Fashion MNIST using improve unified interface

test/fashion-mnist/Readme.md

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+#INTRODUCTION
+This directory contains the code to run the fashion-mnist example and explains the changes made to the original python notebook to make it work with IMPROVE.
+
+The python notebook ***fashion-mnist.ipynb*** consists of 3 sections. 
+Please read the comments on top of that notebook.
+
+Notice the changes marked by comment line *## IMPROVE*
+in the following files:
+#Running 
+```
+PYTHONPATH=<PATH-TO-IMPROVE>:$PYTHONPATH
+IMPROVE_DATA_DIR=<PATH-TO-IMPROVE-DATA-DIR>
+python preprocess.py
+python train.py (if you rerun this, it will use the checkpointed model and restart)
+python infer.py
+```
+#NOTES
+IMPROVE_DATA_DIR is the directory where the data is downloaded and preprocessed. It is also the place where the model checkpoints are stored. It also saves the model and inference output under the same directory.
+
+All the above files read the configuration and hyperparameters from the file *fashion-mnist_default_model.txt*
+
+The 3 subsections in *fashion-mnist_default_model.txt* are [preprocess], [train] and [infer]. The [preprocess] section is used by preprocess.py, [train] section is used by train.py and [infer] section is used by infer.py. Train has a few more parameters than the other two. It includes the checkpointing parameters. Checkpointing functions used in the model can be found [here]([text](https://candle-lib.readthedocs.io/en/latest/api_ckpt_pytorch_utils/_autosummary/candle.ckpt_pytorch_utils.CandleCkptPyTorch.html))
+
+```
+save_path='save/'
+ckpt_save_best=True
+ckpt_save_interval=1
+```
+
+
+Note that checkpointing is not enabled in the original python notebook, but it is enabled in train.py. See train.py for implementation details.
+

test/fashion-mnist/fashion-mnist.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fashion MNIST Image Classification with PyTorch\n",
+    "\n",
+    "## Overview\n",
+    "The [IMPROVE Project](https://jdacs4c-improve.github.io/docs/index.html) focus on data standardization/quality/integration for a variety of cancer drug response problems. The intention with this example is to show how to modify such an example to comply with the [IMPROVE library:](https://github.com/JDACS4C-IMPROVE/IMPROVE). See the [Unified Interface Documentation](https://jdacs4c-improve.github.io/docs/content/unified_interface.html) \n",
+    "\n",
+    "Note: This model does not use any of the cancer data, it simply classifies images of clothing items from the Fashion MNIST dataset into 10 categories:\n",
+    "\n",
+    "T-shirt/top\n",
+    "Trouser\n",
+    "Pullover\n",
+    "Dress\n",
+    "Coat\n",
+    "Sandal\n",
+    "Shirt\n",
+    "Sneaker\n",
+    "Bag\n",
+    "Ankle boot\n",
+    "\n",
+    "## Requirements\n",
+    "Break the model's workflow into three primary steps:\n",
+    "\n",
+    "Preprocessing\n",
+    "Training\n",
+    "Inference\n",
+    "\n",
+    "## Preprocessing\n",
+    "\n",
+    "Load the Fashion MNIST dataset using PyTorch's torchvision.datasets.FashionMNIST class.\n",
+    "Split the data into training and validation sets.\n",
+    "Normalize pixel values to a range of [0, 1] and ensure them to have a mean of 0.5 and a standard deviation of 0.5.\n",
+    "This helps stabilize the training process and improve model convergence.\n",
+    "Use torchvision.datasets.FashionMNIST with ``Download=True`` to download the dataset locally.\n",
+    "\n",
+    "## Training\n",
+    "This section assumes that the data has been preprocessed and is available for training.\n",
+    "We first define a neural network architecture suitable for image classification (e.g., a convolutional neural network).\n",
+    "Instantiate the model with PyTorch's torch.nn modules.\n",
+    "Select a loss function: cross-entropy loss. Choose the optimizer SGD to update model parameters during training.\n",
+    "Iterate through training data in batches for a fixed number of epochs:\n",
+    "Feed a batch of images and labels to the model.\n",
+    "Calculate the loss based on the model's predictions and true labels.\n",
+    "Backpropagate the loss to update model parameters using the optimizer.\n",
+    "\n",
+    "## Inference\n",
+    "\n",
+    "Load the trained model's state.\n",
+    "For new, unseen images:\n",
+    "Preprocess them as done during training.\n",
+    "Pass the preprocessed images through the model to compute predictions and compare with the ground truth labels.\n",
+    "## Implementation Details\n",
+    "\n",
+    "Refer to the code for specific model architecture, hyperparameter choices, and implementation choices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torchvision\n",
+    "import torchvision.transforms as transforms\n",
+    "\n",
+    "# PREPARE DATA\n",
+    "\n",
+    "# Define transformations for data preprocessing\n",
+    "transform = transforms.Compose([\n",
+    "    transforms.ToTensor(),\n",
+    "    transforms.Normalize((0.5,), (0.5,))\n",
+    "])\n",
+    "\n",
+    "# Load Fashion MNIST dataset\n",
+    "# NOTE: train=True for trainset and train=False for testset; dowload=True for both.\n",
+    "trainset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)\n",
+    "testset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-- Epoch: 1, Loss: 1.027475444969338\n",
+      "-- Epoch: 2, Loss: 0.5553531648317126\n",
+      "-- Epoch: 3, Loss: 0.4895101859371291\n",
+      "-- Epoch: 4, Loss: 0.45323895183262797\n",
+      "-- Epoch: 5, Loss: 0.429904751845006\n",
+      "-- Epoch: 6, Loss: 0.41118921979721673\n",
+      "-- Epoch: 7, Loss: 0.3971544050458652\n",
+      "-- Epoch: 8, Loss: 0.3844668244454525\n",
+      "-- Epoch: 9, Loss: 0.37378820707040555\n",
+      "-- Epoch: 10, Loss: 0.3639335038978408\n",
+      "Training finished.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Part 2: Model Training\n",
+    "\n",
+    "# Create data loaders using the trainset and testset in Part 1, with batch size 64 and shuffle=True for trainset\n",
+    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
+    "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)\n",
+    "\n",
+    "# Define the neural network model\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.fc1 = nn.Linear(784, 256)\n",
+    "        self.fc2 = nn.Linear(256, 128)\n",
+    "        self.fc3 = nn.Linear(128, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = x.view(x.size(0), -1)\n",
+    "        x = torch.relu(self.fc1(x))\n",
+    "        x = torch.relu(self.fc2(x))\n",
+    "        x = self.fc3(x)\n",
+    "        return x\n",
+    "\n",
+    "# Create an instance of the model\n",
+    "model = Net()\n",
+    "\n",
+    "# Define loss function and optimizer\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
+    "\n",
+    "# Train the model\n",
+    "for epoch in range(10):\n",
+    "    running_loss = 0.0\n",
+    "    for i, data in enumerate(trainloader, 0):\n",
+    "        inputs, labels = data\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        outputs = model(inputs)\n",
+    "        loss = criterion(outputs, labels)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "        # Print loss every 200 mini-batches\n",
+    "        # if i % 200 == 199:\n",
+    "        #     print(f'Epoch: {epoch + 1}, Batch: {i + 1}, Loss: {running_loss / 200}')\n",
+    "        #     running_loss = 0.0\n",
+    "            \n",
+    "    loss = running_loss / len(trainloader)\n",
+    "    print(f'-- Epoch: {epoch + 1}, Loss: {loss}')\n",
+    "    \n",
+    "print('Training finished.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on test set: 85.62%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Part 3: Model Inferencing\n",
+    "\n",
+    "correct = 0\n",
+    "total = 0\n",
+    "with torch.no_grad():\n",
+    "    for data in testloader:\n",
+    "        images, labels = data\n",
+    "        outputs = model(images)\n",
+    "        _, predicted = torch.max(outputs.data, 1)\n",
+    "        total += labels.size(0)\n",
+    "        correct += (predicted == labels).sum().item()\n",
+    "\n",
+    "print(f'Accuracy on test set: {(100 * correct / total):.2f}%')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Benchmarks",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

test/fashion-mnist/infer.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+# Define the neural network model
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = nn.Linear(784, 256)
+        self.fc2 = nn.Linear(256, 128)
+        self.fc3 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = x.view(x.size(0), -1)
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+## IMPROVE
+from improve import framework as frm
+import candle
+from pathlib import Path
+
+filepath = Path(__file__).resolve().parent
+
+
+# Part 3: Model Testing
+## IMPROVE
+def run(params):
+##
+
+   # Need to get testloader from Part 1.
+   transform = transforms.Compose([
+      transforms.ToTensor(),
+      transforms.Normalize((0.5,), (0.5,))
+   ])
+
+   # Get the data directory, batch size and other hyperparameters from params 
+   ##IMPROVE 
+   batch_size = params["batch_size"]
+   learning_rate = params["learning_rate"]
+   momentum = params["momentum"]
+   dataset_dir = params["data_dir"]
+
+   # NOTE: using false now for data loading
+   testset = torchvision.datasets.FashionMNIST(root=dataset_dir, train=False, download=False, transform=transform)
+   testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True)
+
+   # Check if GPU is available, else use CPU
+   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+   # Create a neural network model
+   model = Net().to(device)
+
+   # Define optimizer
+   optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)
+
+   ##IMPROVE 
+   # Use CANDLE checkpointing to load the model weights for inferencing
+   ckpt = candle.CandleCkptPyTorch(params)
+   ckpt.set_model({"model": model, "optimizer": optimizer})
+   J = ckpt.restart(model)
+   ## 
+
+   correct = 0
+   total = 0
+   with torch.no_grad():
+      for data in testloader:
+         images, labels = data
+         outputs = model(images)
+         _, predicted = torch.max(outputs.data, 1)
+         total += labels.size(0)
+         correct += (predicted == labels).sum().item()
+
+   print(f'Accuracy on test set: {(100 * correct / total):.2f}%')
+
+
+
+
+## IMPROVE
+# Note some of these are similar to previous section and may be adjusted as per model requirements
+model_infer_params = [
+
+    {"name": "data_dir", # default
+     "type": str,
+     "help": "Directory containing the Fashion MNIST dataset.",
+    },
+]
+
+infer_params = [
+
+    {"name": "batch_size", # default
+     "type": int,
+     "help": "Batch size for creating data loaders.",
+    },
+]
+
+req_infer_args = [ll["name"] for ll in infer_params]
+
+def main():
+    params = frm.initialize_parameters(
+        filepath,
+        default_model="fashion-mnist_default_model.txt",
+        additional_definitions=model_infer_params,
+        required=req_infer_args,
+    )
+    run(params)
+
+if __name__ == "__main__":
+    main()
+
+## END IMPROVE