diff --git a/README.md b/README.md
deleted file mode 100644
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--- a/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# Pattern Analysis
-Pattern Analysis of various datasets by COMP3710 students at the University of Queensland.
-
-We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX.
-
-This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students.
-
-The library includes the following implemented in Tensorflow:
-* fractals
-* recognition problems
-
-In the recognition folder, you will find many recognition problems solved including:
-* OASIS brain segmentation
-* Classification
-etc.
diff --git a/recognition/47184530_VQVAE_Oasis_BrainMRI/47184530.ipynb b/recognition/47184530_VQVAE_Oasis_BrainMRI/47184530.ipynb
new file mode 100644
index 000000000..65e023b47
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+++ b/recognition/47184530_VQVAE_Oasis_BrainMRI/47184530.ipynb
@@ -0,0 +1,951 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "gpuType": "V100",
+ "machine_shape": "hm",
+ "authorship_tag": "ABX9TyMRBrqbl6pY/4sFtaxfnYyZ",
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "import zipfile\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from torch import nn, optim\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from PIL import Image\n",
+ "import matplotlib.pyplot as plt\n",
+ "from skimage.metrics import structural_similarity as ssim\n",
+ "from prettytable import PrettyTable\n",
+ "import matplotlib.pyplot as plt\n",
+ "import torch.nn.functional as F\n",
+ "from google.colab import drive\n",
+ "import zipfile\n",
+ "from torchvision.utils import save_image"
+ ],
+ "metadata": {
+ "id": "C1oCBoLsedJV"
+ },
+ "execution_count": 14,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Ensure that PyTorch uses the GPU (if available) or CPU otherwise\n",
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "\n",
+ "# Mounting Google Drive to access files. Note: This is specific to Google Colab.\n",
+ "drive.mount('/content/drive')\n",
+ "\n",
+ "# Define the directory where the output will be saved\n",
+ "OUTPUT_DIR = \"/content/drive/MyDrive/Colab_Notebooks_Course/image_process/A3/OUTPUT2\"\n",
+ "\n",
+ "# Create the directory if it doesn't exist\n",
+ "if not os.path.exists(OUTPUT_DIR):\n",
+ " os.makedirs(OUTPUT_DIR)\n",
+ "\n",
+ "# Dataset class to handle brain slice images\n",
+ "class BrainSlicesDataset(Dataset):\n",
+ " def __init__(self, image_slices):\n",
+ " self.image_slices = image_slices\n",
+ "\n",
+ " def __len__(self):\n",
+ " # Return the total number of image slices\n",
+ " return len(self.image_slices)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " image = self.image_slices[idx]\n",
+ "\n",
+ " # Ensure the image has a channel dimension (grayscale images may not have one)\n",
+ " if len(image.shape) == 2: # If the image is of shape [Height, Width]\n",
+ " image = torch.unsqueeze(image, 0) # Convert it to [1, Height, Width]\n",
+ "\n",
+ " return image\n",
+ "\n",
+ "\n",
+ "# Function to load and extract image slices from a zip file\n",
+ "def get_image_slices():\n",
+ " # Path to the zipped dataset\n",
+ " zip_path = \"/content/drive/MyDrive/Colab_Notebooks_Course/image_process/A3/testgans/GAN_Dataset.zip\"\n",
+ " extraction_path = \"/content/GAN_Dataset\"\n",
+ " # Extract the zip file\n",
+ " with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
+ " zip_ref.extractall(extraction_path)\n",
+ "\n",
+ " # Define the directories for training, testing, and validation datasets\n",
+ " parent_dir = \"/content/GAN_Dataset\"\n",
+ " train_path = os.path.join(parent_dir, \"keras_png_slices_train\")\n",
+ " test_path = os.path.join(parent_dir, \"keras_png_slices_test\")\n",
+ " val_path = os.path.join(parent_dir, \"keras_png_slices_validate\")\n",
+ "\n",
+ " # Helper function to load images from a directory\n",
+ " def load_images_from_folder(folder_path):\n",
+ " images = []\n",
+ " for filename in os.listdir(folder_path):\n",
+ " # Open the image, convert to grayscale, and resize to 128x128 pixels\n",
+ " img = Image.open(os.path.join(folder_path, filename)).convert('L').resize((128, 128))\n",
+ " if img is not None:\n",
+ " # Convert the image to a tensor and append to the list\n",
+ " images.append(torch.tensor(np.array(img, dtype=np.float32)))\n",
+ " return torch.stack(images) # Convert list of tensors to a single tensor\n",
+ "\n",
+ " # Load images from each directory\n",
+ " train_images = load_images_from_folder(train_path)\n",
+ " test_images = load_images_from_folder(test_path)\n",
+ " validate_images = load_images_from_folder(val_path)\n",
+ "\n",
+ " return train_images, test_images, validate_images\n",
+ "\n",
+ "\n",
+ "# Function to retrieve the image slices and provide a summary with a table and example images\n",
+ "def get_image_slices_with_table():\n",
+ " train_images, test_images, validate_images = get_image_slices()\n",
+ "\n",
+ " # Display a summary table using PrettyTable\n",
+ " table = PrettyTable()\n",
+ " table.field_names = [\"Data Split\", \"Total Images\", \"Image Shape\"]\n",
+ " table.add_row([\"Training\", len(train_images), train_images[0].shape])\n",
+ " table.add_row([\"Testing\", len(test_images), test_images[0].shape])\n",
+ " table.add_row([\"Validation\", len(validate_images), validate_images[0].shape])\n",
+ "\n",
+ " print(table)\n",
+ "\n",
+ " # Plot an example image from each dataset split\n",
+ " fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n",
+ " axs[0].imshow(train_images[0], cmap='gray')\n",
+ " axs[0].set_title(\"Training Image\")\n",
+ " axs[0].axis('off')\n",
+ "\n",
+ " axs[1].imshow(test_images[0], cmap='gray')\n",
+ " axs[1].set_title(\"Testing Image\")\n",
+ " axs[1].axis('off')\n",
+ "\n",
+ " axs[2].imshow(validate_images[0], cmap='gray')\n",
+ " axs[2].set_title(\"Validation Image\")\n",
+ " axs[2].axis('off')\n",
+ "\n",
+ " plt.show()\n",
+ "\n",
+ " return train_images, test_images, validate_images\n",
+ "\n",
+ "# Call the function to display the dataset summary and example images\n",
+ "get_image_slices_with_table()"
+ ],
+ "metadata": {
+ "id": "4cFXg40HfDzG",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "outputId": "e96106c1-3cfc-475d-9cd6-bcf8fe866190"
+ },
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
+ "+------------+--------------+------------------------+\n",
+ "| Data Split | Total Images | Image Shape |\n",
+ "+------------+--------------+------------------------+\n",
+ "| Training | 9664 | torch.Size([128, 128]) |\n",
+ "| Testing | 544 | torch.Size([128, 128]) |\n",
+ "| Validation | 1120 | torch.Size([128, 128]) |\n",
+ "+------------+--------------+------------------------+\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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+ ]
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# 2. Model Definitions\n",
+ "\n",
+ "# The Vector Quantizer layer performs the quantization of the encoder's outputs.\n",
+ "# This is where the continuous representations from the encoder are mapped to a discrete set of embeddings.\n",
+ "class VectorQuantizer(nn.Module):\n",
+ " def __init__(self, num_embeddings, embedding_dim, beta=0.25):\n",
+ " super(VectorQuantizer, self).__init__()\n",
+ "\n",
+ " # Embedding dimension: size of each embedding vector\n",
+ " self.embedding_dim = embedding_dim\n",
+ "\n",
+ " # Number of embeddings: total number of discrete embeddings in our codebook\n",
+ " self.num_embeddings = num_embeddings\n",
+ "\n",
+ " # Beta is a hyperparameter that weights the commitment loss\n",
+ " self.beta = beta\n",
+ "\n",
+ " # Initialize the embeddings (codebook) with random values. It's a learnable parameter.\n",
+ " self.embeddings = nn.Parameter(torch.randn(embedding_dim, num_embeddings))\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # Reshape the tensor to compute distances\n",
+ " z_e_x = x.permute(0, 2, 3, 1).contiguous()\n",
+ " z_e_x_ = z_e_x.view(-1, self.embedding_dim)\n",
+ "\n",
+ " # Compute pairwise distances between input and the codebook\n",
+ " distances = (torch.sum(z_e_x_**2, dim=1, keepdim=True)\n",
+ " + torch.sum(self.embeddings**2, dim=0)\n",
+ " - 2 * torch.matmul(z_e_x_, self.embeddings))\n",
+ "\n",
+ " # Find the closest embedding index for each item in the batch\n",
+ " encoding_indices = torch.argmin(distances, dim=1).unsqueeze(1)\n",
+ "\n",
+ " # Create a one-hot encoding of the indices\n",
+ " encodings = torch.zeros(encoding_indices.shape[0], self.num_embeddings).to(x.device)\n",
+ " encodings.scatter_(1, encoding_indices, 1)\n",
+ "\n",
+ " # Reshape the encoding indices to have the same spatial dimensions as input\n",
+ " encoding_indices = encoding_indices.view(*z_e_x.shape[:-1])\n",
+ "\n",
+ " # Use the encodings to get the quantized values from the codebook\n",
+ " quantized = torch.matmul(encodings, self.embeddings.t()).view(*z_e_x.shape)\n",
+ "\n",
+ " # Compute the commitment loss and the quantization loss\n",
+ " e_latent_loss = F.mse_loss(quantized.detach(), z_e_x)\n",
+ " q_latent_loss = F.mse_loss(quantized, z_e_x.detach())\n",
+ " loss = q_latent_loss + self.beta * e_latent_loss\n",
+ "\n",
+ " # Straight-through estimator: gradients bypass the non-differentiable operation\n",
+ " quantized = z_e_x + (quantized - z_e_x).detach()\n",
+ "\n",
+ " # Compute perplexity to check how many codebook entries are being used\n",
+ " avg_probs = torch.mean(encodings, dim=0)\n",
+ " perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))\n",
+ "\n",
+ " return loss, quantized.permute(0, 3, 1, 2).contiguous(), perplexity, encoding_indices\n",
+ "\n",
+ "# The Encoder module maps the input images to a continuous representation that will be quantized by the Vector Quantizer.\n",
+ "class Encoder(nn.Module):\n",
+ " def __init__(self, input_channels, hidden_channels, embedding_dim):\n",
+ " super(Encoder, self).__init__()\n",
+ "\n",
+ " # Define the encoder neural network\n",
+ " # The encoder consists of three convolutional layers with ReLU activations.\n",
+ " self.encoder = nn.Sequential(\n",
+ " # First convolutional layer: it takes the input image and produces 'hidden_channels' feature maps.\n",
+ " nn.Conv2d(input_channels, hidden_channels, kernel_size=4, stride=2, padding=1),\n",
+ " nn.ReLU(),\n",
+ "\n",
+ " # Second convolutional layer: reduces the spatial dimensions by half and reduces the number of feature maps.\n",
+ " nn.Conv2d(hidden_channels, hidden_channels // 2, kernel_size=4, stride=2, padding=1),\n",
+ " nn.ReLU(),\n",
+ "\n",
+ " # Third convolutional layer: prepares the tensor for quantization by setting the number of channels to 'embedding_dim'.\n",
+ " nn.Conv2d(hidden_channels // 2, embedding_dim, kernel_size=3, padding=1)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # Forward propagation of input through the encoder\n",
+ " return self.encoder(x)\n",
+ "\n",
+ "# The Decoder module maps the quantized representation back to the space of the original image.\n",
+ "class Decoder(nn.Module):\n",
+ " def __init__(self, input_channels, hidden_channels):\n",
+ " super(Decoder, self).__init__()\n",
+ "\n",
+ " # Define the decoder neural network\n",
+ " # The decoder consists of three transposed convolutional layers (sometimes called \"deconvolutional layers\") with ReLU activations.\n",
+ " self.decoder = nn.Sequential(\n",
+ " # First transposed convolutional layer: it takes the quantized representation and increases the spatial dimensions.\n",
+ " nn.ConvTranspose2d(input_channels, hidden_channels, kernel_size=3, stride=2, padding=1, output_padding=1),\n",
+ " nn.ReLU(),\n",
+ "\n",
+ " # Second transposed convolutional layer: further increases the spatial dimensions.\n",
+ " nn.ConvTranspose2d(hidden_channels, hidden_channels // 2, kernel_size=3, stride=2, padding=1, output_padding=1),\n",
+ " nn.ReLU(),\n",
+ "\n",
+ " # Third transposed convolutional layer: produces the final output with the same shape as the original image.\n",
+ " nn.ConvTranspose2d(hidden_channels // 2, 1, kernel_size=3, padding=1)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # Forward propagation of the quantized representation through the decoder\n",
+ " return self.decoder(x)\n",
+ "\n",
+ "# The VQ-VAE module combines the encoder, vector quantizer, and decoder components.\n",
+ "class VQVAE(nn.Module):\n",
+ " def __init__(self, input_channels, hidden_channels, num_embeddings, embedding_dim):\n",
+ " super(VQVAE, self).__init__()\n",
+ "\n",
+ " # Initialize the encoder module\n",
+ " self.encoder = Encoder(input_channels, hidden_channels, embedding_dim)\n",
+ "\n",
+ " # Initialize the vector quantization module\n",
+ " self.quantize = VectorQuantizer(num_embeddings, embedding_dim)\n",
+ "\n",
+ " # Initialize the decoder module\n",
+ " self.decoder = Decoder(embedding_dim, hidden_channels)\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # Encode the input image to a continuous representation\n",
+ " z = self.encoder(x)\n",
+ "\n",
+ " # Quantize the continuous representation\n",
+ " loss, quantized, perplexity, _ = self.quantize(z)\n",
+ "\n",
+ " # Decode the quantized representation to produce the reconstruction\n",
+ " x_recon = self.decoder(quantized)\n",
+ "\n",
+ " return loss, x_recon, perplexity\n",
+ "\n",
+ "# The VQVAETrainer module facilitates the training of the VQ-VAE model.\n",
+ "class VQVAETrainer(nn.Module):\n",
+ " def __init__(self, train_variance, input_channels, hidden_channels, num_embeddings, embedding_dim):\n",
+ " super(VQVAETrainer, self).__init__()\n",
+ "\n",
+ " # Store the variance of the training data (used for normalization)\n",
+ " self.train_variance = train_variance\n",
+ "\n",
+ " # Initialize the VQ-VAE model\n",
+ " self.vqvae = VQVAE(input_channels, hidden_channels, num_embeddings, embedding_dim)\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # Forward propagation of the input through the VQ-VAE\n",
+ " vq_loss, x_recon, perplexity = self.vqvae(x)\n",
+ "\n",
+ " # Compute the reconstruction loss normalized by the training data variance\n",
+ " recon_loss_value = F.mse_loss(x_recon, x) / self.train_variance\n",
+ "\n",
+ " # Overall loss is the sum of reconstruction loss and vector quantization loss\n",
+ " loss = recon_loss_value + vq_loss\n",
+ "\n",
+ " return x_recon, perplexity, loss\n",
+ "\n",
+ "# The PixelConvLayer is a custom convolutional layer used in the PixelCNN.\n",
+ "# It ensures that each pixel only depends on other pixels above it or to its left.\n",
+ "class PixelConvLayer(nn.Module):\n",
+ " def __init__(self, in_channels, out_channels, kernel_size, mask_type, **kwargs):\n",
+ " super(PixelConvLayer, self).__init__()\n",
+ "\n",
+ " # Define the mask type (either 'A' or 'B')\n",
+ " self.mask_type = mask_type\n",
+ "\n",
+ " # Compute padding to ensure the convolution is 'same' (output size == input size)\n",
+ " self.padding = (kernel_size - 1) // 2\n",
+ "\n",
+ " # Define the convolutional layer\n",
+ " self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs, padding=self.padding)\n",
+ "\n",
+ " # Initialize the mask to be applied on the convolutional weights\n",
+ " self.mask = self.conv.weight.data.clone()\n",
+ "\n",
+ " # Create the mask\n",
+ " self.create_mask()\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # Apply the mask to the convolutional weights\n",
+ " self.conv.weight.data *= self.mask.to(self.conv.weight.device)\n",
+ "\n",
+ " # Apply the convolution\n",
+ " return self.conv(x)\n",
+ "\n",
+ " def create_mask(self):\n",
+ " _, _, H, W = self.conv.weight.size()\n",
+ "\n",
+ " # Set the mask to ones initially\n",
+ " self.mask.fill_(1)\n",
+ "\n",
+ " # For mask type 'A', the center pixel and all pixels to the right are set to zero\n",
+ " # For mask type 'B', all pixels to the right of the center pixel are set to zero\n",
+ " self.mask[:, :, H // 2, W // 2 + (self.mask_type == 'A'):] = 0\n",
+ "\n",
+ " # All pixels below the center pixel are set to zero\n",
+ " self.mask[:, :, H // 2 + 1:] = 0\n",
+ "\n",
+ "# The PixelCNN model comprises several PixelConvLayers.\n",
+ "class PixelCNN(nn.Module):\n",
+ " def __init__(self, input_shape, num_embeddings, embedding_dim):\n",
+ " super(PixelCNN, self).__init__()\n",
+ "\n",
+ " # Define the input shape of the image\n",
+ " self.input_shape = input_shape\n",
+ "\n",
+ " # Define the embedding dimension\n",
+ " self.embedding_dim = embedding_dim\n",
+ "\n",
+ " # Define the number of embeddings (or the number of different pixel values)\n",
+ " self.num_embeddings = num_embeddings\n",
+ "\n",
+ " # Define the architecture of the PixelCNN\n",
+ " self.layers = nn.ModuleList()\n",
+ "\n",
+ " # The first layer has a mask type 'A'\n",
+ " self.layers.append(PixelConvLayer(input_shape[0], embedding_dim, 7, mask_type='A'))\n",
+ "\n",
+ " # Subsequent layers have a mask type 'B'\n",
+ " for _ in range(5):\n",
+ " self.layers.append(PixelConvLayer(embedding_dim, embedding_dim, 7, mask_type='B'))\n",
+ "\n",
+ " # The final layer reduces the number of channels to the number of embeddings\n",
+ " self.layers.append(nn.Conv2d(embedding_dim, num_embeddings, 1))\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # Forward propagation through the PixelCNN\n",
+ " for layer in self.layers:\n",
+ " x = F.relu(layer(x))\n",
+ " return x\n",
+ "\n",
+ "\n",
+ "# 3. Training Functions\n",
+ "\n",
+ "# This function trains the VQ-VAE model.\n",
+ "def train_vqvae(vqvae, train_loader, num_epochs, learning_rate, test_samples, recon_losses, vq_losses, perplexities):\n",
+ " # Set up the optimizer for training (Adam in this case).\n",
+ " optimizer = optim.Adam(vqvae.parameters(), lr=learning_rate)\n",
+ "\n",
+ " # Loop through each epoch.\n",
+ " for epoch in range(num_epochs):\n",
+ " # Loop through each batch of images from the DataLoader.\n",
+ " for batch_idx, images in enumerate(train_loader):\n",
+ " images = images.to(device) # Transfer images to the GPU if available.\n",
+ "\n",
+ " # Zero the gradients.\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " # Forward pass through the VQ-VAE.\n",
+ " x_recon, perplexity, loss = vqvae(images)\n",
+ "\n",
+ " # Compute reconstruction and VQ losses.\n",
+ " recon_loss_value = F.mse_loss(x_recon, images) / vqvae.train_variance\n",
+ " vq_loss_value = loss - recon_loss_value\n",
+ "\n",
+ " # Record the losses and perplexity for plotting later.\n",
+ " recon_losses.append(recon_loss_value.item())\n",
+ " vq_losses.append(vq_loss_value.item())\n",
+ " perplexities.append(perplexity.item())\n",
+ "\n",
+ " # Backward pass.\n",
+ " loss.backward()\n",
+ "\n",
+ " # Update the weights.\n",
+ " optimizer.step()\n",
+ "\n",
+ " # At the end of each epoch, visualize some reconstructed images.\n",
+ " with torch.no_grad():\n",
+ " reconstructions, _, _ = vqvae(test_samples)\n",
+ " visualize_reconstructions(test_samples.cpu(), reconstructions.cpu())\n",
+ "\n",
+ " # Save the generated images\n",
+ " save_path = os.path.join(OUTPUT_DIR, f\"{epoch}.png\")\n",
+ " save_image(reconstructions, save_path)\n",
+ "\n",
+ " # Print the loss for the current epoch.\n",
+ " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}\")\n",
+ "\n",
+ " # At the end of training, plot the recorded losses and perplexity.\n",
+ " plt.figure(figsize=(10,5))\n",
+ " plt.plot(recon_losses, label='Reconstruction Loss')\n",
+ " plt.plot(vq_losses, label='VQ Loss')\n",
+ " plt.legend()\n",
+ " plt.title('Losses over Training')\n",
+ " plt.xlabel('Training Iterations')\n",
+ " plt.ylabel('Loss Value')\n",
+ " plt.show()\n",
+ "\n",
+ " plt.figure(figsize=(10,5))\n",
+ " plt.plot(perplexities)\n",
+ " plt.title('Perplexity over Training')\n",
+ " plt.xlabel('Training Iterations')\n",
+ " plt.ylabel('Perplexity')\n",
+ " plt.show()\n",
+ "\n",
+ " # Visualize the histogram of encoding indices.\n",
+ " with torch.no_grad():\n",
+ " _, _, _, encoding_indices = vqvae.vqvae.quantize(vqvae.vqvae.encoder(test_samples))\n",
+ " encoding_indices = encoding_indices.flatten().cpu().numpy()\n",
+ "\n",
+ " plt.figure(figsize=(10,5))\n",
+ " plt.hist(encoding_indices, bins=np.arange(vqvae.vqvae.quantize.num_embeddings+1)-0.5, rwidth=0.8)\n",
+ " plt.title('Histogram of Encoding Indices')\n",
+ " plt.xlabel('Encoding Index')\n",
+ " plt.ylabel('Frequency')\n",
+ " plt.xticks(np.arange(vqvae.vqvae.quantize.num_embeddings))\n",
+ " plt.show()\n",
+ "\n",
+ " # Print the recorded losses and perplexities\n",
+ " print(\"Reconstruction Losses:\", recon_losses)\n",
+ " print(\"VQ Losses:\", vq_losses)\n",
+ " print(\"Perplexities:\", perplexities)\n",
+ "\n",
+ "# This function trains the PixelCNN model.\n",
+ "def train_pixelcnn(pixelcnn, train_loader, num_epochs, learning_rate):\n",
+ " optimizer = optim.Adam(pixelcnn.parameters(), lr=learning_rate)\n",
+ " criterion = nn.CrossEntropyLoss()\n",
+ "\n",
+ " # Loop through each epoch.\n",
+ " for epoch in range(num_epochs):\n",
+ " # Loop through each batch of images from the DataLoader.\n",
+ " for images in train_loader:\n",
+ " images = images.to(device) # Transfer images to the GPU if available.\n",
+ "\n",
+ " # Zero the gradients.\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " # Forward pass through the PixelCNN.\n",
+ " logits = pixelcnn(images)\n",
+ "\n",
+ " # Compute the loss.\n",
+ " loss = criterion(logits, images.squeeze(1).long())\n",
+ "\n",
+ " # Backward pass.\n",
+ " loss.backward()\n",
+ "\n",
+ " # Update the weights.\n",
+ " optimizer.step()\n",
+ "\n",
+ " # Print the loss for the current epoch.\n",
+ " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}\")\n",
+ "\n",
+ "# 4. Visualization\n",
+ "\n",
+ "# This function visualizes original vs reconstructed images.\n",
+ "def visualize_reconstructions(originals, reconstructions, num_samples=3):\n",
+ " # Loop through the number of samples specified.\n",
+ " for i in range(num_samples):\n",
+ " # Create a subplot for the original and reconstructed images.\n",
+ " fig, axs = plt.subplots(1, 2)\n",
+ "\n",
+ " # Display the original image.\n",
+ " axs[0].imshow(originals[i, 0].detach().numpy(), cmap='gray')\n",
+ " axs[0].set_title(\"Original\")\n",
+ "\n",
+ " # Display the reconstructed image.\n",
+ " axs[1].imshow(reconstructions[i, 0].detach().numpy(), cmap='gray')\n",
+ " axs[1].set_title(\"Reconstruction\")\n",
+ "\n",
+ " # Remove axis ticks and labels.\n",
+ " plt.show()\n",
+ "\n",
+ "# This function visualizes generated samples.\n",
+ "def visualize_samples(samples, num_samples=3):\n",
+ " # Loop through the number of samples specified.\n",
+ " for i in range(num_samples):\n",
+ " # Display the generated image.\n",
+ " plt.imshow(samples[i, 0].detach().cpu().numpy(), cmap='gray')\n",
+ " plt.title(\"Generated Sample\")\n",
+ " plt.show()\n",
+ "\n",
+ "# This function visualizes images generated using PixelCNN.\n",
+ "def visualize_pixelcnn_generation_batch(pixelcnn, batch_size, img_size=(1, 128, 128)):\n",
+ " # Create a batch of empty images.\n",
+ " samples = torch.zeros(batch_size, *img_size).to(device)\n",
+ "\n",
+ " # Generate images pixel by pixel.\n",
+ " for i in range(img_size[1]):\n",
+ " for j in range(img_size[2]):\n",
+ " out = pixelcnn(samples)\n",
+ " probs = F.softmax(out[:, :, i, j], dim=1)\n",
+ " for b in range(batch_size):\n",
+ " samples[b, :, i, j] = torch.multinomial(probs[b], 1).float() / 255.0\n",
+ "\n",
+ " # Display the generated images.\n",
+ " for b in range(batch_size):\n",
+ " plt.imshow(samples[b, 0].cpu().detach().numpy(), cmap='gray')\n",
+ " plt.title(f\"PixelCNN Generated Sample {b+1}\")\n",
+ " plt.show()\n",
+ "\n",
+ "# This function compares an original image with one generated by PixelCNN.\n",
+ "def compare_original_and_generated(original, pixelcnn, img_size=(1, 128, 128)):\n",
+ " # Generate an image using PixelCNN.\n",
+ " generated = torch.zeros(img_size).to(device)\n",
+ " for i in range(img_size[1]):\n",
+ " for j in range(img_size[2]):\n",
+ " out = pixelcnn(generated)\n",
+ " probs = F.softmax(out[:, :, i, j], dim=1)\n",
+ " generated[:, :, i, j] = torch.multinomial(probs, 1).float() / 255.0\n",
+ "\n",
+ " # Create a subplot for the original and PixelCNN generated images.\n",
+ " fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
+ "\n",
+ " # Display the original image.\n",
+ " axs[0].imshow(original[0, 0].cpu().detach().numpy(), cmap='gray')\n",
+ " axs[0].set_title(\"Original\")\n",
+ "\n",
+ " # Display the PixelCNN generated image.\n",
+ " axs[1].imshow(generated[0, 0].cpu().detach().numpy(), cmap='gray')\n",
+ " axs[1].set_title(\"PixelCNN Generated\")\n",
+ "\n",
+ " # Remove axis ticks and labels.\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "# 5. Main Function\n",
+ "\n",
+ "def main():\n",
+ "\n",
+ " # Lists to store loss values and perplexities for visualization\n",
+ " recon_losses = []\n",
+ " vq_losses = []\n",
+ " perplexities = []\n",
+ "\n",
+ " # Load the brain slices images\n",
+ " train_images, test_images, _ = get_image_slices()\n",
+ " # Create a dataset and data loader using the train images\n",
+ " dataset = BrainSlicesDataset(train_images)\n",
+ " train_loader = DataLoader(dataset, batch_size=32, shuffle=True)\n",
+ "\n",
+ " # Create a batch of test images for visualization purposes\n",
+ " test_samples_for_viz = torch.stack([test_images[i].unsqueeze(0) for i in range(3)]).to(device)\n",
+ "\n",
+ " # Initialize the VQ-VAE model and move it to the appropriate device (GPU or CPU)\n",
+ " vqvae_model = VQVAE(input_channels=1, hidden_channels=128, num_embeddings=512, embedding_dim=32).to(device)\n",
+ " optimizer = torch.optim.Adam(vqvae_model.parameters(), lr=0.001)\n",
+ "\n",
+ " # Initialize the VQVAE trainer model and move it to the appropriate device\n",
+ " vqvae = VQVAETrainer(train_images.var(), 1, 128, 512, 32).to(device)\n",
+ " # Train the VQVAE model\n",
+ " train_vqvae(vqvae, train_loader, num_epochs=20, learning_rate=0.001, test_samples=test_samples_for_viz, recon_losses=recon_losses, vq_losses=vq_losses, perplexities=perplexities)\n",
+ "\n",
+ " # Initialize the PixelCNN model and move it to the appropriate device\n",
+ " pixelcnn = PixelCNN((1, 128, 128), 256, 10).to(device)\n",
+ " # Train the PixelCNN model\n",
+ " train_pixelcnn(pixelcnn, train_loader, num_epochs=40, learning_rate=0.001)\n",
+ "\n",
+ " # Generate images using the trained PixelCNN\n",
+ " with torch.no_grad():\n",
+ " pixelcnn_generated_samples = torch.zeros(3, 1, 128, 128).to(device) # batch of 3 empty images\n",
+ " for i in range(128):\n",
+ " for j in range(128):\n",
+ " out = pixelcnn(pixelcnn_generated_samples)\n",
+ " probs = F.softmax(out[:, :, i, j], dim=1)\n",
+ " for b in range(3): # For each image in the batch\n",
+ " pixelcnn_generated_samples[b, :, i, j] = torch.multinomial(probs[b], 1).float() / 255.0\n",
+ " # Visualize the images generated by the PixelCNN\n",
+ " visualize_samples(pixelcnn_generated_samples)\n",
+ "\n",
+ " # Visualization of reconstructions using the VQ-VAE model\n",
+ " with torch.no_grad():\n",
+ " # Get some test images for reconstruction visualization\n",
+ " test_samples = torch.stack([test_images[i] for i in range(3)]).to(device)\n",
+ " reconstructions, _, _ = vqvae(test_samples)\n",
+ " # Visualize the reconstructions\n",
+ " visualize_reconstructions(test_samples, reconstructions)\n",
+ "\n",
+ " # Visualize multiple images generated by the PixelCNN\n",
+ " visualize_pixelcnn_generation_batch(pixelcnn, batch_size=5)\n",
+ "\n",
+ " # Compare an original image with an image generated by the PixelCNN\n",
+ " for i in range(3): # For 3 examples\n",
+ " compare_original_and_generated(test_samples[i], pixelcnn)\n",
+ " return recon_losses, vq_losses, perplexities\n",
+ "\n",
+ " # Print the recorded losses and perplexities\n",
+ " print(\"Reconstruction Losses:\", recon_losses)\n",
+ " print(\"VQ Losses:\", vq_losses)\n",
+ " print(\"Perplexities:\", perplexities)\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " main()\n",
+ "\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "H1hto2HnA0RW",
+ "outputId": "081e2e97-0985-460f-a5c5-384224cff430"
+ },
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch [1/20], Loss: 1.1359\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch [2/20], Loss: 0.9987\n"
+ ]
+ },
+ {
+ "output_type": "error",
+ "ename": "KeyboardInterrupt",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 436\u001b[0m \u001b[0mvqvae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVQVAETrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_images\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[0;31m# Train the VQVAE model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 438\u001b[0;31m \u001b[0mtrain_vqvae\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvqvae\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_samples_for_viz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecon_losses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecon_losses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvq_losses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvq_losses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperplexities\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mperplexities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;31m# Initialize the PixelCNN model and move it to the appropriate device\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mtrain_vqvae\u001b[0;34m(vqvae, train_loader, num_epochs, learning_rate, test_samples, recon_losses, vq_losses, perplexities)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;31m# Forward pass through the VQ-VAE.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m \u001b[0mx_recon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperplexity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvqvae\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0;31m# Compute reconstruction and VQ losses.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;31m# Forward propagation of the input through the VQ-VAE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m \u001b[0mvq_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_recon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperplexity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvqvae\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;31m# Compute the reconstruction loss normalized by the training data variance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;31m# Quantize the continuous representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquantized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperplexity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0;31m# Decode the quantized representation to produce the reconstruction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;31m# Create a one-hot encoding of the indices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mencodings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoding_indices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_embeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0mencodings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "X0JCu_4quBrG"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/LICENSE b/recognition/47184530_VQVAE_Oasis_BrainMRI/LICENSE
similarity index 100%
rename from LICENSE
rename to recognition/47184530_VQVAE_Oasis_BrainMRI/LICENSE
diff --git a/recognition/47184530_VQVAE_Oasis_BrainMRI/README.md b/recognition/47184530_VQVAE_Oasis_BrainMRI/README.md
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+++ b/recognition/47184530_VQVAE_Oasis_BrainMRI/README.md
@@ -0,0 +1,337 @@
+# COMP3710 Report - VQ-VAE (Brain MRI)
+---
+# Table of Contents
+
+- [**Introduction**](#introduction)
+- [**Dataset**](#dataset)
+ - [Training, Testing, and Validation Splits](#dataset)
+ - [Image Specifications](#dataset)
+ - [Dataset Samples](#dataset)
+- [**Model Definitions**](#model-definitions)
+ - [**VQ-VAE**](#vq-vae)
+ - [VQ-VAE Architecture](#vq-vae)
+ - [VQ-VAE Framework](#vq-vae)
+ - [Description and Components](#vq-vae)
+ - [Loss Mechanisms](#vq-vae)
+ - [Pseudocode](#vq-vae)
+ - [**PixelCNN**](#pixelcnn)
+ - [PixelCNN Architecture](#pixelcnn)
+ - [How it Works](#pixelcnn)
+ - [The Loss Mechanism](#pixelcnn)
+- [**Visualization**](#visualization)
+ - [Sample Output Images](#visualization)
+ - [Training Progress GIFs](#visualization)
+- [**Training Insights**](#training-insights)
+ - [Overview of Metrics](#training-insights)
+ - [Graphs and Observations](#training-insights)
+- [**Scope of Improvement**](#scope-of-improvement)
+ - [Model Architecture Enhancements](#scope-of-improvement)
+ - [Hyperparameter Tuning](#scope-of-improvement)
+ - [Data Augmentation](#scope-of-improvement)
+ - [And more...](#scope-of-improvement)
+- [**Future Roadmap**](#future-roadmap)
+- [**Dependencies**](#dependencies)
+- [**Directory Structure**](#directory-structure)
+- [**Usage**](#usage)
+ - [Mounting Google Drive](#usage)
+ - [Path Set-up](#usage)
+ - [Running the Main Function](#usage)
+- [**Training**](#training)
+- [**Prediction with Optional Pre-trained Model**](#prediction-with-optional-pre-trained-model)
+- [**Output**](#output)
+- [**References**](#references)
+
+
+
+# VQ-VAE with PixelCNN for Oasis Brain MRI Image Reconstruction and Generation
+
+## Introduction
+
+This project explores the use of Vector Quantized Variational Autoencoders (VQ-VAE) combined with PixelCNN for the purpose of brain image reconstruction and generation. The code demonstrates the complete pipeline, starting from dataset loading and preprocessing, to model training, and finally visualization of results.
+
+## Dataset
+
+The dataset consists of brain slice images. The data is zipped and stored in Google Drive, but can be easily extracted and processed for use in the project.
+
+The dataset is split into:
+- Training: 9664 images
+- Testing: 544 images
+- Validation: 1120 images
+
+Each image is of shape 128x128.
+
+
+
+## Model Definitions
+
+### VQ-VAE
+
+#### VQ-VAE Architecture
+
+
+#### VQ-VAE Framework
+
+
+VQ-VAE is used for the compression of brain images. It comprises three main components: an encoder, a vector quantizer, and a decoder. The encoder maps input images to a continuous representation, which is then quantized by the vector quantizer. The quantized representation is finally mapped back to the original image space using the decoder.
+
+When we talk about the loss in the VQ-VAE model, it's a blend of three primary components:
+
+1. **Total Loss**: This is like the grand total on a bill. It combines the losses from the vector-quantization layer and the image reconstructions.
+2. **Vector Quantization (VQ) Loss**: This is further split into two parts:
+ - **Commitment Loss**: This ensures that the encoder remains loyal to a particular codebook. It's essential because while our encoder learns pretty quickly,
+ our codebook takes its sweet time. The commitment loss is like a gentle nudge to ensure they remain in sync. We also introduce a scaling factor, termed as the beta parameter.
+ Even though the original VQ-VAE paper mentioned that the model is sturdy against changes in this parameter, it still plays a role in the commitment.
+ - **Codebook Loss**: This is simply the L2-norm error, which nudges our embedding or codebook vectors to align better with the encoder's output.
+3. **Reconstruction Loss**: At the end of the day, we want our reconstructed image to resemble the original. This loss measures how well we're doing in that aspect.
+
+ The formula for the total loss can be represented as:
+
+**Total Loss** = Reconstruction Loss + VQ Loss
+
+Where:
+
+**VQ Loss** = Commitment Loss + Codebook Loss
+
+### Pseudocode
+```
+INITIALIZE necessary libraries
+SET device, paths, and directories
+
+DEFINE BrainSlicesDataset:
+ INITIALIZE with image slices
+ DEFINE methods to get length and item
+
+DEFINE function to load and extract image slices
+DEFINE function to retrieve image slices and provide summary
+
+DEFINE VectorQuantizer class:
+ INITIALIZE embeddings and parameters
+ FORWARD function to perform quantization and compute loss
+
+DEFINE Encoder class:
+ INITIALIZE encoder neural network
+ FORWARD function to encode input
+
+DEFINE Decoder class:
+ INITIALIZE decoder neural network
+ FORWARD function to decode input
+
+DEFINE VQVAE class:
+ INITIALIZE encoder, vector quantizer, and decoder
+ FORWARD function to perform end-to-end VQ-VAE processing
+
+DEFINE VQVAETrainer class:
+ INITIALIZE VQ-VAE model
+ FORWARD function to compute losses and perform reconstruction
+
+DEFINE PixelConvLayer class:
+ INITIALIZE convolutional layer with mask
+ FORWARD function to apply masked convolution
+ CREATE mask based on mask type
+
+DEFINE PixelCNN class:
+ INITIALIZE layers for PixelCNN
+ FORWARD function for end-to-end PixelCNN processing
+
+DEFINE training functions:
+ TRAIN VQ-VAE
+ LOOP through epochs:
+ FORWARD pass through VQ-VAE
+ COMPUTE losses
+ BACKWARD pass
+ UPDATE model weights
+ VISUALIZE results after each epoch
+ TRAIN PixelCNN
+ LOOP through epochs:
+ FORWARD pass through PixelCNN
+ COMPUTE loss
+ BACKWARD pass
+ UPDATE model weights
+
+DEFINE visualization functions:
+ VISUALIZE original vs reconstructed images
+ VISUALIZE samples generated by PixelCNN
+ COMPARE original with PixelCNN generated image
+
+MAIN function:
+ LOAD brain slice images
+ INITIALIZE and TRAIN VQ-VAE
+ INITIALIZE and TRAIN PixelCNN
+ VISUALIZE results using the trained models
+
+EXECUTE main function
+```
+
+### PixelCNN
+PixelCNN is like an artist with a paintbrush, creating images one pixel at a time. It's a generative model that cleverly utilizes convolutional and residual blocks.
+The idea is to compute the distribution of prior pixels to guess the next pixel.
+
+
+
+**How it Works:**
+1. **Initial Convolution**: The input image is passed through a convolutional layer.
+ This process is a bit like using a magnifying glass to inspect the image, where the "receptive fields" help the model learn features for all the pixels simultaneously.
+ But there's a catch! We use masks, termed 'A' and 'B', to ensure that we're not "cheating" by looking at pixels we shouldn't.
+ The 'A' mask restricts connections to only the pixels we've already predicted, while the 'B' mask allows connections only from predicted pixels to the current ones.
+
+2. **Residual Blocks**: After the initial convolution, the data flows through residual blocks.
+ These blocks are smart! Instead of trying to learn the output directly, they focus on learning the difference (or residuals) between the expected output and the current one.
+ This is achieved by creating shortcuts (or skip connections) between layers.
+
+### The Loss Mechanism:
+For PixelCNN, the loss metric used is the Sparse Categorical Crossentropy loss. This quantifies the error in selecting the right latent vectors (or pages from our codebook) for image generation.
+PixelCNN is a generative model trained to predict the next pixel's value in an image given all the previous pixels. It's employed post-VQ-VAE training to refine the generated images, making them more realistic.
+
+## Visualization
+
+Functions are provided to visualize the reconstructions made by the VQ-VAE, as well as images generated by the PixelCNN. This includes side-by-side comparisons of original and reconstructed/generated images, histograms of encoding indices, and various loss plots.
+
+#### Sample Output Image
+Output at epoch = 2
+
+
+Output at epoch = 30
+
+
+#### Training Progress
+ 
+
+## Training Insights
+
+The training phase of the project was critical. The VQ-VAE's loss and the PixelCNN's loss provided insights into how well the models were learning and reconstructing the brain images. Additionally, metrics like perplexity gave a deeper understanding of the model's predictive distribution in comparison to the actual data distribution.
+
+From the data provided:
+- **Reconstruction Loss** - This measures how well the reconstructed output matches the original input. A lower reconstruction loss indicates that the VQ-VAE is able to more accurately reproduce the original images from its encoded representations.
+- **VQ Loss** - Vector Quantization (VQ) loss measures the difference between the encoder's output and the nearest embedding from the codebook. It ensures that the continuous representations from the encoder are effectively quantized to discrete values.
+- **Perplexity** - Perplexity provides insights into the diversity of the embeddings being used. A higher perplexity indicates that more embeddings from the codebook are being actively used.
+
+1. **Reconstruction Loss:**
+
+This graph showcases the reconstruction loss over epochs. The reconstruction loss quantifies how well the reconstructed output from the VQ-VAE matches the original input. A lower value of this loss indicates that the VQ-VAE is effectively reproducing the original images from its encoded representations.
+
+
+
+#### Observations:
+
+The reconstruction loss demonstrates a declining trend, which suggests that as the training progresses, the model becomes better at reconstructing the input data.
+This is expected behavior during training as the model adapts its weights and biases to minimize the difference between the original input and the reconstructed output.
+
+2. **VQ Loss:**
+
+This graph depicts the vector quantization (VQ) loss over epochs. The VQ loss measures the discrepancy between the encoder's output and the nearest embedding from the codebook. It ensures that the continuous representations from the encoder are effectively transformed to discrete values that can be looked up in the codebook.
+
+
+
+#### Observations:
+
+The VQ loss also displays a general decreasing trend, albeit with some fluctuations. This indicates that, over time, the encoder's outputs are getting closer to the codebook embeddings, ensuring effective quantization.
+The fluctuations might suggest that the model is exploring different parts of the latent space during training.
+
+3. **Perplexity:**
+
+This graph illustrates the perplexity over epochs. Perplexity offers insights into the diversity of the embeddings being used. A higher perplexity indicates that a wider range of embeddings from the codebook is being actively utilized.
+
+
+
+
+#### Observations:
+
+The perplexity seems to rise initially and then stabilizes, which implies that as the model trains, it starts using a broader variety of embeddings from the codebook.
+The stabilization of perplexity suggests that the model has reached a point where it consistently uses a certain number of embeddings from the codebook for representation.
+Overall, these graphs provide insights into the training dynamics of the VQ-VAE model. The decreasing reconstruction and VQ losses indicate that the model is learning effectively. The behavior of perplexity suggests that the model is leveraging a diverse set of embeddings from the codebook for representation, which is a good sign of a well-trained model.
+
+# Scope of Improvement
+- **Model Architecture Enhancements**: The current architecture can be improved by adding more convolutional layers or integrating techniques like batch normalization to stabilize and accelerate the training process.
+
+- **Hyperparameter Tuning**: There's always room to experiment with hyperparameters such as learning rate, batch size, and the number of epochs. Automated hyperparameter optimization tools like Optuna or Ray Tune can be used for this purpose.
+
+- **Data Augmentation**: Introducing data augmentation can help in enhancing the diversity of the training dataset, leading to better generalization during reconstruction.
+
+- **Loss Function Refinements**: Modifying the loss function or incorporating additional loss terms can lead to better reconstructions or faster training.
+
+- **Integration with other GANs**: The current setup can be integrated with other Generative Adversarial Networks (GANs) to improve the quality of generated images.
+
+- **Code Optimization**: From a coding perspective, some parts of the code can be modularized further, making it easier for community contributions and extensions.
+
+- **Parallel Processing**: Leveraging GPU parallel processing capabilities more efficiently can reduce the training time.
+
+- **Regularization**: Implementing dropout or other regularization techniques might improve the model's robustness and prevent overfitting.
+
+- **Evaluation Metrics**: Incorporating additional evaluation metrics can give a clearer picture of the model's performance, such as PSNR or MAE for reconstruction tasks.
+
+- **Model Interpretability**: Leveraging tools like TensorBoard or integrating modules to visualize the intermediate activations and embeddings can help in understanding and debugging the model better.
+
+### Future Roadmap
+1. **Integration with Advanced GANs**: Explore the integration of VQ-VAE with advanced Generative Adversarial Networks like CycleGAN or BigGAN for improved image synthesis.
+2. **Expand Dataset**: Incorporate more diverse brain images, possibly from different imaging techniques.
+3. **Model Pruning and Optimization**: Aim to make the model lighter while retaining its performance, making it suitable for real-time applications.
+4. **Deploy on Edge Devices**: With optimized models, plan to deploy the VQ-VAE on edge devices for real-time brain image processing.
+
+
+## Dependencies
+
+The list of dependencies required for this implementation are as follows:
+```
+- Python
+- PyTorch
+- NumPy
+- PIL
+- Matplotlib
+- scikit-image
+- prettytable
+- Google Colab utilities (for mounting drive)
+```
+## Directory Structure
+
+```
+ /content/GAN_Dataset/
+
+ |-- keras_png_slices_train/
+
+ |-- keras_png_slices_test/
+
+ |-- keras_png_slices_validate/
+```
+
+## Usage
+
+To use the code:
+1. Mount Google Drive (specific to Google Colab).
+2. Set the path for the output directory and dataset zip file.
+3. Run the main function to start the training and visualization process.
+
+## Training
+To train the model based on the VQ-VAE architecture:
+Two main training functions are present: one for the VQ-VAE and the other for the PixelCNN. The VQ-VAE training involves both reconstruction loss and vector quantization loss.
+After VQ-VAE training, the PixelCNN is trained to refine the outputs further.
+
+```
+$ python3 train.py
+```
+## Prediction with Optional Pre-trained Model
+If preferable different VQ-VAE Model can be used, add an optional -m command to load a prebuilt VQ-VAE model. Ensure the folder containing the **VQ-VAE** model is labeled** "VQVAE_Model"**.
+If model is not set up correctly or the system is unable to load it, the script will use the default model.
+
+```
+$ python3 predict.py [-m ]
+```
+## Output
+
+The output consists of various visualizations showcasing original vs. reconstructed/generated images, histograms of encoding indices, and loss plots.
+
+Sample Outputs during training:
+
+## References
+
+1. [Papers with Code. (n.d.). VQ-VAE Explained.](https://paperswithcode.com/method/vq-vae)
+
+2. [Keras. (n.d.). Vector-Quantized Variational Autoencoders.](https://keras.io/examples/generative/vq_vae/)
+
+3. [GitHub. (2023, February 15). PyTorch implementation of VQ-VAE-2 from "Generating Diverse High-Fidelity Images with VQ-VAE-2".](https://github.com/topics/vq-vae)
+
+4. [Stack Overflow. (n.d.). Implementation of VQ-VAE-2 paper.](https://stackoverflow.com/questions/55125010/implementation-of-vq-vae-2-paper)
+
+5. [van den Oord, A., et al. (n.d.). Neural Discrete Representation Learning. arXiv.](https://arxiv.org/abs/1711.00937)
+
+6. [Royer, A. (n.d.). VQ-VAE Implementation in Keras / Tensorflow. Amélie Royer.](https://ameroyer.github.io/research/2019/08/28/VQ-VAE.html)
+
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diff --git a/recognition/47184530_VQVAE_Oasis_BrainMRI/dataset.py b/recognition/47184530_VQVAE_Oasis_BrainMRI/dataset.py
new file mode 100644
index 000000000..551f085bf
--- /dev/null
+++ b/recognition/47184530_VQVAE_Oasis_BrainMRI/dataset.py
@@ -0,0 +1,107 @@
+import os
+import zipfile
+import torch
+import numpy as np
+from torch.utils.data import Dataset
+from PIL import Image
+from prettytable import PrettyTable
+
+# Ensure that PyTorch uses the GPU (if available) or CPU otherwise
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Mounting Google Drive to access files. Note: This is specific to Google Colab.
+drive.mount('/content/drive')
+
+# Define the directory where the output will be saved
+OUTPUT_DIR = "/content/drive/MyDrive/Colab_Notebooks_Course/image_process/A3/OUTPUT2"
+
+# Create the directory if it doesn't exist
+if not os.path.exists(OUTPUT_DIR):
+ os.makedirs(OUTPUT_DIR)
+
+# Dataset class to handle brain slice images
+class BrainSlicesDataset(Dataset):
+ def __init__(self, image_slices):
+ self.image_slices = image_slices
+
+ def __len__(self):
+ # Return the total number of image slices
+ return len(self.image_slices)
+
+ def __getitem__(self, idx):
+ image = self.image_slices[idx]
+
+ # Ensure the image has a channel dimension (grayscale images may not have one)
+ if len(image.shape) == 2: # If the image is of shape [Height, Width]
+ image = torch.unsqueeze(image, 0) # Convert it to [1, Height, Width]
+
+ return image
+
+
+# Function to load and extract image slices from a zip file
+def get_image_slices():
+ # Path to the zipped dataset
+ zip_path = "/content/drive/MyDrive/Colab_Notebooks_Course/image_process/A3/testgans/GAN_Dataset.zip"
+ extraction_path = "/content/GAN_Dataset"
+ # Extract the zip file
+ with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+ zip_ref.extractall(extraction_path)
+
+ # Define the directories for training, testing, and validation datasets
+ parent_dir = "/content/GAN_Dataset"
+ train_path = os.path.join(parent_dir, "keras_png_slices_train")
+ test_path = os.path.join(parent_dir, "keras_png_slices_test")
+ val_path = os.path.join(parent_dir, "keras_png_slices_validate")
+
+ # Helper function to load images from a directory
+ def load_images_from_folder(folder_path):
+ images = []
+ for filename in os.listdir(folder_path):
+ # Open the image, convert to grayscale, and resize to 128x128 pixels
+ img = Image.open(os.path.join(folder_path, filename)).convert('L').resize((128, 128))
+ if img is not None:
+ # Convert the image to a tensor and append to the list
+ images.append(torch.tensor(np.array(img, dtype=np.float32)))
+ return torch.stack(images) # Convert list of tensors to a single tensor
+
+ # Load images from each directory
+ train_images = load_images_from_folder(train_path)
+ test_images = load_images_from_folder(test_path)
+ validate_images = load_images_from_folder(val_path)
+
+ return train_images, test_images, validate_images
+
+
+# Function to retrieve the image slices and provide a summary with a table and example images
+def get_image_slices_with_table():
+ train_images, test_images, validate_images = get_image_slices()
+
+ # Display a summary table using PrettyTable
+ table = PrettyTable()
+ table.field_names = ["Data Split", "Total Images", "Image Shape"]
+ table.add_row(["Training", len(train_images), train_images[0].shape])
+ table.add_row(["Testing", len(test_images), test_images[0].shape])
+ table.add_row(["Validation", len(validate_images), validate_images[0].shape])
+
+ print(table)
+
+ # Plot an example image from each dataset split
+ fig, axs = plt.subplots(1, 3, figsize=(15, 5))
+ axs[0].imshow(train_images[0], cmap='gray')
+ axs[0].set_title("Training Image")
+ axs[0].axis('off')
+
+ axs[1].imshow(test_images[0], cmap='gray')
+ axs[1].set_title("Testing Image")
+ axs[1].axis('off')
+
+ axs[2].imshow(validate_images[0], cmap='gray')
+ axs[2].set_title("Validation Image")
+ axs[2].axis('off')
+
+ plt.show()
+
+ return train_images, test_images, validate_images
+
+# Call the function to display the dataset summary and example images
+get_image_slices_with_table()
\ No newline at end of file
diff --git a/recognition/47184530_VQVAE_Oasis_BrainMRI/modules.py b/recognition/47184530_VQVAE_Oasis_BrainMRI/modules.py
new file mode 100644
index 000000000..357d0647b
--- /dev/null
+++ b/recognition/47184530_VQVAE_Oasis_BrainMRI/modules.py
@@ -0,0 +1,229 @@
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+# The Vector Quantizer layer performs the quantization of the encoder's outputs.
+# This is where the continuous representations from the encoder are mapped to a discrete set of embeddings.
+class VectorQuantizer(nn.Module):
+ def __init__(self, num_embeddings, embedding_dim, beta=0.25):
+ super(VectorQuantizer, self).__init__()
+
+ # Embedding dimension: size of each embedding vector
+ self.embedding_dim = embedding_dim
+
+ # Number of embeddings: total number of discrete embeddings in our codebook
+ self.num_embeddings = num_embeddings
+
+ # Beta is a hyperparameter that weights the commitment loss
+ self.beta = beta
+
+ # Initialize the embeddings (codebook) with random values. It's a learnable parameter.
+ self.embeddings = nn.Parameter(torch.randn(embedding_dim, num_embeddings))
+
+ def forward(self, x):
+ # Reshape the tensor to compute distances
+ z_e_x = x.permute(0, 2, 3, 1).contiguous()
+ z_e_x_ = z_e_x.view(-1, self.embedding_dim)
+
+ # Compute pairwise distances between input and the codebook
+ distances = (torch.sum(z_e_x_**2, dim=1, keepdim=True)
+ + torch.sum(self.embeddings**2, dim=0)
+ - 2 * torch.matmul(z_e_x_, self.embeddings))
+
+ # Find the closest embedding index for each item in the batch
+ encoding_indices = torch.argmin(distances, dim=1).unsqueeze(1)
+
+ # Create a one-hot encoding of the indices
+ encodings = torch.zeros(encoding_indices.shape[0], self.num_embeddings).to(x.device)
+ encodings.scatter_(1, encoding_indices, 1)
+
+ # Reshape the encoding indices to have the same spatial dimensions as input
+ encoding_indices = encoding_indices.view(*z_e_x.shape[:-1])
+
+ # Use the encodings to get the quantized values from the codebook
+ quantized = torch.matmul(encodings, self.embeddings.t()).view(*z_e_x.shape)
+
+ # Compute the commitment loss and the quantization loss
+ e_latent_loss = F.mse_loss(quantized.detach(), z_e_x)
+ q_latent_loss = F.mse_loss(quantized, z_e_x.detach())
+ loss = q_latent_loss + self.beta * e_latent_loss
+
+ # Straight-through estimator: gradients bypass the non-differentiable operation
+ quantized = z_e_x + (quantized - z_e_x).detach()
+
+ # Compute perplexity to check how many codebook entries are being used
+ avg_probs = torch.mean(encodings, dim=0)
+ perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
+
+ return loss, quantized.permute(0, 3, 1, 2).contiguous(), perplexity, encoding_indices
+
+# The Encoder module maps the input images to a continuous representation that will be quantized by the Vector Quantizer.
+class Encoder(nn.Module):
+ def __init__(self, input_channels, hidden_channels, embedding_dim):
+ super(Encoder, self).__init__()
+
+ # Define the encoder neural network
+ # The encoder consists of three convolutional layers with ReLU activations.
+ self.encoder = nn.Sequential(
+ # First convolutional layer: it takes the input image and produces 'hidden_channels' feature maps.
+ nn.Conv2d(input_channels, hidden_channels, kernel_size=4, stride=2, padding=1),
+ nn.ReLU(),
+
+ # Second convolutional layer: reduces the spatial dimensions by half and reduces the number of feature maps.
+ nn.Conv2d(hidden_channels, hidden_channels // 2, kernel_size=4, stride=2, padding=1),
+ nn.ReLU(),
+
+ # Third convolutional layer: prepares the tensor for quantization by setting the number of channels to 'embedding_dim'.
+ nn.Conv2d(hidden_channels // 2, embedding_dim, kernel_size=3, padding=1)
+ )
+
+ def forward(self, x):
+ # Forward propagation of input through the encoder
+ return self.encoder(x)
+
+# The Decoder module maps the quantized representation back to the space of the original image.
+class Decoder(nn.Module):
+ def __init__(self, input_channels, hidden_channels):
+ super(Decoder, self).__init__()
+
+ # Define the decoder neural network
+ # The decoder consists of three transposed convolutional layers (sometimes called "deconvolutional layers") with ReLU activations.
+ self.decoder = nn.Sequential(
+ # First transposed convolutional layer: it takes the quantized representation and increases the spatial dimensions.
+ nn.ConvTranspose2d(input_channels, hidden_channels, kernel_size=3, stride=2, padding=1, output_padding=1),
+ nn.ReLU(),
+
+ # Second transposed convolutional layer: further increases the spatial dimensions.
+ nn.ConvTranspose2d(hidden_channels, hidden_channels // 2, kernel_size=3, stride=2, padding=1, output_padding=1),
+ nn.ReLU(),
+
+ # Third transposed convolutional layer: produces the final output with the same shape as the original image.
+ nn.ConvTranspose2d(hidden_channels // 2, 1, kernel_size=3, padding=1)
+ )
+
+ def forward(self, x):
+ # Forward propagation of the quantized representation through the decoder
+ return self.decoder(x)
+
+# The VQ-VAE module combines the encoder, vector quantizer, and decoder components.
+class VQVAE(nn.Module):
+ def __init__(self, input_channels, hidden_channels, num_embeddings, embedding_dim):
+ super(VQVAE, self).__init__()
+
+ # Initialize the encoder module
+ self.encoder = Encoder(input_channels, hidden_channels, embedding_dim)
+
+ # Initialize the vector quantization module
+ self.quantize = VectorQuantizer(num_embeddings, embedding_dim)
+
+ # Initialize the decoder module
+ self.decoder = Decoder(embedding_dim, hidden_channels)
+
+ def forward(self, x):
+ # Encode the input image to a continuous representation
+ z = self.encoder(x)
+
+ # Quantize the continuous representation
+ loss, quantized, perplexity, _ = self.quantize(z)
+
+ # Decode the quantized representation to produce the reconstruction
+ x_recon = self.decoder(quantized)
+
+ return loss, x_recon, perplexity
+
+# The VQVAETrainer module facilitates the training of the VQ-VAE model.
+class VQVAETrainer(nn.Module):
+ def __init__(self, train_variance, input_channels, hidden_channels, num_embeddings, embedding_dim):
+ super(VQVAETrainer, self).__init__()
+
+ # Store the variance of the training data (used for normalization)
+ self.train_variance = train_variance
+
+ # Initialize the VQ-VAE model
+ self.vqvae = VQVAE(input_channels, hidden_channels, num_embeddings, embedding_dim)
+
+ def forward(self, x):
+ # Forward propagation of the input through the VQ-VAE
+ vq_loss, x_recon, perplexity = self.vqvae(x)
+
+ # Compute the reconstruction loss normalized by the training data variance
+ recon_loss_value = F.mse_loss(x_recon, x) / self.train_variance
+
+ # Overall loss is the sum of reconstruction loss and vector quantization loss
+ loss = recon_loss_value + vq_loss
+
+ return x_recon, perplexity, loss
+
+# The PixelConvLayer is a custom convolutional layer used in the PixelCNN.
+# It ensures that each pixel only depends on other pixels above it or to its left.
+class PixelConvLayer(nn.Module):
+ def __init__(self, in_channels, out_channels, kernel_size, mask_type, **kwargs):
+ super(PixelConvLayer, self).__init__()
+
+ # Define the mask type (either 'A' or 'B')
+ self.mask_type = mask_type
+
+ # Compute padding to ensure the convolution is 'same' (output size == input size)
+ self.padding = (kernel_size - 1) // 2
+
+ # Define the convolutional layer
+ self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs, padding=self.padding)
+
+ # Initialize the mask to be applied on the convolutional weights
+ self.mask = self.conv.weight.data.clone()
+
+ # Create the mask
+ self.create_mask()
+
+ def forward(self, x):
+ # Apply the mask to the convolutional weights
+ self.conv.weight.data *= self.mask.to(self.conv.weight.device)
+
+ # Apply the convolution
+ return self.conv(x)
+
+ def create_mask(self):
+ _, _, H, W = self.conv.weight.size()
+
+ # Set the mask to ones initially
+ self.mask.fill_(1)
+
+ # For mask type 'A', the center pixel and all pixels to the right are set to zero
+ # For mask type 'B', all pixels to the right of the center pixel are set to zero
+ self.mask[:, :, H // 2, W // 2 + (self.mask_type == 'A'):] = 0
+
+ # All pixels below the center pixel are set to zero
+ self.mask[:, :, H // 2 + 1:] = 0
+
+# The PixelCNN model comprises several PixelConvLayers.
+class PixelCNN(nn.Module):
+ def __init__(self, input_shape, num_embeddings, embedding_dim):
+ super(PixelCNN, self).__init__()
+
+ # Define the input shape of the image
+ self.input_shape = input_shape
+
+ # Define the embedding dimension
+ self.embedding_dim = embedding_dim
+
+ # Define the number of embeddings (or the number of different pixel values)
+ self.num_embeddings = num_embeddings
+
+ # Define the architecture of the PixelCNN
+ self.layers = nn.ModuleList()
+
+ # The first layer has a mask type 'A'
+ self.layers.append(PixelConvLayer(input_shape[0], embedding_dim, 7, mask_type='A'))
+
+ # Subsequent layers have a mask type 'B'
+ for _ in range(5):
+ self.layers.append(PixelConvLayer(embedding_dim, embedding_dim, 7, mask_type='B'))
+
+ # The final layer reduces the number of channels to the number of embeddings
+ self.layers.append(nn.Conv2d(embedding_dim, num_embeddings, 1))
+
+ def forward(self, x):
+ # Forward propagation through the PixelCNN
+ for layer in self.layers:
+ x = F.relu(layer(x))
+ return x
diff --git a/recognition/47184530_VQVAE_Oasis_BrainMRI/predict.py b/recognition/47184530_VQVAE_Oasis_BrainMRI/predict.py
new file mode 100644
index 000000000..c766f68c3
--- /dev/null
+++ b/recognition/47184530_VQVAE_Oasis_BrainMRI/predict.py
@@ -0,0 +1,74 @@
+import torch
+import matplotlib.pyplot as plt
+from modules import VQVAE, PixelCNN
+from dataset import get_image_slices
+
+# This function visualizes original vs reconstructed images.
+def visualize_reconstructions(originals, reconstructions, num_samples=3):
+ # Loop through the number of samples specified.
+ for i in range(num_samples):
+ # Create a subplot for the original and reconstructed images.
+ fig, axs = plt.subplots(1, 2)
+
+ # Display the original image.
+ axs[0].imshow(originals[i, 0].detach().numpy(), cmap='gray')
+ axs[0].set_title("Original")
+
+ # Display the reconstructed image.
+ axs[1].imshow(reconstructions[i, 0].detach().numpy(), cmap='gray')
+ axs[1].set_title("Reconstruction")
+
+ # Remove axis ticks and labels.
+ plt.show()
+
+# This function visualizes generated samples.
+def visualize_samples(samples, num_samples=3):
+ # Loop through the number of samples specified.
+ for i in range(num_samples):
+ # Display the generated image.
+ plt.imshow(samples[i, 0].detach().cpu().numpy(), cmap='gray')
+ plt.title("Generated Sample")
+ plt.show()
+
+# This function visualizes images generated using PixelCNN.
+def visualize_pixelcnn_generation_batch(pixelcnn, batch_size, img_size=(1, 128, 128)):
+ # Create a batch of empty images.
+ samples = torch.zeros(batch_size, *img_size).to(device)
+
+ # Generate images pixel by pixel.
+ for i in range(img_size[1]):
+ for j in range(img_size[2]):
+ out = pixelcnn(samples)
+ probs = F.softmax(out[:, :, i, j], dim=1)
+ for b in range(batch_size):
+ samples[b, :, i, j] = torch.multinomial(probs[b], 1).float() / 255.0
+
+ # Display the generated images.
+ for b in range(batch_size):
+ plt.imshow(samples[b, 0].cpu().detach().numpy(), cmap='gray')
+ plt.title(f"PixelCNN Generated Sample {b+1}")
+ plt.show()
+
+# This function compares an original image with one generated by PixelCNN.
+def compare_original_and_generated(original, pixelcnn, img_size=(1, 128, 128)):
+ # Generate an image using PixelCNN.
+ generated = torch.zeros(img_size).to(device)
+ for i in range(img_size[1]):
+ for j in range(img_size[2]):
+ out = pixelcnn(generated)
+ probs = F.softmax(out[:, :, i, j], dim=1)
+ generated[:, :, i, j] = torch.multinomial(probs, 1).float() / 255.0
+
+ # Create a subplot for the original and PixelCNN generated images.
+ fig, axs = plt.subplots(1, 2, figsize=(10, 5))
+
+ # Display the original image.
+ axs[0].imshow(original[0, 0].cpu().detach().numpy(), cmap='gray')
+ axs[0].set_title("Original")
+
+ # Display the PixelCNN generated image.
+ axs[1].imshow(generated[0, 0].cpu().detach().numpy(), cmap='gray')
+ axs[1].set_title("PixelCNN Generated")
+
+ # Remove axis ticks and labels.
+ plt.show()
diff --git a/recognition/47184530_VQVAE_Oasis_BrainMRI/run.py b/recognition/47184530_VQVAE_Oasis_BrainMRI/run.py
new file mode 100644
index 000000000..978fad5ca
--- /dev/null
+++ b/recognition/47184530_VQVAE_Oasis_BrainMRI/run.py
@@ -0,0 +1,71 @@
+from dataset import get_image_slices, get_image_slices_with_table
+from train import train_vqvae, train_pixelcnn
+from predict import visualize_reconstructions, visualize_samples, visualize_pixelcnn_generation_batch, compare_original_and_generated
+from modules import VQVAE, VQVAETrainer, PixelCNN
+import torch
+
+def main():
+
+ # Lists to store loss values and perplexities for visualization
+ recon_losses = []
+ vq_losses = []
+ perplexities = []
+
+ # Load the brain slices images
+ train_images, test_images, _ = get_image_slices()
+ # Create a dataset and data loader using the train images
+ dataset = BrainSlicesDataset(train_images)
+ train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
+
+ # Create a batch of test images for visualization purposes
+ test_samples_for_viz = torch.stack([test_images[i].unsqueeze(0) for i in range(3)]).to(device)
+
+ # Initialize the VQ-VAE model and move it to the appropriate device (GPU or CPU)
+ vqvae_model = VQVAE(input_channels=1, hidden_channels=128, num_embeddings=512, embedding_dim=32).to(device)
+ optimizer = torch.optim.Adam(vqvae_model.parameters(), lr=0.001)
+
+ # Initialize the VQVAE trainer model and move it to the appropriate device
+ vqvae = VQVAETrainer(train_images.var(), 1, 128, 512, 32).to(device)
+ # Train the VQVAE model
+ train_vqvae(vqvae, train_loader, num_epochs=20, learning_rate=0.0001, test_samples=test_samples_for_viz, recon_losses=recon_losses, vq_losses=vq_losses, perplexities=perplexities)
+
+ # Initialize the PixelCNN model and move it to the appropriate device
+ pixelcnn = PixelCNN((1, 128, 128), 256, 10).to(device)
+ # Train the PixelCNN model
+ train_pixelcnn(pixelcnn, train_loader, num_epochs=40, learning_rate=0.001)
+
+ # Generate images using the trained PixelCNN
+ with torch.no_grad():
+ pixelcnn_generated_samples = torch.zeros(3, 1, 128, 128).to(device) # batch of 3 empty images
+ for i in range(128):
+ for j in range(128):
+ out = pixelcnn(pixelcnn_generated_samples)
+ probs = F.softmax(out[:, :, i, j], dim=1)
+ for b in range(3): # For each image in the batch
+ pixelcnn_generated_samples[b, :, i, j] = torch.multinomial(probs[b], 1).float() / 255.0
+ # Visualize the images generated by the PixelCNN
+ visualize_samples(pixelcnn_generated_samples)
+
+ # Visualization of reconstructions using the VQ-VAE model
+ with torch.no_grad():
+ # Get some test images for reconstruction visualization
+ test_samples = torch.stack([test_images[i] for i in range(3)]).to(device)
+ reconstructions, _, _ = vqvae(test_samples)
+ # Visualize the reconstructions
+ visualize_reconstructions(test_samples, reconstructions)
+
+ # Visualize multiple images generated by the PixelCNN
+ visualize_pixelcnn_generation_batch(pixelcnn, batch_size=5)
+
+ # Compare an original image with an image generated by the PixelCNN
+ for i in range(3): # For 3 examples
+ compare_original_and_generated(test_samples[i], pixelcnn)
+ return recon_losses, vq_losses, perplexities
+
+ # Print the recorded losses and perplexities
+ print("Reconstruction Losses:", recon_losses)
+ print("VQ Losses:", vq_losses)
+ print("Perplexities:", perplexities)
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/recognition/47184530_VQVAE_Oasis_BrainMRI/train.py b/recognition/47184530_VQVAE_Oasis_BrainMRI/train.py
new file mode 100644
index 000000000..232942d3a
--- /dev/null
+++ b/recognition/47184530_VQVAE_Oasis_BrainMRI/train.py
@@ -0,0 +1,113 @@
+import torch
+from torch import optim, nn
+from modules import VQVAE, VQVAETrainer, PixelCNN
+from dataset import BrainSlicesDataset, get_image_slices
+import matplotlib.pyplot as plt
+
+# This function trains the VQ-VAE model.
+def train_vqvae(vqvae, train_loader, num_epochs, learning_rate, test_samples, recon_losses, vq_losses, perplexities):
+ # Set up the optimizer for training (Adam in this case).
+ optimizer = optim.Adam(vqvae.parameters(), lr=learning_rate)
+
+ # Loop through each epoch.
+ for epoch in range(num_epochs):
+ # Loop through each batch of images from the DataLoader.
+ for batch_idx, images in enumerate(train_loader):
+ images = images.to(device) # Transfer images to the GPU if available.
+
+ # Zero the gradients.
+ optimizer.zero_grad()
+
+ # Forward pass through the VQ-VAE.
+ x_recon, perplexity, loss = vqvae(images)
+
+ # Compute reconstruction and VQ losses.
+ recon_loss_value = F.mse_loss(x_recon, images) / vqvae.train_variance
+ vq_loss_value = loss - recon_loss_value
+
+ # Record the losses and perplexity for plotting later.
+ recon_losses.append(recon_loss_value.item())
+ vq_losses.append(vq_loss_value.item())
+ perplexities.append(perplexity.item())
+
+ # Backward pass.
+ loss.backward()
+
+ # Update the weights.
+ optimizer.step()
+
+ # At the end of each epoch, visualize some reconstructed images.
+ with torch.no_grad():
+ reconstructions, _, _ = vqvae(test_samples)
+ visualize_reconstructions(test_samples.cpu(), reconstructions.cpu())
+
+ # Save the generated images
+ save_path = os.path.join(OUTPUT_DIR, f"{epoch}.png")
+ save_image(reconstructions, save_path)
+
+ # Print the loss for the current epoch.
+ print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")
+
+ # At the end of training, plot the recorded losses and perplexity.
+ plt.figure(figsize=(10,5))
+ plt.plot(recon_losses, label='Reconstruction Loss')
+ plt.plot(vq_losses, label='VQ Loss')
+ plt.legend()
+ plt.title('Losses over Training')
+ plt.xlabel('Training Iterations')
+ plt.ylabel('Loss Value')
+ plt.show()
+
+ plt.figure(figsize=(10,5))
+ plt.plot(perplexities)
+ plt.title('Perplexity over Training')
+ plt.xlabel('Training Iterations')
+ plt.ylabel('Perplexity')
+ plt.show()
+
+ # Visualize the histogram of encoding indices.
+ with torch.no_grad():
+ _, _, _, encoding_indices = vqvae.vqvae.quantize(vqvae.vqvae.encoder(test_samples))
+ encoding_indices = encoding_indices.flatten().cpu().numpy()
+
+ plt.figure(figsize=(10,5))
+ plt.hist(encoding_indices, bins=np.arange(vqvae.vqvae.quantize.num_embeddings+1)-0.5, rwidth=0.8)
+ plt.title('Histogram of Encoding Indices')
+ plt.xlabel('Encoding Index')
+ plt.ylabel('Frequency')
+ plt.xticks(np.arange(vqvae.vqvae.quantize.num_embeddings))
+ plt.show()
+
+ # Print the recorded losses and perplexities
+ print("Reconstruction Losses:", recon_losses)
+ print("VQ Losses:", vq_losses)
+ print("Perplexities:", perplexities)
+
+# This function trains the PixelCNN model.
+def train_pixelcnn(pixelcnn, train_loader, num_epochs, learning_rate):
+ optimizer = optim.Adam(pixelcnn.parameters(), lr=learning_rate)
+ criterion = nn.CrossEntropyLoss()
+
+ # Loop through each epoch.
+ for epoch in range(num_epochs):
+ # Loop through each batch of images from the DataLoader.
+ for images in train_loader:
+ images = images.to(device) # Transfer images to the GPU if available.
+
+ # Zero the gradients.
+ optimizer.zero_grad()
+
+ # Forward pass through the PixelCNN.
+ logits = pixelcnn(images)
+
+ # Compute the loss.
+ loss = criterion(logits, images.squeeze(1).long())
+
+ # Backward pass.
+ loss.backward()
+
+ # Update the weights.
+ optimizer.step()
+
+ # Print the loss for the current epoch.
+ print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")
|