predicted}", Actual: "{actual}"') ################################################################## -# -# .. include:: /beginner_source/quickstart/qs_toc.txt # # *Authors: Seth Juarez, Ari Bornstein, Cassie Breviu, Dmitry Soshnikov* From d90b8b1116dfecc109ecc008b6784a98688fcab2 Mon Sep 17 00:00:00 2001 From: Dmitri Soshnikov Date: Wed, 13 Jan 2021 19:19:47 +0300 Subject: [PATCH 036/120] Make misc improvements to quickstart (#43) --- .../quickstart/autograd_tutorial.py | 7 +- .../quickstart/dataquickstart_tutorial.py | 37 ++-- beginner_source/quickstart/tensor_tutorial.py | 19 +- .../quickstart/transforms_tutorial.py | 178 ++++++++++-------- 4 files changed, 128 insertions(+), 113 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 8f927f82c27..a5046866467 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -19,6 +19,7 @@ """ import torch + x = torch.ones(5) # input tensor y = torch.zeros(3) # expected output w = torch.randn(5, 3, requires_grad=True) @@ -55,7 +56,8 @@ # documentation `__. # -print(z.grad_fn, loss.grad_fn, sep='\n') +print('Gradient function for z =',z.grad_fn) +print('Gradient function for loss =', loss.grad_fn) ###################################################################### # Computing Gradients @@ -152,8 +154,9 @@ # x = torch.zeros(2, requires_grad=True) -def f(x): return (x-torch.tensor([3, -2])).pow(2).sum() +def f(x): + return (x-torch.tensor([3, -2])).pow(2).sum() lr = 0.1 diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 409887ae55d..bc0e1aac2f0 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -23,7 +23,7 @@ # # If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking managing collections of data. # -# A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. They are subclasses of torch.utils.data.Dataset that have parameters and functions specific to the type of data and the particular dataset. The actual data samples can be downloaded from the internet.These are useful for benchmarking and testing your models before training on your own custom datasets. +# A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. They are subclasses of `torch.utils.data.Dataset` that have parameters and functions specific to the type of data and the particular dataset. The actual data samples can be downloaded from the internet. These are useful for benchmarking and testing your models before training on your own custom datasets. # # You can find some of them below. # @@ -36,7 +36,7 @@ # Iterating through a Dataset # ----------------- # -# Once we have a Dataset we can index it manually like a list `clothing[index]`. +# Once we have a Dataset ``ds``, we can index it manually like a list: ``ds[index]``. # # Here is an example of how to load the `Fashion-MNIST `_ dataset from torch vision. # `Fashion-MNIST `_ is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. @@ -60,7 +60,7 @@ cols, rows = 3, 3 for i in range(1, cols*rows +1): sample_idx = np.random.randint(len(clothing)) - img = clothing[sample_idx][0][0,:,:] + img = clothing[sample_idx][0] figure.add_subplot(rows, cols, i) plt.title(labels_map[clothing[sample_idx][1]]) plt.axis('off') @@ -68,7 +68,8 @@ plt.show() ################################################################# -# .. figure:: /_static/img/quickstart/fashion_mnist.png +# .. +# .. figure:: /_static/img/quickstart/fashion_mnist.png # :alt: fashion_mnist # @@ -76,7 +77,7 @@ # Creating a Custom Dataset # ----------------- # -# To work with your own data lets look at the a simple custom image Dataset implementation: +# To work with your own data, we need to implement a custom class that inherits from ``Dataset```. Let's look at a custom image dataset implementation. In this example, we have a number of images stored in a directory, and their labels stored separately in CSV annotation file. # import os @@ -114,7 +115,7 @@ def __getitem__(self, idx): # Imports # ------- # -# Import os for file handling, torch for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and Dataset to implement the Dataset interface. +# Import `os` for file handling, torch for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and Dataset to implement the Dataset interface. # # Example: # @@ -130,16 +131,13 @@ def __getitem__(self, idx): # Init # ----------------- # -# The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and the keep track of directory of our image file. Note that different types of data can take different init inputs you are not limited to just an annotations file, directory_path and transforms but for images this is a standard practice. -# A sample csv annotations file may look as follows: -# -# tshirt1.jpg, 0 -# -# tshirt2.jpg, 0 +# The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and the keep track of directory of our image file. Note that different types of data can take different init inputs. You are not limited to just an annotations file, directory path and transforms, but for images this is a standard practice. +# A sample csv annotations file may look as follows: :: # +# tshirt1.jpg, 0 +# tshirt2.jpg, 0 # ...... -# -# ankleboot999.jpg, 9 +# ankleboot999.jpg, 9 # # Example: # @@ -153,7 +151,7 @@ def __init__(self, annotations_file, img_dir, transform=None): # __len__ # ----------------- # -# The __len__ function is very simple here we just need to return the number of samples in our dataset. +# The __len__ function is very simple, we just need to return the number of samples in our dataset. # # Example: @@ -164,9 +162,9 @@ def __len__(self): # __getitem__ # ----------------- # -# The __getitem__ function is the most important function in the Datasets interface this. It takes a tensor or an index as input and returns a loaded sample from you dataset at from the given indecies. +# The __getitem__ function is the most important function in the Datasets interface. It takes a tensor or an index as input and returns a loaded sample from you dataset at the given indices. # -# In this sample if provided a tensor we convert the tensor to a list containing our index. We then load the file at the given index from our image directory as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a Transform on and return. To learn more about Transforms see the next section of the Blitz. +# If provided a tensor as an index, we convert the tensor to a list first. We then load the file at the given index from our image directory, as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a Transform on and return. To learn more about Transforms see the next section of the Blitz. # # Example: # @@ -190,16 +188,17 @@ def __getitem__(self, idx): # Now we have a organized mechansim for managing data which is great, but there is still a lot of manual work we would have to do train a model with our Dataset. # # For example we would have to manually maintain the code for: +# # * Batching # * Suffling # * Parallel batch distribution # -# The PyTorch Dataloader *torch.utils.data.DataLoader* is an iterator that handles all of this complexity for us enabling us to load a dataset and focusing on train our model. +# The PyTorch Dataloader ``torch.utils.data.DataLoader`` is an iterator that handles all of this complexity for us, enabling us to load a dataset and focus on training our model. dataloader = DataLoader(clothing, batch_size=4, shuffle=True, num_workers=0) ################################################################# -# With this we have all we need to know to load an process data of any kind in PyTorch to train deep learning models. +# With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. # # Next: Learn more about how to `transform that data for training `_. # diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 3f6658032d9..1a3a509a366 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -8,9 +8,9 @@ similar to **NumPy array**, and supports similar operations. However, there are two very important features of Torch tensors that make them especially useful for training large-scale neural networks: -- Tensor operations can be performed on GPUs or other specialized hardware to accelerate computing -- Tensor operations support automatic differentiation using - `pytorch.autograd engine `__ + + * Tensor operations can be performed on GPUs or other specialized hardware to accelerate computing + * Tensor operations support automatic differentiation using `pytorch.autograd engine `__ Conversion between Torch tensors and NumPy arrays can be done easily: """ @@ -178,9 +178,9 @@ # using different dimensions: # -print(x.size()) # original size of x is 3x5 -print(x.view(5, 3, 1).size()) # will give size 5x3x1 -print(x.view(5, -1)) # will result in size 5x3 +print('Original size of x =',x.size()) # original size of x is 3x5 +print('Size after reshaping is',x.view(5, 3, 1).size()) # will give size 5x3x1 +print('Reshaped tensor:\n',x.view(5, -1)) # will result in size 5x3 ###################################################################### @@ -205,7 +205,9 @@ # print(x.size()) # original size of x is 3x5 -print(x[0].size(), x[:, 0].size(), x[..., 1].size()) # will give 5, 3, 3 +print('First row: ',x[0]) +print('First column: ', x[:, 0]) +print('Last column:', x[..., -1]) ###################################################################### @@ -215,7 +217,7 @@ # val = x.sum().item() # will compute the sum of all elements - +print(val) ###################################################################### # Hardware-Accelerated Computations @@ -241,7 +243,6 @@ y = torch.ones_like(x) # create tensor on CPU y = y.to(device) # move tensor to another device z = x+y # this is performed on GPU if it is available -print(z) print(z.to("cpu", torch.double)) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index d52f429607d..24dacbd1519 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -4,26 +4,34 @@ .. include:: /beginner_source/quickstart/qs_toc.txt -Data does not come ready to be processed in the machine learning algorithm. We need to do different data manipulations or transforms to prepare it for training. There are many types of transformations and it depends on the type of model you are building and the state of your data as to which ones you should use. - -In the below example, for our FashionMNIT image dataset, we are taking our image features (x), turning it into a tensor and normalizing it. Then taking the labels (y) padding with zeros to get a consistent shape. We will break down each of these steps and the why below. +In most of the practical tasks, data does not come in its final form that is required for training machine learning algorithm. We need to do different data manipulations or **transformations** to prepare it for training. There are many types of transformations, and it depends on the type of model you are building and the state of your data as to which ones you should use. +In the example below, let's take FashionMNIST image dataset, which is available from ``torchvision.datasets`` using the following function: """ +import torchvision + +ds = torchvision.datasets.FashionMNIST( + 'data', # specifies data directory to store data + train=True, # specifies training or test dataset to use + transform=None, # specifies transforms to apply to features (images) + target_transform=None, # specifies transforms to apply to labels + download=False) # should the data be downloaded from the Internet + +################################ +#To prepare data for training the neural network, we need to take our image (also called features, x), turn it into a tensor and normalize it. We also need to convert labels (y) into one-hot encoding. +# +#We will break down each of these steps below. ############################################## # Pytorch Datasets # -------------------------- # -# We are using the built-in open FashionMNIST datasets from the PyTorch library. -# For more info on the Datasets and Loaders check out `this `_ resource. +# We are using the built-in FashionMNIST dataset from the PyTorch library. +# For more info on the Datasets and Loaders check out `this `_ section of the tutorial. # The ``Train=True`` indicates we want to download the training dataset from the # built-in datasets, ``Train=False`` indicates to download the testing dataset. # This way we have data partitioned out for training and testing within the provided PyTorch datasets. # We will apply the same transforms to both the training and testing datasets. -# -# From the docs: -# -# ``torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=False)`` # import packages import os @@ -39,107 +47,111 @@ "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] ############################################## -# Transform: Features -# --------------------------- +# Feature Transforms and Label Transforms +# --------------------------------------- # +# Below is the code to load the FashionMNIST dataset and apply required transforms: -# Create training data from the built in PyTorch dataset. -training_data = datasets.FashionMNIST('data', train=True, download=True, - transform=transforms.Compose( - [transforms.ToTensor()]), - target_transform=transforms.Compose([ - transforms.Lambda(lambda y: torch.zeros( - 10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) - ]) - ) +training_data = datasets.FashionMNIST( + 'data', + train=True, download=True, + transform=transforms.ToTensor(), + target_transform=transforms.Lambda( + lambda y: torch.zeros(10, dtype=torch.float) + .scatter_(0, torch.tensor(y), value=1))) -##################################################### -# Compose -# ------------------------ +######################################## +# Here we define two transformations: # -# The ``transforms.compose`` allows us to string together different steps of transformations in a -# sequential order. This allows us to add an array of transforms for both the features and labels -# when preparing our data for training. -# -transform = transforms.Compose([transforms.ToTensor()]) +# * ``transform`` is the transformation we apply to features, in our case - to images. Because dataset contains images in PIL format, we need to convert them to tensors using ``ToTensor()`` transform. +# * ``target_transform`` defines a transformation that is applied to labels. In our case label is a class number from 0 to 9, and we need to convert it to one-hot encoding. ################################################# # ToTensor() # ------------------------------- # -# For the feature transforms we have an array of transforms to process our image data for training. The first transform in the array is ``transforms.ToTensor()`` this is from class `torchvision.transforms.ToTensor `_. We need to take our images and turn them into a tensor. (To learn more about Tensors check out `this `_ resource.) The ``ToTensor()`` transformation is doing more than converting our image into a tensor. Its also normalizing our data for us by scaling the images to be between 0 and 1. -# +# `torchvision.transforms.ToTensor `_ transform is required to prepare an image for training. It takes PIL image, converts it into a `tensor `_, and also normalizes our data by scaling the image pixel intensity values to be between 0 and 1. # # .. note:: ToTensor only normalized image data that is in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling. # -# -# Check out the other `TorchVision Transforms `_ -# ############################################## -# Target_Transform: Labels +# Lambda Transform # ------------------------------- # +# To turn class number into one-hot encoding, we use **lambda transform**, i.e. a transformation defined by an arbitrary function. This function takes y as an input and creates a zero tensor of size 10. Then it calls scatter `torch.Tensor.scatter_ class `_ to take a value 1 and store it into the correct position of the zero vector defined by the class number. -# Create testing data from the built in PyTorch dataset. -test_data = datasets.FashionMNIST('data', train=False, download=True, - transform=transforms.Compose( - [transforms.ToTensor()]), - target_transform=transforms.Compose([ - transforms.Lambda(lambda y: torch.zeros( - 10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) - ]) - ) +target_transform = transforms.Lambda(lambda y: torch.zeros( + 10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)) +############################################### +# Check out the other `TorchVision Transforms `_ +# -################################################# -# This function is taking the y input and creating a tensor of size 10 with a float datatype. Then its calling scatter `torch.Tensor.scatter_ class `_ to send each item to torch.zeros, according to the row, index and current item value. -# - Dim=0 is row wise index -# - index = torchtensor(y) is the index of the element toscatter -# - value = 1 is the source elemnt +##################################################### +# Compose +# ------------------------ +# +# In many cases, we need to perform several transformations on the data sequentially. ``transforms.compose`` allows us to string together different steps of transformations in a sequential order. We will see an example of using composition transform in the next section. -target_transform = transforms.Lambda(lambda y: torch.zeros( - 10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)) ############################################## # Using your own data # -------------------------------------- # -# Below is an example for processing image data using a dataset from a local directory. +# Below is an example for processing image data using a dataset from a local directory. It assumes the we have ``train`` and ``val`` subdirectories with training and validation dataset accordingly, and we want to apply different sets of transforms for training and validation dataset: # -# Example: +# * For training data, we want to perform some **data augmentation**, and do random croping/resizing of the original image. We also introduce random horizontal flips. +# * For testing, we typically want to be consistent and always use the same images - thus we do not do any augmentation, just resizing. +# +# We also normalize values by subtracting the mean, which was computed along the whole dataset. +# +# To be able to unify the code for train and validation datasets, we use a special trick and create a dictionary of transforms for each dataset: train and validation: +# +# .. code-block:: Python +# +# data_transforms = { +# 'train': +# transforms.Compose([ +# transforms.RandomResizedCrop(224), +# transforms.RandomHorizontalFlip(), +# transforms.ToTensor(), +# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) +# ]), +# 'val': +# transforms.Compose([ +# transforms.Resize(256), +# transforms.CenterCrop(224), +# transforms.ToTensor(), +# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) +# ]), +# } +# +# Next, we define a similar dictionary of train and validation datasets by using ``datasets.ImageFolder`` class. This class allows us to create a dataset from all files in a folder, and apply any transformations to them: +# +# .. code-block:: Python +# +# data_dir = 'data' +# +# image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), +# data_transforms[x]) +# for x in ['train', 'val']} +# +# In a similar manner, we define a dictionary of dataloaders, which can prepare our datasets for neural network training. They allow us to shuffle data and group them into batches of a specified size: +# +# .. code-block:: Python +# +# batch_size = 4 +# +# dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], +# batch_size=batch_size, +# shuffle=True, num_workers=4) +# for x in ['train', 'val']} +# +# dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} +# +# class_names = image_datasets['train'].classes # - -data_dir = 'data' -batch_size = 4 - -data_transforms = { - 'train': transforms.Compose([ - transforms.RandomResizedCrop(224), - transforms.RandomHorizontalFlip(), - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]), - 'val': transforms.Compose([ - transforms.Resize(256), - transforms.CenterCrop(224), - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]), -} -image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), - data_transforms[x]) - for x in ['train', 'val']} - -dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], - batch_size=batch_size, - shuffle=True, num_workers=4) - for x in ['train', 'val']} - -dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} - -class_names = image_datasets['train'].classes - ################################################################## # Next learn how to `build the model `_ From 3c91291410d27b9d39cd1da52710905504db766b Mon Sep 17 00:00:00 2001 From: Dmitri Soshnikov Date: Thu, 14 Jan 2021 22:51:20 +0300 Subject: [PATCH 037/120] Refactor buildmodel_tutorial.py to exclude sequential net definition (#44) --- .../quickstart/buildmodel_tutorial.py | 77 +++++++++---------- 1 file changed, 35 insertions(+), 42 deletions(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index b42e8629dc0..d1f1b167fbb 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -9,14 +9,13 @@ # Get Started Building the Model # ----------------- # -# The data has been loaded and transformed we can now build the model. -# We will leverage `torch.nn `_ predefined layers that PyTorch has that can simplify our code. +# Having loaded and transformed the data, we can now build the neural network model. In many simple cases neural network consists of a number of layers, and `torch.nn `_ namespace provides predefined layers that can simplify our code. # -# In the below example, for our FashionMNIT image dataset, we are using `nn.Module `_ -# A big plus with using a class that inherits ``nn.Module`` is better parameter management across all nested submodules. -# This gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. +# The most common way to define a neural network is to use a class inherited from `nn.Module `_ +# It provides great parameter management across all nested submodules, which gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. # -# Lets break down the steps to build this model below +# In the below example, for our FashionMNIST image dataset, we will create simplest dense multi-layer network. +# Lets break down the steps to build this model below. # ############################################# @@ -27,6 +26,7 @@ import os import torch import torch.nn as nn +import torch.nn.functional as F import torch.onnx as onnx from torch.utils.data import DataLoader from torchvision import datasets, transforms @@ -35,7 +35,7 @@ ############################################# # Get Device for Training # ----------------------- -# Here we check to see if `torch.cuda `_ +# We want to be able to train our model on both CPU and GPU, if it is available. It is common practice to define a variable ``device`` which will designate the device we will be training on. We check to see if `torch.cuda `_ # is available to use the GPU, else we will use the CPU. # # Example: @@ -71,6 +71,7 @@ def forward(self, x): x = F.relu(self.layer2(x)) x = self.output(x) return F.softmax(x, dim=1) + model = NeuralNetwork().to(device) print(model) @@ -79,12 +80,12 @@ def forward(self, x): # The Model Module Layers # ------------------------- # -# Lets break down each model layer in the FashionMNIST model. In the below example we are using a `Sequential` -# container from class `torch.nn. Sequential `_ -# that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` -# method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined. +# Lets break down each model layer in the FashionMNIST model. To illustrate it, we will take a sample minibatch of 100 images of size 28x28 and see what happens to it as we pass it through the network. The code in the sections below would essentially explain what happens inside the ``forward`` method of our ``NeuralNetwork`` class. # +input_image = torch.rand(100,28,28) +print(input_image.size()) + ################################################## # `nn.Flatten `_ # ----------------------------------------------- @@ -94,15 +95,10 @@ def forward(self, x): # From the docs: # ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)`` # -# Here is an example using one of the training_data set items: -tensor = training_data[0][0] -print(tensor.size()) - -model = nn.Sequential( - nn.Flatten() -) -flattened_tensor = model(tensor) -flattened_tensor.size() +# In our case, flatten keeps the minibatch dimension, but two image dimensions are reduced to one: +flatten = nn.Flatten() +flat_image = flatten(input_image) +print(flat_image.size()) ############################################## # `nn.Linear `_ to add a linear layer to the model. @@ -116,36 +112,33 @@ def forward(self, x): # ``torch.nn.Linear(in_features: int, out_features: int, bias: bool = True)`` # -input = training_data[0][0] -print(input.size()) -model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), -) -output = model(input) -output.size() +layer1 = nn.Linear(28*28,512) +hidden1 = layer1(flat_image) +print(hidden1.size()) ################################################# # Activation Functions # ------------------------- # -# After the first two linear layer we will call the `nn.ReLU `_ -# activation function. Then after the third linear layer we call the `nn.Softmax `_ -# activation to rescale between 0 and 1 and sum to one. +# In between layers of a neural network, we need to put non-linear activation functions, such as `nn.ReLU `_ (which is often used in between hidden layers) or `nn.Softmax `_, which turns output of the network into probabilities by rescaling them in such a way that all values are between 0 and 1, and all sum to one. # -model = nn.Sequential( - nn.Flatten(), - nn.Linear(28*28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, len(classes)), - nn.Softmax(dim=1) - ).to(device) - -print(model) +layer2 = nn.Linear(512,512) +output = nn.Linear(512,10) +hidden2 = layer2(F.relu(hidden1)) +print('Hidden 2 output size =',hidden2.size()) +z = output(F.relu(hidden2)) +out = F.softmax(z) +print('Output size =',out.size()) + +################################################# +# Parameter Tracking +# ------------------------- +# +# The main reason to put all code inside a class inherited from ``nn.Module`` is to utilize **parameter tracking**. Most of the layers inside a neural network, in our case all linear layers, have associtated weights and biases that need to be adjusted during training. ``nn.Module`` automatically tracks all fields defined inside the class, and makes all parameters accessible using ``parameters()`` or ``named_parameters()`` methods. Let's have a look at the first two parameters of our neural network that has been defined in the beginning of this section: +# +print(list(model.named_parameters())[0:2]) ################################################ # In the next section you will learn about how to train the model and the optimization loop for this example. From 83255032efd44344ccefb77f435ba2f8091a569c Mon Sep 17 00:00:00 2001 From: Dmitri Soshnikov Date: Fri, 15 Jan 2021 00:11:22 +0000 Subject: [PATCH 038/120] moved quickstart home folder, added breadcrumb --- .../quickstart/autograd_tutorial.py | 14 ++++++++++--- .../quickstart/buildmodel_tutorial.py | 13 ++++++++++-- .../quickstart/dataquickstart_tutorial.py | 14 ++++++++++--- .../quickstart/optimization_tutorial.py | 13 +++++++++--- beginner_source/quickstart/qs_toc.txt | 3 --- .../{ => quickstart}/quickstart_tutorial.py | 20 +++++++++---------- .../quickstart/saveloadrun_tutorial.py | 13 +++++++++--- beginner_source/quickstart/tensor_tutorial.py | 13 +++++++++--- .../quickstart/transforms_tutorial.py | 13 +++++++++--- index.rst | 2 +- 10 files changed, 84 insertions(+), 34 deletions(-) rename beginner_source/{ => quickstart}/quickstart_tutorial.py (93%) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index a5046866467..6915c43384e 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -1,8 +1,16 @@ """ -5. Automatic Differentiation with ``torch.autograd`` -======================================= -.. include:: /beginner_source/quickstart/qs_toc.txt +`Quickstart `_ > +`Tensors `_ > +`DataSets & DataLoaders `_ > +`Transforms `_ > +`Build Model `_ > +**Autograd** > +`Optimization `_ > +`Save & Load Model `_ + +Automatic Differentiation with ``torch.autograd`` +======================================= When training neural networks, the most frequently used algorithm is **back propagation**. In this algorithm, parameters (model weights) are diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index d1f1b167fbb..195f5191aa0 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -1,8 +1,17 @@ """ -4. Build the Neural Network + +`Quickstart `_ > +`Tensors `_ > +`DataSets & DataLoaders `_ > +`Transforms `_ > +**Build Model** > +`Autograd `_ > +`Optimization `_ > +`Save & Load Model `_ + +Build the Neural Network =================== -.. include:: /beginner_source/quickstart/qs_toc.txt """ ################################################################# diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index bc0e1aac2f0..976949518ea 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -1,8 +1,16 @@ """ -2. Datasets & Dataloaders -=================== -.. include:: /beginner_source/quickstart/qs_toc.txt +`Quickstart `_ > +`Tensors `_ > +**DataSets & DataLoaders** > +`Transforms `_ > +`Build Model `_ > +`Autograd `_ > +`Optimization `_ > +`Save & Load Model `_ + +Datasets & Dataloaders +=================== """ diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 797b7bd8e8a..5dd375b534e 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -1,9 +1,16 @@ """ -6. Optimizing Model Parameters +`Quickstart `_ > +`Tensors `_ > +`DataSets & DataLoaders `_ > +`Transforms `_ > +`Build Model `_ > +`Automgrad `_ > +**Optimization** > +`Save & Load Model `_ + +Optimizing Model Parameters =========================== -.. include:: /beginner_source/quickstart/qs_toc.txt - Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! To get started lets take a look at some example model optimization code: """ diff --git a/beginner_source/quickstart/qs_toc.txt b/beginner_source/quickstart/qs_toc.txt index ae71517323b..fc0fb747714 100644 --- a/beginner_source/quickstart/qs_toc.txt +++ b/beginner_source/quickstart/qs_toc.txt @@ -1,6 +1,3 @@ - -Pytorch Quickstart Topics -------------------------- | 1. `Tensors `_ | 2. `DataSets and DataLoaders `_ | 3. `Transforms `_ diff --git a/beginner_source/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py similarity index 93% rename from beginner_source/quickstart_tutorial.py rename to beginner_source/quickstart/quickstart_tutorial.py index d5ebc537b25..6b0f1fb36b0 100644 --- a/beginner_source/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -1,4 +1,13 @@ """ +**Quickstart** > +`Tensors `_ > +`DataSets & DataLoaders `_ > +`Transforms `_ > +`Build Model `_ > +`Autograd `_ > +`Optimization `_ > +`Save & Load Model `_ + PyTorch Quickstart =================== @@ -19,16 +28,7 @@ into the concepts introduced in each section to get more details and explanations to better understand each concept and how to apply them with PyTorch. The topics are introduced in a sequenced order as listed below: - -Pytorch Quickstart Topics -------------------------- -| 1. `Tensors `_ -| 2. `DataSets and DataLoaders `_ -| 3. `Transforms `_ -| 4. `Build Model `_ -| 5. `Automatic Differentiation `_ -| 6. `Optimization Loop `_ -| 7. `Save, Load and Use Model `_ +.. include:: /beginner_source/quickstart/qs_toc.txt Working with data ----------------- diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index ec7a8a99d52..15dc7580568 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -1,8 +1,15 @@ """ -7. Save, Load and Use the Model -============================ +`Quickstart `_ > +`Tensors `_ > +`DataSets & DataLoaders `_ > +`Transforms `_ > +`Build Model `_ > +`Automgrad `_ > +`Optimization `_ > +**Save & Load Model** -.. include:: /beginner_source/quickstart/qs_toc.txt +Save, Load and Use the Model +============================ In this section we will look at how to save, load and use persisted model state to run predictions. diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 1a3a509a366..f2710416d25 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -1,9 +1,16 @@ """ -1. Tensors and Operations +`Quickstart `_ > +**Tensors** > +`DataSets & DataLoaders `_ > +`Transforms `_ > +`Build Model `_ > +`Autograd `_ > +`Optimization `_ > +`Save & Load Model `_ + +Tensors and Operations ========================== -.. include:: /beginner_source/quickstart/qs_toc.txt - **Tensor** is the basic computational unit in PyTorch. It is very similar to **NumPy array**, and supports similar operations. However, there are two very important features of Torch tensors that make them diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 24dacbd1519..807502a7f58 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -1,9 +1,16 @@ """ -3. Transforms +`Quickstart `_ > +`Tensors `_ > +`DataSets & DataLoaders `_ > +**Transforms** > +`Build Model `_ > +`Automgrad `_ > +`Optimization `_ > +`Save & Load Model `_ + +Transforms =================== -.. include:: /beginner_source/quickstart/qs_toc.txt - In most of the practical tasks, data does not come in its final form that is required for training machine learning algorithm. We need to do different data manipulations or **transformations** to prepare it for training. There are many types of transformations, and it depends on the type of model you are building and the state of your data as to which ones you should use. In the example below, let's take FashionMNIST image dataset, which is available from ``torchvision.datasets`` using the following function: diff --git a/index.rst b/index.rst index 3f616b7800a..28b6e52bb44 100644 --- a/index.rst +++ b/index.rst @@ -437,7 +437,7 @@ Additional Resources :includehidden: :caption: Learning PyTorch - beginner/quickstart_tutorial + beginner/quickstart/quickstart_tutorial beginner/deep_learning_60min_blitz beginner/pytorch_with_examples beginner/nn_tutorial From a204fe66345010e472432289eea557b39f304c01 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Fri, 15 Jan 2021 19:09:22 +0000 Subject: [PATCH 039/120] add callout quickstart on tutorials page, cap fix --- beginner_source/quickstart/quickstart_tutorial.py | 2 +- beginner_source/quickstart/transforms_tutorial.py | 2 +- index.rst | 13 +++++++++++++ 3 files changed, 15 insertions(+), 2 deletions(-) diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 6b0f1fb36b0..5f733ef809b 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -191,7 +191,7 @@ def test(dataloader, model): # parameters includes re-creating the model shape and then loading # the state dictionary. Once loaded the model can be used for either # retraining or inference purposes (in this example it is used for -# inference). Check out more details on `saving, loading and running models with Pytorch `_ +# inference). Check out more details on `saving, loading and running models with PyTorch `_ # loaded_model = NeuralNetwork() diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 807502a7f58..24cee6999f6 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -30,7 +30,7 @@ #We will break down each of these steps below. ############################################## -# Pytorch Datasets +# PyTorch Datasets # -------------------------- # # We are using the built-in FashionMNIST dataset from the PyTorch library. diff --git a/index.rst b/index.rst index 28b6e52bb44..3f249ff3e61 100644 --- a/index.rst +++ b/index.rst @@ -14,6 +14,12 @@ Welcome to PyTorch Tutorials :button_link: beginner/deep_learning_60min_blitz.html :button_text: Start 60-min blitz +.. customcalloutitem:: + :description: In this quickstart we will cover the basics of machine learning and how to apply them with PyTorch. You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step! + :header: PyTorch Quickstart + :button_link: beginner/quickstart/quickstart_tutorial.html + :button_text: Get started with PyTorch + .. customcalloutitem:: :description: Bite-size, ready-to-deploy PyTorch code examples. :header: PyTorch Recipes @@ -57,6 +63,13 @@ Welcome to PyTorch Tutorials :link: beginner/deep_learning_60min_blitz.html :tags: Getting-Started +.. customcarditem:: + :header: PyTorch Quickstart + :card_description: Get started with a step-by-step guide to building neural networks with PyTorch. + :image: _static/img/thumbnails/cropped/60-min-blitz.png + :link: beginner/quickstart/quickstart_tutorial.html + :tags: Getting-Started + .. customcarditem:: :header: Learning PyTorch with Examples :card_description: This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. From 70f90b90a348f605965fb6c7f53b948ab0fa63f2 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Sat, 16 Jan 2021 17:16:54 +0000 Subject: [PATCH 040/120] checkin by Cassie: how to run code, fixed typo --- beginner_source/quickstart/quickstart_tutorial.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 5f733ef809b..fc2d511843e 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -13,7 +13,7 @@ The basic machine learning concepts in any framework should include: Working with data, Creating models, Optimizing Parameters, Saving and Loading Models. In this PyTorch Quickstart we will -go through these concepts and how to apply them with PyTorch. That dataset we will be using is the +go through these concepts and how to apply them with PyTorch. The dataset we will be using is the FashionMNIST clothing images dataset that demonstrates these core steps applied to create ML Models. You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step. @@ -30,6 +30,16 @@ .. include:: /beginner_source/quickstart/qs_toc.txt +Running the Tutorial Code +------------------ +To run the tutorial code you have some options. The navigation above allows you to run the Jupyter Notebook on the cloud. +If you want to run the code locally on your machine you will need some tools you may or may not have installed already. +Below are some good tool options for configuring local development: + +- `Visual Studio Code `_ : You can open run python code in Visual Studio Code or open a Jupyter Notebook in VS Code. + +- `Anaconda for Package Management `_ : You will need to install the package using the either ``pip`` or ``conda`` to run the code locally. + Working with data ----------------- """ From 82ed7fb61acd601375863b501a6c806e610a80e5 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Sat, 16 Jan 2021 21:55:58 +0000 Subject: [PATCH 041/120] checked in by cassie: quickstart updates typo fix --- .../quickstart/buildmodel_tutorial.py | 2 +- .../quickstart/quickstart_tutorial.py | 20 ++++++++++++------- .../quickstart/saveloadrun_tutorial.py | 2 +- .../quickstart/transforms_tutorial.py | 2 +- 4 files changed, 16 insertions(+), 10 deletions(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 195f5191aa0..9a797744cbd 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -23,7 +23,7 @@ # The most common way to define a neural network is to use a class inherited from `nn.Module `_ # It provides great parameter management across all nested submodules, which gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. # -# In the below example, for our FashionMNIST image dataset, we will create simplest dense multi-layer network. +# In the below example, for our FashionMNIST image dataset, we will create a dense multi-layer network. # Lets break down the steps to build this model below. # diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index fc2d511843e..13d16414384 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -32,13 +32,14 @@ Running the Tutorial Code ------------------ -To run the tutorial code you have some options. The navigation above allows you to run the Jupyter Notebook on the cloud. +The navigation above allows you to run the Jupyter Notebook on the cloud, download the Jupyter Notebook +or download the python file to run locally. If you want to run the code locally on your machine you will need some tools you may or may not have installed already. Below are some good tool options for configuring local development: - `Visual Studio Code `_ : You can open run python code in Visual Studio Code or open a Jupyter Notebook in VS Code. -- `Anaconda for Package Management `_ : You will need to install the package using the either ``pip`` or ``conda`` to run the code locally. +- `Anaconda for Package Management `_ : You will need to install the packages using either ``pip`` or ``conda`` to run the code locally. Working with data ----------------- @@ -50,7 +51,8 @@ # The `torchvision.datasets` ``DataSet`` object includes a ``transforms`` mechanism to # modify data in-place. Below is an example of how to load that data from the PyTorch open datasets and transform the data to a normalized tensor. # This example is using the `torchvision.datasets` which is a subclass from the primitive `torch.utils.data.Dataset`. Note that the primitive dataset doesnt have the built in transforms param like the built in dataset in `torchvision.datasets.` -# +# For more details on datasets and dataloaders check out `DataSets & DataLoaders `_. +# import torch import torch.nn as nn @@ -85,8 +87,12 @@ # Creating Models # --------------- # -# There are two ways of creating models: in-line or as a class. This -# quickstart will consider a class definition. For more examples checkout `building the model `_. +# There are two ways of creating models: in-line or as a class. +# The most common way to define a neural network is to use a class inherited +# from `nn.Module `_. +# It provides great parameter management across all nested submodules, which gives us more +# flexibility, because we can construct layers of any complexity, including the ones with shared weights. +# For more details checkout `building the model `_. device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) @@ -115,7 +121,7 @@ def forward(self, x): # --------------------- # # Optimizing model parameters requires a loss function, optimizer, -# and the optimization loop. +# and the optimization loop. Read more about the `Optimization Loop`_. # # cost function used to determine best parameters @@ -201,7 +207,7 @@ def test(dataloader, model): # parameters includes re-creating the model shape and then loading # the state dictionary. Once loaded the model can be used for either # retraining or inference purposes (in this example it is used for -# inference). Check out more details on `saving, loading and running models with PyTorch `_ +# inference). Check out more details on `saving, loading and running models with PyTorch `_ # loaded_model = NeuralNetwork() diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 15dc7580568..272b69e8578 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -4,7 +4,7 @@ `DataSets & DataLoaders `_ > `Transforms `_ > `Build Model `_ > -`Automgrad `_ > +`Autograd `_ > `Optimization `_ > **Save & Load Model** diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 24cee6999f6..3a507c66c1f 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -4,7 +4,7 @@ `DataSets & DataLoaders `_ > **Transforms** > `Build Model `_ > -`Automgrad `_ > +`Autograd `_ > `Optimization `_ > `Save & Load Model `_ From cc364531193c3cf481a12f7f124b199680fcb7ef Mon Sep 17 00:00:00 2001 From: Cassieview Date: Sun, 17 Jan 2021 00:20:21 +0000 Subject: [PATCH 042/120] Refactor optimization tutorial --- .../quickstart/optimization_tutorial.py | 337 ++++++++---------- 1 file changed, 157 insertions(+), 180 deletions(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 5dd375b534e..cc9447bf699 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -4,7 +4,7 @@ `DataSets & DataLoaders `_ > `Transforms `_ > `Build Model `_ > -`Automgrad `_ > +`Autograd `_ > **Optimization** > `Save & Load Model `_ @@ -12,195 +12,172 @@ =========================== Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! -To get started lets take a look at some example model optimization code: -""" -# Initilize hyper parameters -learning_rate = 0.01 -num_epochs = 100 +Training a model is essentially an optimization process similar to the one we described in the previous section on `Autograd `_. We run the optimization process on the whole dataset several times, and each run is refered to as an **epoch**. During each run, we present data in **minibatches**, and for each minibatch compute gradients and correct parameters of the model according to back propagation algorithm. + +Hyperparameters +----------------- + +Hyperparameters are adjustable parameters that let you control the model optimization process. Unlike model parameters that we will optimize during training, hyperparameters are configured for the whole training process. However, you may achieve different model performance with different hyperparameters, so you may want to try out different values for them to perform hyperparameter optimization. + +In our case, we need to define the following hyperparameters: + + - **Number of Epochs**- the number times to iterate over the dataset + - **Batch Size** - the number of samples in the dataset to take for each update cycle + - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training. + +.. code-block:: Python + learning_rate = 1e-3 + batch_size = 64 + epochs = 5 + +We also need to create the model class instance (defined in the previous section): +..code-block:: Python + # Initilize model + model = NeuralNework() + +Optimizaton Loop +----------------- + +.. figure:: /_static/img/quickstart/optimizationloops.png + :alt: + +Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. Each iteration of the optimiziation loop is called an **epoch**. Each epoch is comprized of two main parts: + + 1. **The Train Loop** - main loop that iterates over all dataset and performs training + 2. **The Validation/Test Loop** - goes through the validation / test dataset to evaluate model performance on the test data. + +Here is a high-level view of optimization loop: + +.. code-block:: Python +for epoch in range(num_epochs): # Iterate over all epochs + + # Training loop: + for train_features, train_labels in train_dataloader: # Go over all minibatches + out = model(train_features) # Compute network output + loss = loss_function(out,train_labels) # Compute loss function + # optimize weights to minimize loss + ... + + # Evaluation loop + model.eval() # set to evaluation mode not to compute gradients + for test_features, test_labels: + out = model(test_features) + loss = loss_function(out,train_labels) + # store / display the loss and/or other metrics + ... + +Complete code for optimization loop will be presented at the end of this section. + +Loss Function +------------- + +When presented with some training data, our untrained network is likely not to give the correct answer. **Loss function** measures the degree of dissimilarity of obtained result to the target value, and it is the loss function that we want to minimize during training. To calculate the loss we make a prediction using the inputs of our given data sample and compare it against the true data label value. + +Common loss functions include `Mean Square Error `_ (for regression tasks), + `Negative Log Likelihood `_, + and `CrossEntropyLoss `_ (for classification tasks). + +In our example, we will use the built-in Cross Entropy Loss function: + +.. code-block:: Python + # Initialize the loss function + loss_function = nn.CrossEntropyLoss() + +Optimizer +--------- -# Initilize model, optimizer and example cost function -model = NeuralNework() # From Previous Model Section -optimizer = optim.SGD(model.parameters(), lr=learning_rate) # optimizer -cost_function = nn.CrossEntropyLoss() +Optimization is the process of adjusting model paramters on each training step. **Optimization algorithm** defines how this process is performed. The standard method for optimization is called Stochastic Gradient Descent. To learn more check out this awesome video by `3blue1brown `_. -# For loop to iterate over epoch -for epoch in range(num_epochs): - # Train loop over batches +All optimization logic is encapsulated in ``optimizer`` object. In our case, we will instantiate the stochastic gradient descent optimizer: + +.. code-block:: Python + optimizer = optim.SGD(model.parameters(), lr=learning_rate) + +In addition to SGD there are many different optimizers and variations of this method in PyTorch such +as ADAM and RMSProp, that work better for different kinds of models. They are outside the scope +of this quickstart, but you can check out the full list of optimizers `here `_. + +Inside the training loop, optimization happens in three steps: + + * Call ``optimizer.zero_grad()` function to zero the gradients. As you have seen in the previous section on automatic differentiation, gradients by default add up, so we need to explicitly zero them on each step. + * Calculate the loss using loss function. This builds a computation graph, which PyTorch uses to automatically update parameters with respect to our model's loss during training. This is done with one call to ``loss.backwards()``. + * Once we have our gradients, we call ``optimizer.step()`` to propgate the gradients from the backwards command to update all the parameters in our model. + +.. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png + :alt: tensor graph + + +Putting it all together +----------------------- + +Below is the complete code for the optimization loop. If you want a complete runnable example of training the model, refer to the `main quickstart page `_. The code below is commented to explain what goes on, but essentially it is put together from concepts that we have described above. + +.. code-block:: Python +for epoch in range(num_epochs): # Do training for each epoch + + # Training loop over all data in minibatches for train_batch, (train_inputs, train_labels) in enumerate(train_dataloader): model.train() # Set model to train mode - train_inputs, train_labels = train_inputs.to( - device), train_labels.to(device) - optimizer.zero_grad() # zero out gradient - pred = model(train_inputs) # make a prediction on this batch! - loss = cost_function(pred, train_labels) # how bad is it? - loss.backward() # compute gradients + # we need to move the data to the devices used for training + train_inputs, train_labels = + train_inputs.to(device), train_labels.to(device) + optimizer.zero_grad() # zero out gradients + pred = model(train_inputs) # make a prediction on the current batch + loss = cost_function(pred, train_labels) # compute loss function + loss.backward() # compute gradients of loss function optimizer.step() # update parameters - # validation loop - model.eval() # Set model to evaluate mode and start validation loop - for val_batch, (val_inputs, val_labels) in enumerate(val_dataloader): - val_inputs, val_labels = val_inputs.to( - device), val_labels.to(device) - pred = model(val_inputs) - test_loss += cost_function(pred, val_labels).item() - correct += (pred.argmax(1) == val_labels.argmax(1) - ).type(torch.float).sum().item() - val_loss /= len(val_dataloader.dataset) - correct /= len(val_dataloader.dataset) - print('\nValidation Error:') - print('acc: {:>0.1f}%, avg loss: {:>8f}'.format(100*correct, val_loss)) - # Make any additonal hyperparameter modifications here - - # Test loop + # Test loop: go over test dataset for test_batch, (test_inputs, test_labels) in enumerate(test_dataloader): - test_inputs, test_labels = test_inputs.to( - device), test_labels.to(device) - pred = model(test_inputs) - test_loss += cost_function(pred, test_labels).item() - correct += (pred.argmax(1) == test_labels.argmax(1) - ).type(torch.float).sum().item() + # move data to the device we use for computations + test_inputs, test_labels = + test_inputs.to(device), test_labels.to(device) + pred = model(test_inputs) # evaluate model on test minibatch + test_loss += cost_function(pred, test_labels).item() # compute loss + # compute the metrics for classification: + # how many classes were guessed correctly + correct += + (pred.argmax(1) == test_labels.argmax(1)) + .type(torch.float).sum().item() + test_loss /= len(test_dataloader.dataset) correct /= len(test_dataloader.dataset) - print('\nTest Error:') + print('\nEpoch {} test Error:'.format(epoch)) print('acc: {:>0.1f}%, avg loss: {:>8f}'.format(100*correct, test_loss)) -###################################################### -# To understand this code need to understand a how to handle 4 core deep learning concepts in PyTorch: -# -# 1. Hyperparameters (learning rates, batch sizes, epochs etc) -# 2. Optimization Loops -# 3. Loss -# 4. Optimizers -# -# Let's dissect these core concepts one by one by the time we end every line the code above will make sense. -# -# Hyperparameters -# ----------------- - - -###################################################### -# Hyperparameters are adjustable parameters that let you control the model optimization process. For example, with neural networks, you can configure: -# -# - **Number of Epochs**- the number times iterate over the dataset to update model parameters -# - **Batch Size** - the number of samples in the dataset to evaluate before you update model parameters -# - **Learning Rate** - how much to update models parameters at each batch/epoch. Set this value too large and your model won't learn optimally if you set it too small and it will learn really slowly. - -learning_rate = 1e-3 -batch_size = 64 -epochs = 5 - -###################################################### -# Optimizaton Loops -# ----------------- -# -# Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. -# -# Each iteration of the optimiziation loop is called an Epoch. Each epoch is comprized of three main subloops in PyTorch. -# - -############################################################ -# .. figure:: /_static/img/quickstart/optimizationloops.png -# :alt: -# - -############################################################# -# 1. **The Train Loop** - Core loop iterates over all the epochs -# 2. **The Validation Loop** - Validate loss after each weight parameter update and can be used to gauge hyper parameter performance and update them for the next batch. -# 3. **The Test Loop** - is used to evaluate our models performance after each epoch on traditional metrics to show how much our model is generalizing from the train and validation dataset to the test dataset it's never seen before. -# - -for epoch in range(num_epochs): # Optimization Loop - # Train loop over batches - model.train() # set model to train - # Model Update Code - model.eval() # After exiting batch loop set model to eval to speed up evaluation and not track gradients (this is explained below) - # Validation Loop - # - Validation metric logging and hyperparameter update happens here - # After exiting train loop set model to eval to speed up evaluation and not track gradients (this is explained below) - # Test Loop - # - Test preformance happens here - -###################################################### -# Loss and Cost Function -# ---------------------- -# -# The loss is the value used to update our parameters. To calculate the loss we make a prediction using the inputs of our given data sample and compare it with a cost function against the true data label value. -# - -preds = model(inputs) -loss = cost_function(preds, labels) - -###################################################### -# Common loss functions include `Mean Square Error `_, -# `Negative Log Likelihood `_, -# and `CrossEntropyLoss `_. -# Here is an example built in Cross Entropy Loss cost function call from the PyTorch nn module. -# - -cost_function = nn.CrossEntropyLoss() -loss = cost_function(model_prediction, true_value) - -###################################################### -# In addition to the included PyTorch cost functions you can create your own custom cost functions as long as they are differentiable. -# -# See this example custom Cross Entropy Loss implementation from the `Stanford CS230 `_ course below. -# - - -def myCrossEntropyLoss(outputs, labels): - batch_size = outputs.size()[0] # batch_size - # compute the log of softmax values - outputs = F.log_softmax(outputs, dim=1) - # pick the values corresponding to the labels - outputs = outputs[range(batch_size), labels] - return -torch.sum(outputs)/num_examples - -###################################################### -# It can be called just like the out of the box implementation above. -# - - -loss = myCrossEntropyLoss(model_prediction, true_value) - -###################################################### -# A more in depth explanation of PyTorch cost functions is outside the scope of the blitz but you can learn more -# about the different common cost functions for deep learning in the PyTorch `documentation `_. -# -# Optimizer -# --------- -# Using the loss, we can then optimize our models parameters. By default, each tensor maintains -# a graph of every operation applied on it unless otherwise specified using the torch.no_grad() command. - -############################################################ -# .. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png -# :alt: tensor graph -# -# PyTorch uses this graph to automatically update parameters with respect to our model's loss during training. This is done with one -# line ``loss.backwards()``. Once we have our gradients the optimizer is used to propgate the gradients from the backwards command -# to update all the parameters in our model. - -optimizer.zero_grad() # make sure previous gradients are cleared -loss.backward() # calculates gradients with respect to loss -optimizer.step() - -###################################################### -# The standard method for optimization is called Stochastic Gradient Descent, to learn more check out this awesome -# video by `3blue1brown `_. -# -# An Optimizer can be initalized with the Pytorch optim module, as an example lets initialize an SGD optimizer. -# The PyTorch SGD optimizer takes our model and our learning rate hyperparameter as input. - -optimizer = optim.SGD(model.parameters(), lr=learning_rate) - -###################################################### -# In addition to SGD there are many different optimizers and variations of this method in PyTorch such -# as ADAM and RMSProp, that work better for different kinds of models. They are outside the scope -# of this Blitz, but can check out the full list of optimizers `here `_. -# -# With this we have all we need to know to train, validate and test PyTorch deep learning models. - -################################################################## -# Next: Learn more about `Automatic Differentiation with AutoGrad `_. -# +Creating Custom Cost Functions +------------------------------ + +In addition to the included PyTorch cost functions you can create your own custom cost functions as long as they are differentiable. Here is an example of custom Cross Entropy Loss implementation from the `Stanford CS230 `_ course: + +.. code-block:: Python + def myCrossEntropyLoss(outputs, labels): + batch_size = outputs.size()[0] + # compute the log of softmax values + outputs = F.log_softmax(outputs, dim=1) + # pick the values corresponding to the labels + outputs = outputs[range(batch_size), labels] + return -torch.sum(outputs)/num_examples + +It can be called just like the out of the box implementation above. + +.. code-block::Python + loss = myCrossEntropyLoss(model_prediction, true_value) + +A more in depth explanation of PyTorch cost functions is outside the scope of the blitz but you can learn more +about the different common cost functions for deep learning in the PyTorch `documentation `_. + +Using Train/Validation/Test Split to Optimize Hyperparameters +------------------------------------------------------------- + +In our example, we have split the data between train and test datasets. However, as we mentioned above, different hyperparameters can yield different model performance. Thus, it makes sense to use a part of the dataset for **hyperparameter optimization**. In this case, we split the dataset into three parts: + + * Training data + * Validation data, which is used inside optimization loop to determine the accuracy of the current model and the optimal number of epochs. After a certain number of epochs, validation accuracy typically starts to decrease, which means that we have reached optimal performance for given hyperparameters. + * Test data, which is used to measure the performance of the model for given hyperparameters. It is important that test data are independent from validation data, i.e. the same dataset cannot be used for both validation and test purposes. + +We will not consider hyperparameter optimization further in this quickstart. + +Next: Learn how to `save our trained model `_. From 49134b5e197554619d2c6af0420ec122440fd3dd Mon Sep 17 00:00:00 2001 From: Cassieview Date: Sun, 17 Jan 2021 01:06:14 +0000 Subject: [PATCH 043/120] Formatting fixes to optimization tutorial --- .../quickstart/optimization_tutorial.py | 32 +++++++++++-------- 1 file changed, 19 insertions(+), 13 deletions(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index cc9447bf699..4a750a13c37 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -27,13 +27,15 @@ - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training. .. code-block:: Python + learning_rate = 1e-3 batch_size = 64 epochs = 5 We also need to create the model class instance (defined in the previous section): -..code-block:: Python - # Initilize model + +.. code-block:: Python + model = NeuralNework() Optimizaton Loop @@ -50,7 +52,8 @@ Here is a high-level view of optimization loop: .. code-block:: Python -for epoch in range(num_epochs): # Iterate over all epochs + + for epoch in range(num_epochs): # Iterate over all epochs # Training loop: for train_features, train_labels in train_dataloader: # Go over all minibatches @@ -74,13 +77,12 @@ When presented with some training data, our untrained network is likely not to give the correct answer. **Loss function** measures the degree of dissimilarity of obtained result to the target value, and it is the loss function that we want to minimize during training. To calculate the loss we make a prediction using the inputs of our given data sample and compare it against the true data label value. -Common loss functions include `Mean Square Error `_ (for regression tasks), - `Negative Log Likelihood `_, - and `CrossEntropyLoss `_ (for classification tasks). +Common loss functions include `Mean Square Error `_ (for regression tasks), `Negative Log Likelihood `_, and `CrossEntropyLoss `_ (for classification tasks). In our example, we will use the built-in Cross Entropy Loss function: .. code-block:: Python + # Initialize the loss function loss_function = nn.CrossEntropyLoss() @@ -92,6 +94,7 @@ All optimization logic is encapsulated in ``optimizer`` object. In our case, we will instantiate the stochastic gradient descent optimizer: .. code-block:: Python + optimizer = optim.SGD(model.parameters(), lr=learning_rate) In addition to SGD there are many different optimizers and variations of this method in PyTorch such @@ -100,13 +103,12 @@ Inside the training loop, optimization happens in three steps: - * Call ``optimizer.zero_grad()` function to zero the gradients. As you have seen in the previous section on automatic differentiation, gradients by default add up, so we need to explicitly zero them on each step. + * Call ``optimizer.zero_grad()`` function to zero the gradients. As you have seen in the previous section on automatic differentiation, gradients by default add up, so we need to explicitly zero them on each step. * Calculate the loss using loss function. This builds a computation graph, which PyTorch uses to automatically update parameters with respect to our model's loss during training. This is done with one call to ``loss.backwards()``. - * Once we have our gradients, we call ``optimizer.step()`` to propgate the gradients from the backwards command to update all the parameters in our model. - -.. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png - :alt: tensor graph + * Once we have our gradients, we call ``optimizer.step()`` to propagate the gradients from the backwards command to update all the parameters in our model. +#.. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png +# :alt: tensor graph Putting it all together ----------------------- @@ -114,7 +116,8 @@ Below is the complete code for the optimization loop. If you want a complete runnable example of training the model, refer to the `main quickstart page `_. The code below is commented to explain what goes on, but essentially it is put together from concepts that we have described above. .. code-block:: Python -for epoch in range(num_epochs): # Do training for each epoch + + for epoch in range(num_epochs): # Do training for each epoch # Training loop over all data in minibatches for train_batch, (train_inputs, train_labels) in enumerate(train_dataloader): @@ -143,7 +146,7 @@ test_loss /= len(test_dataloader.dataset) correct /= len(test_dataloader.dataset) - print('\nEpoch {} test Error:'.format(epoch)) + print('Epoch {} test Error:'.format(epoch)) print('acc: {:>0.1f}%, avg loss: {:>8f}'.format(100*correct, test_loss)) Creating Custom Cost Functions @@ -152,6 +155,7 @@ In addition to the included PyTorch cost functions you can create your own custom cost functions as long as they are differentiable. Here is an example of custom Cross Entropy Loss implementation from the `Stanford CS230 `_ course: .. code-block:: Python + def myCrossEntropyLoss(outputs, labels): batch_size = outputs.size()[0] # compute the log of softmax values @@ -163,6 +167,7 @@ def myCrossEntropyLoss(outputs, labels): It can be called just like the out of the box implementation above. .. code-block::Python + loss = myCrossEntropyLoss(model_prediction, true_value) A more in depth explanation of PyTorch cost functions is outside the scope of the blitz but you can learn more @@ -181,3 +186,4 @@ def myCrossEntropyLoss(outputs, labels): Next: Learn how to `save our trained model `_. +""" \ No newline at end of file From 39b1c3ce2ece628f12d944e1460d5fe1637a6d24 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Sun, 17 Jan 2021 17:54:10 +0000 Subject: [PATCH 044/120] quickstart home page updates --- .../quickstart/quickstart_tutorial.py | 41 ++++++++++++------- 1 file changed, 27 insertions(+), 14 deletions(-) diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 13d16414384..b5f81ae5b5e 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -51,7 +51,9 @@ # The `torchvision.datasets` ``DataSet`` object includes a ``transforms`` mechanism to # modify data in-place. Below is an example of how to load that data from the PyTorch open datasets and transform the data to a normalized tensor. # This example is using the `torchvision.datasets` which is a subclass from the primitive `torch.utils.data.Dataset`. Note that the primitive dataset doesnt have the built in transforms param like the built in dataset in `torchvision.datasets.` -# For more details on datasets and dataloaders check out `DataSets & DataLoaders `_. +# For more details on the concepts introduced here check out `Tensors `_, +# `DataSets & DataLoaders `_, +# and `Transforms `_. # import torch @@ -64,6 +66,7 @@ classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] +# Download training data from open datasets. training_data = datasets.FashionMNIST('data', train=True, download=True, transform=transforms.Compose([transforms.ToTensor()]), target_transform=transforms.Compose([ @@ -71,15 +74,18 @@ ]) ) +# Download test data from open datasets. test_data = datasets.FashionMNIST('data', train=False, download=True, transform=transforms.Compose([transforms.ToTensor()]), target_transform=transforms.Compose([ transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ]) + ) batch_size = 64 +# Create data loaders. train_dataloader = DataLoader(training_data, batch_size=batch_size, num_workers=0, pin_memory=True) test_dataloader = DataLoader(test_data, batch_size=batch_size, num_workers=0, pin_memory=True) @@ -94,6 +100,7 @@ # flexibility, because we can construct layers of any complexity, including the ones with shared weights. # For more details checkout `building the model `_. +# Get cpu or gpu device for training. device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) @@ -117,22 +124,26 @@ def forward(self, x): print(model) ###################################################################### -# Optimizing Parameters +# Optimizing Parameters and Training # --------------------- # # Optimizing model parameters requires a loss function, optimizer, -# and the optimization loop. Read more about the `Optimization Loop`_. +# and the optimization loop. +# Training a model is essentially an optimization process similar to the one we described in the +# `Autograd `_ section. We run the optimization process on the whole dataset +# several times, and each run is refered to as an **epoch**. During each run, we present data +# in **minibatches**, and for each minibatch compute gradients and correct parameters of the model +# according to back propagation algorithm. Read more about the `Optimization Loop `_. # -# cost function used to determine best parameters +# Cost function used to determine best parameters. cost = torch.nn.BCELoss() -# used to create optimal parameters +# This is used to create optimal parameters. learning_rate = 1e-3 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) -# Create the training function - +# Create the training function. def train(dataloader, model, loss, optimizer): size = len(dataloader.dataset) for batch, (X, Y) in enumerate(dataloader): @@ -168,12 +179,8 @@ def test(dataloader, model): print(f'\nTest Error:\nacc: {(100*correct):>0.1f}%, avg loss: {test_loss:>8f}\n') -###################################################################### -# Training Models -# ------------- -# -# Call the train and test function in a training loop with the number of epochs indicated -# + +# Call the train and test function in a training loop with the number of epochs indicated. epochs = 5 @@ -222,7 +229,13 @@ def test(dataloader, model): predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] print(f'Predicted: "{predicted}", Actual: "{actual}"') + +############################################################# +# +# Looking for more resources? Check out the other tutorials on the `tutorials home page `_. +# + ################################################################## # -# *Authors: Seth Juarez, Ari Bornstein, Cassie Breviu, Dmitry Soshnikov* +# *Authors: Seth Juarez, Cassie Breviu, Dmitry Soshnikov, Ari Bornstein,* From fc8beae0bb62b8dc00912bab9428897665890326 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Sun, 17 Jan 2021 19:23:09 +0000 Subject: [PATCH 045/120] tensor page edits --- beginner_source/quickstart/tensor_tutorial.py | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index f2710416d25..a2523a0131f 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -14,11 +14,11 @@ **Tensor** is the basic computational unit in PyTorch. It is very similar to **NumPy array**, and supports similar operations. However, there are two very important features of Torch tensors that make them -especially useful for training large-scale neural networks: +especially useful for training large-scale neural networks. First, tensor operations can be performed on +GPUs or other specialized hardware to accelerate computing. Second, tensor operations support +automatic differentiation using `pytorch.autograd engine `__. - * Tensor operations can be performed on GPUs or other specialized hardware to accelerate computing - * Tensor operations support automatic differentiation using `pytorch.autograd engine `__ -Conversion between Torch tensors and NumPy arrays can be done easily: +Lets look at how we can easily convert between Torch tensors and NumPy arrays: """ import torch @@ -43,8 +43,8 @@ # ~~~~~~~~~~~~~~~~ # # The fastest way to create a tensor is to define an *uninitialized* -# tensor - the values of this tensor are not set, and depend on the -# whatever data was there in memory: +# tensor. This means the values of this tensor are not set and depend on the +# data that was there in memory: # x = torch.empty(3, 6) @@ -52,7 +52,7 @@ ###################################################################### # In practice, we often want to create tensors initialized to some values, -# such as zeros, ones or random values. Note that you can also specify the +# such as zeros, ones or random values. You can also specify the # type of elements using ``dtype`` parameter, and chosing one of ``torch`` # types: # @@ -62,7 +62,7 @@ z = torch.ones(3, 5, dtype=torch.double) ###################################################################### -# You can also create random tensors with values sampled from different +# You can create random tensors with values sampled from different # distributions, as described `in the # documentation `__. # @@ -74,7 +74,7 @@ ###################################################################### -# You can also create new tensors with the same properties or size as +# You can create new tensors with the same properties or size as # existing tensors: # @@ -208,7 +208,7 @@ ###################################################################### -# Tensors support all slicing operations that exist in NymPy: +# Tensors support all slicing operations that exist in NumPy: # print(x.size()) # original size of x is 3x5 From 824d71f9effb01cf6714f76e0d47e6a47837a736 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Sun, 17 Jan 2021 20:09:49 +0000 Subject: [PATCH 046/120] cassie checkin: data page review --- .../quickstart/dataquickstart_tutorial.py | 37 +++++++++---------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 976949518ea..b0a8f4ab676 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -29,15 +29,14 @@ ############################################################ # Different data types require different python libraries to load and process such as `openCV `_ and `PIL `_ for images, `NLTK `_ and `spaCy `_ for text and `Librosa `_ for audio. # -# If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking managing collections of data. +# If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking and managing collections of data. # # A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. They are subclasses of `torch.utils.data.Dataset` that have parameters and functions specific to the type of data and the particular dataset. The actual data samples can be downloaded from the internet. These are useful for benchmarking and testing your models before training on your own custom datasets. # -# You can find some of them below. -# -# - `Image Datasets `_ -# - `Text Datasets `_ -# - `Audio Datasets `_ +# You can find some of these datasets +# here: `Image Datasets `_, +# `Text Datasets `_, and +# `Audio Datasets `_ # ################################################################# @@ -51,9 +50,9 @@ # Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. Read more `here `_. # # To load the FashionMNIST Dataset we need to provide the following three parameters: -# - root is the path where the train/test data is stored. -# - train includes the training dataset. -# - setting download to true downloads the data from the internet if it's not available at root. +# - ``root`` is the path where the train/test data is stored. +# - ``train`` includes the training dataset. +# - ``download=True`` downloads the data from the internet if it's not available at root. import torch @@ -85,7 +84,7 @@ # Creating a Custom Dataset # ----------------- # -# To work with your own data, we need to implement a custom class that inherits from ``Dataset```. Let's look at a custom image dataset implementation. In this example, we have a number of images stored in a directory, and their labels stored separately in CSV annotation file. +# To work with your own data, we need to implement a custom class that inherits from ``Dataset``. Let's look at a custom image dataset implementation. In this example, we have a number of images stored in a directory, and their labels stored separately in CSV annotation file. Below is the full example and we will break down whats happening in each function. # import os @@ -120,10 +119,10 @@ def __getitem__(self, idx): return sample ################################################################# -# Imports +# Import the packages # ------- # -# Import `os` for file handling, torch for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and Dataset to implement the Dataset interface. +# Import ``os`` for file handling, ``torch`` for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and ``Dataset`` to implement the Dataset interface. # # Example: # @@ -139,7 +138,7 @@ def __getitem__(self, idx): # Init # ----------------- # -# The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and the keep track of directory of our image file. Note that different types of data can take different init inputs. You are not limited to just an annotations file, directory path and transforms, but for images this is a standard practice. +# The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and then keep track of the directory of our image file. Note that different types of data can take different init inputs. You are not limited to just an annotations file, directory path and transforms, but for images this is a standard practice. # A sample csv annotations file may look as follows: :: # # tshirt1.jpg, 0 @@ -159,7 +158,7 @@ def __init__(self, annotations_file, img_dir, transform=None): # __len__ # ----------------- # -# The __len__ function is very simple, we just need to return the number of samples in our dataset. +# The __len__ function is needed to return the number of samples in our dataset. # # Example: @@ -170,9 +169,9 @@ def __len__(self): # __getitem__ # ----------------- # -# The __getitem__ function is the most important function in the Datasets interface. It takes a tensor or an index as input and returns a loaded sample from you dataset at the given indices. +# The __getitem__ function is the most important function in the Datasets interface. It takes a tensor or an index as input and returns a loaded sample from your dataset at the given indices. # -# If provided a tensor as an index, we convert the tensor to a list first. We then load the file at the given index from our image directory, as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a Transform on and return. To learn more about Transforms see the next section of the Blitz. +# If provided a tensor as an index, we convert the tensor to a list first. We then load the file at the given index from our image directory, as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a transform on and return. Transforms will be discussed in more detail in the next section: `Transforms `_ # # Example: # @@ -193,12 +192,12 @@ def __getitem__(self, idx): # Preparing your data for training with DataLoaders # ------------------------------------------------- # -# Now we have a organized mechansim for managing data which is great, but there is still a lot of manual work we would have to do train a model with our Dataset. +# Now we have an organized mechanism for managing data which is great, but there is still a lot of manual work we would have to do to train a model with our Dataset. # # For example we would have to manually maintain the code for: # # * Batching -# * Suffling +# * Shuffling # * Parallel batch distribution # # The PyTorch Dataloader ``torch.utils.data.DataLoader`` is an iterator that handles all of this complexity for us, enabling us to load a dataset and focus on training our model. @@ -208,6 +207,6 @@ def __getitem__(self, idx): ################################################################# # With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. # -# Next: Learn more about how to `transform that data for training `_. +# Next learn more about how to `transform data for training `_. # From c89a337a6d4fb0353374e316ba496e693b922ad6 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Mon, 18 Jan 2021 17:10:09 +0000 Subject: [PATCH 047/120] Update save-load-run quickstart tutorial --- .../quickstart/saveloadrun_tutorial.py | 126 +++++++++++------- 1 file changed, 80 insertions(+), 46 deletions(-) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 272b69e8578..0f1512bd3dc 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -16,63 +16,97 @@ """ import torch -import torch.nn as nn import torch.onnx as onnx ####################################################################### -# Save the Model -# -------------- +# Pre-trained Models +# ------------------ +# +# Many tasks, such as object classification in computer vision, rely on some pre-trained models. While you can find some pre-trained models for common tasks online, PyTorch already includes the most common model architectures. For example, to initialize a VGG-16 model for image classification, we can use the following code: + +import torchvision.models as models +model = models.vgg16(pretrained=True) + +############################# +# To use this network on the input batch of images ``imgs``, we can just call it as an ordinary function: +# +# .. code-block:: Python +# +# res = model(imgs) +# +# Let us see how a picture of a cat can be classified using this VGG-16 model. Once we load a picture from the internet, we need to apply a series of trainsformations to it, to turn it into a tensor of the appropriate size: +# * Resize image to 224x224 pixels +# * Convert it to tensor +# * Apply normalization with a given mean and standard deviation +# Also, we need to turn a single tensor into a batch by adding one more dimension with ``unsqueeze()`` call. +# After doing the inference, we obtain a tensor of probabilities for each of the classes, and we get the index of most probable class by calling ``.argmax()``. + +import matplotlib.pyplot as plt +from PIL import Image +import requests +import torchvision.transforms as T + +url = "https://upload.wikimedia.org/wikipedia/commons/6/66/An_up-close_picture_of_a_curious_male_domestic_shorthair_tabby_cat.jpg" +im = Image.open(requests.get(url, stream=True).raw) +plt.imshow(im) + +transform = T.Compose([T.Resize(224), T.ToTensor(), + T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])]) +input_image = transform(im).unsqueeze(0) +with torch.no_grad(): + res = model(input_image).argmax().item() + print(res) + +###################### +# The result obtained is a number of imagenet predicted class, in this case, *tiger cat*. +# .. note:: When running model inference, it is recommended to wrap the code into ``torch.no_grad()``, because `automatic differentiation `_ is unnecessary. + +####################################################################### +# Saving and Loading Model Weights +# -------------------------------- # PyTorch stores the learned parameters in the model's internal -# state dictionary. These are persisted via the `torch.save` +# state dictionary, called ``state_dict``. These can be persisted via the ``torch.save`` # method: -# saving PyTorch Model Dictionary -torch.save(model.state_dict(), 'model.pth') +torch.save(model.state_dict(), 'model_weights.pth') + +########################## +# To load model weights, you need to create a model class first, and then load the parameters using ``load_state_dict()`` method. + +model = models.vgg16() # we do not specify pretrained=True, i.e. do not load default weights +model.load_state_dict(torch.load('model_weights.pth')) +model.eval() + +########################### +# .. note:: Before inference, do not forget to call ``model.eval()`` method to set dropout and batch normalization layers to evaluation mode. Failing to do this will yield inconsistent inference results. ####################################################################### -# PyTorch also has native ONNX export support. Given the dynamic nature of the -# PyTorch execution graph however, the export process must -# traverse the execution graph to produce a persisted onnx model. As such, a -# test variable of the appropriate size should be passed in to the -# export routine: +# Saving and Loading Models with Shapes +# ------------------------------------- +# When loading model weights, we needed to instantiate the model class first, because the class defines the structure of a network. We might want to save the structure of this class together with the model, in which case we can pass ``model`` (and not ``model.state_dict()``) to the saving function: -# create test variable to traverse graph -x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255 -onnx.export(model, x, 'model.onnx') +torch.save(model, 'model.pth') +######################## +# We can then load the model like this: + +model = torch.load('model.pth') + +######################## +# .. note:: This approach uses Python `pickle `_ module when serializing the model, this it relies on the actual class definition to be available when loading the model. ####################################################################### -# Load the Model -# -------------- -# Loading a persisted PyTorch model consists of two primary steps: -# -# 1. Recreating the appropriate model shape, and -# 2. Rehydrating the parameters into the newly created model's state dictionary -# -# These two steps are illustrated here: - -# recreate model -loaded_model = NeuralNetwork() -# hydrate state dictionary -loaded_model.load_state_dict(torch.load('model.pth')) - - -###################################################################### -# Use the Model -# ------------- -# Once the model is loaded it can be used for both training as well -# as inference. The model's ``eval()`` method is called in this case -# to indicate the model will be used for inference. This method -# only affects internal modules like Dropout and BatchNorm which -# are not necessary for inference. Using ``torch.no_grad()`` turns off -# `automatic differentiation `_ since it -# is also unnecessary: - -loaded_model.eval() -x, y = test_data[0][0], test_data[0][1] -with torch.no_grad(): - pred = loaded_model(x) - predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] - print(f'Predicted: "{predicted}", Actual: "{actual}"') +# Exporting Model to ONNX +# ----------------------- +# PyTorch also has native ONNX export support. Given the dynamic nature of the +# PyTorch execution graph, however, the export process must +# traverse the execution graph to produce a persisted ONNX model. For this reason, a +# test variable of the appropriate size should be passed in to the +# export routine: +onnx.export(model, input_image, 'model.onnx') +########################### +# There are a lot of things you can do with ONNX model, including running inference on different platforms and in different programming languages. For more details, we recommend visiting `ONNX tutorial `_. +# +# **You have reached the end of the detailed PyTorch beginner tutorial.** You can now `return to the first page `_ and go over the sample code again - we hope you have gained much better understanding of all the details. From 15fb7bca019c29ceca193ece6c3e4de877ace3c9 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Mon, 18 Jan 2021 20:58:04 +0000 Subject: [PATCH 048/120] checkin by cassie: review/fix transforms page --- .gitignore | 15 ++++++++++++ .../quickstart/quickstart_tutorial.py | 2 +- .../quickstart/transforms_tutorial.py | 23 ++++++++----------- 3 files changed, 26 insertions(+), 14 deletions(-) diff --git a/.gitignore b/.gitignore index 2834a874f07..61b12b300f5 100644 --- a/.gitignore +++ b/.gitignore @@ -72,6 +72,9 @@ coverage.xml *,cover .hypothesis/ +# wav files +*.wav + # Translations *.mo *.pot @@ -124,3 +127,15 @@ cleanup.sh # vscode things .vscode/ +beginner_source/quickstart/data/FashionMNIST/raw/* +beginner_source/*.gz +quickstartbuild.sh +beginner_source/model.onnx +beginner_source/model.pth +beginner_source/waves_yesno/ +beginner_source/waves_yesno/README +_config/quickstart/custom_directives.py +_config/quickstart/conf.py +beginner_source/quickstart/model.pth +beginner_source/quickstart/model.onnx +beginner_source/quickstart/model_weights.pth diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index b5f81ae5b5e..5e56f530cd1 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -237,5 +237,5 @@ def test(dataloader, model): ################################################################## # -# *Authors: Seth Juarez, Cassie Breviu, Dmitry Soshnikov, Ari Bornstein,* +# *Authors: Seth Juarez, Cassie Breviu, Dmitry Soshnikov, Ari Bornstein* diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 3a507c66c1f..f5d0bffb3c9 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -11,7 +11,7 @@ Transforms =================== -In most of the practical tasks, data does not come in its final form that is required for training machine learning algorithm. We need to do different data manipulations or **transformations** to prepare it for training. There are many types of transformations, and it depends on the type of model you are building and the state of your data as to which ones you should use. +In most cases data does not come in its final processed form that is required for training machine learning algorithms. We need to do different data manipulations or **transformations** to prepare it for training. There are many types of transformations, and it depends on the type of model you are building and the state of your data as to which ones you should use. In the example below, let's take FashionMNIST image dataset, which is available from ``torchvision.datasets`` using the following function: """ @@ -25,7 +25,7 @@ download=False) # should the data be downloaded from the Internet ################################ -#To prepare data for training the neural network, we need to take our image (also called features, x), turn it into a tensor and normalize it. We also need to convert labels (y) into one-hot encoding. +#To prepare data for training we need to take our image (also called features, x), turn it into a tensor and normalize it. Then we need to convert labels (y) into one-hot encoding. # #We will break down each of these steps below. @@ -57,7 +57,7 @@ # Feature Transforms and Label Transforms # --------------------------------------- # -# Below is the code to load the FashionMNIST dataset and apply required transforms: +# Below is the code to load the FashionMNIST dataset and apply the transforms: training_data = datasets.FashionMNIST( 'data', @@ -70,23 +70,23 @@ ######################################## # Here we define two transformations: # -# * ``transform`` is the transformation we apply to features, in our case - to images. Because dataset contains images in PIL format, we need to convert them to tensors using ``ToTensor()`` transform. +# * ``transform`` is the transformation we apply to features, in our case - to images. The dataset contains images in PIL format so we need to convert them to tensors using the ``ToTensor()`` transform. # * ``target_transform`` defines a transformation that is applied to labels. In our case label is a class number from 0 to 9, and we need to convert it to one-hot encoding. ################################################# # ToTensor() # ------------------------------- # -# `torchvision.transforms.ToTensor `_ transform is required to prepare an image for training. It takes PIL image, converts it into a `tensor `_, and also normalizes our data by scaling the image pixel intensity values to be between 0 and 1. +# `torchvision.transforms.ToTensor `_ transform is required to prepare an image for training. It takes the PIL image, converts it into a `tensor `_, and normalizes our data by scaling the image pixel intensity values to be between 0 and 1. # -# .. note:: ToTensor only normalized image data that is in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling. +# .. note:: ToTensor only normalizes images that are in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling. # ############################################## # Lambda Transform # ------------------------------- # -# To turn class number into one-hot encoding, we use **lambda transform**, i.e. a transformation defined by an arbitrary function. This function takes y as an input and creates a zero tensor of size 10. Then it calls scatter `torch.Tensor.scatter_ class `_ to take a value 1 and store it into the correct position of the zero vector defined by the class number. +# We use a **lambda transform** to turn the class number into one-hot encoding. This function takes y as an input and creates a zero tensor of size 10. Then it calls scatter `torch.Tensor.scatter_ class `_ to take a value 1 and store it into the correct position of the zero vector defined by the class number. target_transform = transforms.Lambda(lambda y: torch.zeros( 10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)) @@ -106,14 +106,14 @@ # Using your own data # -------------------------------------- # -# Below is an example for processing image data using a dataset from a local directory. It assumes the we have ``train`` and ``val`` subdirectories with training and validation dataset accordingly, and we want to apply different sets of transforms for training and validation dataset: +# Below is an example for processing image data using a dataset from a local directory. It assumes that we have ``train`` and ``val`` subdirectories with training and validation dataset. In this example we want to apply different sets of transforms for training and validation dataset: # # * For training data, we want to perform some **data augmentation**, and do random croping/resizing of the original image. We also introduce random horizontal flips. # * For testing, we typically want to be consistent and always use the same images - thus we do not do any augmentation, just resizing. # # We also normalize values by subtracting the mean, which was computed along the whole dataset. # -# To be able to unify the code for train and validation datasets, we use a special trick and create a dictionary of transforms for each dataset: train and validation: +# To be able to unify the code for train and validation datasets, we use a special trick and create a dictionary of transforms for the train and validation dataset: # # .. code-block:: Python # @@ -144,7 +144,7 @@ # data_transforms[x]) # for x in ['train', 'val']} # -# In a similar manner, we define a dictionary of dataloaders, which can prepare our datasets for neural network training. They allow us to shuffle data and group them into batches of a specified size: +# Similarly we define a dictionary of dataloaders to prepare our datasets for training. They allow us to shuffle data and group them into batches of a specified size: # # .. code-block:: Python # @@ -159,7 +159,4 @@ # # class_names = image_datasets['train'].classes # - -################################################################## # Next learn how to `build the model `_ -# \ No newline at end of file From 044b0ebaf3c821f152e63bfc38a62cc10ab90f3f Mon Sep 17 00:00:00 2001 From: Cassieview Date: Tue, 19 Jan 2021 19:25:58 +0000 Subject: [PATCH 049/120] checkin by cassie: Fixed build model page --- .../quickstart/buildmodel_tutorial.py | 45 +++++++++++++------ 1 file changed, 31 insertions(+), 14 deletions(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 9a797744cbd..e5fb419c869 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -9,19 +9,21 @@ `Optimization `_ > `Save & Load Model `_ -Build the Neural Network +Build the Neural Network Model =================== """ ################################################################# -# Get Started Building the Model -# ----------------- # -# Having loaded and transformed the data, we can now build the neural network model. In many simple cases neural network consists of a number of layers, and `torch.nn `_ namespace provides predefined layers that can simplify our code. +# Now that we have loaded and transformed the data, we can build the neural network model. +# Neural network consists of a number of layers and PyTorch `torch.nn `_ namespace provides predefined layers +# that helps us build the model. # -# The most common way to define a neural network is to use a class inherited from `nn.Module `_ -# It provides great parameter management across all nested submodules, which gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. +# The most common way to define a neural network is to use a class inherited +# from `nn.Module `_. +# It provides great parameter management across all nested submodules, which gives us more +# flexibility, because we can construct layers of any complexity, including ones with shared weights. # # In the below example, for our FashionMNIST image dataset, we will create a dense multi-layer network. # Lets break down the steps to build this model below. @@ -44,7 +46,9 @@ ############################################# # Get Device for Training # ----------------------- -# We want to be able to train our model on both CPU and GPU, if it is available. It is common practice to define a variable ``device`` which will designate the device we will be training on. We check to see if `torch.cuda `_ +# We want to be able to train our model on both CPU and GPU, if it is available. It is common practice to +# define a variable ``device`` which will designate the device we will be training on. +# We check to see if `torch.cuda `_ # is available to use the GPU, else we will use the CPU. # # Example: @@ -89,7 +93,10 @@ def forward(self, x): # The Model Module Layers # ------------------------- # -# Lets break down each model layer in the FashionMNIST model. To illustrate it, we will take a sample minibatch of 100 images of size 28x28 and see what happens to it as we pass it through the network. The code in the sections below would essentially explain what happens inside the ``forward`` method of our ``NeuralNetwork`` class. +# Lets break down each model layer in the FashionMNIST model. To illustrate it, we +# will take a sample minibatch of 100 images of size 28x28 and see what happens to it as +# we pass it through the network. The code in the sections below would essentially explain +# what happens inside the ``forward`` method of our ``NeuralNetwork`` class. # input_image = torch.rand(100,28,28) @@ -104,13 +111,15 @@ def forward(self, x): # From the docs: # ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)`` # -# In our case, flatten keeps the minibatch dimension, but two image dimensions are reduced to one: +# In our case, flatten keeps the minibatch dimension, but two image dimensions are +# reduced to one: + flatten = nn.Flatten() flat_image = flatten(input_image) print(flat_image.size()) ############################################## -# `nn.Linear `_ to add a linear layer to the model. +# `nn.Linear `_ to add a linear layer # ------------------------------- # # Now that we have flattened our tensor dimension we will apply a linear layer. The linear layer is @@ -129,7 +138,10 @@ def forward(self, x): # Activation Functions # ------------------------- # -# In between layers of a neural network, we need to put non-linear activation functions, such as `nn.ReLU `_ (which is often used in between hidden layers) or `nn.Softmax `_, which turns output of the network into probabilities by rescaling them in such a way that all values are between 0 and 1, and all sum to one. +# In between layers of a neural network, we need to put non-linear activation functions, +# such as `nn.ReLU `_ (which is often +# used in between hidden layers) or `nn.Softmax `_, +# which turns the output of the network into probabilities by rescaling values between 0 and 1, and all sum to one. # layer2 = nn.Linear(512,512) @@ -144,13 +156,18 @@ def forward(self, x): # Parameter Tracking # ------------------------- # -# The main reason to put all code inside a class inherited from ``nn.Module`` is to utilize **parameter tracking**. Most of the layers inside a neural network, in our case all linear layers, have associtated weights and biases that need to be adjusted during training. ``nn.Module`` automatically tracks all fields defined inside the class, and makes all parameters accessible using ``parameters()`` or ``named_parameters()`` methods. Let's have a look at the first two parameters of our neural network that has been defined in the beginning of this section: +# The main reason to put all code inside a class inherited from ``nn.Module`` is to +# utilize **parameter tracking**. Most of the layers inside a neural network, +# in our case all linear layers, have associated weights and biases that need to +# be adjusted during training. ``nn.Module`` automatically tracks all fields defined +# inside the class, and makes all parameters accessible using ``parameters()`` +# or ``named_parameters()`` methods. Let's have a look at the first two parameters of +# our neural network that were defined in the beginning of this section: # print(list(model.named_parameters())[0:2]) ################################################ -# In the next section you will learn about how to train the model and the optimization loop for this example. # -# Next: Learn more about how the `optimzation loop works with this example `_. +# Next learn more about how the `optimization loop works with this example `_. # From a3757b7fd8c4da42fcaf6046a7924a8337ecdb34 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Tue, 19 Jan 2021 22:44:54 +0000 Subject: [PATCH 050/120] checkin by cassie: optimization page, add setup --- .../quickstart/autograd_tutorial.py | 2 +- .../quickstart/buildmodel_tutorial.py | 2 +- .../quickstart/optimization_tutorial.py | 33 +++++++++++++------ .../quickstart/quickstart_tutorial.py | 7 ++-- 4 files changed, 29 insertions(+), 15 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 6915c43384e..d1bc909c85d 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -285,6 +285,6 @@ def f(x): ###################################################################### -# Next: Learn more about `how to use automatic differentiation to train a neural network model `_. +# Next learn more about how the `optimization loop works with this example `_. # diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index e5fb419c869..da2233c40c7 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -169,5 +169,5 @@ def forward(self, x): ################################################ # -# Next learn more about how the `optimization loop works with this example `_. +# Next learn more about `how to use automatic differentiation to train a neural network model `_. # diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 4a750a13c37..88e17e13373 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -11,14 +11,21 @@ Optimizing Model Parameters =========================== -Now that we have a model and data it's time to train, validate and test our model by optimizating it's paramerters on our data! +Now that we have a model and data it's time to train, validate and test our model by optimizing it's parameters on +our data! -Training a model is essentially an optimization process similar to the one we described in the previous section on `Autograd `_. We run the optimization process on the whole dataset several times, and each run is refered to as an **epoch**. During each run, we present data in **minibatches**, and for each minibatch compute gradients and correct parameters of the model according to back propagation algorithm. +Training a model is essentially an optimization process similar to the one we described in the previous section +on `Autograd `_. We run the optimization process on the whole dataset several times, +and each run is referred to as an **epoch**. During each run, we present data in **minibatches**, and for each +minibatch compute gradients and correct parameters of the model according to back propagation algorithm. Hyperparameters ----------------- -Hyperparameters are adjustable parameters that let you control the model optimization process. Unlike model parameters that we will optimize during training, hyperparameters are configured for the whole training process. However, you may achieve different model performance with different hyperparameters, so you may want to try out different values for them to perform hyperparameter optimization. +Hyperparameters are adjustable parameters that let you control the model optimization process. +Unlike model parameters that we will optimize during training, hyperparameters are configured for the whole +training process. However, you may achieve different model performance with different hyperparameters, so you may + want to try out different values for them to perform hyperparameter optimization. In our case, we need to define the following hyperparameters: @@ -38,13 +45,14 @@ model = NeuralNework() -Optimizaton Loop +Optimization Loop ----------------- .. figure:: /_static/img/quickstart/optimizationloops.png :alt: -Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. Each iteration of the optimiziation loop is called an **epoch**. Each epoch is comprized of two main parts: +Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. Each +iteration of the optimization loop is called an **epoch**. Each epoch is comprized of two main parts: 1. **The Train Loop** - main loop that iterates over all dataset and performs training 2. **The Validation/Test Loop** - goes through the validation / test dataset to evaluate model performance on the test data. @@ -75,7 +83,10 @@ Loss Function ------------- -When presented with some training data, our untrained network is likely not to give the correct answer. **Loss function** measures the degree of dissimilarity of obtained result to the target value, and it is the loss function that we want to minimize during training. To calculate the loss we make a prediction using the inputs of our given data sample and compare it against the true data label value. +When presented with some training data, our untrained network is likely not to give the correct +answer. **Loss function** measures the degree of dissimilarity of obtained result to the target value, +and it is the loss function that we want to minimize during training. To calculate the loss we make a +prediction using the inputs of our given data sample and compare it against the true data label value. Common loss functions include `Mean Square Error `_ (for regression tasks), `Negative Log Likelihood `_, and `CrossEntropyLoss `_ (for classification tasks). @@ -89,7 +100,9 @@ Optimizer --------- -Optimization is the process of adjusting model paramters on each training step. **Optimization algorithm** defines how this process is performed. The standard method for optimization is called Stochastic Gradient Descent. To learn more check out this awesome video by `3blue1brown `_. +Optimization is the process of adjusting model paramters on each training step. **Optimization algorithm** defines +how this process is performed. The standard method for optimization is called Stochastic Gradient Descent. +To learn more check out this awesome video by `3blue1brown `_. All optimization logic is encapsulated in ``optimizer`` object. In our case, we will instantiate the stochastic gradient descent optimizer: @@ -107,8 +120,8 @@ * Calculate the loss using loss function. This builds a computation graph, which PyTorch uses to automatically update parameters with respect to our model's loss during training. This is done with one call to ``loss.backwards()``. * Once we have our gradients, we call ``optimizer.step()`` to propagate the gradients from the backwards command to update all the parameters in our model. -#.. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png -# :alt: tensor graph +.. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png + :alt: tensor graph Putting it all together ----------------------- @@ -184,6 +197,6 @@ def myCrossEntropyLoss(outputs, labels): We will not consider hyperparameter optimization further in this quickstart. -Next: Learn how to `save our trained model `_. +Next learn how to `save our trained model `_. """ \ No newline at end of file diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 5e56f530cd1..4da20d24c44 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -34,8 +34,9 @@ ------------------ The navigation above allows you to run the Jupyter Notebook on the cloud, download the Jupyter Notebook or download the python file to run locally. -If you want to run the code locally on your machine you will need some tools you may or may not have installed already. -Below are some good tool options for configuring local development: +If you want to run the code locally on your machine you will need some tools you +may or may not have installed already. +Below are some good tool options for configuring local development or for more detailed instructions check out `Get Started Locally `_ - `Visual Studio Code `_ : You can open run python code in Visual Studio Code or open a Jupyter Notebook in VS Code. @@ -195,7 +196,7 @@ def test(dataloader, model): # ------------- # # PyTorch has different ways you can save your model. One way is to serialize the internal model state to a file. Another would be to use the built-in `ONNX `_ support. -# Saving PyTorch Model Dictionary +# torch.save(model.state_dict(), 'model.pth') print('Saved PyTorch Model to model.pth') From 59a04d2f7f52c59980baf4a0982712c933ac1d8c Mon Sep 17 00:00:00 2001 From: Cassieview Date: Wed, 20 Jan 2021 20:07:31 +0000 Subject: [PATCH 051/120] autograd and optimzation page updates --- .../quickstart/autograd_tutorial.py | 18 +++++++++--------- .../quickstart/optimization_tutorial.py | 10 ++++------ 2 files changed, 13 insertions(+), 15 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index d1bc909c85d..4896861001a 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -60,7 +60,7 @@ # compute the function in the *forward* direction, and also how to compute # it's derivative during the *backward propagation* step. A reference to # the backward propagation function is stored in ``grad_fn`` property of a -# tensor. You can find more information of ``Function`` `in +# tensor. You can find more information of ``Function`` `in the # documentation `__. # @@ -89,7 +89,7 @@ # .. note:: # - We can only obtain the ``grad`` properties for the leaf # nodes of the computational graph, which have ``requires_grad`` property -# set to ``True``. For all other nodes in our graph gradients will not be +# set to ``True``. All other nodes in our graph gradients will not be # available. # - We can only perform gradient calculations using # ``backward`` once on a given graph, for performance reasons. If we need @@ -108,7 +108,7 @@ # trained the model and just want to apply it to some input data, i.e. we # only want to do *forward* computations through the network. We can stop # tracking computations by surrounding our computation code with -# ``with torch.no_grad()`` block: +# ``torch.no_grad()`` block: # z = torch.matmul(x, w)+b @@ -129,7 +129,7 @@ print(z_det.requires_grad) ###################################################################### -# There are several reasons you might want to disable gradient tracking: +# There are reasons you might want to disable gradient tracking: # - To mark some parameters in your neural network at **frozen parameters**. This is # a very common scenario for # `finetuning a pretrained network `__ @@ -186,14 +186,14 @@ def f(x): ###################################################################### # As you can see, we have obtained the values close to the optimal point -# :math:`(3,-2)`. `Training a neural network `_ is in fact a very similar +# :math:`(3,-2)`. Training a neural network is in fact a very similar # process, we will need to do a number of iterations to minimize the value -# of **loss function**. +# of the **loss function**. ###################################################################### # More on Computational Graphs # ---------------------------- -# Conceptually, autograd keeps a record of data (tensors) & all executed +# Conceptually, autograd keeps a record of data (tensors) and all executed # operations (along with the resulting new tensors) in a directed acyclic # graph (DAG) consisting of # `Function `__ @@ -203,14 +203,14 @@ def f(x): # # In a forward pass, autograd does two things simultaneously: # -# - run the requested operation to compute a resulting tensor, and +# - run the requested operation to compute a resulting tensor # - maintain the operation’s *gradient function* in the DAG. # # The backward pass kicks off when ``.backward()`` is called on the DAG # root. ``autograd`` then: # # - computes the gradients from each ``.grad_fn``, -# - accumulates them in the respective tensor’s ``.grad`` attribute, and +# - accumulates them in the respective tensor’s ``.grad`` attribute # - using the chain rule, propagates all the way to the leaf tensors. # # .. note:: diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 88e17e13373..2c46feb041f 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -12,9 +12,7 @@ =========================== Now that we have a model and data it's time to train, validate and test our model by optimizing it's parameters on -our data! - -Training a model is essentially an optimization process similar to the one we described in the previous section +our data. Training a model is essentially an optimization process similar to the one we described in the previous section on `Autograd `_. We run the optimization process on the whole dataset several times, and each run is referred to as an **epoch**. During each run, we present data in **minibatches**, and for each minibatch compute gradients and correct parameters of the model according to back propagation algorithm. @@ -25,7 +23,7 @@ Hyperparameters are adjustable parameters that let you control the model optimization process. Unlike model parameters that we will optimize during training, hyperparameters are configured for the whole training process. However, you may achieve different model performance with different hyperparameters, so you may - want to try out different values for them to perform hyperparameter optimization. +want to try out different values for them to perform hyperparameter optimization. In our case, we need to define the following hyperparameters: @@ -179,11 +177,11 @@ def myCrossEntropyLoss(outputs, labels): It can be called just like the out of the box implementation above. -.. code-block::Python +.. code-block:: Python loss = myCrossEntropyLoss(model_prediction, true_value) -A more in depth explanation of PyTorch cost functions is outside the scope of the blitz but you can learn more +A more in depth explanation of PyTorch cost functions is outside the scope of the quickstart but you can learn more about the different common cost functions for deep learning in the PyTorch `documentation `_. Using Train/Validation/Test Split to Optimize Hyperparameters From a96ec6046654c7aacf18d0b7ecde63ad59d07248 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Wed, 20 Jan 2021 20:58:03 +0000 Subject: [PATCH 052/120] checkin by cassie: save run load page review --- .../quickstart/quickstart_tutorial.py | 2 +- .../quickstart/saveloadrun_tutorial.py | 36 +++++++++++++------ 2 files changed, 27 insertions(+), 11 deletions(-) diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 4da20d24c44..3473e41874b 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -36,7 +36,7 @@ or download the python file to run locally. If you want to run the code locally on your machine you will need some tools you may or may not have installed already. -Below are some good tool options for configuring local development or for more detailed instructions check out `Get Started Locally `_ +Below are some good tool options for configuring local development or for more detailed instructions check out `get started locally `_. - `Visual Studio Code `_ : You can open run python code in Visual Studio Code or open a Jupyter Notebook in VS Code. diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 0f1512bd3dc..dd686dcd3e6 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -11,8 +11,7 @@ Save, Load and Use the Model ============================ -In this section we will look at how to save, load and use persisted model state -to run predictions. +In this section we will look at how to presist model state with saving, loading and running model predictions. """ import torch @@ -22,7 +21,10 @@ # Pre-trained Models # ------------------ # -# Many tasks, such as object classification in computer vision, rely on some pre-trained models. While you can find some pre-trained models for common tasks online, PyTorch already includes the most common model architectures. For example, to initialize a VGG-16 model for image classification, we can use the following code: +# Many tasks, such as object classification in computer vision, rely on some pre-trained models. +# While you can find some pre-trained models for common tasks online, PyTorch already includes the most +# common model architectures. For example, to initialize a VGG-16 model for image classification, +# we can use the following code: import torchvision.models as models model = models.vgg16(pretrained=True) @@ -34,12 +36,17 @@ # # res = model(imgs) # -# Let us see how a picture of a cat can be classified using this VGG-16 model. Once we load a picture from the internet, we need to apply a series of trainsformations to it, to turn it into a tensor of the appropriate size: +# Let us see how a picture of a cat can be classified using this VGG-16 model. Once we load a +# picture from the internet, we need to apply a series of transformations to it, to turn it into a +# tensor of the appropriate size: +# # * Resize image to 224x224 pixels # * Convert it to tensor # * Apply normalization with a given mean and standard deviation +# # Also, we need to turn a single tensor into a batch by adding one more dimension with ``unsqueeze()`` call. -# After doing the inference, we obtain a tensor of probabilities for each of the classes, and we get the index of most probable class by calling ``.argmax()``. +# After doing the inference, we obtain a tensor of probabilities for each of the classes, and we get the index of the +# most probable class by calling ``.argmax()``. import matplotlib.pyplot as plt from PIL import Image @@ -59,6 +66,7 @@ ###################### # The result obtained is a number of imagenet predicted class, in this case, *tiger cat*. +# # .. note:: When running model inference, it is recommended to wrap the code into ``torch.no_grad()``, because `automatic differentiation `_ is unnecessary. ####################################################################### @@ -71,19 +79,22 @@ torch.save(model.state_dict(), 'model_weights.pth') ########################## -# To load model weights, you need to create a model class first, and then load the parameters using ``load_state_dict()`` method. +# To load model weights, you need to create a model class first, and then load the parameters +# using ``load_state_dict()`` method. model = models.vgg16() # we do not specify pretrained=True, i.e. do not load default weights model.load_state_dict(torch.load('model_weights.pth')) model.eval() ########################### -# .. note:: Before inference, do not forget to call ``model.eval()`` method to set dropout and batch normalization layers to evaluation mode. Failing to do this will yield inconsistent inference results. +# .. note:: be sure to call ``model.eval()`` method before inferencing to set the dropout and batch normalization layers to evaluation mode. Failing to do this will yield inconsistent inference results. ####################################################################### # Saving and Loading Models with Shapes # ------------------------------------- -# When loading model weights, we needed to instantiate the model class first, because the class defines the structure of a network. We might want to save the structure of this class together with the model, in which case we can pass ``model`` (and not ``model.state_dict()``) to the saving function: +# When loading model weights, we needed to instantiate the model class first, because the class +# defines the structure of a network. We might want to save the structure of this class together with +# the model, in which case we can pass ``model`` (and not ``model.state_dict()``) to the saving function: torch.save(model, 'model.pth') @@ -107,6 +118,11 @@ onnx.export(model, input_image, 'model.onnx') ########################### -# There are a lot of things you can do with ONNX model, including running inference on different platforms and in different programming languages. For more details, we recommend visiting `ONNX tutorial `_. +# There are a lot of things you can do with ONNX model, including running inference on different platforms +# and in different programming languages. For more details, we recommend +# visiting `ONNX tutorial `_. # -# **You have reached the end of the detailed PyTorch beginner tutorial.** You can now `return to the first page `_ and go over the sample code again - we hope you have gained much better understanding of all the details. +# Congratulations! You have completed the PyTorch beginner tutorial! You can +# now `return to the first page `_ and go over the sample code +# again and we hope you have a better understanding of how to do deep learning with PyTorch. +# Good luck on your deep learning journey! From 8fa8e8215a215b8baf7745acfb3eea56a8fa176f Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:22:09 -0600 Subject: [PATCH 053/120] Update beginner_source/quickstart/dataquickstart_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/dataquickstart_tutorial.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index b0a8f4ab676..307ca13632a 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -149,7 +149,7 @@ def __getitem__(self, idx): # Example: # -def __init__(self, annotations_file, img_dir, transform=None): +def __init__(self, labels_file, img_dir, transform=None): self.img_labels = pd.read_csv(annotations_file) self.img_dir = img_dir self.transform = transform @@ -209,4 +209,3 @@ def __getitem__(self, idx): # # Next learn more about how to `transform data for training `_. # - From 94d1a43fbe5ea169b8188250ba1706a50e7fa43f Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:22:20 -0600 Subject: [PATCH 054/120] Update beginner_source/quickstart/dataquickstart_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/dataquickstart_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 307ca13632a..adf04c2502f 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -150,7 +150,7 @@ def __getitem__(self, idx): # def __init__(self, labels_file, img_dir, transform=None): - self.img_labels = pd.read_csv(annotations_file) + self.img_labels = pd.read_csv(labels_file) self.img_dir = img_dir self.transform = transform From 450adea138d24cecf2e5e9a90773685183bb4198 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:23:17 -0600 Subject: [PATCH 055/120] Update beginner_source/quickstart/dataquickstart_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/dataquickstart_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index adf04c2502f..4f3bc26a8d3 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -158,7 +158,7 @@ def __init__(self, labels_file, img_dir, transform=None): # __len__ # ----------------- # -# The __len__ function is needed to return the number of samples in our dataset. +# The __len__ function returns the number of samples in our dataset. # # Example: From 99702d198e28930597e5b67bb19ddd16c47c15e4 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:23:37 -0600 Subject: [PATCH 056/120] Update beginner_source/quickstart/dataquickstart_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/dataquickstart_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 4f3bc26a8d3..7a8c8bf90d3 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -135,7 +135,7 @@ def __getitem__(self, idx): from torch.utils.data import DataLoader ################################################################# -# Init +# __init__ # ----------------- # # The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and then keep track of the directory of our image file. Note that different types of data can take different init inputs. You are not limited to just an annotations file, directory path and transforms, but for images this is a standard practice. From 448e9171d523411ef33c20e4db6894eaea962ab0 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:24:17 -0600 Subject: [PATCH 057/120] Update beginner_source/quickstart/dataquickstart_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/dataquickstart_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 7a8c8bf90d3..0f05cfc65e2 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -84,7 +84,7 @@ # Creating a Custom Dataset # ----------------- # -# To work with your own data, we need to implement a custom class that inherits from ``Dataset``. Let's look at a custom image dataset implementation. In this example, we have a number of images stored in a directory, and their labels stored separately in CSV annotation file. Below is the full example and we will break down whats happening in each function. +# To work with your own data, we can implement a custom class that inherits from ``Dataset``. This custom class must implement three functions: `__init__`, `__len__`, and `__getitem__`. Let's look at a custom image dataset implementation. In this example, we have a number of images stored in a directory, and their labels stored separately in a CSV file. Here's what it looks like; in the following sections, we will break down what's happening in each function. # import os From 624a2d6a9a27d0bf7fbabfd594ca052ddd9ae63c Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:27:41 -0600 Subject: [PATCH 058/120] Update beginner_source/quickstart/transforms_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/transforms_tutorial.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index f5d0bffb3c9..26bb0745eb5 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -60,12 +60,15 @@ # Below is the code to load the FashionMNIST dataset and apply the transforms: training_data = datasets.FashionMNIST( - 'data', - train=True, download=True, + "data", + train=True, + download=True, transform=transforms.ToTensor(), target_transform=transforms.Lambda( lambda y: torch.zeros(10, dtype=torch.float) - .scatter_(0, torch.tensor(y), value=1))) + .scatter_(0, torch.tensor(y), value=1) + ) +) ######################################## # Here we define two transformations: From bf6d1377eecd0fd9ad4c7bd0c4809c8a8b868bac Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:28:52 -0600 Subject: [PATCH 059/120] Update beginner_source/quickstart/transforms_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/transforms_tutorial.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 26bb0745eb5..14c5a064d9f 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -35,8 +35,7 @@ # # We are using the built-in FashionMNIST dataset from the PyTorch library. # For more info on the Datasets and Loaders check out `this `_ section of the tutorial. -# The ``Train=True`` indicates we want to download the training dataset from the -# built-in datasets, ``Train=False`` indicates to download the testing dataset. +# The ``train=True`` argument indicates we want the training split of the dataset (``train=False`` downloads the test split instead). # This way we have data partitioned out for training and testing within the provided PyTorch datasets. # We will apply the same transforms to both the training and testing datasets. From dc3d460a48646f14a8acac913a675328557e5966 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:45:38 -0600 Subject: [PATCH 060/120] Update beginner_source/quickstart/transforms_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/transforms_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 14c5a064d9f..e3d5f6ae933 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -101,7 +101,7 @@ # Compose # ------------------------ # -# In many cases, we need to perform several transformations on the data sequentially. ``transforms.compose`` allows us to string together different steps of transformations in a sequential order. We will see an example of using composition transform in the next section. +# In many cases, we need to perform several transformations on the data sequentially. `transforms.Compose __` allows us to string together different steps of transformations in a sequential order. We will see an example of using composition transform in the next section. ############################################## From 91bbfcbee8f27677409e4ae778b1dc427ede868d Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:46:00 -0600 Subject: [PATCH 061/120] Update beginner_source/quickstart/transforms_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/transforms_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index e3d5f6ae933..6c8430cacb8 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -94,7 +94,7 @@ 10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)) ############################################### -# Check out the other `TorchVision Transforms `_ +# Check out more `torchvision transforms `_ # ##################################################### From 923672cd96cef581fdd40f58f353554b7fc44543 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:46:11 -0600 Subject: [PATCH 062/120] Update beginner_source/quickstart/transforms_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/transforms_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 6c8430cacb8..c541e26b922 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -85,7 +85,7 @@ # ############################################## -# Lambda Transform +# Lambda Transforms # ------------------------------- # # We use a **lambda transform** to turn the class number into one-hot encoding. This function takes y as an input and creates a zero tensor of size 10. Then it calls scatter `torch.Tensor.scatter_ class `_ to take a value 1 and store it into the correct position of the zero vector defined by the class number. From 273348a4dc57e79c7ddde8f59fa0d4dd51b78096 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:47:10 -0600 Subject: [PATCH 063/120] Update beginner_source/quickstart/transforms_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/transforms_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index c541e26b922..2af43fa79ed 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -79,7 +79,7 @@ # ToTensor() # ------------------------------- # -# `torchvision.transforms.ToTensor `_ transform is required to prepare an image for training. It takes the PIL image, converts it into a `tensor `_, and normalizes our data by scaling the image pixel intensity values to be between 0 and 1. +# `transforms.ToTensor `_ transform is required to prepare an image for training. It takes the PIL image, converts it into a `tensor `_, and normalizes our data by scaling the image pixel intensity values to be between 0 and 1. # # .. note:: ToTensor only normalizes images that are in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling. # From 0ac7b2083007faf9f1f57bc03b2c4031b02b2da4 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:48:14 -0600 Subject: [PATCH 064/120] Update beginner_source/quickstart/transforms_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/transforms_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 2af43fa79ed..031dffdc52d 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -73,7 +73,7 @@ # Here we define two transformations: # # * ``transform`` is the transformation we apply to features, in our case - to images. The dataset contains images in PIL format so we need to convert them to tensors using the ``ToTensor()`` transform. -# * ``target_transform`` defines a transformation that is applied to labels. In our case label is a class number from 0 to 9, and we need to convert it to one-hot encoding. +# * ``target_transform`` defines a transformation that is applied to labels in the dataset. Here, the label is a class number from 0 to 9, and we need to convert it to one-hot encoding. ################################################# # ToTensor() From 3e678184e9bd745d2161e183ad00ec564fd0c401 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 15:51:51 -0600 Subject: [PATCH 065/120] Update beginner_source/quickstart/transforms_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/transforms_tutorial.py | 1 - 1 file changed, 1 deletion(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 031dffdc52d..1f2177b1c1d 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -81,7 +81,6 @@ # # `transforms.ToTensor `_ transform is required to prepare an image for training. It takes the PIL image, converts it into a `tensor `_, and normalizes our data by scaling the image pixel intensity values to be between 0 and 1. # -# .. note:: ToTensor only normalizes images that are in PIL mode of (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8. In the other cases, tensors are returned without scaling. # ############################################## From 0bd38592a3387797e5d4c8157dce71955417f3e5 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:38:19 -0600 Subject: [PATCH 066/120] Update beginner_source/quickstart/autograd_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- .../quickstart/autograd_tutorial.py | 52 ------------------- 1 file changed, 52 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 4896861001a..becd033eb72 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -138,57 +138,6 @@ ###################################################################### -# Example of Gradient Descent -# --------------------------- -# -# Let's use the AutoGrad functionality to minimize a simple function of -# two variables :math:`f(x_1,x_2)=(x_1-3)^2+(x_2+2)^2`. We will use the -# ``x`` tensor to represent the coordinates of a point. To do the gradient -# descent, we start with some initial value :math:`x^{(0)}=(0,0)`, and -# compute each consecutive step using: -# -# .. math:: -# -# -# x^{(n+1)} = x^{(n)} - \eta\nabla f -# -# Here :math:`\eta` is so-called **learning rate** (we will call it ``lr`` -# in our code), and -# :math:`\nabla f = (\frac{\partial f}{\partial x_1},\frac{\partial f}{\partial x_2})` -# is the gradient of :math:`f`. -# -# We will start by defining the initial value of ``x`` and the function -# ``f``: -# - -x = torch.zeros(2, requires_grad=True) - -def f(x): - return (x-torch.tensor([3, -2])).pow(2).sum() - -lr = 0.1 - - -###################################################################### -# For the gradient descent, let's do 15 iterations. On each iteration, we -# will update the coordinate tensor ``x`` and print its coordinates to -# make sure that we are approaching the minimum: -# - -for i in range(15): - y = f(x) - y.backward() - gr = x.grad - x.data.add_(-lr*gr) - x.grad.zero_() - print("Step {}: x[0]={}, x[1]={}".format(i, x[0], x[1])) - - -###################################################################### -# As you can see, we have obtained the values close to the optimal point -# :math:`(3,-2)`. Training a neural network is in fact a very similar -# process, we will need to do a number of iterations to minimize the value -# of the **loss function**. ###################################################################### # More on Computational Graphs @@ -287,4 +236,3 @@ def f(x): ###################################################################### # Next learn more about how the `optimization loop works with this example `_. # - From 71df3363df757a5a6bcfcbbffc3194045b6f119f Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:38:35 -0600 Subject: [PATCH 067/120] Update beginner_source/quickstart/autograd_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/autograd_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index becd033eb72..75e893a22c8 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -89,7 +89,7 @@ # .. note:: # - We can only obtain the ``grad`` properties for the leaf # nodes of the computational graph, which have ``requires_grad`` property -# set to ``True``. All other nodes in our graph gradients will not be +# set to ``True``. For all other nodes in our graph, gradients will not be # available. # - We can only perform gradient calculations using # ``backward`` once on a given graph, for performance reasons. If we need From 3742e2e48f565679664e3ab3eddbb6973133981b Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:39:21 -0600 Subject: [PATCH 068/120] Update beginner_source/quickstart/buildmodel_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/buildmodel_tutorial.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index da2233c40c7..66c8034cfbd 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -86,9 +86,13 @@ def forward(self, x): return F.softmax(x, dim=1) model = NeuralNetwork().to(device) - print(model) +input = torch.rand(5, 28, 28) + +# equivalent to model.forward(input) +model(input) + ############################################## # The Model Module Layers # ------------------------- From c4e814181db3761b7ecc2a4a9c294a388ef01a30 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:40:15 -0600 Subject: [PATCH 069/120] Update beginner_source/quickstart/buildmodel_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/buildmodel_tutorial.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 66c8034cfbd..a2dd030ebf1 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -61,9 +61,8 @@ # Define the Class # ------------------------- # -# Here we define the `NeuralNetwork` class which inherits from ``nn.Module`` which is the base class for -# building neural network modules. The ``init`` function defines the layers in the neural network -# then it initializes the modules to be called in the ``forward`` function. +# First we define the `NeuralNetwork` class which inherits from ``nn.Module``, the base class for +# building all neural network modules in PyTorch. We use the ``__init__`` function to define and initialize the NN layers that will be then called in the module's ``forward`` function. # Then we call the ``NeuralNetwork`` class and assign the device. When training # the model we will call ``model`` and pass the data (x) into the forward function and # through each layer of our network. From 44fe2cd20da30cf6ec8927917af7d636a3954e35 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:40:48 -0600 Subject: [PATCH 070/120] Update beginner_source/quickstart/buildmodel_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/buildmodel_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index a2dd030ebf1..01d1bb7011e 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -106,7 +106,7 @@ def forward(self, x): print(input_image.size()) ################################################## -# `nn.Flatten `_ +# nn.Flatten # ----------------------------------------------- # # First we call nn.Flatten to reduce tensor dimensions to one. From 85ba9d1b3f6f1cb77b14fd75fc37bd9b25caaef2 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:41:12 -0600 Subject: [PATCH 071/120] Update beginner_source/quickstart/buildmodel_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/buildmodel_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 01d1bb7011e..c9b7e80529e 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -96,7 +96,7 @@ def forward(self, x): # The Model Module Layers # ------------------------- # -# Lets break down each model layer in the FashionMNIST model. To illustrate it, we +# Lets break down each layer in the FashionMNIST model. To illustrate it, we # will take a sample minibatch of 100 images of size 28x28 and see what happens to it as # we pass it through the network. The code in the sections below would essentially explain # what happens inside the ``forward`` method of our ``NeuralNetwork`` class. From b4fd18f1abfbe3908d03a8062215f9e39cf5bf4f Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:41:52 -0600 Subject: [PATCH 072/120] Update beginner_source/quickstart/buildmodel_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/buildmodel_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index c9b7e80529e..4105274946f 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -122,7 +122,7 @@ def forward(self, x): print(flat_image.size()) ############################################## -# `nn.Linear `_ to add a linear layer +# nn.Linear # ------------------------------- # # Now that we have flattened our tensor dimension we will apply a linear layer. The linear layer is From 0c9a3a3fbe8ca77360abff1dbe673ffed695c6e5 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:42:09 -0600 Subject: [PATCH 073/120] Update beginner_source/quickstart/buildmodel_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/buildmodel_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 4105274946f..f195f8eeeef 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -133,7 +133,7 @@ def forward(self, x): # ``torch.nn.Linear(in_features: int, out_features: int, bias: bool = True)`` # -layer1 = nn.Linear(28*28,512) +layer1 = nn.Linear(in_features=28*28, out_features=512) hidden1 = layer1(flat_image) print(hidden1.size()) From a5d7b0ab2452f999b5fb04f21a7fc94aa213e9f8 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:42:24 -0600 Subject: [PATCH 074/120] Update beginner_source/quickstart/buildmodel_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/buildmodel_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index f195f8eeeef..40e10827bba 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -93,7 +93,7 @@ def forward(self, x): model(input) ############################################## -# The Model Module Layers +# Model Layers # ------------------------- # # Lets break down each layer in the FashionMNIST model. To illustrate it, we From 7d58e9fad16db0a4102975e598da3d3a2dba9054 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:54:07 -0600 Subject: [PATCH 075/120] Update beginner_source/quickstart/buildmodel_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/buildmodel_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 40e10827bba..f11fa8de539 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -125,7 +125,7 @@ def forward(self, x): # nn.Linear # ------------------------------- # -# Now that we have flattened our tensor dimension we will apply a linear layer. The linear layer is +# Now that we have flattened our tensor dimension we will pass our data through a `linear layer `_. The linear layer is # a module that applies a linear transformation on the input using it's stored weights and biases. # # From the docs: From 38745c4bdc6e8959a1f474aad5af77b12a4b5dff Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 16:54:22 -0600 Subject: [PATCH 076/120] Update beginner_source/quickstart/buildmodel_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/buildmodel_tutorial.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index f11fa8de539..5417641d8cf 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -128,10 +128,6 @@ def forward(self, x): # Now that we have flattened our tensor dimension we will pass our data through a `linear layer `_. The linear layer is # a module that applies a linear transformation on the input using it's stored weights and biases. # -# From the docs: -# -# ``torch.nn.Linear(in_features: int, out_features: int, bias: bool = True)`` -# layer1 = nn.Linear(in_features=28*28, out_features=512) hidden1 = layer1(flat_image) From f89daf09b5a260c4b2756d7c2d4e57c01fd06ae5 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Wed, 20 Jan 2021 17:37:04 -0600 Subject: [PATCH 077/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 2c46feb041f..fe1f3e109e4 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -12,11 +12,7 @@ =========================== Now that we have a model and data it's time to train, validate and test our model by optimizing it's parameters on -our data. Training a model is essentially an optimization process similar to the one we described in the previous section -on `Autograd `_. We run the optimization process on the whole dataset several times, -and each run is referred to as an **epoch**. During each run, we present data in **minibatches**, and for each -minibatch compute gradients and correct parameters of the model according to back propagation algorithm. - +our data. Training a model is an iterative process; in each iteration(called an *epoch*) the model makes a guess about the output, calculates the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in the `previous section `_), and **optimizes** these parameters using gradient descent. For a more detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown `__. Hyperparameters ----------------- @@ -197,4 +193,4 @@ def myCrossEntropyLoss(outputs, labels): Next learn how to `save our trained model `_. -""" \ No newline at end of file +""" From eb66e9d4a76bafd36bd2a512a493ac5e7e43e6c6 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Thu, 21 Jan 2021 00:06:12 +0000 Subject: [PATCH 078/120] checkin by cassie: fix spacing --- beginner_source/quickstart/optimization_tutorial.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index fe1f3e109e4..af4bdb4d1ba 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -12,7 +12,8 @@ =========================== Now that we have a model and data it's time to train, validate and test our model by optimizing it's parameters on -our data. Training a model is an iterative process; in each iteration(called an *epoch*) the model makes a guess about the output, calculates the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in the `previous section `_), and **optimizes** these parameters using gradient descent. For a more detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown `__. +our data. Training a model is an iterative process; in each iteration (called an *epoch*) the model makes a guess about the output, calculates the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in the `previous section `_), and **optimizes** these parameters using gradient descent. For a more detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown `__. + Hyperparameters ----------------- From 91707c8a63e07c3342ce97ed53ad44fe7b063dd1 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:09:05 -0600 Subject: [PATCH 079/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index af4bdb4d1ba..4a70960e817 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -18,9 +18,7 @@ ----------------- Hyperparameters are adjustable parameters that let you control the model optimization process. -Unlike model parameters that we will optimize during training, hyperparameters are configured for the whole -training process. However, you may achieve different model performance with different hyperparameters, so you may -want to try out different values for them to perform hyperparameter optimization. +Different hyperparameter values can impact model training and convergence rates (`read more `__ about hyperparameter tuning) In our case, we need to define the following hyperparameters: From 441b5cda81444e500125b3bad61d195bbe7a3034 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:09:29 -0600 Subject: [PATCH 080/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 4a70960e817..594a7168cb1 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -45,7 +45,7 @@ :alt: Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. Each -iteration of the optimization loop is called an **epoch**. Each epoch is comprized of two main parts: +iteration of the optimization loop is called an **epoch**. Each epoch consists of two main parts: 1. **The Train Loop** - main loop that iterates over all dataset and performs training 2. **The Validation/Test Loop** - goes through the validation / test dataset to evaluate model performance on the test data. From 7830517a6c744f94e4b36d92dea170294431dd8f Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:09:41 -0600 Subject: [PATCH 081/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 594a7168cb1..561ff04a19a 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -36,7 +36,7 @@ .. code-block:: Python - model = NeuralNework() + model = NeuralNetwork() Optimization Loop ----------------- From 76e518d7273f66fa7e93d2c2feb24e8eab02cc16 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:12:38 -0600 Subject: [PATCH 082/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 561ff04a19a..746b7d19ced 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -94,9 +94,7 @@ --------- Optimization is the process of adjusting model paramters on each training step. **Optimization algorithm** defines -how this process is performed. The standard method for optimization is called Stochastic Gradient Descent. -To learn more check out this awesome video by `3blue1brown `_. - +how this process is performed. In this example we use Stochastic Gradient Descent. All optimization logic is encapsulated in ``optimizer`` object. In our case, we will instantiate the stochastic gradient descent optimizer: .. code-block:: Python From d6273ba907c995259719bddfe7d08526c203a300 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:13:26 -0600 Subject: [PATCH 083/120] Update beginner_source/quickstart/saveloadrun_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/saveloadrun_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index dd686dcd3e6..853049f9ebc 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -104,7 +104,7 @@ model = torch.load('model.pth') ######################## -# .. note:: This approach uses Python `pickle `_ module when serializing the model, this it relies on the actual class definition to be available when loading the model. +# .. note:: This approach uses Python `pickle `_ module when serializing the model, thus it relies on the actual class definition to be available when loading the model. ####################################################################### # Exporting Model to ONNX From dfa5a0a13bf3eac178de51fd9f5bb4367aef9e6a Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:15:05 -0600 Subject: [PATCH 084/120] Update beginner_source/quickstart/saveloadrun_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/saveloadrun_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 853049f9ebc..81b5682000c 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -8,7 +8,7 @@ `Optimization `_ > **Save & Load Model** -Save, Load and Use the Model +Save and Load the Model ============================ In this section we will look at how to presist model state with saving, loading and running model predictions. From 9c19a3e55dd2c65c57989856c1ae1b211471d5c3 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:15:15 -0600 Subject: [PATCH 085/120] Update beginner_source/quickstart/saveloadrun_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/saveloadrun_tutorial.py | 1 + 1 file changed, 1 insertion(+) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 81b5682000c..cc4b59ccc44 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -76,6 +76,7 @@ # state dictionary, called ``state_dict``. These can be persisted via the ``torch.save`` # method: +model = models.vgg16(pretrained=True) torch.save(model.state_dict(), 'model_weights.pth') ########################## From 3f538de1418009a24021dd58c53089754be5aac9 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:19:45 -0600 Subject: [PATCH 086/120] Update beginner_source/quickstart/saveloadrun_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/saveloadrun_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index cc4b59ccc44..84802b5a6e0 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -80,7 +80,7 @@ torch.save(model.state_dict(), 'model_weights.pth') ########################## -# To load model weights, you need to create a model class first, and then load the parameters +# To load model weights, you need to create an instance of the same model first, and then load the parameters # using ``load_state_dict()`` method. model = models.vgg16() # we do not specify pretrained=True, i.e. do not load default weights From beecc68685cbad58d6d1fea3bd7d3b7d2c20a44f Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:19:56 -0600 Subject: [PATCH 087/120] Update beginner_source/quickstart/saveloadrun_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/saveloadrun_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 84802b5a6e0..65d8770cc88 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -72,7 +72,7 @@ ####################################################################### # Saving and Loading Model Weights # -------------------------------- -# PyTorch stores the learned parameters in the model's internal +# PyTorch models store the learned parameters in an internal # state dictionary, called ``state_dict``. These can be persisted via the ``torch.save`` # method: From ab71869175fec8e40273f4a931578740620f90f2 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:20:09 -0600 Subject: [PATCH 088/120] Update beginner_source/quickstart/saveloadrun_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/saveloadrun_tutorial.py | 1 + 1 file changed, 1 insertion(+) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 65d8770cc88..c8e3b58e6dc 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -16,6 +16,7 @@ import torch import torch.onnx as onnx +import torchvision.models as models ####################################################################### # Pre-trained Models From b68484e149d3a2b94618b6631dd2b7f467f5f6c4 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:31:24 -0600 Subject: [PATCH 089/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 746b7d19ced..501ff98f620 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -23,7 +23,7 @@ In our case, we need to define the following hyperparameters: - **Number of Epochs**- the number times to iterate over the dataset - - **Batch Size** - the number of samples in the dataset to take for each update cycle + - **Batch Size** - the number of data samples seen by the model in each epoch - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training. .. code-block:: Python From aef05f5b5ebbac8b7b65cb95a6661de80350c30c Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:39:06 -0600 Subject: [PATCH 090/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 501ff98f620..9d0bcad13ce 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -109,7 +109,7 @@ * Call ``optimizer.zero_grad()`` function to zero the gradients. As you have seen in the previous section on automatic differentiation, gradients by default add up, so we need to explicitly zero them on each step. * Calculate the loss using loss function. This builds a computation graph, which PyTorch uses to automatically update parameters with respect to our model's loss during training. This is done with one call to ``loss.backwards()``. - * Once we have our gradients, we call ``optimizer.step()`` to propagate the gradients from the backwards command to update all the parameters in our model. + * Once we have our gradients, we call ``optimizer.step()`` to adjust the parameters by the gradients collected in the backward pass. .. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png :alt: tensor graph From f99e918d7f258284cf7495b0192aedc4d9854dee Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:55:49 -0600 Subject: [PATCH 091/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 9d0bcad13ce..5fd49c6b372 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -101,9 +101,7 @@ optimizer = optim.SGD(model.parameters(), lr=learning_rate) -In addition to SGD there are many different optimizers and variations of this method in PyTorch such -as ADAM and RMSProp, that work better for different kinds of models. They are outside the scope -of this quickstart, but you can check out the full list of optimizers `here `_. +In addition to SGD there are many `different optimizers `_ available in PyTorch such as ADAM and RMSProp, that work better for different kinds of models. Inside the training loop, optimization happens in three steps: From 0934d327cf38feb6988c7b840a55ae37221205ba Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 10:57:38 -0600 Subject: [PATCH 092/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 5fd49c6b372..a3843bd213d 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -175,16 +175,6 @@ def myCrossEntropyLoss(outputs, labels): A more in depth explanation of PyTorch cost functions is outside the scope of the quickstart but you can learn more about the different common cost functions for deep learning in the PyTorch `documentation `_. -Using Train/Validation/Test Split to Optimize Hyperparameters -------------------------------------------------------------- - -In our example, we have split the data between train and test datasets. However, as we mentioned above, different hyperparameters can yield different model performance. Thus, it makes sense to use a part of the dataset for **hyperparameter optimization**. In this case, we split the dataset into three parts: - - * Training data - * Validation data, which is used inside optimization loop to determine the accuracy of the current model and the optimal number of epochs. After a certain number of epochs, validation accuracy typically starts to decrease, which means that we have reached optimal performance for given hyperparameters. - * Test data, which is used to measure the performance of the model for given hyperparameters. It is important that test data are independent from validation data, i.e. the same dataset cannot be used for both validation and test purposes. - -We will not consider hyperparameter optimization further in this quickstart. Next learn how to `save our trained model `_. From 42a158bbea78e49f6a0fbe379cd8abfeaf3fc562 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Thu, 21 Jan 2021 18:32:58 +0000 Subject: [PATCH 093/120] fix link --- beginner_source/quickstart/transforms_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 1f2177b1c1d..40179f0fb68 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -100,7 +100,7 @@ # Compose # ------------------------ # -# In many cases, we need to perform several transformations on the data sequentially. `transforms.Compose __` allows us to string together different steps of transformations in a sequential order. We will see an example of using composition transform in the next section. +# In many cases, we need to perform several transformations on the data sequentially. `transforms.Compose _` allows us to string together different steps of transformations in a sequential order. We will see an example of using composition transform in the next section. ############################################## From 3e0a445624a03f7946933e154f163417778567f2 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 13:06:21 -0600 Subject: [PATCH 094/120] Update beginner_source/quickstart/optimization_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- beginner_source/quickstart/optimization_tutorial.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index a3843bd213d..827c93af207 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -93,9 +93,8 @@ Optimizer --------- -Optimization is the process of adjusting model paramters on each training step. **Optimization algorithm** defines -how this process is performed. In this example we use Stochastic Gradient Descent. -All optimization logic is encapsulated in ``optimizer`` object. In our case, we will instantiate the stochastic gradient descent optimizer: +Optimization is the process of adjusting model parameters to reduce model error in each training step. **Optimization algorithms** define how this process is performed (in this example we use Stochastic Gradient Descent). +All optimization logic is encapsulated in the``optimizer`` object. In this case, we use the SGD optimizer: .. code-block:: Python From 483c7484c297f9bd19619932b2c8b930b59d04e5 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Thu, 21 Jan 2021 19:09:59 +0000 Subject: [PATCH 095/120] fix link --- beginner_source/quickstart/transforms_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 40179f0fb68..d5b52f16fe4 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -100,7 +100,7 @@ # Compose # ------------------------ # -# In many cases, we need to perform several transformations on the data sequentially. `transforms.Compose _` allows us to string together different steps of transformations in a sequential order. We will see an example of using composition transform in the next section. +# In many cases, we need to perform several transformations on the data sequentially. `transforms.Compose `_ allows us to string together different steps of transformations in a sequential order. We will see an example of using composition transform in the next section. ############################################## From 6536a1c4530588d885f7acafe39978b974027ad0 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Thu, 21 Jan 2021 15:42:08 -0600 Subject: [PATCH 096/120] Update beginner_source/quickstart/saveloadrun_tutorial.py Co-authored-by: suraj813 <5676233+suraj813@users.noreply.github.com> --- .../quickstart/saveloadrun_tutorial.py | 51 ------------------- 1 file changed, 51 deletions(-) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index c8e3b58e6dc..99ec50bb21b 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -18,57 +18,6 @@ import torch.onnx as onnx import torchvision.models as models -####################################################################### -# Pre-trained Models -# ------------------ -# -# Many tasks, such as object classification in computer vision, rely on some pre-trained models. -# While you can find some pre-trained models for common tasks online, PyTorch already includes the most -# common model architectures. For example, to initialize a VGG-16 model for image classification, -# we can use the following code: - -import torchvision.models as models -model = models.vgg16(pretrained=True) - -############################# -# To use this network on the input batch of images ``imgs``, we can just call it as an ordinary function: -# -# .. code-block:: Python -# -# res = model(imgs) -# -# Let us see how a picture of a cat can be classified using this VGG-16 model. Once we load a -# picture from the internet, we need to apply a series of transformations to it, to turn it into a -# tensor of the appropriate size: -# -# * Resize image to 224x224 pixels -# * Convert it to tensor -# * Apply normalization with a given mean and standard deviation -# -# Also, we need to turn a single tensor into a batch by adding one more dimension with ``unsqueeze()`` call. -# After doing the inference, we obtain a tensor of probabilities for each of the classes, and we get the index of the -# most probable class by calling ``.argmax()``. - -import matplotlib.pyplot as plt -from PIL import Image -import requests -import torchvision.transforms as T - -url = "https://upload.wikimedia.org/wikipedia/commons/6/66/An_up-close_picture_of_a_curious_male_domestic_shorthair_tabby_cat.jpg" -im = Image.open(requests.get(url, stream=True).raw) -plt.imshow(im) - -transform = T.Compose([T.Resize(224), T.ToTensor(), - T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])]) -input_image = transform(im).unsqueeze(0) -with torch.no_grad(): - res = model(input_image).argmax().item() - print(res) - -###################### -# The result obtained is a number of imagenet predicted class, in this case, *tiger cat*. -# -# .. note:: When running model inference, it is recommended to wrap the code into ``torch.no_grad()``, because `automatic differentiation `_ is unnecessary. ####################################################################### # Saving and Loading Model Weights From 327f7ecfe12022406db3fa01eeb33b4a8a572ee1 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Fri, 22 Jan 2021 16:18:20 +0000 Subject: [PATCH 097/120] cassie checkin: add next prev links --- beginner_source/quickstart/autograd_tutorial.py | 3 ++- beginner_source/quickstart/buildmodel_tutorial.py | 5 +++-- beginner_source/quickstart/dataquickstart_tutorial.py | 3 ++- beginner_source/quickstart/optimization_tutorial.py | 3 ++- beginner_source/quickstart/saveloadrun_tutorial.py | 3 +++ beginner_source/quickstart/tensor_tutorial.py | 5 +++-- beginner_source/quickstart/transforms_tutorial.py | 3 ++- 7 files changed, 17 insertions(+), 8 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 75e893a22c8..a27688ff42a 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -234,5 +234,6 @@ ###################################################################### -# Next learn more about how the `optimization loop works with this example `_. +# `< prev `_ | +# `next > `_ # diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 5417641d8cf..4bab57b03cf 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -168,5 +168,6 @@ def forward(self, x): ################################################ # -# Next learn more about `how to use automatic differentiation to train a neural network model `_. -# +# `< prev `_ | +# `next > `_ +# diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 0f05cfc65e2..a50166dbb2b 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -207,5 +207,6 @@ def __getitem__(self, idx): ################################################################# # With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. # -# Next learn more about how to `transform data for training `_. +# `< prev `_ | +# `next > `_ # diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 827c93af207..ca2795df043 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -175,6 +175,7 @@ def myCrossEntropyLoss(outputs, labels): about the different common cost functions for deep learning in the PyTorch `documentation `_. -Next learn how to `save our trained model `_. +`< prev `_ | +`next > `_ """ diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 99ec50bb21b..2ca71679694 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -77,3 +77,6 @@ # now `return to the first page `_ and go over the sample code # again and we hope you have a better understanding of how to do deep learning with PyTorch. # Good luck on your deep learning journey! +# +# +# `< prev `_ \ No newline at end of file diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index a2523a0131f..5516bf7395e 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -259,5 +259,6 @@ # computational time, because we need to copy and transform the data when # moving it from GPU anyway. # -# Next learn how to load built in and custom `datasets with dataloaders `_ -# +# `< prev `_ | +# `next > `_ +# diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index d5b52f16fe4..70886ab3ad1 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -160,4 +160,5 @@ # # class_names = image_datasets['train'].classes # -# Next learn how to `build the model `_ +# `< prev `_ | +# `next > `_ \ No newline at end of file From 8ebac6956bc69741c842f6baa9cd7c12340b257a Mon Sep 17 00:00:00 2001 From: Cassieview Date: Fri, 22 Jan 2021 16:46:45 +0000 Subject: [PATCH 098/120] fix links --- beginner_source/quickstart/autograd_tutorial.py | 4 ++-- beginner_source/quickstart/buildmodel_tutorial.py | 4 ++-- beginner_source/quickstart/dataquickstart_tutorial.py | 4 ++-- beginner_source/quickstart/optimization_tutorial.py | 4 ++-- beginner_source/quickstart/saveloadrun_tutorial.py | 3 ++- beginner_source/quickstart/tensor_tutorial.py | 4 ++-- beginner_source/quickstart/transforms_tutorial.py | 5 +++-- 7 files changed, 15 insertions(+), 13 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index a27688ff42a..50909dc668e 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -234,6 +234,6 @@ ###################################################################### -# `< prev `_ | -# `next > `_ +# `< Previous `_ | +# `Next > `_ # diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 4bab57b03cf..b4ed77463ae 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -168,6 +168,6 @@ def forward(self, x): ################################################ # -# `< prev `_ | -# `next > `_ +# `< Previous `_ | +# `Next > `_ # diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index a50166dbb2b..8dc76973d4e 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -207,6 +207,6 @@ def __getitem__(self, idx): ################################################################# # With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. # -# `< prev `_ | -# `next > `_ +# `< Previous `_ | +# `Next > `_ # diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index ca2795df043..84342219a13 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -175,7 +175,7 @@ def myCrossEntropyLoss(outputs, labels): about the different common cost functions for deep learning in the PyTorch `documentation `_. -`< prev `_ | -`next > `_ +`< Previous `_ | +`Next > `_ """ diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 2ca71679694..28cff280577 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -79,4 +79,5 @@ # Good luck on your deep learning journey! # # -# `< prev `_ \ No newline at end of file +# `< Previous `_ +# \ No newline at end of file diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 5516bf7395e..a0d4da2921c 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -259,6 +259,6 @@ # computational time, because we need to copy and transform the data when # moving it from GPU anyway. # -# `< prev `_ | -# `next > `_ +# `< Previous `_ | +# `Next > `_ # diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 70886ab3ad1..83d7672c75c 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -160,5 +160,6 @@ # # class_names = image_datasets['train'].classes # -# `< prev `_ | -# `next > `_ \ No newline at end of file +# `< Previous `_ | +# `Next > `_ +# \ No newline at end of file From 88d0879f203fcd61f91391d047defd740e6735f4 Mon Sep 17 00:00:00 2001 From: Cassieview Date: Fri, 22 Jan 2021 17:01:06 +0000 Subject: [PATCH 099/120] fix links --- beginner_source/quickstart/saveloadrun_tutorial.py | 2 +- beginner_source/quickstart/transforms_tutorial.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 28cff280577..62ea6bad49f 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -80,4 +80,4 @@ # # # `< Previous `_ -# \ No newline at end of file +# \ No newline at end of file diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 83d7672c75c..ac2c3dfe7f3 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -159,7 +159,8 @@ # dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} # # class_names = image_datasets['train'].classes -# + +######################################### # `< Previous `_ | # `Next > `_ -# \ No newline at end of file +# \ No newline at end of file From dfd9f5f6d1db975c3824a7edec428e680f56291c Mon Sep 17 00:00:00 2001 From: Cassieview Date: Fri, 22 Jan 2021 20:21:18 +0000 Subject: [PATCH 100/120] toc tree test --- beginner_source/quickstart/quickstart_tutorial.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 3473e41874b..71c17aff440 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -30,6 +30,18 @@ .. include:: /beginner_source/quickstart/qs_toc.txt +.. toctree:: + :hidden: + + /beginner/quickstart/tensor_tutorial + /beginner/quickstart/dataquickstart_tutorial + /beginner/quickstart/transforms_tutorial + /beginner/quickstart/buildmodel_tutorial + /beginner/quickstart/autograd_tutorial + /beginner/quickstart/optimization_tutorial + /beginner/quickstart/saveloadrun_tutorial + + Running the Tutorial Code ------------------ The navigation above allows you to run the Jupyter Notebook on the cloud, download the Jupyter Notebook From 3594483a89689e091368a2a4dc89c452695f7fff Mon Sep 17 00:00:00 2001 From: Cassieview Date: Fri, 22 Jan 2021 20:30:09 +0000 Subject: [PATCH 101/120] cassie checkin: removed old next prev --- beginner_source/quickstart/autograd_tutorial.py | 6 ------ beginner_source/quickstart/buildmodel_tutorial.py | 5 ----- beginner_source/quickstart/dataquickstart_tutorial.py | 3 --- beginner_source/quickstart/optimization_tutorial.py | 4 ---- beginner_source/quickstart/saveloadrun_tutorial.py | 4 +--- beginner_source/quickstart/tensor_tutorial.py | 5 +---- beginner_source/quickstart/transforms_tutorial.py | 4 ---- 7 files changed, 2 insertions(+), 29 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 50909dc668e..5c5f6a46391 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -231,9 +231,3 @@ # gradients in case of a scalar-valued function, such as loss during # neural network training. # - - -###################################################################### -# `< Previous `_ | -# `Next > `_ -# diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index b4ed77463ae..fad87d32a81 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -166,8 +166,3 @@ def forward(self, x): print(list(model.named_parameters())[0:2]) -################################################ -# -# `< Previous `_ | -# `Next > `_ -# diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 8dc76973d4e..9dd7be809f9 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -207,6 +207,3 @@ def __getitem__(self, idx): ################################################################# # With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. # -# `< Previous `_ | -# `Next > `_ -# diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 84342219a13..2ec7bea72ce 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -174,8 +174,4 @@ def myCrossEntropyLoss(outputs, labels): A more in depth explanation of PyTorch cost functions is outside the scope of the quickstart but you can learn more about the different common cost functions for deep learning in the PyTorch `documentation `_. - -`< Previous `_ | -`Next > `_ - """ diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 62ea6bad49f..01f49add796 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -78,6 +78,4 @@ # again and we hope you have a better understanding of how to do deep learning with PyTorch. # Good luck on your deep learning journey! # -# -# `< Previous `_ -# \ No newline at end of file +# \ No newline at end of file diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index a0d4da2921c..3f87910bd1e 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -258,7 +258,4 @@ # also change the ``dtype``. This does not result in additional # computational time, because we need to copy and transform the data when # moving it from GPU anyway. -# -# `< Previous `_ | -# `Next > `_ -# +# \ No newline at end of file diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index ac2c3dfe7f3..cfd2b35b011 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -159,8 +159,4 @@ # dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} # # class_names = image_datasets['train'].classes - -######################################### -# `< Previous `_ | -# `Next > `_ # \ No newline at end of file From a4c89b65bbaaccb84a54e71bf125850e0092febe Mon Sep 17 00:00:00 2001 From: Cassieview Date: Sat, 23 Jan 2021 15:34:52 +0000 Subject: [PATCH 102/120] Fix missing variable in saveloadrun_tutorial --- beginner_source/quickstart/saveloadrun_tutorial.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 01f49add796..f1374133074 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -11,7 +11,7 @@ Save and Load the Model ============================ -In this section we will look at how to presist model state with saving, loading and running model predictions. +In this section we will look at how to persist model state with saving, loading and running model predictions. """ import torch @@ -64,8 +64,9 @@ # PyTorch execution graph, however, the export process must # traverse the execution graph to produce a persisted ONNX model. For this reason, a # test variable of the appropriate size should be passed in to the -# export routine: +# export routine (in our case, we will create a dummy zero tensor of the correct size): +input_image = torch.zeros((1,3,224,224)) onnx.export(model, input_image, 'model.onnx') ########################### From e9731c0da7c06a701b2cf589369d14bd74d40856 Mon Sep 17 00:00:00 2001 From: suraj813 <5676233+suraj813@users.noreply.github.com> Date: Mon, 25 Jan 2021 12:09:47 -0500 Subject: [PATCH 103/120] Update beginner_source/quickstart/quickstart_tutorial.py --- beginner_source/quickstart/quickstart_tutorial.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 71c17aff440..7e0c763951a 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -1,7 +1,7 @@ """ **Quickstart** > `Tensors `_ > -`DataSets & DataLoaders `_ > +`Datasets & DataLoaders `_ > `Transforms `_ > `Build Model `_ > `Autograd `_ > @@ -251,4 +251,3 @@ def test(dataloader, model): ################################################################## # # *Authors: Seth Juarez, Cassie Breviu, Dmitry Soshnikov, Ari Bornstein* - From 805a759f0de0fab41015f80e8ab352e1624b7dfc Mon Sep 17 00:00:00 2001 From: suraj813 <5676233+suraj813@users.noreply.github.com> Date: Mon, 25 Jan 2021 12:17:03 -0500 Subject: [PATCH 104/120] "DataSet" -> "Dataset" --- beginner_source/quickstart/autograd_tutorial.py | 2 +- beginner_source/quickstart/buildmodel_tutorial.py | 3 +-- beginner_source/quickstart/dataquickstart_tutorial.py | 2 +- beginner_source/quickstart/optimization_tutorial.py | 2 +- beginner_source/quickstart/qs_toc.txt | 2 +- beginner_source/quickstart/saveloadrun_tutorial.py | 4 ++-- beginner_source/quickstart/tensor_tutorial.py | 4 ++-- beginner_source/quickstart/transforms_tutorial.py | 4 ++-- 8 files changed, 11 insertions(+), 12 deletions(-) diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 5c5f6a46391..eb0eba1e698 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -2,7 +2,7 @@ `Quickstart `_ > `Tensors `_ > -`DataSets & DataLoaders `_ > +`Datasets & DataLoaders `_ > `Transforms `_ > `Build Model `_ > **Autograd** > diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index fad87d32a81..2e653016c53 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -2,7 +2,7 @@ `Quickstart `_ > `Tensors `_ > -`DataSets & DataLoaders `_ > +`Datasets & DataLoaders `_ > `Transforms `_ > **Build Model** > `Autograd `_ > @@ -165,4 +165,3 @@ def forward(self, x): # print(list(model.named_parameters())[0:2]) - diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 9dd7be809f9..69940a889de 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -2,7 +2,7 @@ `Quickstart `_ > `Tensors `_ > -**DataSets & DataLoaders** > +**Datasets & DataLoaders** > `Transforms `_ > `Build Model `_ > `Autograd `_ > diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 2ec7bea72ce..bebe2ceaf97 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -1,7 +1,7 @@ """ `Quickstart `_ > `Tensors `_ > -`DataSets & DataLoaders `_ > +`Datasets & DataLoaders `_ > `Transforms `_ > `Build Model `_ > `Autograd `_ > diff --git a/beginner_source/quickstart/qs_toc.txt b/beginner_source/quickstart/qs_toc.txt index fc0fb747714..b88bf1645e3 100644 --- a/beginner_source/quickstart/qs_toc.txt +++ b/beginner_source/quickstart/qs_toc.txt @@ -1,5 +1,5 @@ | 1. `Tensors `_ -| 2. `DataSets and DataLoaders `_ +| 2. `Datasets and DataLoaders `_ | 3. `Transforms `_ | 4. `Build Model `_ | 5. `Automatic Differentiation `_ diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index f1374133074..e97df39e6fe 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -1,7 +1,7 @@ """ `Quickstart `_ > `Tensors `_ > -`DataSets & DataLoaders `_ > +`Datasets & DataLoaders `_ > `Transforms `_ > `Build Model `_ > `Autograd `_ > @@ -79,4 +79,4 @@ # again and we hope you have a better understanding of how to do deep learning with PyTorch. # Good luck on your deep learning journey! # -# \ No newline at end of file +# diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 3f87910bd1e..c8ff6940580 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -1,7 +1,7 @@ """ `Quickstart `_ > **Tensors** > -`DataSets & DataLoaders `_ > +`Datasets & DataLoaders `_ > `Transforms `_ > `Build Model `_ > `Autograd `_ > @@ -258,4 +258,4 @@ # also change the ``dtype``. This does not result in additional # computational time, because we need to copy and transform the data when # moving it from GPU anyway. -# \ No newline at end of file +# diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index cfd2b35b011..6b7b0733cba 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -1,7 +1,7 @@ """ `Quickstart `_ > `Tensors `_ > -`DataSets & DataLoaders `_ > +`Datasets & DataLoaders `_ > **Transforms** > `Build Model `_ > `Autograd `_ > @@ -159,4 +159,4 @@ # dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} # # class_names = image_datasets['train'].classes -# \ No newline at end of file +# From 5f242f9e6360a8ed171d1755137b148fb59945d8 Mon Sep 17 00:00:00 2001 From: suraj813 <5676233+suraj813@users.noreply.github.com> Date: Mon, 25 Jan 2021 12:30:03 -0500 Subject: [PATCH 105/120] Apply suggestions from code review -- buildmodel --- beginner_source/quickstart/buildmodel_tutorial.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 2e653016c53..1ffce4532e2 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -109,15 +109,12 @@ def forward(self, x): # nn.Flatten # ----------------------------------------------- # -# First we call nn.Flatten to reduce tensor dimensions to one. -# -# From the docs: -# ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)`` +# First we call `nn.Flatten `_ to reduce tensor dimensions to one. # # In our case, flatten keeps the minibatch dimension, but two image dimensions are # reduced to one: -flatten = nn.Flatten() +flatten = nn.Flatten(start_dim=1, end_dim=2) flat_image = flatten(input_image) print(flat_image.size()) From ef2e8814f144a8d22e2aff9801d1928225a97bbb Mon Sep 17 00:00:00 2001 From: Cassieview Date: Mon, 25 Jan 2021 17:57:22 +0000 Subject: [PATCH 106/120] add links to author names --- beginner_source/quickstart/quickstart_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 7e0c763951a..6099ac2cb08 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -250,4 +250,4 @@ def test(dataloader, model): ################################################################## # -# *Authors: Seth Juarez, Cassie Breviu, Dmitry Soshnikov, Ari Bornstein* +# *Authors: `Seth Juarez `_, `Cassie Breviu `_, `Dmitry Soshnikov `_, `Ari Bornstein `_* From 42b03767e5421ac2b4b714eef275c243e74d2e8a Mon Sep 17 00:00:00 2001 From: Cassieview Date: Mon, 25 Jan 2021 18:31:56 +0000 Subject: [PATCH 107/120] formatting --- beginner_source/quickstart/quickstart_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 6099ac2cb08..5361cd69741 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -250,4 +250,4 @@ def test(dataloader, model): ################################################################## # -# *Authors: `Seth Juarez `_, `Cassie Breviu `_, `Dmitry Soshnikov `_, `Ari Bornstein `_* +# Authors: `Seth Juarez `_, `Cassie Breviu `_, `Dmitry Soshnikov `_, `Ari Bornstein `_ From d1b37792831ba4ca12dbfc4018970317f7ee17a0 Mon Sep 17 00:00:00 2001 From: Cassie Breviu Date: Mon, 25 Jan 2021 13:01:26 -0600 Subject: [PATCH 108/120] add suraj as author --- beginner_source/quickstart/quickstart_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 5361cd69741..74f8eb0ff12 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -250,4 +250,4 @@ def test(dataloader, model): ################################################################## # -# Authors: `Seth Juarez `_, `Cassie Breviu `_, `Dmitry Soshnikov `_, `Ari Bornstein `_ +# Authors: `Seth Juarez `_, `Cassie Breviu `_, `Dmitry Soshnikov `_, `Ari Bornstein `_, `Suraj Subramanian `_ From 698fb874b31e9d6cdc32ac09e48d6485e302e731 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Mon, 25 Jan 2021 19:46:30 +0000 Subject: [PATCH 109/120] add loading dataset to data quickstart --- .../quickstart/dataquickstart_tutorial.py | 25 +++++++++++++------ 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 69940a889de..7cf195d164a 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -39,11 +39,9 @@ # `Audio Datasets `_ # -################################################################# -# Iterating through a Dataset -# ----------------- -# -# Once we have a Dataset ``ds``, we can index it manually like a list: ``ds[index]``. +############################################################ +# Loading a Dataset +# ------------------- # # Here is an example of how to load the `Fashion-MNIST `_ dataset from torch vision. # `Fashion-MNIST `_ is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. @@ -53,7 +51,7 @@ # - ``root`` is the path where the train/test data is stored. # - ``train`` includes the training dataset. # - ``download=True`` downloads the data from the internet if it's not available at root. - +# import torch from torch.utils.data import Dataset @@ -61,7 +59,20 @@ import matplotlib.pyplot as plt import numpy as np -clothing = datasets.FashionMNIST(root='data', train=True, download=True) +clothing = torchvision.datasets.FashionMNIST( + 'data', # specifies data directory to store data + train=True, # specifies training or test dataset to use + transform=None, # specifies transforms to apply to features (images) + target_transform=None, # specifies transforms to apply to labels + download=True) # should the data be downloaded from the Internet + + +################################################################# +# Iterating and Visualizing the Dataset +# ----------------- +# +# Once we have the ``clothing`` dataset, we can index it manually like a list: ``clothing[index]``. Then use ``matplotlib`` to visualize the dataset. + labels_map = {0 : 'T-Shirt', 1 : 'Trouser', 2 : 'Pullover', 3 : 'Dress', 4 : 'Coat', 5 : 'Sandal', 6 : 'Shirt', 7 : 'Sneaker', 8 : 'Bag', 9 : 'Ankle Boot'} figure = plt.figure(figsize=(8,8)) cols, rows = 3, 3 From ca92b817e2d1392a6af5f4c6288e4eafa340c04e Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Mon, 25 Jan 2021 20:05:56 +0000 Subject: [PATCH 110/120] init fucntion wording update on datasets --- beginner_source/quickstart/dataquickstart_tutorial.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 7cf195d164a..847589963cb 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -149,8 +149,11 @@ def __getitem__(self, idx): # __init__ # ----------------- # -# The init function is used for all the first time operations when our Dataset is loaded. In this case we use it to load our annotation labels to memory and then keep track of the directory of our image file. Note that different types of data can take different init inputs. You are not limited to just an annotations file, directory path and transforms, but for images this is a standard practice. -# A sample csv annotations file may look as follows: :: +# The __init__ function is run once when instantiating our Dataset object. Here, we use it to load +# the directory containing the images, and their labels (contained in a csv file). While creating the +# Dataset object, we can optionally pass it the transform that should be run on the images. +# +# The labels.csv file looks like: :: # # tshirt1.jpg, 0 # tshirt2.jpg, 0 From e1e4d5a91665fb571b5e84df90d326f2fc3d1237 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Mon, 25 Jan 2021 23:03:54 +0000 Subject: [PATCH 111/120] added iterate to datasets section --- .../quickstart/dataquickstart_tutorial.py | 24 ++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 847589963cb..5c2a3bba838 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -59,7 +59,7 @@ import matplotlib.pyplot as plt import numpy as np -clothing = torchvision.datasets.FashionMNIST( +clothing = datasets.FashionMNIST( 'data', # specifies data directory to store data train=True, # specifies training or test dataset to use transform=None, # specifies transforms to apply to features (images) @@ -218,6 +218,28 @@ def __getitem__(self, idx): dataloader = DataLoader(clothing, batch_size=4, shuffle=True, num_workers=0) +########################### +# Iterate through the Dataset +# -------------------------- +# +# We have loaded that dataset into the ``dataloader`` and can iterate through the dataset as needed. +# Below is a simple example of how to iterate and display an image or return a label count: + + +# Display image and label. +for train_features, train_labels in dataloader.dataset: + print(train_labels) + plt.imshow(train_features, cmap='gray') + plt.show() + break; + +# Count the number of occurances for label number 9 which is for the 'Bag' +count = 0 +for train_features, train_labels in dataloader.dataset: + if(train_labels==9): + count+=1 +print(count) + ################################################################# # With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. # From cbcfa05354d96ed4ee3fbe41be209aba95d54ec1 Mon Sep 17 00:00:00 2001 From: Cassie <46505951+cassieview@users.noreply.github.com> Date: Tue, 26 Jan 2021 23:33:42 +0000 Subject: [PATCH 112/120] update quickstart title to 'learn the basics' --- beginner_source/quickstart/README.txt | 2 +- beginner_source/quickstart/autograd_tutorial.py | 2 +- beginner_source/quickstart/buildmodel_tutorial.py | 2 +- beginner_source/quickstart/dataquickstart_tutorial.py | 2 +- beginner_source/quickstart/optimization_tutorial.py | 6 +++--- beginner_source/quickstart/quickstart_tutorial.py | 6 +++--- beginner_source/quickstart/saveloadrun_tutorial.py | 2 +- beginner_source/quickstart/tensor_tutorial.py | 2 +- beginner_source/quickstart/transforms_tutorial.py | 2 +- index.rst | 4 ++-- 10 files changed, 15 insertions(+), 15 deletions(-) diff --git a/beginner_source/quickstart/README.txt b/beginner_source/quickstart/README.txt index 0dcf2df4681..062e373ddd6 100644 --- a/beginner_source/quickstart/README.txt +++ b/beginner_source/quickstart/README.txt @@ -1,4 +1,4 @@ -PyTorch Quickstart +Learn the Basics ---------------------------------- 1. data_tutorial.py diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index eb0eba1e698..023dd138879 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -1,6 +1,6 @@ """ -`Quickstart `_ > +`Learn the Basics `_ > `Tensors `_ > `Datasets & DataLoaders `_ > `Transforms `_ > diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 1ffce4532e2..283453aa1ef 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -1,6 +1,6 @@ """ -`Quickstart `_ > +`Learn the Basics `_ > `Tensors `_ > `Datasets & DataLoaders `_ > `Transforms `_ > diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 5c2a3bba838..79d14554de2 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -1,6 +1,6 @@ """ -`Quickstart `_ > +`Learn the Basics `_ > `Tensors `_ > **Datasets & DataLoaders** > `Transforms `_ > diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index bebe2ceaf97..6e8982866e0 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -1,5 +1,5 @@ """ -`Quickstart `_ > +`Learn the Basics `_ > `Tensors `_ > `Datasets & DataLoaders `_ > `Transforms `_ > @@ -114,7 +114,7 @@ Putting it all together ----------------------- -Below is the complete code for the optimization loop. If you want a complete runnable example of training the model, refer to the `main quickstart page `_. The code below is commented to explain what goes on, but essentially it is put together from concepts that we have described above. +Below is the complete code for the optimization loop. If you want a complete runnable example of training the model, refer to the `main page `_. The code below is commented to explain what goes on, but essentially it is put together from concepts that we have described above. .. code-block:: Python @@ -171,7 +171,7 @@ def myCrossEntropyLoss(outputs, labels): loss = myCrossEntropyLoss(model_prediction, true_value) -A more in depth explanation of PyTorch cost functions is outside the scope of the quickstart but you can learn more +A more in depth explanation of PyTorch cost functions is outside the scope of the tutorial but you can learn more about the different common cost functions for deep learning in the PyTorch `documentation `_. """ diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 74f8eb0ff12..68e38ab08e2 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -1,5 +1,5 @@ """ -**Quickstart** > +**Learn the Basics** > `Tensors `_ > `Datasets & DataLoaders `_ > `Transforms `_ > @@ -8,11 +8,11 @@ `Optimization `_ > `Save & Load Model `_ -PyTorch Quickstart +Learn the Basics =================== The basic machine learning concepts in any framework should include: Working with data, -Creating models, Optimizing Parameters, Saving and Loading Models. In this PyTorch Quickstart we will +Creating models, Optimizing Parameters, Saving and Loading Models. In this PyTorch tutorial we will go through these concepts and how to apply them with PyTorch. The dataset we will be using is the FashionMNIST clothing images dataset that demonstrates these core steps applied to create ML Models. diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index e97df39e6fe..2f16a09f5b5 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -1,5 +1,5 @@ """ -`Quickstart `_ > +`Learn the Basics `_ > `Tensors `_ > `Datasets & DataLoaders `_ > `Transforms `_ > diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index c8ff6940580..4d8941251d2 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -1,5 +1,5 @@ """ -`Quickstart `_ > +`Learn the Basics `_ > **Tensors** > `Datasets & DataLoaders `_ > `Transforms `_ > diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 6b7b0733cba..9eef4202d1f 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -1,5 +1,5 @@ """ -`Quickstart `_ > +`Learn the Basics `_ > `Tensors `_ > `Datasets & DataLoaders `_ > **Transforms** > diff --git a/index.rst b/index.rst index 3f249ff3e61..97fafb10718 100644 --- a/index.rst +++ b/index.rst @@ -16,7 +16,7 @@ Welcome to PyTorch Tutorials .. customcalloutitem:: :description: In this quickstart we will cover the basics of machine learning and how to apply them with PyTorch. You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step! - :header: PyTorch Quickstart + :header: Learn the Basics :button_link: beginner/quickstart/quickstart_tutorial.html :button_text: Get started with PyTorch @@ -64,7 +64,7 @@ Welcome to PyTorch Tutorials :tags: Getting-Started .. customcarditem:: - :header: PyTorch Quickstart + :header: Learn the Basics :card_description: Get started with a step-by-step guide to building neural networks with PyTorch. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: beginner/quickstart/quickstart_tutorial.html From c45be42717373a5a9e95c3db11a6c38b695fb529 Mon Sep 17 00:00:00 2001 From: suraj813 Date: Thu, 28 Jan 2021 02:16:52 -0500 Subject: [PATCH 113/120] refactor - till custom datasets --- .../quickstart/dataquickstart_tutorial.py | 174 +++++----- .../quickstart/quickstart_tutorial.py | 298 ++++++++++-------- beginner_source/quickstart/tensor_tutorial.py | 294 ++++++++--------- 3 files changed, 388 insertions(+), 378 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 79d14554de2..d06813e85aa 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -15,25 +15,17 @@ """ ################################################################# -# Getting Started With Data in PyTorch -# ----------------- -# -# Before we start building models with PyTorch, let's first learn how to load and process data. Data can be sourced from local files, cloud datastores and database queries. It comes in all sorts of forms and formats from structured tables to image, audio, text, video files and more. +# Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code +# to be decoupled from our model training code for better readability and modularity. +# PyTorch provides two data primitives: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``, and a number of other abstractions +# that allow you to use pre-loaded as well as your own custom datasets easily. +# ``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around +# the ``Dataset`` to enable easy access to the samples. # - -############################################################### -# .. figure:: /_static/img/quickstart/typesdata.png -# :alt: typesdata -# - -############################################################ -# Different data types require different python libraries to load and process such as `openCV `_ and `PIL `_ for images, `NLTK `_ and `spaCy `_ for text and `Librosa `_ for audio. -# -# If not properly organized, code for processing data samples can quickly get messy and become hard to maintain. Since different model architectures can be applied to many data types, we ideally want our dataset code to be decoupled from our model training code. To this end, PyTorch provides a simple Datasets interface for linking and managing collections of data. -# -# A whole set of example datasets such as Fashion MNIST that implement this interface are built into PyTorch extension libraries. They are subclasses of `torch.utils.data.Dataset` that have parameters and functions specific to the type of data and the particular dataset. The actual data samples can be downloaded from the internet. These are useful for benchmarking and testing your models before training on your own custom datasets. -# -# You can find some of these datasets +# A number of pre-loaded datasets such as FashionMNIST that implement this interface are built into PyTorch domain libraries. +# They all subclass ``torch.utils.data.Dataset`` and implement functions specific to the particular dataset. +# These are useful for prototyping and benchmarking your model before training it on your own custom datasets. +# You can find some of these datasets # here: `Image Datasets `_, # `Text Datasets `_, and # `Audio Datasets `_ @@ -42,69 +34,90 @@ ############################################################ # Loading a Dataset # ------------------- -# -# Here is an example of how to load the `Fashion-MNIST `_ dataset from torch vision. -# `Fashion-MNIST `_ is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. -# Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. Read more `here `_. -# -# To load the FashionMNIST Dataset we need to provide the following three parameters: -# - ``root`` is the path where the train/test data is stored. -# - ``train`` includes the training dataset. +# +# Here is an example of how to load the `Fashion-MNIST `_ dataset from TorchVision. +# Fashion-MNIST is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. +# Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. +# +# To load the `FashionMNIST Dataset `_ we need to provide the following three parameters: +# - ``root`` is the path where the train/test data is stored. +# - ``train`` includes the training dataset. # - ``download=True`` downloads the data from the internet if it's not available at root. # -import torch -from torch.utils.data import Dataset -import torchvision.datasets as datasets +import torch +from torch.utils.data import Dataset, DataLoader +from torchvision import datasets import matplotlib.pyplot as plt import numpy as np clothing = datasets.FashionMNIST( - 'data', # specifies data directory to store data - train=True, # specifies training or test dataset to use - transform=None, # specifies transforms to apply to features (images) - target_transform=None, # specifies transforms to apply to labels - download=True) # should the data be downloaded from the Internet + "data", # specifies data directory to store data + train=True, # specifies training or test dataset to use + transform=None, # specifies transforms to apply to features (images) + target_transform=None, # specifies transforms to apply to labels + download=True, # should the data be downloaded from the Internet +) ################################################################# # Iterating and Visualizing the Dataset # ----------------- -# -# Once we have the ``clothing`` dataset, we can index it manually like a list: ``clothing[index]``. Then use ``matplotlib`` to visualize the dataset. - -labels_map = {0 : 'T-Shirt', 1 : 'Trouser', 2 : 'Pullover', 3 : 'Dress', 4 : 'Coat', 5 : 'Sandal', 6 : 'Shirt', 7 : 'Sneaker', 8 : 'Bag', 9 : 'Ankle Boot'} -figure = plt.figure(figsize=(8,8)) +# +# Once we have the ``clothing`` dataset, we can index it manually like a list: ``clothing[index]``. +# Then use ``matplotlib`` to visualize the dataset. + +labels_map = { + 0: "T-Shirt", + 1: "Trouser", + 2: "Pullover", + 3: "Dress", + 4: "Coat", + 5: "Sandal", + 6: "Shirt", + 7: "Sneaker", + 8: "Bag", + 9: "Ankle Boot", +} +figure = plt.figure(figsize=(8, 8)) cols, rows = 3, 3 -for i in range(1, cols*rows +1): - sample_idx = np.random.randint(len(clothing)) - img = clothing[sample_idx][0] +for i in range(1, cols * rows + 1): + sample_idx = torch.random.randint(len(clothing), size=(1,)).item() + img, label = clothing[sample_idx] figure.add_subplot(rows, cols, i) - plt.title(labels_map[clothing[sample_idx][1]]) - plt.axis('off') - plt.imshow(img, cmap='gray') + plt.title(labels_map[label]) + plt.axis("off") + plt.imshow(img, cmap="gray") plt.show() ################################################################# # .. # .. figure:: /_static/img/quickstart/fashion_mnist.png # :alt: fashion_mnist + + +###################################################################### +# -------------- # ################################################################# # Creating a Custom Dataset # ----------------- # -# To work with your own data, we can implement a custom class that inherits from ``Dataset``. This custom class must implement three functions: `__init__`, `__len__`, and `__getitem__`. Let's look at a custom image dataset implementation. In this example, we have a number of images stored in a directory, and their labels stored separately in a CSV file. Here's what it looks like; in the following sections, we will break down what's happening in each function. +# To work with your own data, we can implement a custom class that inherits from ``Dataset``. +# This custom class must implement three functions: `__init__`, `__len__`, and `__getitem__`. +# Let's look at a custom image dataset implementation. In this example, we have a number of images stored +# in a directory, and their labels stored separately in a CSV file. +# in the following sections, we will break down what's happening in each function. # import os -import torch import pandas as pd from torch.utils.data import Dataset from torchvision import transforms, utils from torchvision.io import read_image + class CustomImageDataset(Dataset): def __init__(self, annotations_file, img_dir, transform=None): self.img_labels = pd.read_csv(annotations_file) @@ -118,23 +131,23 @@ def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() - img_path = os.path.join(self.root_dir, - self.img_labels.iloc[idx, 0]) + img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) image = read_image(img_path) label = self.img_labels.iloc[idx, 1:] - sample = {'image': image, 'label': label} + sample = {"image": image, "label": label} if self.transform: sample = self.transform(sample) - return sample - + return sample + + ################################################################# # Import the packages # ------- -# +# # Import ``os`` for file handling, ``torch`` for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and ``Dataset`` to implement the Dataset interface. -# +# # Example: # @@ -149,8 +162,8 @@ def __getitem__(self, idx): # __init__ # ----------------- # -# The __init__ function is run once when instantiating our Dataset object. Here, we use it to load -# the directory containing the images, and their labels (contained in a csv file). While creating the +# The __init__ function is run once when instantiating our Dataset object. Here, we use it to load +# the directory containing the images, and their labels (contained in a csv file). While creating the # Dataset object, we can optionally pass it the transform that should be run on the images. # # The labels.csv file looks like: :: @@ -159,61 +172,66 @@ def __getitem__(self, idx): # tshirt2.jpg, 0 # ...... # ankleboot999.jpg, 9 -# +# # Example: -# +# + def __init__(self, labels_file, img_dir, transform=None): self.img_labels = pd.read_csv(labels_file) self.img_dir = img_dir self.transform = transform + ################################################################# # __len__ # ----------------- # -# The __len__ function returns the number of samples in our dataset. -# +# The __len__ function returns the number of samples in our dataset. +# # Example: + def __len__(self): return len(self.img_labels) + ################################################################# # __getitem__ # ----------------- # # The __getitem__ function is the most important function in the Datasets interface. It takes a tensor or an index as input and returns a loaded sample from your dataset at the given indices. -# +# # If provided a tensor as an index, we convert the tensor to a list first. We then load the file at the given index from our image directory, as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a transform on and return. Transforms will be discussed in more detail in the next section: `Transforms `_ -# +# # Example: # + def __getitem__(self, idx): if torch.is_tensor(idx): idx = idx.tolist() - img_path = os.path.join(self.root_dir, - self.img_labels.iloc[idx, 0]) + img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) image = read_image(img_path) label = self.img_labels.iloc[idx, 1:] - sample = {'image': image, 'label': label} + sample = {"image": image, "label": label} if self.transform: sample = self.transform(sample) - return sample + return sample + ################################################################# # Preparing your data for training with DataLoaders # ------------------------------------------------- # -# Now we have an organized mechanism for managing data which is great, but there is still a lot of manual work we would have to do to train a model with our Dataset. -# -# For example we would have to manually maintain the code for: +# Now we have an organized mechanism for managing data which is great, but there is still a lot of manual work we would have to do to train a model with our Dataset. +# +# For example we would have to manually maintain the code for: +# +# * Batching +# * Shuffling +# * Parallel batch distribution # -# * Batching -# * Shuffling -# * Parallel batch distribution -# # The PyTorch Dataloader ``torch.utils.data.DataLoader`` is an iterator that handles all of this complexity for us, enabling us to load a dataset and focus on training our model. dataloader = DataLoader(clothing, batch_size=4, shuffle=True, num_workers=0) @@ -229,17 +247,17 @@ def __getitem__(self, idx): # Display image and label. for train_features, train_labels in dataloader.dataset: print(train_labels) - plt.imshow(train_features, cmap='gray') + plt.imshow(train_features, cmap="gray") plt.show() - break; + break # Count the number of occurances for label number 9 which is for the 'Bag' count = 0 for train_features, train_labels in dataloader.dataset: - if(train_labels==9): - count+=1 + if train_labels == 9: + count += 1 print(count) ################################################################# # With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. -# +# diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 68e38ab08e2..7fa6e3c7665 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -11,22 +11,37 @@ Learn the Basics =================== -The basic machine learning concepts in any framework should include: Working with data, -Creating models, Optimizing Parameters, Saving and Loading Models. In this PyTorch tutorial we will -go through these concepts and how to apply them with PyTorch. The dataset we will be using is the -FashionMNIST clothing images dataset that demonstrates these core steps applied to create ML Models. +Authors: +`Suraj Subramanian `_, +`Seth Juarez `_, +`Cassie Breviu `_, +`Dmitry Soshnikov `_, +`Ari Bornstein `_ -You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step. -Using this dataset we will be able to predict if the image is one of the following classes: T-shirt/top, -Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, or Ankle boot. Lets get started! +A basic machine learning workflow involves working with data, creating models, optimizing model +parameters, and saving the trained models. This tutorial introduces you to the complete ML workflow +as implemented in PyTorch, with links to learn more about of these concepts. + +We'll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs +to one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, +Bag, or Ankle boot. + +This tutorial assumes a basic familiarity with Python and Deep Learning concepts. + +Running the Tutorial Code +------------------ +You can run this tutorial in a few ways: + +- **In the cloud**: This is the easiest way to get started! Each section has a Colab link at the top, which opens a notebook with the code in a fully-hosted environment. Pro tip: Use Colab with a GPU runtime to speed up operations *Runtime > Change runtime type > GPU* +- **Locally**: This option requires you to setup PyTorch and TorchVision first on your local machine (`installation instructions `_). Download the notebook or copy the code into your favorite IDE. How to Use this Guide ----------------- -This guide is setup to cover machine learning concepts and how to apply them with PyTorch. This main page is a -highlevel intro to each step with the code examples to build the model. You have the option to jump -into the concepts introduced in each section to get more details and explanations to better understand each concept -and how to apply them with PyTorch. The topics are introduced in a sequenced order as listed below: +This page contains an overview of the code used at each step of the tutorial. If you're familiar with +other deep learning frameworks, this is a quick way to get acquainted with PyTorch's API. + +If this is your first time, head right into our step-by-step guide: .. include:: /beginner_source/quickstart/qs_toc.txt @@ -42,200 +57,220 @@ /beginner/quickstart/saveloadrun_tutorial -Running the Tutorial Code ------------------- -The navigation above allows you to run the Jupyter Notebook on the cloud, download the Jupyter Notebook -or download the python file to run locally. -If you want to run the code locally on your machine you will need some tools you -may or may not have installed already. -Below are some good tool options for configuring local development or for more detailed instructions check out `get started locally `_. -- `Visual Studio Code `_ : You can open run python code in Visual Studio Code or open a Jupyter Notebook in VS Code. +-------------- -- `Anaconda for Package Management `_ : You will need to install the packages using either ``pip`` or ``conda`` to run the code locally. Working with data ----------------- -""" +PyTorch has two data primitives to work with data: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``. +``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around +the ``Dataset``. -###################################################################### -# -# PyTorch has two basic data primitives: ``DataSet`` and ``DataLoader``. -# The `torchvision.datasets` ``DataSet`` object includes a ``transforms`` mechanism to -# modify data in-place. Below is an example of how to load that data from the PyTorch open datasets and transform the data to a normalized tensor. -# This example is using the `torchvision.datasets` which is a subclass from the primitive `torch.utils.data.Dataset`. Note that the primitive dataset doesnt have the built in transforms param like the built in dataset in `torchvision.datasets.` -# For more details on the concepts introduced here check out `Tensors `_, -# `DataSets & DataLoaders `_, -# and `Transforms `_. -# +""" import torch -import torch.nn as nn -import torch.onnx as onnx -import matplotlib.pyplot as plt +from torch import nn from torch.utils.data import DataLoader -from torchvision import datasets, transforms -import torch.nn.functional as F +from torchvision import datasets +from torchvision.transforms import ToTensor, Lambda, Compose +import matplotlib.pyplot as plt -classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] +###################################################################### +# The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like ImageNet, +# CIFAR, COCO (`full list here `_). In this tutorial, we +# use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and +# ``target_transform`` to modify the samples and labels respectively. + +classes = [ + "T-shirt/top", + "Trouser", + "Pullover", + "Dress", + "Coat", + "Sandal", + "Shirt", + "Sneaker", + "Bag", + "Ankle boot", +] # Download training data from open datasets. -training_data = datasets.FashionMNIST('data', train=True, download=True, - transform=transforms.Compose([transforms.ToTensor()]), - target_transform=transforms.Compose([ - transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) - ]) +training_data = datasets.FashionMNIST( + root="data", + train=True, + download=True, + transform=ToTensor(), + target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) # Download test data from open datasets. -test_data = datasets.FashionMNIST('data', train=False, download=True, - transform=transforms.Compose([transforms.ToTensor()]), - target_transform=transforms.Compose([ - transforms.Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) - ]) - +test_data = datasets.FashionMNIST( + root="data", + train=False, + download=True, + transform=ToTensor(), + target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) +###################################################################### +# We pass the ``Dataset`` as an argument to ``DataLoader``. This wraps an iterable over our dataset, and supports +# automatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element +# in the dataloader iterable will return a batch of 64 features and labels. + batch_size = 64 # Create data loaders. -train_dataloader = DataLoader(training_data, batch_size=batch_size, num_workers=0, pin_memory=True) -test_dataloader = DataLoader(test_data, batch_size=batch_size, num_workers=0, pin_memory=True) +train_dataloader = DataLoader(training_data, batch_size=batch_size) +test_dataloader = DataLoader(test_data, batch_size=batch_size) + +for X, y in test_dataloader: + print("Shape of X [N, C, H, W]: ", X.shape) + print("Shape of y: ", y.shape) + break + +###################################################################### +# -------------- +# ################################ # Creating Models -# --------------- -# -# There are two ways of creating models: in-line or as a class. -# The most common way to define a neural network is to use a class inherited -# from `nn.Module `_. -# It provides great parameter management across all nested submodules, which gives us more -# flexibility, because we can construct layers of any complexity, including the ones with shared weights. -# For more details checkout `building the model `_. +# ------------------ +# To define a neural network in PyTorch, we create a class that inherits +# from `nn.Module `_. We define the layers of the network +# in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate +# operations in the NN, we move it to the GPU if available. # Get cpu or gpu device for training. -device = 'cuda' if torch.cuda.is_available() else 'cpu' -print('Using {} device'.format(device)) +device = "cuda" if torch.cuda.is_available() else "cpu" +print("Using {} device".format(device)) # Define model class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() self.flatten = nn.Flatten() - self.layer1 = nn.Linear(28*28, 512) - self.layer2 = nn.Linear(512, 512) - self.output = nn.Linear(512, 10) - + self.softmax = nn.Softmax(dim=1) + self.nn_layers = nn.Sequential( + nn.Linear(28 * 28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, 10) + ) def forward(self, x): x = self.flatten(x) - x = F.relu(self.layer1(x)) - x = F.relu(self.layer2(x)) - x = self.output(x) - return F.softmax(x, dim=1) + x = self.nn_layers(x) + return self.softmax(x) + model = NeuralNetwork().to(device) - print(model) ###################################################################### -# Optimizing Parameters and Training -# --------------------- -# -# Optimizing model parameters requires a loss function, optimizer, -# and the optimization loop. -# Training a model is essentially an optimization process similar to the one we described in the -# `Autograd `_ section. We run the optimization process on the whole dataset -# several times, and each run is refered to as an **epoch**. During each run, we present data -# in **minibatches**, and for each minibatch compute gradients and correct parameters of the model -# according to back propagation algorithm. Read more about the `Optimization Loop `_. +# Read more about `building neural networks in PyTorch `_. # -# Cost function used to determine best parameters. -cost = torch.nn.BCELoss() -# This is used to create optimal parameters. -learning_rate = 1e-3 -optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) +###################################################################### +# -------------- +# + +##################################################################### +# Training the Model +# ---------------------------------------- +# To train a model, we need a `loss function `_ +# and an `optimizer `_. -# Create the training function. -def train(dataloader, model, loss, optimizer): +loss_fn = nn.BCELoss() +optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) + +####################################################################### +# In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and +# backpropagates the prediction error to adjust the model's parameters. + +def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) - for batch, (X, Y) in enumerate(dataloader): - X, Y = X.to(device), Y.to(device) - optimizer.zero_grad() + for batch, (X, y) in enumerate(dataloader): + X, y = X.to(device), y.to(device) + + # Compute prediction error pred = model(X) - loss = cost(pred, Y) + loss = loss_fn(pred, y) + + # Backpropagation + optimizer.zero_grad() loss.backward() optimizer.step() - + if batch % 100 == 0: loss, current = loss.item(), batch * len(X) - print(f'loss: {loss:>7f} [{current:>5d}/{size:>5d}]') - + print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") -# Create the validation/test function +############################################################################## +# We also check the model's performance against the test dataset to ensure it is learning. def test(dataloader, model): size = len(dataloader.dataset) model.eval() test_loss, correct = 0, 0 - with torch.no_grad(): - for batch, (X, Y) in enumerate(dataloader): - X, Y = X.to(device), Y.to(device) + for X, y in dataloader: + X, y = X.to(device), y.to(device) pred = model(X) - - test_loss += cost(pred, Y).item() - correct += (pred.argmax(1) == Y.argmax(1)).type(torch.float).sum().item() - + test_loss += loss_fn(pred, y).item() + correct += (pred.argmax(1) == y.argmax(1)).type(torch.float).sum().item() test_loss /= size correct /= size + print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n") - print(f'\nTest Error:\nacc: {(100*correct):>0.1f}%, avg loss: {test_loss:>8f}\n') - - -# Call the train and test function in a training loop with the number of epochs indicated. +############################################################################## +# The training process is conducted over several iterations (*epochs*). During each epoch, the model learns +# parameters to make better predictions. We print the model's accuracy and loss at each epoch; we'd like to see the +# accuracy increase and the loss decrease with every epoch. epochs = 5 - for t in range(epochs): - print(f'Epoch {t+1}\n-------------------------------') - train(train_dataloader, model, cost, optimizer) + print(f"Epoch {t+1}\n-------------------------------") + train(train_dataloader, model, loss_fn, optimizer) test(test_dataloader, model) -print('Done!') +print("Done!") + +###################################################################### +# Read more about `Training your model `_. +# + +###################################################################### +# -------------- +# ###################################################################### # Saving Models # ------------- -# -# PyTorch has different ways you can save your model. One way is to serialize the internal model state to a file. Another would be to use the built-in `ONNX `_ support. -# +# A common way to save a model is to serialize the internal state dictionary (containing the model parameters). + +torch.save(model.state_dict(), "model.pth") +print("Saved PyTorch Model State to model.pth") -torch.save(model.state_dict(), 'model.pth') -print('Saved PyTorch Model to model.pth') -# Save to ONNX, create dummy variable to traverse graph +###################################################################### +# -------------- +# -x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255 -onnx.export(model, x, 'model.onnx') -print('Saved onnx model to model.onnx') ###################################################################### # Loading Models # ---------------------------- -# -# Once a model has been serialized the process for loading the -# parameters includes re-creating the model shape and then loading -# the state dictionary. Once loaded the model can be used for either -# retraining or inference purposes (in this example it is used for -# inference). Check out more details on `saving, loading and running models with PyTorch `_ # +# The process for loading a model includes re-creating the model structure and loading +# the state dictionary into it. -loaded_model = NeuralNetwork() +model = NeuralNetwork() +model.load_state_dict(torch.load("model.pth")) -loaded_model.load_state_dict(torch.load('model.pth')) -loaded_model.eval() +############################################################# +# This model can now be used to make predictions. -# inference +loaded_model.eval() x, y = test_data[0][0], test_data[0][1] with torch.no_grad(): pred = loaded_model(x) @@ -244,10 +279,9 @@ def test(dataloader, model): ############################################################# +# Read more on `saving, loading and running models with PyTorch `_ # -# Looking for more resources? Check out the other tutorials on the `tutorials home page `_. # - -################################################################## +# # -# Authors: `Seth Juarez `_, `Cassie Breviu `_, `Dmitry Soshnikov `_, `Ari Bornstein `_, `Suraj Subramanian `_ + diff --git a/beginner_source/quickstart/tensor_tutorial.py b/beginner_source/quickstart/tensor_tutorial.py index 4d8941251d2..e194b949098 100644 --- a/beginner_source/quickstart/tensor_tutorial.py +++ b/beginner_source/quickstart/tensor_tutorial.py @@ -8,254 +8,212 @@ `Optimization `_ > `Save & Load Model `_ -Tensors and Operations +Tensors ========================== -**Tensor** is the basic computational unit in PyTorch. It is very -similar to **NumPy array**, and supports similar operations. However, -there are two very important features of Torch tensors that make them -especially useful for training large-scale neural networks. First, tensor operations can be performed on -GPUs or other specialized hardware to accelerate computing. Second, tensor operations support -automatic differentiation using `pytorch.autograd engine `__. +Tensors are a specialized data structure that are very similar to arrays and matrices. +In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model’s parameters. -Lets look at how we can easily convert between Torch tensors and NumPy arrays: +Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators. Tensors +are also optimized for automatic differentiation (we'll see more about that later in the `Autograd `__ +section). If you’re familiar with ndarrays, you’ll be right at home with the Tensor API. If not, follow along! """ import torch import numpy as np -np_array = np.arange(10) -tensor = torch.from_numpy(np_array) - -print(f"Tensor={tensor}, Array={tensor.numpy()}") - ###################################################################### -# .. note:: -# When using CPU for computations, tensors converted from arrays -# share the same memory for data. Thus, changing the underlying array -# will also affect the tensor. +# Tensor Initialization +# ~~~~~~~~~~~~~~~~~~~~~ # - - -###################################################################### -# Creating Tensors -# ~~~~~~~~~~~~~~~~ +# Tensors can be initialized in various ways. Take a look at the following examples: # -# The fastest way to create a tensor is to define an *uninitialized* -# tensor. This means the values of this tensor are not set and depend on the -# data that was there in memory: +# **Directly from data** # +# Tensors can be created directly from data. The data type is automatically inferred. -x = torch.empty(3, 6) - +data = [[1, 2],[3, 4]] +x_data = torch.tensor(data) ###################################################################### -# In practice, we often want to create tensors initialized to some values, -# such as zeros, ones or random values. You can also specify the -# type of elements using ``dtype`` parameter, and chosing one of ``torch`` -# types: +# **From a NumPy array** # +# Tensors can be created from NumPy arrays (and vice versa - see :ref:`bridge-to-np-label`). +np_array = np.array(data) +x_np = torch.from_numpy(np_array) -x = torch.randn(3, 5) -y = torch.zeros(3, 5, dtype=torch.int) -z = torch.ones(3, 5, dtype=torch.double) -###################################################################### -# You can create random tensors with values sampled from different -# distributions, as described `in the -# documentation `__. -# -# Similarly to NumPy, you can use ``eye`` to create a diagonal identity -# matrix: +############################################################### +# **From another tensor:** # +# The new tensor retains the properties (shape, datatype) of the argument tensor, unless explicitly overridden. + +x_ones = torch.ones_like(x_data) # retains the properties of x_data +print(f"Ones Tensor: \n {x_ones} \n") -I = torch.eye(10) +x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides the datatype of x_data +print(f"Random Tensor: \n {x_rand} \n") ###################################################################### -# You can create new tensors with the same properties or size as -# existing tensors: +# **With random or constant values:** # +# ``shape`` is a tuple of tensor dimensions. In the functions below, it determines the dimensionality of the output tensor. -print(z.new_ones(2, 2)) # new_ method allows specifying new size -# _like method supports overriding dtype -print(torch.zeros_like(x, dtype=torch.long)) +shape = (2,3,) +rand_tensor = torch.rand(shape) +ones_tensor = torch.ones(shape) +zeros_tensor = torch.zeros(shape) +print(f"Random Tensor: \n {rand_tensor} \n") +print(f"Ones Tensor: \n {ones_tensor} \n") +print(f"Zeros Tensor: \n {zeros_tensor}") -###################################################################### -# Size of the tensor can be obtained using ``.size()`` method, which -# returns a tuple-like object: -# -print(z.size()) # Prints [3.0] +###################################################################### +# -------------- +# ###################################################################### -# Tensor Operations +# Tensor Attributes # ~~~~~~~~~~~~~~~~~ # -# Tensors support all basic arithmetic operations, which can be specified -# in different ways: -# - Using operators, such as ``+``, ``-``, etc. \* -# - Using functions such as ``add``, ``mult``, etc. Functions can either return values, or store them in the specified ouput variable (using ``out=`` parameter) -# - In-place operations, which modify one of the arguments. Those operations have ``_`` appended to their name, eg. ``add_``. -# Complete reference to all tensor operations can be found `in the -# documentation `__. -# -# Let us see examples of those operations on two tensors, ``x`` and ``y``. -# +# Tensor attributes describe their shape, datatype, and the device on which they are stored. -x = torch.randn(3, 5) -y = torch.randn(3, 5) +tensor = torch.rand(3,4) + +print(f"Shape of tensor: {tensor.shape}") +print(f"Datatype of tensor: {tensor.dtype}") +print(f"Device tensor is stored on: {tensor.device}") ###################################################################### -# Using operator notation -# ^^^^^^^^^^^^^^^^^^^^^^^ -# -# We can use overloaded arithmetic operators, such as ``+`` and ``*``: +# -------------- # -z = x*y - - ###################################################################### -# Note, that ``*`` means elementwise product, and not the matrix product. -# To compute matrix product, we need to use `@` operator or ``matmul`` function, as shown -# below. -# -# Using functions -# ^^^^^^^^^^^^^^^ +# Tensor Operations +# ~~~~~~~~~~~~~~~~~ # -# While only some operations are available as Python operators, `many more -# functions `__ -# can be specified using the full name. In the example below, ``t`` -# transposes the matrix, and ``matmul`` means matrix multiplication: +# Over 100 tensor operations, including arithmetic, linear algebra, matrix manipulation (transposing, +# indexing, slicing), sampling and more are +# comprehensively described `here `__. # +# Each of these operations can be run on the GPU (at typically higher speeds than on a +# CPU). If you’re using Colab, allocate a GPU by going to Runtime > Change runtime type > GPU. +# +# By default, tensors are created on the CPU. We need to explicitly move tensors to the GPU using +# ``.to`` method (after checking for GPU availability). Keep in mind that copying large tensors +# across devices can be expensive in terms of time and memory! -z = torch.matmul(x, y.t()) +# We move our tensor to the GPU if available +if torch.cuda.is_available(): + tensor = tensor.to('cuda') ###################################################################### -# Simple operations (addition, multiplication, etc.) also have -# corresponsing functions, and can be called either as methods, or as -# functions: +# Try out some of the operations from the list. +# If you're familiar with the NumPy API, you'll find the Tensor API a breeze to use. # -z = x.add(y) -z = torch.add(x, y) +############################################################### +# **Standard numpy-like indexing and slicing:** +tensor = torch.ones(4, 4) +print('First row: ',tensor[0]) +print('First column: ', tensor[:, 0]) +print('Last column:', tensor[..., -1]) +tensor[:,1] = 0 +print(tensor) ###################################################################### -# Sometimes it may be more convenient to store the result into specified -# variable, instead of returning it from a function. In this case you can -# use ``out=`` parameter: -# - -torch.add(x, y, out=z) - +# **Joining tensors** You can use ``torch.cat`` to concatenate a sequence of tensors along a given dimension. +# See also `torch.stack `__, +# another tensor joining op that is subtly different from ``torch.cat``. +t1 = torch.cat([tensor, tensor, tensor], dim=1) +print(t1) ###################################################################### -# In-place operations -# ^^^^^^^^^^^^^^^^^^^ -# -# When training neural networks, you often need to **modify** the weights, -# i.e. perform some operation and then store the result into the original -# variable. Those operations are called **in-place operations**, and they -# are marked by the ``_`` symbol at the end of their name: -# +# **Arithmetic operations** -x.add_(y) # x will be modified +# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value +y1 = tensor @ tensor.T +y2 = tensor.matmul(tensor.T) +torch.matmul(tensor, tensor.T, out=y3) -###################################################################### -# .. note:: -# In-place operations save some memory, but can be problematic when -# computing derivatives because of an immediate loss -# of history. Hence, their use is discouraged. + +# This computes the element-wise product. z1, z2, z3 will have the same value +z1 = tensor * tensor +z2 = tensor.mul(tensor) +torch.mul(tensor, tensor, out=z3) ###################################################################### -# Resizing and Indexing -# ~~~~~~~~~~~~~~~~~~~~~ -# -# Often you need to change the shape of the tensor without modifying -# its values, eg. to add an extra dimension. To do that, you can use -# ``view`` method, which provides a **view** to the same in-memory values -# using different dimensions: -# +# **Single-element tensors** If you have a one-element tensor, for example by aggregating all +# values of a tensor into one value, you can convert it to a Python +# numerical value using ``item()``: -print('Original size of x =',x.size()) # original size of x is 3x5 -print('Size after reshaping is',x.view(5, 3, 1).size()) # will give size 5x3x1 -print('Reshaped tensor:\n',x.view(5, -1)) # will result in size 5x3 +agg = tensor.sum() +agg_item = agg.item() +print(agg_item, type(agg_item)) ###################################################################### -# The number of elements in a view should be the same as in the -# original tensor. You can use ``-1`` in one of the dimensions to -# figure out this dimension automatically. -# +# **In-place operations** +# Operations that store the result into the operand are called in-place. They are denoted by a ``_`` suffix. +# For example: ``x.copy_(y)``, ``x.t_()``, will change ``x``. +print(tensor, "\n") +tensor.add_(5) +print(tensor) ###################################################################### -# .. note:: ``view`` is similar to ``reshape`` operation in NumPy. There -# is also a ``reshape`` method available in PyTorch, and it is more -# powerful than ``view``, because it can also reshape non-contiguous -# arrays by copying them to the new shape. However, in vast majority of -# cases you can use ``view`` and make sure that no data copying occurs, -# and the operation is always efficient. -# +# .. note:: +# In-place operations save some memory, but can be problematic when computing derivatives because of an immediate loss +# of history. Hence, their use is discouraged. + ###################################################################### -# Tensors support all slicing operations that exist in NumPy: +# -------------- # -print(x.size()) # original size of x is 3x5 -print('First row: ',x[0]) -print('First column: ', x[:, 0]) -print('Last column:', x[..., -1]) - ###################################################################### -# If you have a one-element tensor, for example, after aggregating all -# values of the tensor into one value, you can convert it to a Python -# numerical value using ``item()``: +# .. _bridge-to-np-label: # +# Bridge with NumPy +# ~~~~~~~~~~~~~~~~~ +# Tensors on the CPU and NumPy arrays can share their underlying memory +# locations, and changing one will change the other. -val = x.sum().item() # will compute the sum of all elements -print(val) ###################################################################### -# Hardware-Accelerated Computations -# ~~~~~~~~~~~~~~~~ -# -# One of the major benefits of using PyTorch is the ability to perform -# tensor operations on GPUs and some other specialized hardware. To do that, -# we need to explicitly **move** tensors to another computing platform using ``.to`` method. -# -# In most of the cases, we check for the availability of GPU in the beginning -# of the script, and define the ``device`` object accordingly. Then we move all -# tensors to that device before performing the computations: -# +# Tensor to NumPy array +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +t = torch.ones(5) +print(f"t: {t}") +n = t.numpy() +print(f"n: {n}") -if torch.cuda.is_available(): - device = torch.device("cuda") -else: - device = torch.device("cpu") +###################################################################### +# A change in the tensor reflects in the NumPy array. -print("Doing computations on {}".format(device)) +t.add_(1) +print(f"t: {t}") +print(f"n: {n}") -x = torch.randn(3, 5, device=device) # create tensor on specified device -y = torch.ones_like(x) # create tensor on CPU -y = y.to(device) # move tensor to another device -z = x+y # this is performed on GPU if it is available -print(z.to("cpu", torch.double)) +###################################################################### +# NumPy array to Tensor +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +n = np.ones(5) +t = torch.from_numpy(n) ###################################################################### -# In the last operation, when we move the tensor back to the CPU, we can -# also change the ``dtype``. This does not result in additional -# computational time, because we need to copy and transform the data when -# moving it from GPU anyway. -# +# Changes in the NumPy array reflects in the tensor. +np.add(n, 1, out=n) +print(f"t: {t}") +print(f"n: {n}") From 68854b46c7f1603afa616921f9653e2870c2298e Mon Sep 17 00:00:00 2001 From: suraj813 Date: Thu, 28 Jan 2021 19:17:17 -0500 Subject: [PATCH 114/120] Updated data --- .../quickstart/dataquickstart_tutorial.py | 171 ++++++++---------- .../quickstart/quickstart_tutorial.py | 2 +- 2 files changed, 75 insertions(+), 98 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index d06813e85aa..e033ff99894 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -17,15 +17,14 @@ ################################################################# # Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code # to be decoupled from our model training code for better readability and modularity. -# PyTorch provides two data primitives: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``, and a number of other abstractions -# that allow you to use pre-loaded as well as your own custom datasets easily. +# PyTorch provides two data primitives: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset`` +# that allow you to use pre-loaded datasets as well as your own data. # ``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around # the ``Dataset`` to enable easy access to the samples. # -# A number of pre-loaded datasets such as FashionMNIST that implement this interface are built into PyTorch domain libraries. -# They all subclass ``torch.utils.data.Dataset`` and implement functions specific to the particular dataset. -# These are useful for prototyping and benchmarking your model before training it on your own custom datasets. -# You can find some of these datasets +# PyTorch domain libraries provide a number of pre-loaded datasets (such as FashionMNIST) that +# subclass ``torch.utils.data.Dataset`` and implement functions specific to the particular data. +# They can be used to prototype and benchmark your model. You can find them # here: `Image Datasets `_, # `Text Datasets `_, and # `Audio Datasets `_ @@ -46,18 +45,19 @@ # import torch -from torch.utils.data import Dataset, DataLoader +from torch.utils.data import Dataset from torchvision import datasets +from torchvision.transforms import ToTensor, Lambda import matplotlib.pyplot as plt -import numpy as np + clothing = datasets.FashionMNIST( - "data", # specifies data directory to store data - train=True, # specifies training or test dataset to use - transform=None, # specifies transforms to apply to features (images) - target_transform=None, # specifies transforms to apply to labels - download=True, # should the data be downloaded from the Internet -) + root="data", + train=True, + download=True, + transform=ToTensor(), + target_transform=None +) ################################################################# @@ -82,12 +82,12 @@ figure = plt.figure(figsize=(8, 8)) cols, rows = 3, 3 for i in range(1, cols * rows + 1): - sample_idx = torch.random.randint(len(clothing), size=(1,)).item() + sample_idx = torch.randint(len(clothing), size=(1,)).item() img, label = clothing[sample_idx] figure.add_subplot(rows, cols, i) plt.title(labels_map[label]) plt.axis("off") - plt.imshow(img, cmap="gray") + plt.imshow(img.squeeze(), cmap="gray") plt.show() ################################################################# @@ -104,67 +104,46 @@ # Creating a Custom Dataset # ----------------- # -# To work with your own data, we can implement a custom class that inherits from ``Dataset``. +# To work with our own data, we can implement a custom class that inherits from ``Dataset``. # This custom class must implement three functions: `__init__`, `__len__`, and `__getitem__`. -# Let's look at a custom image dataset implementation. In this example, we have a number of images stored -# in a directory, and their labels stored separately in a CSV file. -# in the following sections, we will break down what's happening in each function. +# Let's look at a custom image dataset implementation. In this example, we have a number of FashionMNIST images stored +# in a directory (``img_dir``), and their labels stored separately in a CSV file (``annotations_file``). # +# In the next sections, we'll break down what's happening in each of these functions. + import os import pandas as pd -from torch.utils.data import Dataset -from torchvision import transforms, utils from torchvision.io import read_image - class CustomImageDataset(Dataset): - def __init__(self, annotations_file, img_dir, transform=None): + def __init__(self, annotations_file, img_dir, transform=None, target_transform=None): self.img_labels = pd.read_csv(annotations_file) self.img_dir = img_dir self.transform = transform + self.target_transform = target_transform def __len__(self): return len(self.img_labels) def __getitem__(self, idx): - if torch.is_tensor(idx): - idx = idx.tolist() - - img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) + img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0]) image = read_image(img_path) - label = self.img_labels.iloc[idx, 1:] - sample = {"image": image, "label": label} - + label = self.img_labels.iloc[idx, 1] if self.transform: - sample = self.transform(sample) - + image = self.transform(image) + if self.target_transform: + label = self.target_transform(label) + sample = {"image": image, "label": label} return sample -################################################################# -# Import the packages -# ------- -# -# Import ``os`` for file handling, ``torch`` for PyTorch, `pandas `_ for loading labels, `torch vision `_ to read image files, and ``Dataset`` to implement the Dataset interface. -# -# Example: -# - -import os -import torch -import pandas as pd -from torchvision.io import read_image -from torch.utils.data import Dataset -from torch.utils.data import DataLoader - ################################################################# # __init__ # ----------------- # -# The __init__ function is run once when instantiating our Dataset object. Here, we use it to load -# the directory containing the images, and their labels (contained in a csv file). While creating the -# Dataset object, we can optionally pass it the transform that should be run on the images. +# The __init__ function is run once when instantiating the Dataset object. We initialize +# the directory containing the images, the annotations file, and both transforms (if). # # The labels.csv file looks like: :: # @@ -172,15 +151,13 @@ def __getitem__(self, idx): # tshirt2.jpg, 0 # ...... # ankleboot999.jpg, 9 -# -# Example: -# -def __init__(self, labels_file, img_dir, transform=None): - self.img_labels = pd.read_csv(labels_file) +def __init__(self, annotations_file, img_dir, transform=None, target_transform=None): + self.img_labels = pd.read_csv(annotations_file) self.img_dir = img_dir self.transform = transform + self.target_transform = target_transform ################################################################# @@ -200,64 +177,64 @@ def __len__(self): # __getitem__ # ----------------- # -# The __getitem__ function is the most important function in the Datasets interface. It takes a tensor or an index as input and returns a loaded sample from your dataset at the given indices. -# -# If provided a tensor as an index, we convert the tensor to a list first. We then load the file at the given index from our image directory, as well as the image label from our pandas annotations DataFrame. This image and label are then wrapped in a single sample dictionary which we can apply a transform on and return. Transforms will be discussed in more detail in the next section: `Transforms `_ -# -# Example: -# - +# The __getitem__ function loads and returns a sample from the dataset at the given index ``idx``. +# Based on the index, it identifies the image's location on disk, converts that to a tensor using ``read_image``, retrieves the +# corresponding label from the csv data in ``self.img_labels``, calls the transform functions on them (if applicable), and returns the +# tensor image and corresponding label in a Python dict. def __getitem__(self, idx): - if torch.is_tensor(idx): - idx = idx.tolist() - img_path = os.path.join(self.root_dir, self.img_labels.iloc[idx, 0]) + img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0]) image = read_image(img_path) - label = self.img_labels.iloc[idx, 1:] - sample = {"image": image, "label": label} + label = self.img_labels.iloc[idx, 1] if self.transform: - sample = self.transform(sample) + image = self.transform(image) + if self.target_transform: + label = self.target_transform(label) + sample = {"image": image, "label": label} return sample +###################################################################### +# -------------- +# + + ################################################################# # Preparing your data for training with DataLoaders # ------------------------------------------------- +# The ``Dataset`` retrieves our dataset's features and labels one sample at a time. While training a model, we typically want to +# pass samples in "minibatches", reshuffle the data at every epoch to reduce model overfitting, and use Python's ``multiprocessing`` to +# speed up data retrieval. # -# Now we have an organized mechanism for managing data which is great, but there is still a lot of manual work we would have to do to train a model with our Dataset. -# -# For example we would have to manually maintain the code for: -# -# * Batching -# * Shuffling -# * Parallel batch distribution -# -# The PyTorch Dataloader ``torch.utils.data.DataLoader`` is an iterator that handles all of this complexity for us, enabling us to load a dataset and focus on training our model. +# ``DataLoader`` is an iterable that abstracts this complexity for us in an easy API. + +from torch.utils.data import DataLoader -dataloader = DataLoader(clothing, batch_size=4, shuffle=True, num_workers=0) +dataloader = DataLoader(clothing, batch_size=64, shuffle=True) ########################### # Iterate through the Dataset # -------------------------- # -# We have loaded that dataset into the ``dataloader`` and can iterate through the dataset as needed. -# Below is a simple example of how to iterate and display an image or return a label count: - +# We have loaded that dataset into the ``Dataloader`` and can iterate through the dataset as needed. +# Each iteration below returns a batch of ``train_features`` and ``train_labels``(containing ``batch_size=64`` features and labels respectively). +# Because we specified ``shuffle=True``, after we iterate over all batches the data is shuffled (for finer-grained control over +# the data loading order, take a look at `Samplers `_). # Display image and label. -for train_features, train_labels in dataloader.dataset: - print(train_labels) - plt.imshow(train_features, cmap="gray") - plt.show() - break - -# Count the number of occurances for label number 9 which is for the 'Bag' -count = 0 -for train_features, train_labels in dataloader.dataset: - if train_labels == 9: - count += 1 -print(count) +train_features, train_labels = next(iter(dataloader)) +print(f"Feature batch shape: {train_features.size()}") +print(f"Labels batch shape: {train_labels.size()}") +img = train_features[0].squeeze() +label = train_labels[0] +plt.imshow(img, cmap="gray") +plt.show() +print(f"Label: {label}") + ################################################################# -# With this we have all we need to know to load and process data of any kind in PyTorch to train deep learning models. -# +# Further Reading +# ~~~~~~~~~~~~~~~~~ +# - `torch.utils.data API `_ + + diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 7fa6e3c7665..4acb4a71628 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -77,7 +77,7 @@ import matplotlib.pyplot as plt ###################################################################### -# The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like ImageNet, +# The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like # CIFAR, COCO (`full list here `_). In this tutorial, we # use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and # ``target_transform`` to modify the samples and labels respectively. From bcfd8205aa1b84967bc1e6d3a07c87f740ead719 Mon Sep 17 00:00:00 2001 From: suraj813 Date: Sat, 30 Jan 2021 16:21:21 -0500 Subject: [PATCH 115/120] updated buildmodel --- .../quickstart/buildmodel_tutorial.py | 199 ++++++++++-------- .../quickstart/dataquickstart_tutorial.py | 22 +- .../quickstart/quickstart_tutorial.py | 13 +- .../quickstart/transforms_tutorial.py | 160 +++----------- 4 files changed, 158 insertions(+), 236 deletions(-) diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index 283453aa1ef..fc157fe6bd3 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -1,5 +1,4 @@ """ - `Learn the Basics `_ > `Tensors `_ > `Datasets & DataLoaders `_ > @@ -9,36 +8,22 @@ `Optimization `_ > `Save & Load Model `_ -Build the Neural Network Model +Build the Neural Network =================== -""" +Neural networks comprise of layers/modules that perform operations on data. +The `torch.nn `_ namespace provides all the building blocks you need to +build your own neural network. Every module in PyTorch subclasses the `nn.Module `_. +A neural network is a module itself that consists of other modules (layers). This nested structure allows for +building and managing complex architectures easily. -################################################################# -# -# Now that we have loaded and transformed the data, we can build the neural network model. -# Neural network consists of a number of layers and PyTorch `torch.nn `_ namespace provides predefined layers -# that helps us build the model. -# -# The most common way to define a neural network is to use a class inherited -# from `nn.Module `_. -# It provides great parameter management across all nested submodules, which gives us more -# flexibility, because we can construct layers of any complexity, including ones with shared weights. -# -# In the below example, for our FashionMNIST image dataset, we will create a dense multi-layer network. -# Lets break down the steps to build this model below. -# +In the following sections, we'll build a neural network to classify images in the FashionMNIST dataset. -############################################# -# Import the Packages -# -------------------------- -# +""" import os import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.onnx as onnx +from torch import nn from torch.utils.data import DataLoader from torchvision import datasets, transforms @@ -46,13 +31,10 @@ ############################################# # Get Device for Training # ----------------------- -# We want to be able to train our model on both CPU and GPU, if it is available. It is common practice to -# define a variable ``device`` which will designate the device we will be training on. -# We check to see if `torch.cuda `_ -# is available to use the GPU, else we will use the CPU. -# -# Example: -# +# We want to be able to train our model on a hardware accelerator like the GPU, +# if it is available. Let's check to see if +# `torch.cuda `_ is available, else we +# continue to use the CPU. device = 'cuda' if torch.cuda.is_available() else 'cpu' print('Using {} device'.format(device)) @@ -60,60 +42,74 @@ ############################################## # Define the Class # ------------------------- -# -# First we define the `NeuralNetwork` class which inherits from ``nn.Module``, the base class for -# building all neural network modules in PyTorch. We use the ``__init__`` function to define and initialize the NN layers that will be then called in the module's ``forward`` function. -# Then we call the ``NeuralNetwork`` class and assign the device. When training -# the model we will call ``model`` and pass the data (x) into the forward function and -# through each layer of our network. -# -# +# We define our neural network by subclassing ``nn.Module``, and +# initialize the neural network layers in ``__init__``. Every ``nn.Module`` subclass implements +# the operations on input data in the ``forward`` method. class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() - self.flatten = nn.Flatten() - self.layer1 = nn.Linear(28*28, 512) - self.layer2 = nn.Linear(512, 512) - self.output = nn.Linear(512, 10) + self.flatten = nn.Flatten(start_dim=1, end_dim=2) + self.linear_relu_stack = nn.Sequential( + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, 10), + nn.ReLU() + ) def forward(self, x): x = self.flatten(x) - x = F.relu(self.layer1(x)) - x = F.relu(self.layer2(x)) - x = self.output(x) - return F.softmax(x, dim=1) + logits = self.linear_relu_stack(x) + return logits + +############################################## +# We create an instance of ``NeuralNetwork``, and move it to the ``device``, and print +# it's structure. model = NeuralNetwork().to(device) print(model) -input = torch.rand(5, 28, 28) -# equivalent to model.forward(input) -model(input) +############################################## +# To use the model, we pass it the input data. This executes the model's ``forward``, +# along with some `background operations `_. +# Do not call ``model.forward()`` directly! +# +# Calling the model on the input returns a 10-dimensional tensor with raw predicted values for each class. +# We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module. + +X = torch.rand(1, 28, 28) +logits = model(X) +pred_probab = nn.Softmax(dim=1)(logits) +y_pred = pred_probab.argmax(1) +print(f"Predicted class: {y_pred}") + + +###################################################################### +# -------------- +# + ############################################## # Model Layers # ------------------------- # -# Lets break down each layer in the FashionMNIST model. To illustrate it, we -# will take a sample minibatch of 100 images of size 28x28 and see what happens to it as -# we pass it through the network. The code in the sections below would essentially explain -# what happens inside the ``forward`` method of our ``NeuralNetwork`` class. -# +# Lets break down the layers in the FashionMNIST model. To illustrate it, we +# will take a sample minibatch of 3 images of size 28x28 and see what happens to it as +# we pass it through the network. -input_image = torch.rand(100,28,28) +input_image = torch.rand(3,28,28) print(input_image.size()) ################################################## # nn.Flatten # ----------------------------------------------- -# -# First we call `nn.Flatten `_ to reduce tensor dimensions to one. -# -# In our case, flatten keeps the minibatch dimension, but two image dimensions are -# reduced to one: - +# We initialize the `nn.Flatten `_ +# layer to convert each 2D 28x28 image into a contiguous array of 784 pixel values ( +# the minibatch dimension (at dim=0) is maintained). + flatten = nn.Flatten(start_dim=1, end_dim=2) flat_image = flatten(input_image) print(flat_image.size()) @@ -121,44 +117,67 @@ def forward(self, x): ############################################## # nn.Linear # ------------------------------- +# The `linear layer `_ +# is a module that applies a linear transformation on the input using it's stored weights and biases. # -# Now that we have flattened our tensor dimension we will pass our data through a `linear layer `_. The linear layer is -# a module that applies a linear transformation on the input using it's stored weights and biases. -# - -layer1 = nn.Linear(in_features=28*28, out_features=512) +layer1 = nn.Linear(in_features=28*28, out_features=20) hidden1 = layer1(flat_image) print(hidden1.size()) + ################################################# -# Activation Functions +# nn.ReLU() # ------------------------- +# Non-linear activations are what create the complex mappings between the model's inputs and outputs. +# They are applied after linear transformations to introduce *nonlinearity*, helping neural networks +# learn a wide variety of phenomena. # -# In between layers of a neural network, we need to put non-linear activation functions, -# such as `nn.ReLU `_ (which is often -# used in between hidden layers) or `nn.Softmax `_, -# which turns the output of the network into probabilities by rescaling values between 0 and 1, and all sum to one. -# +# In this model, we use `nn.ReLU `_ between our +# linear layers, but there's other activations to introduce non-linearity in your model. + +print(f"Before ReLU: {hidden1}\n\n") +hidden1 = nn.ReLU()(hidden1) +print(f"After ReLU: {hidden1}") + +# nn.Sequential +# ------------------------------- +# `nn.Sequential `_ is an ordered +# container of modules. The data is passed through all the modules in the same order as defined. You can use +# sequential containers to put together a quick network like ``seq_modules``. + +seq_modules = nn.Sequential( + flatten, + layer1, + nn.ReLU(), + nn.Linear(20, 10) +) +input_image = torch.rand(3,28,28) +logits = seq_modules(input_image) + +################################################################ +# nn.Softmax() +# ------------------------- +# The last linear layer of the neural network returns `logits` - raw values in [-\infty, \infty] - which are passed to the +# `nn.Softmax `_ module. The logits are scaled to values +# [0, 1] representing the model's predicted probabilities for each class. ``dim`` parameter indicates the dimension along +# which the values must sum to 1. + +softmax = nn.Softmax(dim=1) +pred_probab = softmax(logits) -layer2 = nn.Linear(512,512) -output = nn.Linear(512,10) -hidden2 = layer2(F.relu(hidden1)) -print('Hidden 2 output size =',hidden2.size()) -z = output(F.relu(hidden2)) -out = F.softmax(z) -print('Output size =',out.size()) ################################################# -# Parameter Tracking +# Model Parameters # ------------------------- +# Many layers inside a neural network are *parameterized*, i.e. have associated weights +# and biases that are optimized during training. Subclassing ``nn.Module`` automatically +# tracks all fields defined inside your model object, and makes all parameters +# accessible using your model's ``parameters()`` or ``named_parameters()`` methods. +# +# In this example, we iterate over each parameter, and print its size and a preview of its values. # -# The main reason to put all code inside a class inherited from ``nn.Module`` is to -# utilize **parameter tracking**. Most of the layers inside a neural network, -# in our case all linear layers, have associated weights and biases that need to -# be adjusted during training. ``nn.Module`` automatically tracks all fields defined -# inside the class, and makes all parameters accessible using ``parameters()`` -# or ``named_parameters()`` methods. Let's have a look at the first two parameters of -# our neural network that were defined in the beginning of this section: -# -print(list(model.named_parameters())[0:2]) +print("Model structure: ", model, "\n\n") + +for name, param in model.named_parameters(): + print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n") \ No newline at end of file diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index e033ff99894..06060392d68 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -38,11 +38,13 @@ # Fashion-MNIST is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. # Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. # -# To load the `FashionMNIST Dataset `_ we need to provide the following three parameters: -# - ``root`` is the path where the train/test data is stored. -# - ``train`` includes the training dataset. -# - ``download=True`` downloads the data from the internet if it's not available at root. -# +# To load the `FashionMNIST Dataset `_ +# we need to provide the following three parameters: +# - ``root`` is the path where the train/test data is stored, +# - ``train`` specifies training or test dataset, +# - ``download=True`` downloads the data from the internet if it's not available at ``root``. +# - ``transform`` and ``target_transform`` specify the feature and label transformations (more on this in the next section) + import torch from torch.utils.data import Dataset @@ -101,8 +103,8 @@ # ################################################################# -# Creating a Custom Dataset -# ----------------- +# Creating a Custom Dataset for your files +# --------------------------------------------------- # # To work with our own data, we can implement a custom class that inherits from ``Dataset``. # This custom class must implement three functions: `__init__`, `__len__`, and `__getitem__`. @@ -143,7 +145,8 @@ def __getitem__(self, idx): # ----------------- # # The __init__ function is run once when instantiating the Dataset object. We initialize -# the directory containing the images, the annotations file, and both transforms (if). +# the directory containing the images, the annotations file, and both transforms (covered +# in more detail in the next section). # # The labels.csv file looks like: :: # @@ -231,6 +234,9 @@ def __getitem__(self, idx): plt.show() print(f"Label: {label}") +###################################################################### +# -------------- +# ################################################################# # Further Reading diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 4acb4a71628..b7d41a4ca78 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -270,18 +270,9 @@ def test(dataloader, model): ############################################################# # This model can now be used to make predictions. -loaded_model.eval() +model.eval() x, y = test_data[0][0], test_data[0][1] with torch.no_grad(): - pred = loaded_model(x) + pred = model(x) predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] print(f'Predicted: "{predicted}", Actual: "{actual}"') - - -############################################################# -# Read more on `saving, loading and running models with PyTorch `_ -# -# -# -# - diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 9eef4202d1f..7676ac085e8 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -11,152 +11,58 @@ Transforms =================== -In most cases data does not come in its final processed form that is required for training machine learning algorithms. We need to do different data manipulations or **transformations** to prepare it for training. There are many types of transformations, and it depends on the type of model you are building and the state of your data as to which ones you should use. - -In the example below, let's take FashionMNIST image dataset, which is available from ``torchvision.datasets`` using the following function: +Data does not always come in its final processed form that is required for +training machine learning algorithms. We use **transforms** to perform some +manipulation of the data and make it suitable for training. + +All TorchVision datasets have two parameters -``transform`` to modify the features and +``target_transform`` to modify the labels - that accept callables containing the transformation logic. +The `torchvision.transforms `_ module offers +several commonly-used transforms out of the box. + +The FashionMNIST features are in PIL Image format, and the labels are integers. +For training, we need the features as normalized tensors, and the labels as one-hot encoded tensors. +To make these transformations, we use ``ToTensor`` and ``Lambda``. """ -import torchvision - -ds = torchvision.datasets.FashionMNIST( - 'data', # specifies data directory to store data - train=True, # specifies training or test dataset to use - transform=None, # specifies transforms to apply to features (images) - target_transform=None, # specifies transforms to apply to labels - download=False) # should the data be downloaded from the Internet -################################ -#To prepare data for training we need to take our image (also called features, x), turn it into a tensor and normalize it. Then we need to convert labels (y) into one-hot encoding. -# -#We will break down each of these steps below. +from torchvision import datasets +from torchvision.transforms import ToTensor, Lambda -############################################## -# PyTorch Datasets -# -------------------------- -# -# We are using the built-in FashionMNIST dataset from the PyTorch library. -# For more info on the Datasets and Loaders check out `this `_ section of the tutorial. -# The ``train=True`` argument indicates we want the training split of the dataset (``train=False`` downloads the test split instead). -# This way we have data partitioned out for training and testing within the provided PyTorch datasets. -# We will apply the same transforms to both the training and testing datasets. - -# import packages -import os -import torch -import torch.nn as nn -import torch.onnx as onnx -import matplotlib.pyplot as plt -from torch.utils.data import DataLoader -from torchvision import datasets, transforms - -# Here we define the image classes. -classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", - "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] - -############################################## -# Feature Transforms and Label Transforms -# --------------------------------------- -# -# Below is the code to load the FashionMNIST dataset and apply the transforms: - -training_data = datasets.FashionMNIST( - "data", +ds = datasets.FashionMNIST( + root="data", train=True, download=True, - transform=transforms.ToTensor(), - target_transform=transforms.Lambda( - lambda y: torch.zeros(10, dtype=torch.float) - .scatter_(0, torch.tensor(y), value=1) - ) + transform=ToTensor(), + target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) -######################################## -# Here we define two transformations: -# -# * ``transform`` is the transformation we apply to features, in our case - to images. The dataset contains images in PIL format so we need to convert them to tensors using the ``ToTensor()`` transform. -# * ``target_transform`` defines a transformation that is applied to labels in the dataset. Here, the label is a class number from 0 to 9, and we need to convert it to one-hot encoding. - ################################################# # ToTensor() # ------------------------------- # -# `transforms.ToTensor `_ transform is required to prepare an image for training. It takes the PIL image, converts it into a `tensor `_, and normalizes our data by scaling the image pixel intensity values to be between 0 and 1. -# +# `ToTensor `_ +# converts a PIL image or NumPy ``ndarray`` into a ``FloatTensor``. and scales +# the image's pixel intensity values in the range [0., 1.] # ############################################## # Lambda Transforms # ------------------------------- # -# We use a **lambda transform** to turn the class number into one-hot encoding. This function takes y as an input and creates a zero tensor of size 10. Then it calls scatter `torch.Tensor.scatter_ class `_ to take a value 1 and store it into the correct position of the zero vector defined by the class number. +# Lambda transforms apply any user-defined lambda function. Here, we define a function +# to turn the integer into a one-hot encoded tensor. +# It first creates a zero tensor of size 10 (the number of labels in our dataset) and calls +# `scatter_ `_ which assigns a +# ``value=1`` on the index as given by the label ``y``. -target_transform = transforms.Lambda(lambda y: torch.zeros( +target_transform = Lambda(lambda y: torch.zeros( 10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1)) -############################################### -# Check out more `torchvision transforms `_ -# - -##################################################### -# Compose -# ------------------------ +###################################################################### +# -------------- # -# In many cases, we need to perform several transformations on the data sequentially. `transforms.Compose `_ allows us to string together different steps of transformations in a sequential order. We will see an example of using composition transform in the next section. - -############################################## -# Using your own data -# -------------------------------------- -# -# Below is an example for processing image data using a dataset from a local directory. It assumes that we have ``train`` and ``val`` subdirectories with training and validation dataset. In this example we want to apply different sets of transforms for training and validation dataset: -# -# * For training data, we want to perform some **data augmentation**, and do random croping/resizing of the original image. We also introduce random horizontal flips. -# * For testing, we typically want to be consistent and always use the same images - thus we do not do any augmentation, just resizing. -# -# We also normalize values by subtracting the mean, which was computed along the whole dataset. -# -# To be able to unify the code for train and validation datasets, we use a special trick and create a dictionary of transforms for the train and validation dataset: -# -# .. code-block:: Python -# -# data_transforms = { -# 'train': -# transforms.Compose([ -# transforms.RandomResizedCrop(224), -# transforms.RandomHorizontalFlip(), -# transforms.ToTensor(), -# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) -# ]), -# 'val': -# transforms.Compose([ -# transforms.Resize(256), -# transforms.CenterCrop(224), -# transforms.ToTensor(), -# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) -# ]), -# } -# -# Next, we define a similar dictionary of train and validation datasets by using ``datasets.ImageFolder`` class. This class allows us to create a dataset from all files in a folder, and apply any transformations to them: -# -# .. code-block:: Python -# -# data_dir = 'data' -# -# image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), -# data_transforms[x]) -# for x in ['train', 'val']} -# -# Similarly we define a dictionary of dataloaders to prepare our datasets for training. They allow us to shuffle data and group them into batches of a specified size: -# -# .. code-block:: Python -# -# batch_size = 4 -# -# dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], -# batch_size=batch_size, -# shuffle=True, num_workers=4) -# for x in ['train', 'val']} -# -# dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} -# -# class_names = image_datasets['train'].classes -# +################################################################# +# Further Reading +# ~~~~~~~~~~~~~~~~~ +# - `torchvision.tranasforms API `_ From 4a6ab4658339c5142cc20860564e6726fa4c5607 Mon Sep 17 00:00:00 2001 From: suraj813 Date: Mon, 1 Feb 2021 13:47:17 -0500 Subject: [PATCH 116/120] update the other sections --- .../quickstart/autograd_tutorial.py | 9 + .../quickstart/buildmodel_tutorial.py | 28 +- .../quickstart/dataquickstart_tutorial.py | 38 +- .../quickstart/optimization_tutorial.py | 346 ++++++++++-------- .../quickstart/quickstart_tutorial.py | 84 ++--- .../quickstart/saveloadrun_tutorial.py | 10 +- .../quickstart/transforms_tutorial.py | 2 +- model.pth | Bin 0 -> 2681451 bytes 8 files changed, 285 insertions(+), 232 deletions(-) create mode 100644 model.pth diff --git a/beginner_source/quickstart/autograd_tutorial.py b/beginner_source/quickstart/autograd_tutorial.py index 023dd138879..93ae25ad8e3 100644 --- a/beginner_source/quickstart/autograd_tutorial.py +++ b/beginner_source/quickstart/autograd_tutorial.py @@ -231,3 +231,12 @@ # gradients in case of a scalar-valued function, such as loss during # neural network training. # + +###################################################################### +# -------------- +# + +################################################################# +# Further Reading +# ~~~~~~~~~~~~~~~~~ +# - `Autograd Mechanics `_ \ No newline at end of file diff --git a/beginner_source/quickstart/buildmodel_tutorial.py b/beginner_source/quickstart/buildmodel_tutorial.py index fc157fe6bd3..b25fc757222 100644 --- a/beginner_source/quickstart/buildmodel_tutorial.py +++ b/beginner_source/quickstart/buildmodel_tutorial.py @@ -49,7 +49,7 @@ class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() - self.flatten = nn.Flatten(start_dim=1, end_dim=2) + self.flatten = nn.Flatten() self.linear_relu_stack = nn.Sequential( nn.Linear(28*28, 512), nn.ReLU(), @@ -105,18 +105,18 @@ def forward(self, x): ################################################## # nn.Flatten -# ----------------------------------------------- +# ^^^^^^^^^^^^^^^^^^^^^^ # We initialize the `nn.Flatten `_ # layer to convert each 2D 28x28 image into a contiguous array of 784 pixel values ( # the minibatch dimension (at dim=0) is maintained). -flatten = nn.Flatten(start_dim=1, end_dim=2) +flatten = nn.Flatten() flat_image = flatten(input_image) print(flat_image.size()) ############################################## # nn.Linear -# ------------------------------- +# ^^^^^^^^^^^^^^^^^^^^^^ # The `linear layer `_ # is a module that applies a linear transformation on the input using it's stored weights and biases. # @@ -126,8 +126,8 @@ def forward(self, x): ################################################# -# nn.ReLU() -# ------------------------- +# nn.ReLU +# ^^^^^^^^^^^^^^^^^^^^^^ # Non-linear activations are what create the complex mappings between the model's inputs and outputs. # They are applied after linear transformations to introduce *nonlinearity*, helping neural networks # learn a wide variety of phenomena. @@ -139,8 +139,10 @@ def forward(self, x): hidden1 = nn.ReLU()(hidden1) print(f"After ReLU: {hidden1}") + +################################################# # nn.Sequential -# ------------------------------- +# ^^^^^^^^^^^^^^^^^^^^^^ # `nn.Sequential `_ is an ordered # container of modules. The data is passed through all the modules in the same order as defined. You can use # sequential containers to put together a quick network like ``seq_modules``. @@ -155,8 +157,8 @@ def forward(self, x): logits = seq_modules(input_image) ################################################################ -# nn.Softmax() -# ------------------------- +# nn.Softmax +# ^^^^^^^^^^^^^^^^^^^^^^ # The last linear layer of the neural network returns `logits` - raw values in [-\infty, \infty] - which are passed to the # `nn.Softmax `_ module. The logits are scaled to values # [0, 1] representing the model's predicted probabilities for each class. ``dim`` parameter indicates the dimension along @@ -180,4 +182,10 @@ def forward(self, x): print("Model structure: ", model, "\n\n") for name, param in model.named_parameters(): - print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n") \ No newline at end of file + print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n") + + +################################################################# +# Further Reading +# ~~~~~~~~~~~~~~~~~ +# - `torch.nn API `_ \ No newline at end of file diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 06060392d68..4fd8be201e1 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -38,8 +38,7 @@ # Fashion-MNIST is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. # Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. # -# To load the `FashionMNIST Dataset `_ -# we need to provide the following three parameters: +# We load the `FashionMNIST Dataset `_ with the following parameters: # - ``root`` is the path where the train/test data is stored, # - ``train`` specifies training or test dataset, # - ``download=True`` downloads the data from the internet if it's not available at ``root``. @@ -53,12 +52,18 @@ import matplotlib.pyplot as plt -clothing = datasets.FashionMNIST( +training_data = datasets.FashionMNIST( root="data", train=True, download=True, - transform=ToTensor(), - target_transform=None + transform=ToTensor() +) + +test_data = datasets.FashionMNIST( + root="data", + train=False, + download=True, + transform=ToTensor() ) @@ -66,8 +71,8 @@ # Iterating and Visualizing the Dataset # ----------------- # -# Once we have the ``clothing`` dataset, we can index it manually like a list: ``clothing[index]``. -# Then use ``matplotlib`` to visualize the dataset. +# We can index ``Datasets`` manually like a list: ``training_data[index]``. +# We use ``matplotlib`` to visualize some samples in our training data. labels_map = { 0: "T-Shirt", @@ -84,8 +89,8 @@ figure = plt.figure(figsize=(8, 8)) cols, rows = 3, 3 for i in range(1, cols * rows + 1): - sample_idx = torch.randint(len(clothing), size=(1,)).item() - img, label = clothing[sample_idx] + sample_idx = torch.randint(len(training_data), size=(1,)).item() + img, label = training_data[sample_idx] figure.add_subplot(rows, cols, i) plt.title(labels_map[label]) plt.axis("off") @@ -142,7 +147,7 @@ def __getitem__(self, idx): ################################################################# # __init__ -# ----------------- +# ^^^^^^^^^^^^^^^^^^^^ # # The __init__ function is run once when instantiating the Dataset object. We initialize # the directory containing the images, the annotations file, and both transforms (covered @@ -165,7 +170,7 @@ def __init__(self, annotations_file, img_dir, transform=None, target_transform=N ################################################################# # __len__ -# ----------------- +# ^^^^^^^^^^^^^^^^^^^^ # # The __len__ function returns the number of samples in our dataset. # @@ -178,7 +183,7 @@ def __len__(self): ################################################################# # __getitem__ -# ----------------- +# ^^^^^^^^^^^^^^^^^^^^ # # The __getitem__ function loads and returns a sample from the dataset at the given index ``idx``. # Based on the index, it identifies the image's location on disk, converts that to a tensor using ``read_image``, retrieves the @@ -213,10 +218,11 @@ def __getitem__(self, idx): from torch.utils.data import DataLoader -dataloader = DataLoader(clothing, batch_size=64, shuffle=True) +train_dataloader = DataLoader(training_data, batch_size=64, shuffle=True) +test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True) ########################### -# Iterate through the Dataset +# Iterate through the DataLoader # -------------------------- # # We have loaded that dataset into the ``Dataloader`` and can iterate through the dataset as needed. @@ -225,7 +231,7 @@ def __getitem__(self, idx): # the data loading order, take a look at `Samplers `_). # Display image and label. -train_features, train_labels = next(iter(dataloader)) +train_features, train_labels = next(iter(train_dataloader)) print(f"Feature batch shape: {train_features.size()}") print(f"Labels batch shape: {train_labels.size()}") img = train_features[0].squeeze() @@ -240,7 +246,7 @@ def __getitem__(self, idx): ################################################################# # Further Reading -# ~~~~~~~~~~~~~~~~~ +# -------------- # - `torch.utils.data API `_ diff --git a/beginner_source/quickstart/optimization_tutorial.py b/beginner_source/quickstart/optimization_tutorial.py index 6e8982866e0..bb7332910e0 100644 --- a/beginner_source/quickstart/optimization_tutorial.py +++ b/beginner_source/quickstart/optimization_tutorial.py @@ -12,166 +12,194 @@ =========================== Now that we have a model and data it's time to train, validate and test our model by optimizing it's parameters on -our data. Training a model is an iterative process; in each iteration (called an *epoch*) the model makes a guess about the output, calculates the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in the `previous section `_), and **optimizes** these parameters using gradient descent. For a more detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown `__. +our data. Training a model is an iterative process; in each iteration (called an *epoch*) the model makes a guess about the output, calculates +the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in +the `previous section `_), and **optimizes** these parameters using gradient descent. For a more +detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown `__. -Hyperparameters +Pre-requisite Code ----------------- +We load the code from the previous sections on `Datasets & DataLoaders `_ +and `Build Model `_. +""" -Hyperparameters are adjustable parameters that let you control the model optimization process. -Different hyperparameter values can impact model training and convergence rates (`read more `__ about hyperparameter tuning) - -In our case, we need to define the following hyperparameters: - - - **Number of Epochs**- the number times to iterate over the dataset - - **Batch Size** - the number of data samples seen by the model in each epoch - - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training. - -.. code-block:: Python - - learning_rate = 1e-3 - batch_size = 64 - epochs = 5 - -We also need to create the model class instance (defined in the previous section): - -.. code-block:: Python - - model = NeuralNetwork() - -Optimization Loop ------------------ - -.. figure:: /_static/img/quickstart/optimizationloops.png - :alt: - -Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. Each -iteration of the optimization loop is called an **epoch**. Each epoch consists of two main parts: - - 1. **The Train Loop** - main loop that iterates over all dataset and performs training - 2. **The Validation/Test Loop** - goes through the validation / test dataset to evaluate model performance on the test data. - -Here is a high-level view of optimization loop: - -.. code-block:: Python - - for epoch in range(num_epochs): # Iterate over all epochs - - # Training loop: - for train_features, train_labels in train_dataloader: # Go over all minibatches - out = model(train_features) # Compute network output - loss = loss_function(out,train_labels) # Compute loss function - # optimize weights to minimize loss - ... - - # Evaluation loop - model.eval() # set to evaluation mode not to compute gradients - for test_features, test_labels: - out = model(test_features) - loss = loss_function(out,train_labels) - # store / display the loss and/or other metrics - ... - -Complete code for optimization loop will be presented at the end of this section. - -Loss Function -------------- - -When presented with some training data, our untrained network is likely not to give the correct -answer. **Loss function** measures the degree of dissimilarity of obtained result to the target value, -and it is the loss function that we want to minimize during training. To calculate the loss we make a -prediction using the inputs of our given data sample and compare it against the true data label value. - -Common loss functions include `Mean Square Error `_ (for regression tasks), `Negative Log Likelihood `_, and `CrossEntropyLoss `_ (for classification tasks). - -In our example, we will use the built-in Cross Entropy Loss function: - -.. code-block:: Python - - # Initialize the loss function - loss_function = nn.CrossEntropyLoss() - -Optimizer ---------- - -Optimization is the process of adjusting model parameters to reduce model error in each training step. **Optimization algorithms** define how this process is performed (in this example we use Stochastic Gradient Descent). -All optimization logic is encapsulated in the``optimizer`` object. In this case, we use the SGD optimizer: - -.. code-block:: Python - - optimizer = optim.SGD(model.parameters(), lr=learning_rate) - -In addition to SGD there are many `different optimizers `_ available in PyTorch such as ADAM and RMSProp, that work better for different kinds of models. - -Inside the training loop, optimization happens in three steps: - - * Call ``optimizer.zero_grad()`` function to zero the gradients. As you have seen in the previous section on automatic differentiation, gradients by default add up, so we need to explicitly zero them on each step. - * Calculate the loss using loss function. This builds a computation graph, which PyTorch uses to automatically update parameters with respect to our model's loss during training. This is done with one call to ``loss.backwards()``. - * Once we have our gradients, we call ``optimizer.step()`` to adjust the parameters by the gradients collected in the backward pass. - -.. figure:: https://discuss.pytorch.org/uploads/default/original/1X/c7e0a44b7bcebfb41315b56f8418ce37f0adbfeb.png - :alt: tensor graph - -Putting it all together ------------------------ - -Below is the complete code for the optimization loop. If you want a complete runnable example of training the model, refer to the `main page `_. The code below is commented to explain what goes on, but essentially it is put together from concepts that we have described above. - -.. code-block:: Python - - for epoch in range(num_epochs): # Do training for each epoch - - # Training loop over all data in minibatches - for train_batch, (train_inputs, train_labels) in enumerate(train_dataloader): - model.train() # Set model to train mode - # we need to move the data to the devices used for training - train_inputs, train_labels = - train_inputs.to(device), train_labels.to(device) - optimizer.zero_grad() # zero out gradients - pred = model(train_inputs) # make a prediction on the current batch - loss = cost_function(pred, train_labels) # compute loss function - loss.backward() # compute gradients of loss function - optimizer.step() # update parameters - - # Test loop: go over test dataset - for test_batch, (test_inputs, test_labels) in enumerate(test_dataloader): - # move data to the device we use for computations - test_inputs, test_labels = - test_inputs.to(device), test_labels.to(device) - pred = model(test_inputs) # evaluate model on test minibatch - test_loss += cost_function(pred, test_labels).item() # compute loss - # compute the metrics for classification: - # how many classes were guessed correctly - correct += - (pred.argmax(1) == test_labels.argmax(1)) - .type(torch.float).sum().item() - - test_loss /= len(test_dataloader.dataset) - correct /= len(test_dataloader.dataset) - print('Epoch {} test Error:'.format(epoch)) - print('acc: {:>0.1f}%, avg loss: {:>8f}'.format(100*correct, test_loss)) - -Creating Custom Cost Functions ------------------------------- - -In addition to the included PyTorch cost functions you can create your own custom cost functions as long as they are differentiable. Here is an example of custom Cross Entropy Loss implementation from the `Stanford CS230 `_ course: - -.. code-block:: Python - - def myCrossEntropyLoss(outputs, labels): - batch_size = outputs.size()[0] - # compute the log of softmax values - outputs = F.log_softmax(outputs, dim=1) - # pick the values corresponding to the labels - outputs = outputs[range(batch_size), labels] - return -torch.sum(outputs)/num_examples - -It can be called just like the out of the box implementation above. - -.. code-block:: Python - - loss = myCrossEntropyLoss(model_prediction, true_value) - -A more in depth explanation of PyTorch cost functions is outside the scope of the tutorial but you can learn more -about the different common cost functions for deep learning in the PyTorch `documentation `_. +import torch +from torch import nn +from torch.utils.data import DataLoader +from torchvision import datasets +from torchvision.transforms import ToTensor, Lambda + +training_data = datasets.FashionMNIST( + root="data", + train=True, + download=True, + transform=ToTensor() +) + +test_data = datasets.FashionMNIST( + root="data", + train=False, + download=True, + transform=ToTensor() +) + +train_dataloader = DataLoader(training_data, batch_size=64) +test_dataloader = DataLoader(test_data, batch_size=64) + +class NeuralNetwork(nn.Module): + def __init__(self): + super(NeuralNetwork, self).__init__() + self.flatten = nn.Flatten() + self.linear_relu_stack = nn.Sequential( + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, 10), + nn.ReLU() + ) + + def forward(self, x): + x = self.flatten(x) + logits = self.linear_relu_stack(x) + return logits + +model = NeuralNetwork() + + +############################################## +# Hyperparameters +# ----------------- +# +# Hyperparameters are adjustable parameters that let you control the model optimization process. +# Different hyperparameter values can impact model training and convergence rates +# (`read more `__ about hyperparameter tuning) +# +# We define the following hyperparameters for training: +# - **Number of Epochs** - the number times to iterate over the dataset +# - **Batch Size** - the number of data samples seen by the model in each epoch +# - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training. +# + +learning_rate = 1e-3 +batch_size = 64 +epochs = 5 + + + +##################################### +# Optimization Loop +# ----------------- +# +# Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. Each +# iteration of the optimization loop is called an **epoch**. +# +# Each epoch consists of two main parts: +# - **The Train Loop** - iterate over the training dataset and try to converge to optimal parameters. +# - **The Validation/Test Loop** - iterate over the test dataset to check if model performance is improving. +# +# Let's briefly familiarize ourselves with some of the concepts used in the training loop. Jump ahead to +# see the :ref:`full-impl-label` of the optimization loop. +# +# Loss Function +# ~~~~~~~~~~~~~~~~~ +# +# When presented with some training data, our untrained network is likely not to give the correct +# answer. **Loss function** measures the degree of dissimilarity of obtained result to the target value, +# and it is the loss function that we want to minimize during training. To calculate the loss we make a +# prediction using the inputs of our given data sample and compare it against the true data label value. +# +# Common loss functions include `nn.MSELoss `_ (Mean Square Error) for regression tasks, and +# `nn.NLLLoss `_ (Negative Log Likelihood) for classification. +# `nn.CrossEntropyLoss `_ combines ``nn.LogSoftmax`` and ``nn.NLLLoss``. +# +# We pass our model's output logits to ``nn.CrossEntropyLoss``, which will normalize the logits and compute the prediction error. + +# Initialize the loss function +loss_fn = nn.CrossEntropyLoss() + + + +##################################### +# Optimizer +# ~~~~~~~~~~~~~~~~~ +# +# Optimization is the process of adjusting model parameters to reduce model error in each training step. **Optimization algorithms** define how this process is performed (in this example we use Stochastic Gradient Descent). +# All optimization logic is encapsulated in the ``optimizer`` object. Here, we use the SGD optimizer; additionally, there are many `different optimizers `_ +# available in PyTorch such as ADAM and RMSProp, that work better for different kinds of models and data. +# +# We initialize the optimizer by registering the model's parameters that need to be trained, and passing in the learning rate hyperparameter. + +optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) + +##################################### +# Inside the training loop, optimization happens in three steps: +# * Call ``optimizer.zero_grad()`` to reset the gradients of model parameters. Gradients by default add up; to prevent double-counting, we explicitly zero them at each iteration. +# * Backpropagate the prediction loss with a call to ``loss.backwards()``. PyTorch deposits the gradients of the loss w.r.t. each parameter. +# * Once we have our gradients, we call ``optimizer.step()`` to adjust the parameters by the gradients collected in the backward pass. + + +######################################## +# .. _full-impl-label: +# +# Full Implementation +# ----------------------- +# We define ``train_loop`` that loops over our optimization code, and ``test_loop`` that +# evaluates the model's performance against our test data. + +def train_loop(dataloader, model, loss_fn, optimizer): + size = len(dataloader.dataset) + for batch, (X, y) in enumerate(dataloader): + # Compute prediction and loss + pred = model(X) + loss = loss_fn(pred, y) + + # Backpropagation + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if batch % 100 == 0: + loss, current = loss.item(), batch * len(X) + print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") + + +def test_loop(dataloader, model, loss_fn): + size = len(dataloader.dataset) + test_loss, correct = 0, 0 + + with torch.no_grad(): + for X, y in dataloader: + pred = model(X) + test_loss += loss_fn(pred, y).item() + correct += (pred.argmax(1) == y).type(torch.float).sum().item() + + test_loss /= size + correct /= size + print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n") + + +######################################## +# We initialize the loss function and optimizer, and pass it to ``train_loop`` and ``test_loop``. +# Feel free to increase the number of epochs to track the model's improving performance. + +loss_fn = nn.CrossEntropyLoss() +optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) + +epochs = 10 +for t in range(epochs): + print(f"Epoch {t+1}\n-------------------------------") + train_loop(train_dataloader, model, loss_fn, optimizer) + test_loop(test_dataloader, model, loss_fn) +print("Done!") + + + +################################################################# +# Further Reading +# ----------------------- +# - `Loss Functions `_ +# - `torch.optim `_ +# - `Warmstart Training a Model `_ +# -""" diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index b7d41a4ca78..248361719a6 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -18,9 +18,9 @@ `Dmitry Soshnikov `_, `Ari Bornstein `_ -A basic machine learning workflow involves working with data, creating models, optimizing model -parameters, and saving the trained models. This tutorial introduces you to the complete ML workflow -as implemented in PyTorch, with links to learn more about of these concepts. +Most machine learning workflows involve working with data, creating models, optimizing model +parameters, and saving the trained models. This tutorial introduces you to a complete ML workflow +implemented in PyTorch, with links to learn more about each of these concepts. We'll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs to one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, @@ -30,7 +30,7 @@ Running the Tutorial Code ------------------ -You can run this tutorial in a few ways: +You can run this tutorial in a couple of ways: - **In the cloud**: This is the easiest way to get started! Each section has a Colab link at the top, which opens a notebook with the code in a fully-hosted environment. Pro tip: Use Colab with a GPU runtime to speed up operations *Runtime > Change runtime type > GPU* - **Locally**: This option requires you to setup PyTorch and TorchVision first on your local machine (`installation instructions `_). Download the notebook or copy the code into your favorite IDE. @@ -38,10 +38,10 @@ How to Use this Guide ----------------- -This page contains an overview of the code used at each step of the tutorial. If you're familiar with -other deep learning frameworks, this is a quick way to get acquainted with PyTorch's API. +The rest of this page contains an *overview* of the code used in the complete ML workflow. +If you're familiar with other deep learning frameworks, this is a quick way to get acquainted with PyTorch's API. -If this is your first time, head right into our step-by-step guide: +If this is your first time working with deep learning frameworks, head right into our step-by-step guide: .. include:: /beginner_source/quickstart/qs_toc.txt @@ -56,14 +56,13 @@ /beginner/quickstart/optimization_tutorial /beginner/quickstart/saveloadrun_tutorial - - -------------- Working with data ----------------- -PyTorch has two data primitives to work with data: ``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``. +PyTorch has two `primitives to work with data `_: +``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``. ``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around the ``Dataset``. @@ -82,26 +81,12 @@ # use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and # ``target_transform`` to modify the samples and labels respectively. -classes = [ - "T-shirt/top", - "Trouser", - "Pullover", - "Dress", - "Coat", - "Sandal", - "Shirt", - "Sneaker", - "Bag", - "Ankle boot", -] - # Download training data from open datasets. training_data = datasets.FashionMNIST( root="data", train=True, download=True, transform=ToTensor(), - target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) # Download test data from open datasets. @@ -110,7 +95,6 @@ train=False, download=True, transform=ToTensor(), - target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1)) ) ###################################################################### @@ -126,9 +110,13 @@ for X, y in test_dataloader: print("Shape of X [N, C, H, W]: ", X.shape) - print("Shape of y: ", y.shape) + print("Shape of y: ", y.shape, y.dtype) break +###################################################################### +# Read more about `loading data in PyTorch `_. +# + ###################################################################### # -------------- # @@ -139,7 +127,7 @@ # To define a neural network in PyTorch, we create a class that inherits # from `nn.Module `_. We define the layers of the network # in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate -# operations in the NN, we move it to the GPU if available. +# operations in the neural network, we move it to the GPU if available. # Get cpu or gpu device for training. device = "cuda" if torch.cuda.is_available() else "cpu" @@ -150,18 +138,19 @@ class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() self.flatten = nn.Flatten() - self.softmax = nn.Softmax(dim=1) - self.nn_layers = nn.Sequential( - nn.Linear(28 * 28, 512), - nn.ReLU(), - nn.Linear(512, 512), - nn.ReLU(), - nn.Linear(512, 10) - ) + self.linear_relu_stack = nn.Sequential( + nn.Linear(28*28, 512), + nn.ReLU(), + nn.Linear(512, 512), + nn.ReLU(), + nn.Linear(512, 10), + nn.ReLU() + ) + def forward(self, x): x = self.flatten(x) - x = self.nn_layers(x) - return self.softmax(x) + logits = self.linear_relu_stack(x) + return logits model = NeuralNetwork().to(device) print(model) @@ -181,8 +170,8 @@ def forward(self, x): # To train a model, we need a `loss function `_ # and an `optimizer `_. -loss_fn = nn.BCELoss() -optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) +loss_fn = nn.CrossEntropyLoss() +optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) ####################################################################### # In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and @@ -218,7 +207,7 @@ def test(dataloader, model): X, y = X.to(device), y.to(device) pred = model(X) test_loss += loss_fn(pred, y).item() - correct += (pred.argmax(1) == y.argmax(1)).type(torch.float).sum().item() + correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= size correct /= size print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n") @@ -270,9 +259,22 @@ def test(dataloader, model): ############################################################# # This model can now be used to make predictions. +classes = [ + "T-shirt/top", + "Trouser", + "Pullover", + "Dress", + "Coat", + "Sandal", + "Shirt", + "Sneaker", + "Bag", + "Ankle boot", +] + model.eval() x, y = test_data[0][0], test_data[0][1] with torch.no_grad(): pred = model(x) - predicted, actual = classes[pred[0].argmax(0)], classes[y.argmax(0)] + predicted, actual = classes[pred[0].argmax(0)], classes[y] print(f'Predicted: "{predicted}", Actual: "{actual}"') diff --git a/beginner_source/quickstart/saveloadrun_tutorial.py b/beginner_source/quickstart/saveloadrun_tutorial.py index 2f16a09f5b5..5642d372b10 100644 --- a/beginner_source/quickstart/saveloadrun_tutorial.py +++ b/beginner_source/quickstart/saveloadrun_tutorial.py @@ -74,9 +74,9 @@ # and in different programming languages. For more details, we recommend # visiting `ONNX tutorial `_. # -# Congratulations! You have completed the PyTorch beginner tutorial! You can -# now `return to the first page `_ and go over the sample code -# again and we hope you have a better understanding of how to do deep learning with PyTorch. -# Good luck on your deep learning journey! -# +# Congratulations! You have completed the PyTorch beginner tutorial! Try +# `revisting the first page `_ to see the tutorial in its entirety +# again. We hope this tutorial has helped you get started with deep learning on PyTorch. +# Good luck! # + diff --git a/beginner_source/quickstart/transforms_tutorial.py b/beginner_source/quickstart/transforms_tutorial.py index 7676ac085e8..36d680b117b 100644 --- a/beginner_source/quickstart/transforms_tutorial.py +++ b/beginner_source/quickstart/transforms_tutorial.py @@ -65,4 +65,4 @@ ################################################################# # Further Reading # ~~~~~~~~~~~~~~~~~ -# - `torchvision.tranasforms API `_ +# - `torchvision.transforms API `_ diff --git a/model.pth b/model.pth new file mode 100644 index 0000000000000000000000000000000000000000..74e7c186ca9cba73be4e4fd72745736105428b3d GIT binary patch literal 2681451 zcmbTdXH=BIvNlSRoO6znbB1}VRYXO_h&hXhf}#=yGeJaxVjzkliU9>h5DD{Eqlg40 zhyfH85m6B_pr`}|?(DtKS@)j%oxOj2v)0U7^fX=l)Ku47UG>xgPeCC*K2cG=|Klag zC(q{{?BnOZ)7N^Vcc`~j;MRZ{Q>FP<{*PCrfRE3PfB;{gQ2!mw7kfuXd#51b<$}CXpTMvnk=cB@!hE4Yq6vekl+7Do`bWqgPo(TtDQ}d 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zHOFD(fDb6RQr#c}d6FO4%dhl`h3F5%_`o|D(*q09?w%Lg+lWhqi8~7loSPuZaVYL&LM`MU=Sf3fsN=0938F_^3WZ;J0fB#+ZzuyxT`~98-{rP?V-_QLoFZHUo literal 0 HcmV?d00001 From 8d88428767ae464b54b9b5f22a31144dffa4d3ff Mon Sep 17 00:00:00 2001 From: suraj813 Date: Mon, 1 Feb 2021 15:09:46 -0500 Subject: [PATCH 117/120] final edits --- beginner_source/quickstart/dataquickstart_tutorial.py | 4 ++-- beginner_source/quickstart/quickstart_tutorial.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/beginner_source/quickstart/dataquickstart_tutorial.py b/beginner_source/quickstart/dataquickstart_tutorial.py index 4fd8be201e1..b953f3567d9 100644 --- a/beginner_source/quickstart/dataquickstart_tutorial.py +++ b/beginner_source/quickstart/dataquickstart_tutorial.py @@ -36,13 +36,13 @@ # # Here is an example of how to load the `Fashion-MNIST `_ dataset from TorchVision. # Fashion-MNIST is a dataset of Zalando’s article images consisting of of 60,000 training examples and 10,000 test examples. -# Each example is comprised of a 28×28 grayscale image, associated with a label from one of 10 classes. +# Each example comprises a 28×28 grayscale image and an associated label from one of 10 classes. # # We load the `FashionMNIST Dataset `_ with the following parameters: # - ``root`` is the path where the train/test data is stored, # - ``train`` specifies training or test dataset, # - ``download=True`` downloads the data from the internet if it's not available at ``root``. -# - ``transform`` and ``target_transform`` specify the feature and label transformations (more on this in the next section) +# - ``transform`` and ``target_transform`` specify the feature and label transformations import torch diff --git a/beginner_source/quickstart/quickstart_tutorial.py b/beginner_source/quickstart/quickstart_tutorial.py index 248361719a6..b4aa5f4f908 100644 --- a/beginner_source/quickstart/quickstart_tutorial.py +++ b/beginner_source/quickstart/quickstart_tutorial.py @@ -165,7 +165,7 @@ def forward(self, x): # ##################################################################### -# Training the Model +# Optimizing the Model Parameters # ---------------------------------------- # To train a model, we need a `loss function `_ # and an `optimizer `_. From ee12bbe6aad3ef636deec796ba356d5f937cdd9f Mon Sep 17 00:00:00 2001 From: suraj813 Date: Mon, 1 Feb 2021 21:28:37 -0500 Subject: [PATCH 118/120] update index.rst --- index.rst | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/index.rst b/index.rst index 97fafb10718..12fc6c31512 100644 --- a/index.rst +++ b/index.rst @@ -8,12 +8,6 @@ Welcome to PyTorch Tutorials .. Add callout items below this line -.. customcalloutitem:: - :description: The 60 min blitz is the most common starting point and provides a broad view on how to use PyTorch. It covers the basics all the way to constructing deep neural networks. - :header: New to PyTorch? - :button_link: beginner/deep_learning_60min_blitz.html - :button_text: Start 60-min blitz - .. customcalloutitem:: :description: In this quickstart we will cover the basics of machine learning and how to apply them with PyTorch. You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step! :header: Learn the Basics @@ -56,13 +50,6 @@ Welcome to PyTorch Tutorials .. Learning PyTorch -.. customcarditem:: - :header: Deep Learning with PyTorch: A 60 Minute Blitz - :card_description: Understand PyTorch’s Tensor library and neural networks at a high level. - :image: _static/img/thumbnails/cropped/60-min-blitz.png - :link: beginner/deep_learning_60min_blitz.html - :tags: Getting-Started - .. customcarditem:: :header: Learn the Basics :card_description: Get started with a step-by-step guide to building neural networks with PyTorch. @@ -70,12 +57,6 @@ Welcome to PyTorch Tutorials :link: beginner/quickstart/quickstart_tutorial.html :tags: Getting-Started -.. customcarditem:: - :header: Learning PyTorch with Examples - :card_description: This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. - :image: _static/img/thumbnails/cropped/learning-pytorch-with-examples.png - :link: beginner/pytorch_with_examples.html - :tags: Getting-Started .. customcarditem:: :header: What is torch.nn really? @@ -452,7 +433,6 @@ Additional Resources beginner/quickstart/quickstart_tutorial beginner/deep_learning_60min_blitz - beginner/pytorch_with_examples beginner/nn_tutorial intermediate/tensorboard_tutorial From d2b6458a161c6e2e7bb0ea2a0341648025cc2eaa Mon Sep 17 00:00:00 2001 From: suraj813 Date: Fri, 5 Feb 2021 11:12:26 -0500 Subject: [PATCH 119/120] delete devcontainer and gh workflows --- .devcontainer/Dockerfile | 7 --- .devcontainer/devcontainer.json | 21 --------- .devcontainer/requirements.txt | 33 --------------- .github/workflows/staging.yml | 40 ------------------ .../quickstart/images/fashion_mnist.png | Bin 33424 -> 0 bytes .../quickstart/images/optimization_loops.PNG | Bin 60630 -> 0 bytes .../quickstart/images/typesofdata.PNG | Bin 14723 -> 0 bytes 7 files changed, 101 deletions(-) delete mode 100644 .devcontainer/Dockerfile delete mode 100644 .devcontainer/devcontainer.json delete mode 100644 .devcontainer/requirements.txt delete mode 100644 .github/workflows/staging.yml delete mode 100644 beginner_source/quickstart/images/fashion_mnist.png delete mode 100644 beginner_source/quickstart/images/optimization_loops.PNG delete mode 100644 beginner_source/quickstart/images/typesofdata.PNG diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile deleted file mode 100644 index 3ca67455049..00000000000 --- a/.devcontainer/Dockerfile +++ /dev/null @@ -1,7 +0,0 @@ -FROM python:3.6-slim - -COPY requirements.txt requirements.txt - -RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \ - && apt-get install git gcc unzip make -y \ - && pip install --no-cache-dir -r requirements.txt \ No newline at end of file diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json deleted file mode 100644 index a0212d506ff..00000000000 --- a/.devcontainer/devcontainer.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "name": "PyTorch", - "build": { - "context": "..", - "dockerfile": "Dockerfile", - "args": { } - }, - "settings": { - "terminal.integrated.shell.linux": "/bin/bash", - "workbench.startupEditor": "none", - "files.autoSave": "afterDelay", - "python.dataScience.enabled": true, - "python.dataScience.alwaysTrustNotebooks": true, - "python.insidersChannel": "weekly", - "python.showStartPage": false - }, - "extensions": [ - "ms-python.python", - "lextudio.restructuredtext" - ] -} \ No newline at end of file diff --git a/.devcontainer/requirements.txt b/.devcontainer/requirements.txt deleted file mode 100644 index 993febbb4c6..00000000000 --- a/.devcontainer/requirements.txt +++ /dev/null @@ -1,33 +0,0 @@ -# Refer to ./jenkins/build.sh for tutorial build instructions - -sphinx==1.8.2 -sphinx-gallery==0.3.1 -tqdm -numpy -matplotlib -torch -torchvision -torchtext -torchaudio -PyHamcrest -bs4 -awscli==1.16.35 -flask -spacy -ray[tune] - -# PyTorch Theme --e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme - -ipython - -# to run examples -pandas -scikit-image -# pillow >= 4.2 will throw error when trying to write mode RGBA as JPEG, -# this is a workaround to the issue. -pillow==4.1.1 -wget - -# for codespaces env -pylint diff --git a/.github/workflows/staging.yml b/.github/workflows/staging.yml deleted file mode 100644 index 1e5eb5a2544..00000000000 --- a/.github/workflows/staging.yml +++ /dev/null @@ -1,40 +0,0 @@ -name: PyTorch Tutorial Staging - -on: - push: - branches: - - seth-blitz -jobs: - tutorial-staging: - runs-on: ubuntu-latest - env: - WEB_PATH: $web - ACCOUNT: ${{ secrets.stagingaccount }} - KEY: ${{ secrets.stagingkey }} - SOURCEDIR: . - BUILDDIR: _build/html - steps: - - uses: actions/checkout@v2 - - uses: actions/setup-python@v2 - with: - python-version: 3.6 - - - # install requirements - name: install requirements - run: | - pip install --no-cache-dir -r requirements.txt - - - # build site - name: sphinx build - run: | - sphinx-build -D plot_gallery=0 -b html "$SOURCEDIR" "$BUILDDIR" - - - # clear old site - name: clear old site - run : | - az storage blob delete-batch --source 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Add callout items below this line .. customcalloutitem:: - :description: In this quickstart we will cover the basics of machine learning and how to apply them with PyTorch. You will be introduced to the complete ML workflow using PyTorch with links to learn more at each step! - :header: Learn the Basics - :button_link: beginner/quickstart/quickstart_tutorial.html - :button_text: Get started with PyTorch + :description: The 60 min blitz is the most common starting point and provides a broad view on how to use PyTorch. It covers the basics all the way to constructing deep neural networks. + :header: New to PyTorch? + :button_link: beginner/deep_learning_60min_blitz.html + :button_text: Start 60-min blitz .. customcalloutitem:: :description: Bite-size, ready-to-deploy PyTorch code examples. @@ -51,12 +51,18 @@ Welcome to PyTorch Tutorials .. Learning PyTorch .. customcarditem:: - :header: Learn the Basics - :card_description: Get started with a step-by-step guide to building neural networks with PyTorch. + :header: Deep Learning with PyTorch: A 60 Minute Blitz + :card_description: Understand PyTorch’s Tensor library and neural networks at a high level. :image: _static/img/thumbnails/cropped/60-min-blitz.png - :link: beginner/quickstart/quickstart_tutorial.html + :link: beginner/deep_learning_60min_blitz.html :tags: Getting-Started +.. customcarditem:: + :header: Learning PyTorch with Examples + :card_description: This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. + :image: _static/img/thumbnails/cropped/learning-pytorch-with-examples.png + :link: beginner/pytorch_with_examples.html + :tags: Getting-Started .. customcarditem:: :header: What is torch.nn really? @@ -111,6 +117,13 @@ Welcome to PyTorch Tutorials :link: beginner/audio_preprocessing_tutorial.html :tags: Audio +.. customcarditem:: + :header: Speech Command Recognition + :card_description: Learn how to correctly format an audio dataset and then train/test an audio classifier network on the dataset. + :image: _static/img/thumbnails/cropped/torchaudio-speech.png + :link: intermediate/speech_command_recognition_with_torchaudio.html + :tags: Audio + .. Text .. customcarditem:: @@ -164,6 +177,14 @@ Welcome to PyTorch Tutorials :link: intermediate/reinforcement_q_learning.html :tags: Reinforcement-Learning +.. customcarditem:: + :header: Train a Mario-playing RL Agent + :card_description: Use PyTorch to train a Double Q-learning agent to play Mario . + :image: _static/img/mario.gif + :link: intermediate/mario_rl_tutorial.html + :tags: Reinforcement-Learning + + .. Deploying PyTorch Models in Production .. customcarditem:: @@ -254,6 +275,13 @@ Welcome to PyTorch Tutorials .. Model Optimization +.. customcarditem:: + :header: Performance Profiling in PyTorch + :card_description: Learn how to use the PyTorch Profiler to benchmark your module's performance. + :image: _static/img/thumbnails/cropped/profiler.png + :link: beginner/profiler.html + :tags: Model-Optimization,Best-Practice,Profiling + .. customcarditem:: :header: Hyperparameter Tuning Tutorial :card_description: Learn how to use Ray Tune to find the best performing set of hyperparameters for your model. @@ -282,20 +310,6 @@ Welcome to PyTorch Tutorials :link: intermediate/dynamic_quantization_bert_tutorial.html :tags: Text,Quantization,Model-Optimization -.. customcarditem:: - :header: (beta) Static Quantization with Eager Mode in PyTorch - :card_description: Learn techniques to impove a model's accuracy = post-training static quantization, per-channel quantization, and quantization-aware training. - :image: _static/img/thumbnails/cropped/experimental-Static-Quantization-with-Eager-Mode-in-PyTorch.png - :link: advanced/static_quantization_tutorial.html - :tags: Image/Video,Quantization,Model-Optimization - -.. customcarditem:: - :header: (beta) Quantized Transfer Learning for Computer Vision Tutorial - :card_description: Learn techniques to impove a model's accuracy - post-training static quantization, per-channel quantization, and quantization-aware training. - :image: _static/img/thumbnails/cropped/experimental-Quantized-Transfer-Learning-for-Computer-Vision-Tutorial.png - :link: advanced/static_quantization_tutorial.html - :tags: Image/Video,Quantization,Model-Optimization - .. Parallel-and-Distributed-Training .. customcarditem:: @@ -353,7 +367,7 @@ Welcome to PyTorch Tutorials :image: _static/img/thumbnails/cropped/Implementing-Batch-RPC-Processing-Using-Asynchronous-Executions.png :link: intermediate/rpc_async_execution.html :tags: Parallel-and-Distributed-Training - + .. customcarditem:: :header: Combining Distributed DataParallel with Distributed RPC Framework :card_description: Walk through a through a simple example of how to combine distributed data parallelism with distributed model parallelism. @@ -431,8 +445,8 @@ Additional Resources :includehidden: :caption: Learning PyTorch - beginner/quickstart/quickstart_tutorial beginner/deep_learning_60min_blitz + beginner/pytorch_with_examples beginner/nn_tutorial intermediate/tensorboard_tutorial @@ -454,6 +468,8 @@ Additional Resources :caption: Audio beginner/audio_preprocessing_tutorial + intermediate/speech_command_recognition_with_torchaudio + .. toctree:: :maxdepth: 2 @@ -476,6 +492,7 @@ Additional Resources :caption: Reinforcement Learning intermediate/reinforcement_q_learning + intermediate/mario_rl_tutorial .. toctree:: :maxdepth: 2 @@ -503,6 +520,7 @@ Additional Resources advanced/torch-script-parallelism advanced/cpp_autograd advanced/dispatcher + advanced/extend_dispatcher .. toctree:: :maxdepth: 2 @@ -510,11 +528,11 @@ Additional Resources :hidden: :caption: Model Optimization + beginner/profiler beginner/hyperparameter_tuning_tutorial intermediate/pruning_tutorial advanced/dynamic_quantization_tutorial intermediate/dynamic_quantization_bert_tutorial - advanced/static_quantization_tutorial intermediate/quantized_transfer_learning_tutorial .. toctree:: @@ -531,4 +549,4 @@ Additional Resources intermediate/rpc_param_server_tutorial intermediate/dist_pipeline_parallel_tutorial intermediate/rpc_async_execution - advanced/rpc_ddp_tutorial + advanced/rpc_ddp_tutorial \ No newline at end of file

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