SectionSeeker_Quickstart_notebook.ipynb

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   "source": [
    "!python -m pip install -r requirements.txt"
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   "source": [
    "#  SectionSeeker - Seismic Image finder Starter Notebook"
   ]
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   "source": [
    "**This notebook and package has been adapted from ThinkOnward's [Reflection Connection Challenge](https://thinkonward.com/app/c/challenges/reflection-connection), which ran in late 2023. The SectionSeeker can be used to train a SiameseNN to identify similar sections to the one a user inputs. This can be extremely useful for seismic interpreters looking for an analog section or basin.**"
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   "source": [
    "# add the following paths to use modules we prepared for you\n",
    "import sys\n",
    "\n",
    "ROOT = \".\"  # project root directory\n",
    "sys.path.append(f\"{ROOT}/SectionSeeker\")\n",
    "sys.path.append(f\"{ROOT}/SectionSeeker/snn\")"
   ]
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   "source": [
    "### Data"
   ]
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   "source": [
    "🚨 To [download the data](https://huggingface.co/datasets/thinkonward/reflection-connection) and the models ([B-16](https://huggingface.co/thinkonward/section-seeker-base-16) or [L-16](https://huggingface.co/thinkonward/section-seeker-large-16)) you will need a [HuggingFace account](https://huggingface.co/) and request data and model access.\n",
    "\n",
    "To make sure that data can be accessed by this notebook unpack archives containing train and test data into `ROOT/data/` directory.\n",
    "\n",
    "The data for this challange consists of four parts:\n",
    "\n",
    "1. Training set (`data/train`) of 561 images, divided in 8 distinct categories \n",
    "2. Validation set (`data/test`) of 105 images, divided in 8 distinct categories \n",
    "3. Image corpus (`data/image_corpus`) of 362 unlabeled images with unknown category.\n",
    "4. Query set (`data/query`) of 50 unlabeled images with unknown category\n",
    "\n",
    "Each image is a 1-channel, 2D patch cut out from multiple sections of 3D seismic volumes. \n",
    "\n",
    "Training and validation set both contain labeled images. Dataset has a structure similar to well-known MNIST dataset - images are stored in subcatalogs that represent class labels. Both training and validation data can be accessed with `SeismicDataset` class that offers an API very similar to a standard MNIST torch vision dataset.\n",
    "\n",
    "Classes in the dataset are as follows:\n",
    "\n",
    "1. `Boring` - No visible interesting features\n",
    "2. `Bright_Planar` - Contains laterally continuous bright seismic amplitudes\n",
    "3. `Bright_Chaotic` - Contains chaotic bright amplitudes\n",
    "4. `Channel` - Visible channels (irrespective of depositional environment)\n",
    "5. `Converging_Amplitudes` - Seismic amplitudes that converge together\n",
    "7. `Fault` - Visible faults offsetting seismic horizons\n",
    "8. `Salt` - Visible salt body which may contain the top or edge of the salt body\n",
    "9. `Transparent_Planar` - Patches where the seismic reflectors are planar and the majority of the reflectors are transparent\n",
    "\n",
    "The split between test and train was arbitrary to demonstrate capabilities of the tools available to you in this challange - you are free to move patches between the test and train sets. Keep in mind that class `Boring` contains only patches of uninteresting seismic - we will not ask you to find images that belong to this class in the image corpus.\n",
    "\n",
    "Image corpus and query contain sets of unlabeled images that can be accessed with the `ImageSet` class. \n",
    "**You must not use this data in training of the model**. \n",
    "\n",
    "Note that some of images in image corpus and query set don't belong to any category defined within training and validation sets and will be used to evaluate performance of your algorithm in one-shot scenario.\n",
    "\n",
    "**Your ultimate goal is to prepare an algorithm that will search through image corpus to find 3 most similar images for each patch in query set**.\n"
   ]
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    "Download the dataset to your machine after requesting access from HuggingFace"
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    "import huggingface_hub\n",
    "\n",
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      },
      "text/plain": [
       "skxvn.png:   0%|          | 0.00/10.9k [00:00<?, ?B/s]"
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      "text/plain": [
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     },
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    },
    {
     "data": {
      "text/plain": [
       "'/home/ec2-user/SageMaker/section-seeker/data'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_REPO_ID = \"thinkonward/reflection-connection\"\n",
    "huggingface_hub.snapshot_download(\n",
    "    repo_id=DATA_REPO_ID, repo_type=\"dataset\", local_dir=\"./data\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import skimage\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from albumentations.augmentations.geometric.transforms import PadIfNeeded\n",
    "import albumentations as A\n",
    "import cv2\n",
    "from PIL import Image\n",
    "from typing import Callable, Union\n",
    "from torch import Tensor\n",
    "import torchvision.transforms as T\n",
    "\n",
    "from SectionSeeker.snn.dataset.dataset import SeismicDataset\n",
    "from SectionSeeker.snn.dataset.wrappers import ContrastiveDataset, TripletDataset\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with creating SeismicDataset on top of training data\n",
    "You specify `build=True` to build the dataset directly from images instead of loading the tensor file. \n",
    "You can create the tensor file from dataset by calling `dataset.save()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# transformation to pad images before passing to ViT. Supports standard and compatibility mode\n",
    "def vit_transform(img: Union[Tensor, Image.Image]) -> Tensor:\n",
    "\n",
    "    pad = PadIfNeeded(224, 224, value=-1, border_mode=cv2.BORDER_CONSTANT)\n",
    "    if isinstance(img, Image.Image):\n",
    "        transformed_img = pad(image=np.array(img)).get(\"image\")\n",
    "    elif isinstance(img, Tensor):\n",
    "        transformed_img = pad(image=img.numpy()).get(\"image\")\n",
    "    else:\n",
    "        transformed_img = pad(image=img).get(\"image\")\n",
    "\n",
    "    return Tensor(transformed_img)\n",
    "\n",
    "\n",
    "# Specify compatibilty_mode=True to support storing and getting data as PIL Images\n",
    "# Note that image-wise normalization is enabled bu default. Specify normalize=False to disable it\n",
    "dataset = SeismicDataset(\n",
    "    ROOT, build=True, greyscale=False, train=True, transform=vit_transform\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# each item of dataset is a tuple of image tensor with shape HxWxC and integer label. You can access names of classes with dataset.classes\n",
    "idx = 0\n",
    "plt.imshow(dataset[idx][0].numpy())\n",
    "plt.title(\n",
    "    f\"Example image from class {dataset[idx][1]}: {dataset.classes[dataset[idx][1]]}\"\n",
    ")\n",
    "# Warnings are only caused by floating-point error\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a next step you create an instance of ContrastiveDataset built on top of training seismic dataset\n",
    "Contrastive dataset builds pairs of images with binary target, indicating whether both images come from the same class\n",
    "\n",
    "Default mode for contrastive dataset is `train`, in which `__getitem__` method will generate random pairs for the indexed element of the seismic dataset each time it's called (lazy generation). In `test` mode, on the other hand, pairs are generated during contstruction of the dataset (eager generation), and therefeore are fixed. This behaviour is ment to make sure that validation scores depend only on model performance, not random pair selection. Same approach was also used in other type of dataset - `TripletDataset`, which will be discussed later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contrastive_dataset = ContrastiveDataset(dataset)\n",
    "\n",
    "# Each item of the contrastive dataset is a tuple of (HxWxC image 1 tensor, CxHxW image 2 tensor) and a binary target variable\n",
    "idx = 0\n",
    "\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "ax[0].imshow(contrastive_dataset[idx][0][0].numpy().transpose(1, 2, 0))\n",
    "ax[1].imshow(contrastive_dataset[idx][0][1].numpy().transpose(1, 2, 0))\n",
    "\n",
    "fig.suptitle(\n",
    "    f\"Pair {idx} from contrastive dataset. Target variable for this pair: {contrastive_dataset[idx][1]}\"\n",
    ")\n",
    "# Warnings are only caused by floating-point error\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Siamese neural networks can be trained in many ways - using `ContrastiveDataset` to create pairs annotated with target variable is only one of them. Other possible approach is to use `TripletDataset`, that creates triplets of anchor image, positive reference (same class) and negative reference (other class). You can build it on top of `SeismicDataset` in a same way that you used for `ContrastiveDataset`\n",
    "\n",
    "Note that for compatibility reasons, each item of TripletDataset is a tuple of (HxWxC anchor image tensor, CxHxW posititive image tensor, CxHxW negative image tensor) and empty list: `((Tensor, Tensor, Tensor), [])`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "triplet_dataset = TripletDataset(dataset)\n",
    "\n",
    "idx = 0\n",
    "\n",
    "fig, ax = plt.subplots(1, 3)\n",
    "ax[0].imshow(triplet_dataset[idx][0][0].numpy().transpose(1, 2, 0))\n",
    "ax[0].set_title(\"Anchor image\")\n",
    "ax[1].imshow(triplet_dataset[idx][0][1].numpy().transpose(1, 2, 0))\n",
    "ax[1].set_title(\"Positive reference\")\n",
    "ax[2].imshow(triplet_dataset[idx][0][2].numpy().transpose(1, 2, 0))\n",
    "ax[2].set_title(\"Negative reference\")\n",
    "\n",
    "# Warnings are only caused by floating-point error\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The order in which you serve pairs or triplets for training Siamese Neural Networks might have a huge impact on model performance. You might want to modify `ContrastiveDataset` or `TripletDataset` wrappers to use better pair matching algorithm than just random selection. Check the original FaceNet paper for more information https://arxiv.org/pdf/1503.03832.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model overview\n",
    "\n",
    "Problem of learning on dataset with sparse labels is generally refered to as few-shot learning. Special case of few-shot learning is one-shot learning, when you ask the model to make predictions for the new class, that was not present in training set and it's seen by the model for the first time as a query image during inference. \n",
    "\n",
    "Since you want to be able to use reference images to query the model for matching pairs - we decided to use Siamese Neural Network architecture with pre-trained ResNet-50 backbone. We encourage you to explore different architectures, backbone models and training procedures to achieve best performance not only in the few-shot problem, but also generalize well enough to maintain perfomance also in one-shot scenario."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "There are many possible approaches to train Siamesese Neural Networks. Most popular strategies include:\n",
    "1. Using binary classifier with standard BCE loss as the last stage of network. This is efficient for simple problems, like classification on MNIST dataset\n",
    "2. Using Contrastive Loss to push embeddings for different classes as far from each other as possible in latent space\n",
    "3. Using Triplet loss to separate embeddings in latent space without additional target variable, using anchor image instead. This was the approach used in FaceNet, achieving SOTA performance in facial recognition.\n",
    "\n",
    "We provide you with all above losses and corresponding datasets to choose the best training approach. In this example we used Contrastive loss to train the Siamese NN. We decided to separate training in two stages - first stage is meant to obtain initial convergence, while second stage with very small batch size is supposed to improve performance in one-shot image recognition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "from SectionSeeker.snn.losses import ContrastiveLoss\n",
    "from SectionSeeker.snn.model import SiameseNetwork\n",
    "from SectionSeeker.snn.train import Trainer\n",
    "from SectionSeeker.snn.config import ModelConfig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will need to now download the model weights from the HuggingFace repositories. To do this you need a HuggingFace account and to request model access."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ca1d6804d3541058b776b64003db910",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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    },
    {
     "data": {
      "text/plain": [
       "'/home/ec2-user/SageMaker/section-seeker/l_16_checkpoint'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B16_MODEL_REPO_ID = \"thinkonward/section-seeker-base-16\"\n",
    "huggingface_hub.snapshot_download(\n",
    "    repo_id=B16_MODEL_REPO_ID,\n",
    "    repo_type=\"model\",\n",
    "    local_dir=\"./b_16_checkpoint\",\n",
    "    allow_patterns=\"*.pth\",\n",
    ")\n",
    "\n",
    "L16_MODEL_REPO_ID = \"thinkonward/section-seeker-large-16\"\n",
    "huggingface_hub.snapshot_download(\n",
    "    repo_id=L16_MODEL_REPO_ID,\n",
    "    repo_type=\"model\",\n",
    "    local_dir=\"./l_16_checkpoint\",\n",
    "    allow_patterns=\"*.pth\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# specify configurations for local checkpoints\n",
    "# Note that the `SiameseNetwork()` constructor will first try to load model weights from Torchvision.\n",
    "# It will only proceed to loading local checkpoint when the first attempt fails\n",
    "# For models that have `fc.in_features` attribute you can specify FC_IN_FEATURES=0 for automatic detection\n",
    "\n",
    "vitBConfigPretrained = ModelConfig(\n",
    "    BACKBONE_MODEL=\"ViT_B_16\",\n",
    "    BACKBONE_MODEL_WEIGHTS=\"./b_16_checkpoint/ViT_B_16_SEISMIC31K.pth\",\n",
    "    LATENT_SPACE_DIM=16,\n",
    "    FC_IN_FEATURES=768,\n",
    ")\n",
    "\n",
    "vitLConfigPretrained = ModelConfig(\n",
    "    BACKBONE_MODEL=\"ViT_L_16\",\n",
    "    BACKBONE_MODEL_WEIGHTS=\"./l_16_checkpoint/ViT_L_16_SEISMIC31K.pth\",\n",
    "    LATENT_SPACE_DIM=16,\n",
    "    FC_IN_FEATURES=1024,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized weights for fully connected component\n"
     ]
    }
   ],
   "source": [
    "cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\") if cuda else torch.device(\"cpu\")\n",
    "model = SiameseNetwork(vitLConfigPretrained)\n",
    "# we recommend freezing pretrained & fine-tuned backbone at least in the first stage of training SNN\n",
    "model.freeze_backbone()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First stage of training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "kwargs = {\"num_workers\": 4, \"pin_memory\": True} if cuda else {}\n",
    "batch_size = 16\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    contrastive_dataset, batch_size=batch_size, shuffle=True, **kwargs\n",
    ")\n",
    "lr = 1e-3\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 10, gamma=0.1, last_epoch=-1)\n",
    "n_epochs = 10\n",
    "contrastive_loss = ContrastiveLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "siamese_trainer = Trainer(\"contrastive\", optimizer, cuda, log_interval=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 [0/451 (0%)]\tLoss: 0.226895\n",
      "Train Epoch: 1 [0/451 (0%)]\tLoss: 0.106775\n",
      "Train Epoch: 2 [0/451 (0%)]\tLoss: 0.148337\n",
      "Train Epoch: 3 [0/451 (0%)]\tLoss: 0.138462\n",
      "Train Epoch: 4 [0/451 (0%)]\tLoss: 0.136163\n",
      "Train Epoch: 5 [0/451 (0%)]\tLoss: 0.074675\n",
      "Train Epoch: 6 [0/451 (0%)]\tLoss: 0.107291\n",
      "Train Epoch: 7 [0/451 (0%)]\tLoss: 0.103328\n",
      "Train Epoch: 8 [0/451 (0%)]\tLoss: 0.105465\n",
      "Train Epoch: 9 [0/451 (0%)]\tLoss: 0.177218\n"
     ]
    }
   ],
   "source": [
    "siamese_trainer.train(model, train_loader, scheduler, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second stage of training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "batch_size = 2\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    contrastive_dataset, batch_size=batch_size, shuffle=True, **kwargs\n",
    ")\n",
    "n_epochs = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 [0/451 (0%)]\tLoss: 0.105039\n",
      "Train Epoch: 0 [200/451 (44%)]\tLoss: 0.069229\n",
      "Train Epoch: 0 [400/451 (88%)]\tLoss: 0.168055\n",
      "Train Epoch: 1 [0/451 (0%)]\tLoss: 0.113765\n",
      "Train Epoch: 1 [200/451 (44%)]\tLoss: 0.085873\n",
      "Train Epoch: 1 [400/451 (88%)]\tLoss: 0.206194\n",
      "Train Epoch: 2 [0/451 (0%)]\tLoss: 0.113211\n",
      "Train Epoch: 2 [200/451 (44%)]\tLoss: 0.278147\n",
      "Train Epoch: 2 [400/451 (88%)]\tLoss: 0.176493\n",
      "Train Epoch: 3 [0/451 (0%)]\tLoss: 0.094507\n",
      "Train Epoch: 3 [200/451 (44%)]\tLoss: 0.148441\n",
      "Train Epoch: 3 [400/451 (88%)]\tLoss: 0.037308\n",
      "Train Epoch: 4 [0/451 (0%)]\tLoss: 0.073663\n",
      "Train Epoch: 4 [200/451 (44%)]\tLoss: 0.066304\n",
      "Train Epoch: 4 [400/451 (88%)]\tLoss: 0.082733\n",
      "Train Epoch: 5 [0/451 (0%)]\tLoss: 0.072768\n",
      "Train Epoch: 5 [200/451 (44%)]\tLoss: 0.041371\n",
      "Train Epoch: 5 [400/451 (88%)]\tLoss: 0.068085\n",
      "Train Epoch: 6 [0/451 (0%)]\tLoss: 0.045705\n",
      "Train Epoch: 6 [200/451 (44%)]\tLoss: 0.130626\n",
      "Train Epoch: 6 [400/451 (88%)]\tLoss: 0.030041\n",
      "Train Epoch: 7 [0/451 (0%)]\tLoss: 0.028792\n",
      "Train Epoch: 7 [200/451 (44%)]\tLoss: 0.032542\n",
      "Train Epoch: 7 [400/451 (88%)]\tLoss: 0.049757\n",
      "Train Epoch: 8 [0/451 (0%)]\tLoss: 0.138647\n",
      "Train Epoch: 8 [200/451 (44%)]\tLoss: 0.122363\n",
      "Train Epoch: 8 [400/451 (88%)]\tLoss: 0.145875\n",
      "Train Epoch: 9 [0/451 (0%)]\tLoss: 0.277508\n",
      "Train Epoch: 9 [200/451 (44%)]\tLoss: 0.159594\n",
      "Train Epoch: 9 [400/451 (88%)]\tLoss: 0.209472\n",
      "Train Epoch: 10 [0/451 (0%)]\tLoss: 0.103222\n",
      "Train Epoch: 10 [200/451 (44%)]\tLoss: 0.047571\n",
      "Train Epoch: 10 [400/451 (88%)]\tLoss: 0.069108\n",
      "Train Epoch: 11 [0/451 (0%)]\tLoss: 0.134292\n",
      "Train Epoch: 11 [200/451 (44%)]\tLoss: 0.033167\n",
      "Train Epoch: 11 [400/451 (88%)]\tLoss: 0.044266\n",
      "Train Epoch: 12 [0/451 (0%)]\tLoss: 0.132249\n",
      "Train Epoch: 12 [200/451 (44%)]\tLoss: 0.155105\n",
      "Train Epoch: 12 [400/451 (88%)]\tLoss: 0.206280\n",
      "Train Epoch: 13 [0/451 (0%)]\tLoss: 0.104575\n",
      "Train Epoch: 13 [200/451 (44%)]\tLoss: 0.160565\n",
      "Train Epoch: 13 [400/451 (88%)]\tLoss: 0.210818\n",
      "Train Epoch: 14 [0/451 (0%)]\tLoss: 0.084480\n",
      "Train Epoch: 14 [200/451 (44%)]\tLoss: 0.116785\n",
      "Train Epoch: 14 [400/451 (88%)]\tLoss: 0.077472\n",
      "Train Epoch: 15 [0/451 (0%)]\tLoss: 0.073574\n",
      "Train Epoch: 15 [200/451 (44%)]\tLoss: 0.011340\n",
      "Train Epoch: 15 [400/451 (88%)]\tLoss: 0.252408\n",
      "Train Epoch: 16 [0/451 (0%)]\tLoss: 0.022928\n",
      "Train Epoch: 16 [200/451 (44%)]\tLoss: 0.062136\n",
      "Train Epoch: 16 [400/451 (88%)]\tLoss: 0.081102\n",
      "Train Epoch: 17 [0/451 (0%)]\tLoss: 0.218194\n",
      "Train Epoch: 17 [200/451 (44%)]\tLoss: 0.048087\n",
      "Train Epoch: 17 [400/451 (88%)]\tLoss: 0.016421\n",
      "Train Epoch: 18 [0/451 (0%)]\tLoss: 0.106768\n",
      "Train Epoch: 18 [200/451 (44%)]\tLoss: 0.101301\n",
      "Train Epoch: 18 [400/451 (88%)]\tLoss: 0.021041\n",
      "Train Epoch: 19 [0/451 (0%)]\tLoss: 0.083145\n",
      "Train Epoch: 19 [200/451 (44%)]\tLoss: 0.035057\n",
      "Train Epoch: 19 [400/451 (88%)]\tLoss: 0.101340\n"
     ]
    }
   ],
   "source": [
    "siamese_trainer.train(model, train_loader, scheduler, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of network performance\n",
    "\n",
    "To evaluate the performance of the embedding network you can simply compare distances in latent space between positive and negative pairs. In a well-trained model, average distance for positive pairs should be considerably smaller than for negative pairs. Bigger differences indicate better separation between embeddings of different classes, leading to better overall performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from snn.utils import all_pairs, contrative_distances\n",
    "from snn.predict import predict_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "val_dataset = SeismicDataset(\n",
    "    ROOT, build=True, greyscale=False, train=False, transform=vit_transform\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "val_contrastive_dataset = ContrastiveDataset(val_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "embeddings = predict_embeddings(model, val_dataset, return_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "embedding_pairs = all_pairs(embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "distances = contrative_distances(embedding_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average distance between negative pairs: 0.646,\n",
      "Average distance between positive pairs: 0.394\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"\"\"Average distance between negative pairs: {np.array(distances[0]).mean():.3f},\n",
    "Average distance between positive pairs: {np.array(distances[1]).mean():.3f}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semantic search across image corpus\n",
    "\n",
    "The ultimate goal of the challange is to find patches belonging to the same object class as the reference image in an image corpus - set of multiple patches, most of which don't contain any distinct patterns (they belong to the \"Boring\" class). To find 3 most similar images for each reference you next search for nearest neighbors in k-d tree build on latent representations of each image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from search import ImageSet, SearchTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Note that image-wise normalization is enabled bu default. Specify normalize=False to disable it\n",
    "corpus = ImageSet(ROOT, build=True, transform=vit_transform).build_embeddings(model)\n",
    "query_set = ImageSet(\n",
    "    ROOT, build=True, base=False, transform=vit_transform\n",
    ").build_embeddings(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building KD-Tree from embeddings\n"
     ]
    }
   ],
   "source": [
    "tree = SearchTree(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "reference_labels, distances, _, matched_labels = tree(query_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have the reference labels, distances, and matched labels you can visualize the sections and see how well the model did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def display_images(reference_labels, matched_labels, ref_img_number):\n",
    "    # Number of images to display per row\n",
    "    num_images_per_row = 4\n",
    "\n",
    "    # Create subplots\n",
    "    f, axarr = plt.subplots(1, num_images_per_row, figsize=(20, 5))\n",
    "\n",
    "    # Function to load and resize an image\n",
    "    def load_and_resize_image(path):\n",
    "        img = cv2.imread(path)\n",
    "        if img is None:\n",
    "            print(f\"Failed to load {path}\")\n",
    "            return np.zeros(\n",
    "                (100, 100, 3), dtype=np.uint8\n",
    "            )  # Return a black image if loading fails\n",
    "        return cv2.resize(img, (100, 100))\n",
    "\n",
    "    # Display reference image\n",
    "    axarr[0].imshow(\n",
    "        load_and_resize_image(\"data/query/\" + reference_labels[ref_img_number])\n",
    "    )\n",
    "    axarr[0].set_title(\"Reference\")\n",
    "    axarr[0].axis(\"off\")\n",
    "\n",
    "    # Display matched images\n",
    "    for i in range(1, num_images_per_row):\n",
    "        if i < len(matched_labels):\n",
    "            axarr[i].imshow(\n",
    "                load_and_resize_image(\n",
    "                    \"data/image_corpus/\" + matched_labels[ref_img_number][i - 1]\n",
    "                )\n",
    "            )\n",
    "            axarr[i].set_title(\n",
    "                \"Matched distance: \" + str(round(distances[ref_img_number][i - 1], 2))\n",
    "            )\n",
    "            axarr[i].axis(\"off\")\n",
    "        else:\n",
    "            # Fill remaining space with blank images\n",
    "            axarr[i].imshow(np.zeros((100, 100, 3), dtype=np.uint8))\n",
    "            axarr[i].axis(\"off\")\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_images(reference_labels, matched_labels, 3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_pytorch_p310",
   "language": "python",
   "name": "conda_pytorch_p310"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}