diff --git a/.gitignore b/.gitignore index f3436fe1fd3e8a7064887098b38e50dfda48b27d..943b72bcfa7f7290f94cb823d6e4ea441d7d85ac 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ # Data data/* +hymenoptera_data/ transfer_learning/hymenoptera_data/* # Torch model diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb deleted file mode 100644 index 2ecfce959ae6b947b633a758433f9bea0bf6992e..0000000000000000000000000000000000000000 --- a/TD2 Deep Learning.ipynb +++ /dev/null @@ -1,953 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7edf7168", - "metadata": {}, - "source": [ - "# TD2: Deep learning" - ] - }, - { - "cell_type": "markdown", - "id": "fbb8c8df", - "metadata": {}, - "source": [ - "In this TD, you must modify this notebook to answer the questions. To do this,\n", - "\n", - "1. Fork this repository\n", - "2. Clone your forked repository on your local computer\n", - "3. Answer the questions\n", - "4. Commit and push regularly\n", - "\n", - "The last commit is due on Sunday, December 1, 11:59 PM. Later commits will not be taken into account." - ] - }, - { - "cell_type": "markdown", - "id": "3d167a29", - "metadata": {}, - "source": [ - "Install and test PyTorch from https://pytorch.org/get-started/locally." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "330a42f5", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install torch torchvision" - ] - }, - { - "cell_type": "markdown", - "id": "0882a636", - "metadata": {}, - "source": [ - "\n", - "To test run the following code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1950f0a", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "\n", - "N, D = 14, 10\n", - "x = torch.randn(N, D).type(torch.FloatTensor)\n", - "print(x)\n", - "\n", - "from torchvision import models\n", - "\n", - "alexnet = models.alexnet()\n", - "print(alexnet)" - ] - }, - { - "cell_type": "markdown", - "id": "23f266da", - "metadata": {}, - "source": [ - "## Exercise 1: CNN on CIFAR10\n", - "\n", - "The goal is to apply a Convolutional Neural Net (CNN) model on the CIFAR10 image dataset and test the accuracy of the model on the basis of image classification. Compare the Accuracy VS the neural network implemented during TD1.\n", - "\n", - "Have a look at the following documentation to be familiar with PyTorch.\n", - "\n", - "https://pytorch.org/tutorials/beginner/pytorch_with_examples.html\n", - "\n", - "https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html" - ] - }, - { - "cell_type": "markdown", - "id": "4ba1c82d", - "metadata": {}, - "source": [ - "You can test if GPU is available on your machine and thus train on it to speed up the process" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e18f2fd", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "\n", - "# check if CUDA is available\n", - "train_on_gpu = torch.cuda.is_available()\n", - "\n", - "if not train_on_gpu:\n", - " print(\"CUDA is not available. Training on CPU ...\")\n", - "else:\n", - " print(\"CUDA is available! Training on GPU ...\")" - ] - }, - { - "cell_type": "markdown", - "id": "5cf214eb", - "metadata": {}, - "source": [ - "Next we load the CIFAR10 dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "462666a2", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from torchvision import datasets, transforms\n", - "from torch.utils.data.sampler import SubsetRandomSampler\n", - "\n", - "# number of subprocesses to use for data loading\n", - "num_workers = 0\n", - "# how many samples per batch to load\n", - "batch_size = 20\n", - "# percentage of training set to use as validation\n", - "valid_size = 0.2\n", - "\n", - "# convert data to a normalized torch.FloatTensor\n", - "transform = transforms.Compose(\n", - " [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n", - ")\n", - "\n", - "# choose the training and test datasets\n", - "train_data = datasets.CIFAR10(\"data\", train=True, download=True, transform=transform)\n", - "test_data = datasets.CIFAR10(\"data\", train=False, download=True, transform=transform)\n", - "\n", - "# obtain training indices that will be used for validation\n", - "num_train = len(train_data)\n", - "indices = list(range(num_train))\n", - "np.random.shuffle(indices)\n", - "split = int(np.floor(valid_size * num_train))\n", - "train_idx, valid_idx = indices[split:], indices[:split]\n", - "\n", - "# define samplers for obtaining training and validation batches\n", - "train_sampler = SubsetRandomSampler(train_idx)\n", - "valid_sampler = SubsetRandomSampler(valid_idx)\n", - "\n", - "# prepare data loaders (combine dataset and sampler)\n", - "train_loader = torch.utils.data.DataLoader(\n", - " train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers\n", - ")\n", - "valid_loader = torch.utils.data.DataLoader(\n", - " train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers\n", - ")\n", - "test_loader = torch.utils.data.DataLoader(\n", - " test_data, batch_size=batch_size, num_workers=num_workers\n", - ")\n", - "\n", - "# specify the image classes\n", - "classes = [\n", - " \"airplane\",\n", - " \"automobile\",\n", - " \"bird\",\n", - " \"cat\",\n", - " \"deer\",\n", - " \"dog\",\n", - " \"frog\",\n", - " \"horse\",\n", - " \"ship\",\n", - " \"truck\",\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "58ec3903", - "metadata": {}, - "source": [ - "CNN definition (this one is an example)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "317bf070", - "metadata": {}, - "outputs": [], - "source": [ - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "\n", - "# define the CNN architecture\n", - "\n", - "\n", - "class Net(nn.Module):\n", - " def __init__(self):\n", - " super(Net, self).__init__()\n", - " self.conv1 = nn.Conv2d(3, 6, 5)\n", - " self.pool = nn.MaxPool2d(2, 2)\n", - " self.conv2 = nn.Conv2d(6, 16, 5)\n", - " self.fc1 = nn.Linear(16 * 5 * 5, 120)\n", - " self.fc2 = nn.Linear(120, 84)\n", - " self.fc3 = nn.Linear(84, 10)\n", - "\n", - " def forward(self, x):\n", - " x = self.pool(F.relu(self.conv1(x)))\n", - " x = self.pool(F.relu(self.conv2(x)))\n", - " x = x.view(-1, 16 * 5 * 5)\n", - " x = F.relu(self.fc1(x))\n", - " x = F.relu(self.fc2(x))\n", - " x = self.fc3(x)\n", - " return x\n", - "\n", - "\n", - "# create a complete CNN\n", - "model = Net()\n", - "print(model)\n", - "# move tensors to GPU if CUDA is available\n", - "if train_on_gpu:\n", - " model.cuda()" - ] - }, - { - "cell_type": "markdown", - "id": "a2dc4974", - "metadata": {}, - "source": [ - "Loss function and training using SGD (Stochastic Gradient Descent) optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b53f229", - "metadata": {}, - "outputs": [], - "source": [ - "import torch.optim as optim\n", - "\n", - "criterion = nn.CrossEntropyLoss() # specify loss function\n", - "optimizer = optim.SGD(model.parameters(), lr=0.01) # specify optimizer\n", - "\n", - "n_epochs = 30 # number of epochs to train the model\n", - "train_loss_list = [] # list to store loss to visualize\n", - "valid_loss_min = np.Inf # track change in validation loss\n", - "\n", - "for epoch in range(n_epochs):\n", - " # Keep track of training and validation loss\n", - " train_loss = 0.0\n", - " valid_loss = 0.0\n", - "\n", - " # Train the model\n", - " model.train()\n", - " for data, target in train_loader:\n", - " # Move tensors to GPU if CUDA is available\n", - " if train_on_gpu:\n", - " data, target = data.cuda(), target.cuda()\n", - " # Clear the gradients of all optimized variables\n", - " optimizer.zero_grad()\n", - " # Forward pass: compute predicted outputs by passing inputs to the model\n", - " output = model(data)\n", - " # Calculate the batch loss\n", - " loss = criterion(output, target)\n", - " # Backward pass: compute gradient of the loss with respect to model parameters\n", - " loss.backward()\n", - " # Perform a single optimization step (parameter update)\n", - " optimizer.step()\n", - " # Update training loss\n", - " train_loss += loss.item() * data.size(0)\n", - "\n", - " # Validate the model\n", - " model.eval()\n", - " for data, target in valid_loader:\n", - " # Move tensors to GPU if CUDA is available\n", - " if train_on_gpu:\n", - " data, target = data.cuda(), target.cuda()\n", - " # Forward pass: compute predicted outputs by passing inputs to the model\n", - " output = model(data)\n", - " # Calculate the batch loss\n", - " loss = criterion(output, target)\n", - " # Update average validation loss\n", - " valid_loss += loss.item() * data.size(0)\n", - "\n", - " # Calculate average losses\n", - " train_loss = train_loss / len(train_loader)\n", - " valid_loss = valid_loss / len(valid_loader)\n", - " train_loss_list.append(train_loss)\n", - "\n", - " # Print training/validation statistics\n", - " print(\n", - " \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n", - " epoch, train_loss, valid_loss\n", - " )\n", - " )\n", - "\n", - " # Save model if validation loss has decreased\n", - " if valid_loss <= valid_loss_min:\n", - " print(\n", - " \"Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...\".format(\n", - " valid_loss_min, valid_loss\n", - " )\n", - " )\n", - " torch.save(model.state_dict(), \"model_cifar.pt\")\n", - " valid_loss_min = valid_loss" - ] - }, - { - "cell_type": "markdown", - "id": "13e1df74", - "metadata": {}, - "source": [ - "Does overfit occur? If so, do an early stopping." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d39df818", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.plot(range(n_epochs), train_loss_list)\n", - "plt.xlabel(\"Epoch\")\n", - "plt.ylabel(\"Loss\")\n", - "plt.title(\"Performance of Model 1\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "11df8fd4", - "metadata": {}, - "source": [ - "Now loading the model with the lowest validation loss value\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e93efdfc", - "metadata": {}, - "outputs": [], - "source": [ - "model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n", - "\n", - "# track test loss\n", - "test_loss = 0.0\n", - "class_correct = list(0.0 for i in range(10))\n", - "class_total = list(0.0 for i in range(10))\n", - "\n", - "model.eval()\n", - "# iterate over test data\n", - "for data, target in test_loader:\n", - " # move tensors to GPU if CUDA is available\n", - " if train_on_gpu:\n", - " data, target = data.cuda(), target.cuda()\n", - " # forward pass: compute predicted outputs by passing inputs to the model\n", - " output = model(data)\n", - " # calculate the batch loss\n", - " loss = criterion(output, target)\n", - " # update test loss\n", - " test_loss += loss.item() * data.size(0)\n", - " # convert output probabilities to predicted class\n", - " _, pred = torch.max(output, 1)\n", - " # compare predictions to true label\n", - " correct_tensor = pred.eq(target.data.view_as(pred))\n", - " correct = (\n", - " np.squeeze(correct_tensor.numpy())\n", - " if not train_on_gpu\n", - " else np.squeeze(correct_tensor.cpu().numpy())\n", - " )\n", - " # calculate test accuracy for each object class\n", - " for i in range(batch_size):\n", - " label = target.data[i]\n", - " class_correct[label] += correct[i].item()\n", - " class_total[label] += 1\n", - "\n", - "# average test loss\n", - "test_loss = test_loss / len(test_loader)\n", - "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n", - "\n", - "for i in range(10):\n", - " if class_total[i] > 0:\n", - " print(\n", - " \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n", - " % (\n", - " classes[i],\n", - " 100 * class_correct[i] / class_total[i],\n", - " np.sum(class_correct[i]),\n", - " np.sum(class_total[i]),\n", - " )\n", - " )\n", - " else:\n", - " print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n", - "\n", - "print(\n", - " \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n", - " % (\n", - " 100.0 * np.sum(class_correct) / np.sum(class_total),\n", - " np.sum(class_correct),\n", - " np.sum(class_total),\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "944991a2", - "metadata": {}, - "source": [ - "Build a new network with the following structure.\n", - "\n", - "- It has 3 convolutional layers of kernel size 3 and padding of 1.\n", - "- The first convolutional layer must output 16 channels, the second 32 and the third 64.\n", - "- At each convolutional layer output, we apply a ReLU activation then a MaxPool with kernel size of 2.\n", - "- Then, three fully connected layers, the first two being followed by a ReLU activation and a dropout whose value you will suggest.\n", - "- The first fully connected layer will have an output size of 512.\n", - "- The second fully connected layer will have an output size of 64.\n", - "\n", - "Compare the results obtained with this new network to those obtained previously." - ] - }, - { - "cell_type": "markdown", - "id": "bc381cf4", - "metadata": {}, - "source": [ - "## Exercise 2: Quantization: try to compress the CNN to save space\n", - "\n", - "Quantization doc is available from https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic\n", - " \n", - "The Exercise is to quantize post training the above CNN model. Compare the size reduction and the impact on the classification accuracy \n", - "\n", - "\n", - "The size of the model is simply the size of the file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef623c26", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "\n", - "def print_size_of_model(model, label=\"\"):\n", - " torch.save(model.state_dict(), \"temp.p\")\n", - " size = os.path.getsize(\"temp.p\")\n", - " print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n", - " os.remove(\"temp.p\")\n", - " return size\n", - "\n", - "\n", - "print_size_of_model(model, \"fp32\")" - ] - }, - { - "cell_type": "markdown", - "id": "05c4e9ad", - "metadata": {}, - "source": [ - "Post training quantization example" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c4c65d4b", - "metadata": {}, - "outputs": [], - "source": [ - "import torch.quantization\n", - "\n", - "\n", - "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n", - "print_size_of_model(quantized_model, \"int8\")" - ] - }, - { - "cell_type": "markdown", - "id": "7b108e17", - "metadata": {}, - "source": [ - "For each class, compare the classification test accuracy of the initial model and the quantized model. Also give the overall test accuracy for both models." - ] - }, - { - "cell_type": "markdown", - "id": "a0a34b90", - "metadata": {}, - "source": [ - "Try training aware quantization to mitigate the impact on the accuracy (doc available here https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic)" - ] - }, - { - "cell_type": "markdown", - "id": "201470f9", - "metadata": {}, - "source": [ - "## Exercise 3: working with pre-trained models.\n", - "\n", - "PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.html \n", - "We will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files `imagenet-simple-labels.json` that contains the imagenet labels and the image dog.png that we will use as test.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4d13080", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from PIL import Image\n", - "\n", - "# Choose an image to pass through the model\n", - "test_image = \"dog.png\"\n", - "\n", - "# Configure matplotlib for pretty inline plots\n", - "#%matplotlib inline\n", - "#%config InlineBackend.figure_format = 'retina'\n", - "\n", - "# Prepare the labels\n", - "with open(\"imagenet-simple-labels.json\") as f:\n", - " labels = json.load(f)\n", - "\n", - "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n", - "data_transform = transforms.Compose(\n", - " [\n", - " transforms.Resize((224, 224)),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", - " ]\n", - ")\n", - "# Load the image\n", - "\n", - "image = Image.open(test_image)\n", - "plt.imshow(image), plt.xticks([]), plt.yticks([])\n", - "\n", - "# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n", - "# image = data_transform(image).unsqueeze(0).cuda()\n", - "image = data_transform(image).unsqueeze(0)\n", - "\n", - "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n", - "model = models.resnet50(pretrained=True)\n", - "# Send the model to the GPU\n", - "# model.cuda()\n", - "# Set layers such as dropout and batchnorm in evaluation mode\n", - "model.eval()\n", - "\n", - "# Get the 1000-dimensional model output\n", - "out = model(image)\n", - "# Find the predicted class\n", - "print(\"Predicted class is: {}\".format(labels[out.argmax()]))" - ] - }, - { - "cell_type": "markdown", - "id": "184cfceb", - "metadata": {}, - "source": [ - "Experiments:\n", - "\n", - "Study the code and the results obtained. Possibly add other images downloaded from the internet.\n", - "\n", - "What is the size of the model? Quantize it and then check if the model is still able to correctly classify the other images.\n", - "\n", - "Experiment with other pre-trained CNN models.\n", - "\n", - " \n" - ] - }, - { - "cell_type": "markdown", - "id": "5d57da4b", - "metadata": {}, - "source": [ - "## Exercise 4: Transfer Learning\n", - " \n", - " \n", - "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n", - "Download and unzip in your working directory the dataset available at the address :\n", - " \n", - "https://download.pytorch.org/tutorial/hymenoptera_data.zip\n", - " \n", - "Execute the following code in order to display some images of the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be2d31f5", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "import torchvision\n", - "from torchvision import datasets, transforms\n", - "\n", - "# Data augmentation and normalization for training\n", - "# Just normalization for validation\n", - "data_transforms = {\n", - " \"train\": transforms.Compose(\n", - " [\n", - " transforms.RandomResizedCrop(\n", - " 224\n", - " ), # ImageNet models were trained on 224x224 images\n", - " transforms.RandomHorizontalFlip(), # flip horizontally 50% of the time - increases train set variability\n", - " transforms.ToTensor(), # convert it to a PyTorch tensor\n", - " transforms.Normalize(\n", - " [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", - " ), # ImageNet models expect this norm\n", - " ]\n", - " ),\n", - " \"val\": transforms.Compose(\n", - " [\n", - " transforms.Resize(256),\n", - " transforms.CenterCrop(224),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", - " ]\n", - " ),\n", - "}\n", - "\n", - "data_dir = \"hymenoptera_data\"\n", - "# Create train and validation datasets and loaders\n", - "image_datasets = {\n", - " x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n", - " for x in [\"train\", \"val\"]\n", - "}\n", - "dataloaders = {\n", - " x: torch.utils.data.DataLoader(\n", - " image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n", - " )\n", - " for x in [\"train\", \"val\"]\n", - "}\n", - "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n", - "class_names = image_datasets[\"train\"].classes\n", - "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", - "\n", - "# Helper function for displaying images\n", - "def imshow(inp, title=None):\n", - " \"\"\"Imshow for Tensor.\"\"\"\n", - " inp = inp.numpy().transpose((1, 2, 0))\n", - " mean = np.array([0.485, 0.456, 0.406])\n", - " std = np.array([0.229, 0.224, 0.225])\n", - "\n", - " # Un-normalize the images\n", - " inp = std * inp + mean\n", - " # Clip just in case\n", - " inp = np.clip(inp, 0, 1)\n", - " plt.imshow(inp)\n", - " if title is not None:\n", - " plt.title(title)\n", - " plt.pause(0.001) # pause a bit so that plots are updated\n", - " plt.show()\n", - "\n", - "\n", - "# Get a batch of training data\n", - "inputs, classes = next(iter(dataloaders[\"train\"]))\n", - "\n", - "# Make a grid from batch\n", - "out = torchvision.utils.make_grid(inputs)\n", - "\n", - "imshow(out, title=[class_names[x] for x in classes])\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "bbd48800", - "metadata": {}, - "source": [ - "Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "572d824c", - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "import os\n", - "import time\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import torchvision\n", - "from torch.optim import lr_scheduler\n", - "from torchvision import datasets, transforms\n", - "\n", - "# Data augmentation and normalization for training\n", - "# Just normalization for validation\n", - "data_transforms = {\n", - " \"train\": transforms.Compose(\n", - " [\n", - " transforms.RandomResizedCrop(\n", - " 224\n", - " ), # ImageNet models were trained on 224x224 images\n", - " transforms.RandomHorizontalFlip(), # flip horizontally 50% of the time - increases train set variability\n", - " transforms.ToTensor(), # convert it to a PyTorch tensor\n", - " transforms.Normalize(\n", - " [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", - " ), # ImageNet models expect this norm\n", - " ]\n", - " ),\n", - " \"val\": transforms.Compose(\n", - " [\n", - " transforms.Resize(256),\n", - " transforms.CenterCrop(224),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", - " ]\n", - " ),\n", - "}\n", - "\n", - "data_dir = \"hymenoptera_data\"\n", - "# Create train and validation datasets and loaders\n", - "image_datasets = {\n", - " x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n", - " for x in [\"train\", \"val\"]\n", - "}\n", - "dataloaders = {\n", - " x: torch.utils.data.DataLoader(\n", - " image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n", - " )\n", - " for x in [\"train\", \"val\"]\n", - "}\n", - "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n", - "class_names = image_datasets[\"train\"].classes\n", - "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", - "\n", - "# Helper function for displaying images\n", - "def imshow(inp, title=None):\n", - " \"\"\"Imshow for Tensor.\"\"\"\n", - " inp = inp.numpy().transpose((1, 2, 0))\n", - " mean = np.array([0.485, 0.456, 0.406])\n", - " std = np.array([0.229, 0.224, 0.225])\n", - "\n", - " # Un-normalize the images\n", - " inp = std * inp + mean\n", - " # Clip just in case\n", - " inp = np.clip(inp, 0, 1)\n", - " plt.imshow(inp)\n", - " if title is not None:\n", - " plt.title(title)\n", - " plt.pause(0.001) # pause a bit so that plots are updated\n", - " plt.show()\n", - "\n", - "\n", - "# Get a batch of training data\n", - "# inputs, classes = next(iter(dataloaders['train']))\n", - "\n", - "# Make a grid from batch\n", - "# out = torchvision.utils.make_grid(inputs)\n", - "\n", - "# imshow(out, title=[class_names[x] for x in classes])\n", - "# training\n", - "\n", - "\n", - "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n", - " since = time.time()\n", - "\n", - " best_model_wts = copy.deepcopy(model.state_dict())\n", - " best_acc = 0.0\n", - "\n", - " epoch_time = [] # we'll keep track of the time needed for each epoch\n", - "\n", - " for epoch in range(num_epochs):\n", - " epoch_start = time.time()\n", - " print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n", - " print(\"-\" * 10)\n", - "\n", - " # Each epoch has a training and validation phase\n", - " for phase in [\"train\", \"val\"]:\n", - " if phase == \"train\":\n", - " scheduler.step()\n", - " model.train() # Set model to training mode\n", - " else:\n", - " model.eval() # Set model to evaluate mode\n", - "\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - "\n", - " # Iterate over data.\n", - " for inputs, labels in dataloaders[phase]:\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device)\n", - "\n", - " # zero the parameter gradients\n", - " optimizer.zero_grad()\n", - "\n", - " # Forward\n", - " # Track history if only in training phase\n", - " with torch.set_grad_enabled(phase == \"train\"):\n", - " outputs = model(inputs)\n", - " _, preds = torch.max(outputs, 1)\n", - " loss = criterion(outputs, labels)\n", - "\n", - " # backward + optimize only if in training phase\n", - " if phase == \"train\":\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " # Statistics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - "\n", - " epoch_loss = running_loss / dataset_sizes[phase]\n", - " epoch_acc = running_corrects.double() / dataset_sizes[phase]\n", - "\n", - " print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n", - "\n", - " # Deep copy the model\n", - " if phase == \"val\" and epoch_acc > best_acc:\n", - " best_acc = epoch_acc\n", - " best_model_wts = copy.deepcopy(model.state_dict())\n", - "\n", - " # Add the epoch time\n", - " t_epoch = time.time() - epoch_start\n", - " epoch_time.append(t_epoch)\n", - " print()\n", - "\n", - " time_elapsed = time.time() - since\n", - " print(\n", - " \"Training complete in {:.0f}m {:.0f}s\".format(\n", - " time_elapsed // 60, time_elapsed % 60\n", - " )\n", - " )\n", - " print(\"Best val Acc: {:4f}\".format(best_acc))\n", - "\n", - " # Load best model weights\n", - " model.load_state_dict(best_model_wts)\n", - " return model, epoch_time\n", - "\n", - "\n", - "# Download a pre-trained ResNet18 model and freeze its weights\n", - "model = torchvision.models.resnet18(pretrained=True)\n", - "for param in model.parameters():\n", - " param.requires_grad = False\n", - "\n", - "# Replace the final fully connected layer\n", - "# Parameters of newly constructed modules have requires_grad=True by default\n", - "num_ftrs = model.fc.in_features\n", - "model.fc = nn.Linear(num_ftrs, 2)\n", - "# Send the model to the GPU\n", - "model = model.to(device)\n", - "# Set the loss function\n", - "criterion = nn.CrossEntropyLoss()\n", - "\n", - "# Observe that only the parameters of the final layer are being optimized\n", - "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n", - "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n", - "model, epoch_time = train_model(\n", - " model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "bbd48800", - "metadata": {}, - "source": [ - "Experiments:\n", - "Study the code and the results obtained.\n", - "\n", - "Modify the code and add an \"eval_model\" function to allow\n", - "the evaluation of the model on a test set (different from the learning and validation sets used during the learning phase). Study the results obtained.\n", - "\n", - "Now modify the code to replace the current classification layer with a set of two layers using a \"relu\" activation function for the middle layer, and the \"dropout\" mechanism for both layers. Renew the experiments and study the results obtained.\n", - "\n", - "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy." - ] - }, - { - "cell_type": "markdown", - "id": "04a263f0", - "metadata": {}, - "source": [ - "## Optional\n", - " \n", - "Try this at home!! \n", - "\n", - "\n", - "Pytorch offers a framework to export a given CNN to your selfphone (either android or iOS). Have a look at the tutorial https://pytorch.org/mobile/home/\n", - "\n", - "The Exercise consists in deploying the CNN of Exercise 4 in your phone and then test it on live.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "fe954ce4", - "metadata": {}, - "source": [ - "## Author\n", - "\n", - "Alberto BOSIO - Ph. D." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.5 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "vscode": { - "interpreter": { - "hash": "9e3efbebb05da2d4a1968abe9a0645745f54b63feb7a85a514e4da0495be97eb" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/TD2_Deep_Learning.ipynb b/TD2_Deep_Learning.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ff9956a31ab59210daed7302545b3247c244c0bc --- /dev/null +++ b/TD2_Deep_Learning.ipynb @@ -0,0 +1,1724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7edf7168", + "metadata": { + "id": "7edf7168" + }, + "source": [ + "# TD2: Deep learning" + ] + }, + { + "cell_type": "markdown", + "id": "5dfebfec", + "metadata": { + "id": "5dfebfec" + }, + "source": [ + "Use the following command to install the required packages:\n", + "\n", + "```\n", + "pip install -r requirements.txt\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "0882a636", + "metadata": { + "id": "0882a636" + }, + "source": [ + "To test the install run the following code" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b1950f0a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b1950f0a", + "outputId": "5e833183-6f84-4437-9df3-667b433e80bc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It works!\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "N, D = 14, 10\n", + "x = torch.randn(N, D).type(torch.FloatTensor)\n", + "\n", + "from torchvision import models\n", + "\n", + "alexnet = models.alexnet()\n", + "print(\"It works!\")" + ] + }, + { + "cell_type": "markdown", + "id": "f5e43d68", + "metadata": { + "id": "f5e43d68" + }, + "source": [ + "To test if GPU available" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6e18f2fd", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6e18f2fd", + "outputId": "07858e1d-8aec-4bc0-b1ae-0d7aca943413" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CUDA is available! Training on GPU ...\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# check if CUDA is available\n", + "train_on_gpu = torch.cuda.is_available()\n", + "\n", + "if not train_on_gpu:\n", + " print(\"CUDA is not available. Training on CPU ...\")\n", + "else:\n", + " print(\"CUDA is available! Training on GPU ...\")" + ] + }, + { + "cell_type": "markdown", + "id": "23f266da", + "metadata": { + "id": "23f266da" + }, + "source": [ + "## Exercise 1: CNN on CIFAR10\n", + "\n", + "The goal is to apply a Convolutional Neural Net (CNN) model on the CIFAR10 image dataset and test the accuracy of the model on the basis of image classification. Compare the Accuracy VS the neural network implemented during TD1.\n", + "\n", + "Have a look at the following documentation to be familiar with PyTorch.\n", + "\n", + "https://pytorch.org/tutorials/beginner/pytorch_with_examples.html\n", + "\n", + "https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html" + ] + }, + { + "cell_type": "markdown", + "id": "5cf214eb", + "metadata": { + "id": "5cf214eb" + }, + "source": [ + "Next we load the CIFAR10 dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "54d3cbef", + "metadata": { + "id": "54d3cbef" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "from torchvision import datasets, transforms\n", + "from torch.utils.data.sampler import SubsetRandomSampler\n", + "\n", + "def load_cifar(batch_size=20, valid_size=0.2):\n", + " # number of subprocesses to use for data loading\n", + " num_workers = 0\n", + "\n", + " # convert data to a normalized torch.FloatTensor\n", + " transform = transforms.Compose(\n", + " [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n", + " )\n", + "\n", + " # choose the training and test datasets\n", + " train_data = datasets.CIFAR10(\"data\", train=True, download=True, transform=transform)\n", + " test_data = datasets.CIFAR10(\"data\", train=False, download=True, transform=transform)\n", + "\n", + " # obtain training indices that will be used for validation\n", + " num_train = len(train_data)\n", + " indices = list(range(num_train))\n", + " np.random.shuffle(indices)\n", + " split = int(np.floor(valid_size * num_train))\n", + " train_idx, valid_idx = indices[split:], indices[:split]\n", + "\n", + " # define samplers for obtaining training and validation batches\n", + " train_sampler = SubsetRandomSampler(train_idx)\n", + " valid_sampler = SubsetRandomSampler(valid_idx)\n", + "\n", + " # prepare data loaders (combine dataset and sampler)\n", + " train_loader = torch.utils.data.DataLoader(\n", + " train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers\n", + " )\n", + " valid_loader = torch.utils.data.DataLoader(\n", + " train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers\n", + " )\n", + " test_loader = torch.utils.data.DataLoader(\n", + " test_data, batch_size=batch_size, num_workers=num_workers\n", + " )\n", + "\n", + " # specify the image classes\n", + " classes = [\n", + " \"airplane\",\n", + " \"automobile\",\n", + " \"bird\",\n", + " \"cat\",\n", + " \"deer\",\n", + " \"dog\",\n", + " \"frog\",\n", + " \"horse\",\n", + " \"ship\",\n", + " \"truck\",\n", + " ]\n", + "\n", + " return train_loader, valid_loader, test_loader, classes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "462666a2", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "462666a2", + "outputId": "477fbc7a-5fc3-4e96-d878-8ad8b532f1cc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# check if CUDA is available\n", + "# train_on_gpu = torch.cuda.is_available() # Uncomment this line if you want to use GPU\n", + "train_on_gpu = False # Fort the last part, we will use CPU, loading data on CPU\n", + "\n", + "train_loader, valid_loader, test_loader, classes = load_cifar(batch_size=20, valid_size=0.2)" + ] + }, + { + "cell_type": "markdown", + "id": "58ec3903", + "metadata": { + "id": "58ec3903" + }, + "source": [ + "CNN definition (this one is an example)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "317bf070", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "317bf070", + "outputId": "f6812fb7-6e55-4553-b405-0a8b4edffff6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n", + " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n", + " (fc1): Linear(in_features=400, out_features=120, bias=True)\n", + " (fc2): Linear(in_features=120, out_features=84, bias=True)\n", + " (fc3): Linear(in_features=84, out_features=10, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "# define the CNN architecture\n", + "class Net(nn.Module):\n", + " def __init__(self):\n", + " super(Net, self).__init__()\n", + " self.conv1 = nn.Conv2d(3, 6, 5)\n", + " self.pool = nn.MaxPool2d(2, 2)\n", + " self.conv2 = nn.Conv2d(6, 16, 5)\n", + " self.fc1 = nn.Linear(16 * 5 * 5, 120)\n", + " self.fc2 = nn.Linear(120, 84)\n", + " self.fc3 = nn.Linear(84, 10)\n", + "\n", + " def forward(self, x):\n", + " x = self.pool(F.relu(self.conv1(x)))\n", + " x = self.pool(F.relu(self.conv2(x)))\n", + " x = x.view(-1, 16 * 5 * 5)\n", + " x = F.relu(self.fc1(x))\n", + " x = F.relu(self.fc2(x))\n", + " x = self.fc3(x)\n", + " return x\n", + "\n", + "\n", + "\"\"\"\"\n", + "- It has 3 convolutional layers of kernel size 3 and padding of 1.\n", + "- The first convolutional layer must output 16 channels, the second 32 and the third 64.\n", + "- At each convolutional layer output, we apply a ReLU activation then a MaxPool with kernel size of 2.\n", + "- Then, three fully connected layers, the first two being followed by a ReLU activation and a dropout whose value you will suggest.\n", + "- The first fully connected layer will have an output size of 512.\n", + "- The second fully connected layer will have an output size of 64.\n", + "\"\"\"\n", + "\n", + "class Net2(nn.Module):\n", + " def __init__(self):\n", + " super(Net2, self).__init__()\n", + " self.conv1 = nn.Conv2d(3,16,3,padding=1)\n", + " self.pool = nn.MaxPool2d(2,2)\n", + " self.conv2 = nn.Conv2d(16,32,3,padding=1)\n", + " self.conv3 = nn.Conv2d(32,64,3,padding=1)\n", + " self.fc1 = nn.Linear(64*4*4,512)\n", + " self.dropout = nn.Dropout(0.2)\n", + " self.fc2 = nn.Linear(512,64)\n", + " self.fc3 = nn.Linear(64,10)\n", + "\n", + " def forward(self, x):\n", + " x = self.pool(F.relu(self.conv1(x)))\n", + " x = self.pool(F.relu(self.conv2(x)))\n", + " x = self.pool(F.relu(self.conv3(x)))\n", + " x = x.view(-1,64*4*4)\n", + " x = F.relu(self.fc1(x))\n", + " x = self.dropout(x)\n", + " x = F.relu(self.fc2(x))\n", + " x = self.dropout(x)\n", + " x = self.fc3(x)\n", + "\n", + " return x\n", + "\n", + "\n", + "# create a complete CNN\n", + "model1 = Net()\n", + "print(model1)\n", + "model2 = Net2()\n", + "# move tensors to GPU if CUDA is available\n", + "if train_on_gpu:\n", + " model1.cuda()\n", + " model2.cuda()" + ] + }, + { + "cell_type": "markdown", + "id": "a2dc4974", + "metadata": { + "id": "a2dc4974" + }, + "source": [ + "Loss function and training using SGD (Stochastic Gradient Descent) optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9f14758f", + "metadata": { + "id": "9f14758f" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.optim as optim\n", + "import numpy as np\n", + "import torch.nn as nn\n", + "\n", + "def train_and_validate_model(model_name,model, train_loader, valid_loader, train_on_gpu, n_epochs=30):\n", + " criterion = nn.CrossEntropyLoss() # specify loss function\n", + " optimizer = optim.SGD(model.parameters(), lr=0.01) # specify optimizer\n", + " train_loss_list = [] # list to store training loss for visualization\n", + " valid_loss_list = [] # list to store validation loss for visualization\n", + " valid_loss_min = np.Inf # track change in validation loss\n", + "\n", + " for epoch in range(n_epochs):\n", + " # Keep track of training and validation loss\n", + " train_loss = 0.0\n", + " valid_loss = 0.0\n", + "\n", + " # Train the model\n", + " model.train()\n", + " for data, target in train_loader:\n", + " # Move tensors to GPU if CUDA is available\n", + " if train_on_gpu:\n", + " data, target = data.cuda(), target.cuda()\n", + " # Clear the gradients of all optimized variables\n", + " optimizer.zero_grad()\n", + " # Forward pass: compute predicted outputs by passing inputs to the model\n", + " output = model(data)\n", + " # Calculate the batch loss\n", + " loss = criterion(output, target)\n", + " # Backward pass: compute gradient of the loss with respect to model parameters\n", + " loss.backward()\n", + " # Perform a single optimization step (parameter update)\n", + " optimizer.step()\n", + " # Update training loss\n", + " train_loss += loss.item() * data.size(0)\n", + "\n", + " # Validate the model\n", + " model.eval()\n", + " for data, target in valid_loader:\n", + " # Move tensors to GPU if CUDA is available\n", + " if train_on_gpu:\n", + " data, target = data.cuda(), target.cuda()\n", + " # Forward pass: compute predicted outputs by passing inputs to the model\n", + " output = model(data)\n", + " # Calculate the batch loss\n", + " loss = criterion(output, target)\n", + " # Update average validation loss\n", + " valid_loss += loss.item() * data.size(0)\n", + "\n", + " # Calculate average losses\n", + " train_loss = train_loss / len(train_loader)\n", + " valid_loss = valid_loss / len(valid_loader)\n", + " train_loss_list.append(train_loss)\n", + " valid_loss_list.append(valid_loss)\n", + "\n", + " # Print training/validation statistics\n", + " print(\n", + " \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n", + " epoch, train_loss, valid_loss\n", + " )\n", + " )\n", + "\n", + " # Save model if validation loss has decreased\n", + " if valid_loss <= valid_loss_min:\n", + " print(\n", + " \"Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...\".format(\n", + " valid_loss_min, valid_loss\n", + " )\n", + " )\n", + " torch.save(model.state_dict(), f\"{model_name}_cifar.pt\")\n", + " valid_loss_min = valid_loss\n", + "\n", + " return train_loss_list, valid_loss_list\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5ec081d5", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5ec081d5", + "outputId": "d4aa3010-fd04-4184-f1d8-017e687361e1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 \tTraining Loss: 43.258028 \tValidation Loss: 38.062765\n", + "Validation loss decreased (inf --> 38.062765). Saving model ...\n", + "Epoch: 1 \tTraining Loss: 34.580243 \tValidation Loss: 31.579632\n", + "Validation loss decreased (38.062765 --> 31.579632). Saving model ...\n", + "Epoch: 2 \tTraining Loss: 30.378717 \tValidation Loss: 28.778084\n", + "Validation loss decreased (31.579632 --> 28.778084). Saving model ...\n", + "Epoch: 3 \tTraining Loss: 27.948270 \tValidation Loss: 28.096334\n", + "Validation loss decreased (28.778084 --> 28.096334). Saving model ...\n", + "Epoch: 4 \tTraining Loss: 26.404041 \tValidation Loss: 25.938475\n", + "Validation loss decreased (28.096334 --> 25.938475). Saving model ...\n", + "Epoch: 5 \tTraining Loss: 25.068198 \tValidation Loss: 25.280806\n", + "Validation loss decreased (25.938475 --> 25.280806). Saving model ...\n", + "Epoch: 6 \tTraining Loss: 23.950154 \tValidation Loss: 24.988249\n", + "Validation loss decreased (25.280806 --> 24.988249). Saving model ...\n", + "Epoch: 7 \tTraining Loss: 22.933311 \tValidation Loss: 23.472714\n", + "Validation loss decreased (24.988249 --> 23.472714). Saving model ...\n", + "Epoch: 8 \tTraining Loss: 22.109952 \tValidation Loss: 24.174300\n", + "Epoch: 9 \tTraining Loss: 21.272829 \tValidation Loss: 22.953755\n", + "Validation loss decreased (23.472714 --> 22.953755). Saving model ...\n", + "Epoch: 10 \tTraining Loss: 20.484192 \tValidation Loss: 22.483841\n", + "Validation loss decreased (22.953755 --> 22.483841). Saving model ...\n", + "Epoch: 11 \tTraining Loss: 19.778212 \tValidation Loss: 22.170418\n", + "Validation loss decreased (22.483841 --> 22.170418). Saving model ...\n", + "Epoch: 12 \tTraining Loss: 19.136106 \tValidation Loss: 21.575561\n", + "Validation loss decreased (22.170418 --> 21.575561). Saving model ...\n", + "Epoch: 13 \tTraining Loss: 18.448002 \tValidation Loss: 21.939356\n", + "Epoch: 14 \tTraining Loss: 17.847050 \tValidation Loss: 22.211895\n", + "Epoch: 15 \tTraining Loss: 17.233599 \tValidation Loss: 22.948780\n", + "Epoch: 16 \tTraining Loss: 16.655973 \tValidation Loss: 21.622474\n", + "Epoch: 17 \tTraining Loss: 16.158409 \tValidation Loss: 22.570078\n", + "Epoch: 18 \tTraining Loss: 15.580986 \tValidation Loss: 22.117972\n", + "Epoch: 19 \tTraining Loss: 15.075462 \tValidation Loss: 22.318929\n", + "Epoch: 0 \tTraining Loss: 45.433551 \tValidation Loss: 41.690622\n", + "Validation loss decreased (inf --> 41.690622). Saving model ...\n", + "Epoch: 1 \tTraining Loss: 37.887017 \tValidation Loss: 34.033954\n", + "Validation loss decreased (41.690622 --> 34.033954). Saving model ...\n", + "Epoch: 2 \tTraining Loss: 32.601712 \tValidation Loss: 30.446206\n", + "Validation loss decreased (34.033954 --> 30.446206). Saving model ...\n", + "Epoch: 3 \tTraining Loss: 29.595037 \tValidation Loss: 27.623077\n", + "Validation loss decreased (30.446206 --> 27.623077). Saving model ...\n", + "Epoch: 4 \tTraining Loss: 27.491482 \tValidation Loss: 25.421230\n", + "Validation loss decreased (27.623077 --> 25.421230). Saving model ...\n", + "Epoch: 5 \tTraining Loss: 25.625940 \tValidation Loss: 23.853680\n", + "Validation loss decreased (25.421230 --> 23.853680). Saving model ...\n", + "Epoch: 6 \tTraining Loss: 23.859033 \tValidation Loss: 23.474796\n", + "Validation loss decreased (23.853680 --> 23.474796). Saving model ...\n", + "Epoch: 7 \tTraining Loss: 22.223135 \tValidation Loss: 20.938170\n", + "Validation loss decreased (23.474796 --> 20.938170). Saving model ...\n", + "Epoch: 8 \tTraining Loss: 20.793524 \tValidation Loss: 19.765336\n", + "Validation loss decreased (20.938170 --> 19.765336). Saving model ...\n", + "Epoch: 9 \tTraining Loss: 19.348566 \tValidation Loss: 18.877729\n", + "Validation loss decreased (19.765336 --> 18.877729). Saving model ...\n", + "Epoch: 10 \tTraining Loss: 18.158774 \tValidation Loss: 18.713025\n", + "Validation loss decreased (18.877729 --> 18.713025). Saving model ...\n", + "Epoch: 11 \tTraining Loss: 16.948841 \tValidation Loss: 17.778589\n", + "Validation loss decreased (18.713025 --> 17.778589). Saving model ...\n", + "Epoch: 12 \tTraining Loss: 15.903731 \tValidation Loss: 17.431191\n", + "Validation loss decreased (17.778589 --> 17.431191). Saving model ...\n", + "Epoch: 13 \tTraining Loss: 14.809540 \tValidation Loss: 16.904930\n", + "Validation loss decreased (17.431191 --> 16.904930). Saving model ...\n", + "Epoch: 14 \tTraining Loss: 13.930096 \tValidation Loss: 16.926807\n", + "Epoch: 15 \tTraining Loss: 12.932234 \tValidation Loss: 15.848456\n", + "Validation loss decreased (16.904930 --> 15.848456). Saving model ...\n", + "Epoch: 16 \tTraining Loss: 12.078476 \tValidation Loss: 16.342780\n", + "Epoch: 17 \tTraining Loss: 11.222488 \tValidation Loss: 16.397700\n", + "Epoch: 18 \tTraining Loss: 10.265272 \tValidation Loss: 16.737279\n", + "Epoch: 19 \tTraining Loss: 9.507789 \tValidation Loss: 16.951351\n" + ] + } + ], + "source": [ + "train_loss_list1, valid_loss_list1 = train_and_validate_model(\"model1\",model1, train_loader, valid_loader, train_on_gpu, n_epochs=20)\n", + "\n", + "train_loss_list2, valid_loss_list2 = train_and_validate_model(\"model2\",model2, train_loader, valid_loader, train_on_gpu, n_epochs=20)" + ] + }, + { + "cell_type": "markdown", + "id": "5799cedf", + "metadata": { + "id": "5799cedf" + }, + "source": [ + "Plot training and validation loss" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "67d9c28f", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "67d9c28f", + "outputId": "bbe6ea2f-e8bc-4e29-e4b8-b891a6eaea30" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Plotting the data\n", + "plt.plot(train_loss_list1, 'r--', label='Training loss 1') # red dotted line\n", + "plt.plot(valid_loss_list1, 'r', label='Validation loss 1') # red solid line\n", + "plt.plot(train_loss_list2, 'b--', label='Training loss 2') # blue dotted line\n", + "plt.plot(valid_loss_list2, 'b', label='Validation loss 2') # blue solid line\n", + "\n", + "plt.title(\"Comparison of Model Performances\")\n", + "plt.legend(frameon=False)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "13e1df74", + "metadata": { + "id": "13e1df74" + }, + "source": [ + "Does overfit occur? If so, do an early stopping.\n", + "It does overfit, so I did an early stopping, stopping at 20 epochs." + ] + }, + { + "cell_type": "markdown", + "id": "11df8fd4", + "metadata": { + "id": "11df8fd4" + }, + "source": [ + "Now loading the model with the lowest validation loss value\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e93efdfc", + "metadata": { + "id": "e93efdfc" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def test_performance(model_name,model,last_checkpoint=True):\n", + "\n", + " print(f\"==== TEST PERFORMANCE OF {model_name}\\n\")\n", + "\n", + " if last_checkpoint :\n", + " model.load_state_dict(torch.load(f\"./{model_name}_cifar.pt\"))\n", + "\n", + " # track test loss\n", + " test_loss = 0.0\n", + " class_correct = list(0.0 for i in range(10))\n", + " class_total = list(0.0 for i in range(10))\n", + "\n", + " criterion = nn.CrossEntropyLoss() # specify loss function\n", + " batch_size = 20\n", + "\n", + " model.eval()\n", + " # iterate over test data\n", + " for data, target in test_loader:\n", + " # move tensors to GPU if CUDA is available\n", + " if train_on_gpu:\n", + " data, target = data.cuda(), target.cuda()\n", + " # forward pass: compute predicted outputs by passing inputs to the model\n", + " output = model(data)\n", + " # calculate the batch loss\n", + " loss = criterion(output, target)\n", + " # update test loss\n", + " test_loss += loss.item() * data.size(0)\n", + " # convert output probabilities to predicted class\n", + " _, pred = torch.max(output, 1)\n", + " # compare predictions to true label\n", + " correct_tensor = pred.eq(target.data.view_as(pred))\n", + " correct = (\n", + " np.squeeze(correct_tensor.numpy())\n", + " if not train_on_gpu\n", + " else np.squeeze(correct_tensor.cpu().numpy())\n", + " )\n", + " # calculate test accuracy for each object class\n", + " for i in range(batch_size):\n", + " label = target.data[i]\n", + " class_correct[label] += correct[i].item()\n", + " class_total[label] += 1\n", + "\n", + " # average test loss\n", + " test_loss = test_loss / len(test_loader)\n", + " print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n", + "\n", + " for i in range(10):\n", + " if class_total[i] > 0:\n", + " print(\n", + " \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n", + " % (\n", + " classes[i],\n", + " 100 * class_correct[i] / class_total[i],\n", + " np.sum(class_correct[i]),\n", + " np.sum(class_total[i]),\n", + " )\n", + " )\n", + " else:\n", + " print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n", + "\n", + " print(\n", + " \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n", + " % (\n", + " 100.0 * np.sum(class_correct) / np.sum(class_total),\n", + " np.sum(class_correct),\n", + " np.sum(class_total),\n", + " )\n", + " )\n", + " print(\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "PSVRjzhfsfB5", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PSVRjzhfsfB5", + "outputId": "63361148-4b9d-4685-e5ad-2cb107c59ac5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==== TEST PERFORMANCE OF model1\n", + "\n", + "Test Loss: 21.598281\n", + "\n", + "Test Accuracy of airplane: 64% (646/1000)\n", + "Test Accuracy of automobile: 81% (816/1000)\n", + "Test Accuracy of bird: 42% (423/1000)\n", + "Test Accuracy of cat: 31% (311/1000)\n", + "Test Accuracy of deer: 57% (576/1000)\n", + "Test Accuracy of dog: 56% (568/1000)\n", + "Test Accuracy of frog: 75% (752/1000)\n", + "Test Accuracy of horse: 70% (706/1000)\n", + "Test Accuracy of ship: 75% (750/1000)\n", + "Test Accuracy of truck: 65% (652/1000)\n", + "\n", + "Test Accuracy (Overall): 62% (6200/10000)\n", + "\n", + "\n", + "==== TEST PERFORMANCE OF model2\n", + "\n", + "Test Loss: 16.317094\n", + "\n", + "Test Accuracy of airplane: 72% (727/1000)\n", + "Test Accuracy of automobile: 84% (841/1000)\n", + "Test Accuracy of bird: 62% (629/1000)\n", + "Test Accuracy of cat: 53% (537/1000)\n", + "Test Accuracy of deer: 65% (659/1000)\n", + "Test Accuracy of dog: 57% (572/1000)\n", + "Test Accuracy of frog: 82% (825/1000)\n", + "Test Accuracy of horse: 74% (743/1000)\n", + "Test Accuracy of ship: 84% (844/1000)\n", + "Test Accuracy of truck: 78% (784/1000)\n", + "\n", + "Test Accuracy (Overall): 71% (7161/10000)\n", + "\n", + "\n" + ] + } + ], + "source": [ + "test_performance(\"model1\",model1)\n", + "test_performance(\"model2\",model2)" + ] + }, + { + "cell_type": "markdown", + "id": "944991a2", + "metadata": { + "id": "944991a2" + }, + "source": [ + "Build a new network with the following structure.\n", + "\n", + "- It has 3 convolutional layers of kernel size 3 and padding of 1.\n", + "- The first convolutional layer must output 16 channels, the second 32 and the third 64.\n", + "- At each convolutional layer output, we apply a ReLU activation then a MaxPool with kernel size of 2.\n", + "- Then, three fully connected layers, the first two being followed by a ReLU activation and a dropout whose value you will suggest.\n", + "- The first fully connected layer will have an output size of 512.\n", + "- The second fully connected layer will have an output size of 64.\n", + "\n", + "Compare the results obtained with this new network to those obtained previously." + ] + }, + { + "cell_type": "markdown", + "id": "bc381cf4", + "metadata": { + "id": "bc381cf4" + }, + "source": [ + "## Exercise 2: Quantization: try to compress the CNN to save space\n", + "\n", + "Quantization doc is available from https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic\n", + " \n", + "The Exercise is to quantize post training the above CNN model. Compare the size reduction and the impact on the classification accuracy\n", + "\n", + "\n", + "The size of the model is simply the size of the file." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ef623c26", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ef623c26", + "outputId": "f4190fb3-1b8f-453c-f13d-a031f91cb6a0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model: fp32 \t Size (KB): 251.278\n" + ] + }, + { + "data": { + "text/plain": [ + "251278" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "#Load model1_cifar.pt on CPU\n", + "model1.load_state_dict(torch.load(\"./model1_cifar.pt\",map_location=torch.device('cpu')))\n", + "\n", + "\n", + "model_cpu = model1.to('cpu')\n", + "\n", + "def print_size_of_model(model_cpu, label=\"\"):\n", + " torch.save(model_cpu.state_dict(), \"temp.p\")\n", + " size = os.path.getsize(\"temp.p\")\n", + " print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n", + " os.remove(\"temp.p\")\n", + " return size\n", + "\n", + "\n", + "print_size_of_model(model_cpu, \"fp32\")" + ] + }, + { + "cell_type": "markdown", + "id": "05c4e9ad", + "metadata": { + "id": "05c4e9ad" + }, + "source": [ + "Post training quantization example" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c4c65d4b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c4c65d4b", + "outputId": "df166d0c-95d0-4490-bcc3-b27227e686a6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model: int8 \t Size (KB): 76.522\n" + ] + }, + { + "data": { + "text/plain": [ + "76522" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch.quantization\n", + "\n", + "\n", + "quantized_model = torch.quantization.quantize_dynamic(model_cpu, dtype=torch.qint8)\n", + "print_size_of_model(quantized_model, \"int8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load model1" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "9FMzAtDeyaB4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9FMzAtDeyaB4", + "outputId": "7af70d67-3d05-4d98-90ce-78ec322454b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==== TEST PERFORMANCE OF notquantified\n", + "\n", + "Test Loss: 21.598281\n", + "\n", + "Test Accuracy of airplane: 64% (646/1000)\n", + "Test Accuracy of automobile: 81% (816/1000)\n", + "Test Accuracy of bird: 42% (423/1000)\n", + "Test Accuracy of cat: 31% (311/1000)\n", + "Test Accuracy of deer: 57% (576/1000)\n", + "Test Accuracy of dog: 56% (568/1000)\n", + "Test Accuracy of frog: 75% (752/1000)\n", + "Test Accuracy of horse: 70% (706/1000)\n", + "Test Accuracy of ship: 75% (750/1000)\n", + "Test Accuracy of truck: 65% (652/1000)\n", + "\n", + "Test Accuracy (Overall): 62% (6200/10000)\n", + "\n", + "\n", + "==== TEST PERFORMANCE OF quantified\n", + "\n", + "Test Loss: 21.629376\n", + "\n", + "Test Accuracy of airplane: 64% (646/1000)\n", + "Test Accuracy of automobile: 82% (821/1000)\n", + "Test Accuracy of bird: 41% (417/1000)\n", + "Test Accuracy of cat: 30% (301/1000)\n", + "Test Accuracy of deer: 57% (572/1000)\n", + "Test Accuracy of dog: 57% (570/1000)\n", + "Test Accuracy of frog: 75% (755/1000)\n", + "Test Accuracy of horse: 70% (704/1000)\n", + "Test Accuracy of ship: 74% (747/1000)\n", + "Test Accuracy of truck: 65% (650/1000)\n", + "\n", + "Test Accuracy (Overall): 61% (6183/10000)\n", + "\n", + "\n" + ] + } + ], + "source": [ + "test_performance(\"notquantified\",model_cpu,False)\n", + "test_performance(\"quantified\",quantized_model,False)" + ] + }, + { + "cell_type": "markdown", + "id": "7b108e17", + "metadata": { + "id": "7b108e17" + }, + "source": [ + "For each class, compare the classification test accuracy of the initial model and the quantized model. Also give the overall test accuracy for both models." + ] + }, + { + "cell_type": "markdown", + "id": "201470f9", + "metadata": { + "id": "201470f9" + }, + "source": [ + "## Exercise 3: working with pre-trained models.\n", + "\n", + "PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.html \n", + "We will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files `imagenet-simple-labels.json` that contains the imagenet labels and the image dog.png that we will use as test.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "b4d13080", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 582 + }, + "id": "b4d13080", + "outputId": "bdaad80a-94df-4311-d613-3dbd7dd85092" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /root/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n", + "100%|██████████| 233M/233M [00:05<00:00, 43.5MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted class is: dining table\n", + "model: fp32 \t Size (KB): 244408.234\n", + "=== QUANT ===\n", + "model: fp32 \t Size (KB): 68544.39\n", + "Predicted class with quant is: dining table\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "from PIL import Image\n", + "\n", + "# Choose an image to pass through the model\n", + "test_image = \"table.png\"\n", + "\n", + "# Configure matplotlib for pretty inline plots\n", + "#%matplotlib inline\n", + "#%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Prepare the labels\n", + "with open(\"imagenet-simple-labels.json\") as f:\n", + " labels = json.load(f)\n", + "\n", + "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n", + "data_transform = transforms.Compose(\n", + " [\n", + " transforms.Resize((224, 224)),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", + " ]\n", + ")\n", + "# Load the image\n", + "\n", + "image = Image.open(test_image)\n", + "plt.imshow(image), plt.xticks([]), plt.yticks([])\n", + "\n", + "# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n", + "# image = data_transform(image).unsqueeze(0).cuda()\n", + "image = data_transform(image).unsqueeze(0)\n", + "\n", + "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n", + "# model = models.resnet50(pretrained=True)\n", + "model = models.alexnet(pretrained=True)\n", + "\n", + "\n", + "# Send the model to the GPU\n", + "# model.cuda()\n", + "# Set layers such as dropout and batchnorm in evaluation mode\n", + "model.eval()\n", + "\n", + "# Get the 1000-dimensional model output\n", + "out = model(image)\n", + "# Find the predicted class\n", + "print(\"Predicted class is: {}\".format(labels[out.argmax()]))\n", + "\n", + "print_size_of_model(model,\"fp32\")\n", + "\n", + "### QUANT :\n", + "\n", + "print(\"=== QUANT ===\")\n", + "\n", + "quantized_resnet_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n", + "\n", + "print_size_of_model(quantized_resnet_model,\"fp32\")\n", + "\n", + "out_quant = quantized_resnet_model(image)\n", + "# Find the predicted class\n", + "print(\"Predicted class with quant is: {}\".format(labels[out_quant.argmax()]))\n" + ] + }, + { + "cell_type": "markdown", + "id": "184cfceb", + "metadata": { + "id": "184cfceb" + }, + "source": [ + "Experiments:\n", + "\n", + "Study the code and the results obtained. Possibly add other images downloaded from the internet.\n", + "\n", + "What is the size of the model? Quantize it and then check if the model is still able to correctly classify the other images.\n", + "\n", + "Experiment with other pre-trained CNN models.\n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "5d57da4b", + "metadata": { + "id": "5d57da4b" + }, + "source": [ + "## Exercise 4: Transfer Learning\n", + " \n", + " \n", + "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n", + "Download and unzip in your working directory the dataset available at the address :\n", + " \n", + "https://download.pytorch.org/tutorial/hymenoptera_data.zip\n", + " \n", + "Execute the following code in order to display some images of the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "XWBrj3tQ7IKb", + "metadata": { + "id": "XWBrj3tQ7IKb" + }, + "outputs": [], + "source": [ + "# Load data in colab -> It will download the data in the VM\n", + "\n", + "from urllib.request import urlopen\n", + "from zipfile import ZipFile\n", + "\n", + "zipurl = 'https://download.pytorch.org/tutorial/hymenoptera_data.zip'\n", + " # Download the file from the URL\n", + "zipresp = urlopen(zipurl)\n", + " # Create a new file on the hard drive\n", + "tempzip = open(\"/tmp/tempfile.zip\", \"wb\")\n", + " # Write the contents of the downloaded file into the new file\n", + "tempzip.write(zipresp.read())\n", + " # Close the newly-created file\n", + "tempzip.close()\n", + " # Re-open the newly-created file with ZipFile()\n", + "zf = ZipFile(\"/tmp/tempfile.zip\")\n", + " # Extract its contents into <extraction_path>\n", + " # note that extractall will automatically create the path\n", + "zf.extractall(path = '/content/')\n", + " # close the ZipFile instance\n", + "zf.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "be2d31f5", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 207 + }, + "id": "be2d31f5", + "outputId": "42b58af1-6642-4097-855c-72884bfd1ec9" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torchvision\n", + "from torchvision import datasets, transforms\n", + "\n", + "# Data augmentation and normalization for training\n", + "# Just normalization for validation\n", + "data_transforms = {\n", + " \"train\": transforms.Compose(\n", + " [\n", + " transforms.RandomResizedCrop(\n", + " 224\n", + " ), # ImageNet models were trained on 224x224 images\n", + " transforms.RandomHorizontalFlip(), # flip horizontally 50% of the time - increases train set variability\n", + " transforms.ToTensor(), # convert it to a PyTorch tensor\n", + " transforms.Normalize(\n", + " [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", + " ), # ImageNet models expect this norm\n", + " ]\n", + " ),\n", + " \"val\": transforms.Compose(\n", + " [\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", + " ]\n", + " ),\n", + "}\n", + "\n", + "data_dir = \"hymenoptera_data\"\n", + "# Create train and validation datasets and loaders\n", + "image_datasets = {\n", + " x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n", + " for x in [\"train\", \"val\"]\n", + "}\n", + "dataloaders = {\n", + " x: torch.utils.data.DataLoader(\n", + " image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n", + " )\n", + " for x in [\"train\", \"val\"]\n", + "}\n", + "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n", + "class_names = image_datasets[\"train\"].classes\n", + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# Helper function for displaying images\n", + "def imshow(inp, title=None):\n", + " \"\"\"Imshow for Tensor.\"\"\"\n", + " inp = inp.numpy().transpose((1, 2, 0))\n", + " mean = np.array([0.485, 0.456, 0.406])\n", + " std = np.array([0.229, 0.224, 0.225])\n", + "\n", + " # Un-normalize the images\n", + " inp = std * inp + mean\n", + " # Clip just in case\n", + " inp = np.clip(inp, 0, 1)\n", + " plt.imshow(inp)\n", + " if title is not None:\n", + " plt.title(title)\n", + " plt.pause(0.001) # pause a bit so that plots are updated\n", + " plt.show()\n", + "\n", + "\n", + "# Get a batch of training data\n", + "inputs, classes = next(iter(dataloaders[\"train\"]))\n", + "\n", + "# Make a grid from batch\n", + "out = torchvision.utils.make_grid(inputs)\n", + "\n", + "imshow(out, title=[class_names[x] for x in classes])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bbd48800", + "metadata": { + "id": "bbd48800" + }, + "source": [ + "Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "572d824c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "572d824c", + "outputId": "5cdde3c3-afb4-4761-fe76-b2bbcc987872" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\FIXE_THEOPHILE\\miniconda3\\envs\\torch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "c:\\Users\\FIXE_THEOPHILE\\miniconda3\\envs\\torch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "----------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\FIXE_THEOPHILE\\miniconda3\\envs\\torch\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n", + " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train Loss: 0.6336 Acc: 0.6557\n", + "val Loss: 0.4062 Acc: 0.8758\n", + "\n", + "Epoch 2/10\n", + "----------\n", + "train Loss: 0.5282 Acc: 0.7459\n", + "val Loss: 0.4154 Acc: 0.7712\n", + "\n", + "Epoch 3/10\n", + "----------\n", + "train Loss: 0.4769 Acc: 0.7541\n", + "val Loss: 0.2701 Acc: 0.9150\n", + "\n", + "Epoch 4/10\n", + "----------\n", + "train Loss: 0.3546 Acc: 0.8361\n", + "val Loss: 0.2138 Acc: 0.9346\n", + "\n", + "Epoch 5/10\n", + "----------\n", + "train Loss: 0.4446 Acc: 0.8156\n", + "val Loss: 0.2529 Acc: 0.9150\n", + "\n", + "Epoch 6/10\n", + "----------\n", + "train Loss: 0.3317 Acc: 0.8443\n", + "val Loss: 0.1966 Acc: 0.9412\n", + "\n", + "Epoch 7/10\n", + "----------\n", + "train Loss: 0.3348 Acc: 0.8730\n", + "val Loss: 0.1944 Acc: 0.9346\n", + "\n", + "Epoch 8/10\n", + "----------\n", + "train Loss: 0.2521 Acc: 0.9098\n", + "val Loss: 0.1958 Acc: 0.9346\n", + "\n", + "Epoch 9/10\n", + "----------\n", + "train Loss: 0.2647 Acc: 0.9098\n", + "val Loss: 0.1979 Acc: 0.9412\n", + "\n", + "Epoch 10/10\n", + "----------\n", + "train Loss: 0.2926 Acc: 0.8811\n", + "val Loss: 0.1938 Acc: 0.9477\n", + "\n", + "Training complete in 3m 32s\n", + "Best val Acc: 0.947712\n" + ] + } + ], + "source": [ + "import copy\n", + "import os\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torchvision\n", + "from torch.optim import lr_scheduler\n", + "from torchvision import datasets, transforms\n", + "\n", + "# Data augmentation and normalization for training\n", + "# Just normalization for validation\n", + "data_transforms = {\n", + " \"train\": transforms.Compose(\n", + " [\n", + " # transforms.RandomResizedCrop(\n", + " # 224\n", + " # ), # ImageNet models were trained on 224x224 images\n", + " # transforms.RandomHorizontalFlip(), # flip horizontally 50% of the time - increases train set variability\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(), # convert it to a PyTorch tensor\n", + " transforms.Normalize(\n", + " [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", + " ), # ImageNet models expect this norm\n", + " ]\n", + " ),\n", + " \"val\": transforms.Compose(\n", + " [\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", + " ]\n", + " ),\n", + "}\n", + "\n", + "data_dir = \"hymenoptera_data\"\n", + "# Create train and validation datasets and loaders\n", + "image_datasets = {\n", + " x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n", + " for x in [\"train\", \"val\"]\n", + "}\n", + "dataloaders = {\n", + " x: torch.utils.data.DataLoader(\n", + " image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n", + " )\n", + " for x in [\"train\", \"val\"]\n", + "}\n", + "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n", + "class_names = image_datasets[\"train\"].classes\n", + "# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "device = torch.device(\"cpu\") # In order to eval with quantized model\n", + "\n", + "# Helper function for displaying images\n", + "def imshow(inp, title=None):\n", + " \"\"\"Imshow for Tensor.\"\"\"\n", + " inp = inp.numpy().transpose((1, 2, 0))\n", + " mean = np.array([0.485, 0.456, 0.406])\n", + " std = np.array([0.229, 0.224, 0.225])\n", + "\n", + " # Un-normalize the images\n", + " inp = std * inp + mean\n", + " # Clip just in case\n", + " inp = np.clip(inp, 0, 1)\n", + " plt.imshow(inp)\n", + " if title is not None:\n", + " plt.title(title)\n", + " plt.pause(0.001) # pause a bit so that plots are updated\n", + " plt.show()\n", + "\n", + "\n", + "# Get a batch of training data\n", + "# inputs, classes = next(iter(dataloaders['train']))\n", + "\n", + "# Make a grid from batch\n", + "# out = torchvision.utils.make_grid(inputs)\n", + "\n", + "# imshow(out, title=[class_names[x] for x in classes])\n", + "# training\n", + "\n", + "\n", + "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n", + " since = time.time()\n", + "\n", + " best_model_wts = copy.deepcopy(model.state_dict())\n", + " best_acc = 0.0\n", + "\n", + " epoch_time = [] # we'll keep track of the time needed for each epoch\n", + "\n", + " for epoch in range(num_epochs):\n", + " epoch_start = time.time()\n", + " print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n", + " print(\"-\" * 10)\n", + "\n", + " # Each epoch has a training and validation phase\n", + " for phase in [\"train\", \"val\"]:\n", + " if phase == \"train\":\n", + " scheduler.step()\n", + " model.train() # Set model to training mode\n", + " else:\n", + " model.eval() # Set model to evaluate mode\n", + "\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + "\n", + " # Iterate over data.\n", + " for inputs, labels in dataloaders[phase]:\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device)\n", + "\n", + " # zero the parameter gradients\n", + " optimizer.zero_grad()\n", + "\n", + " # Forward\n", + " # Track history if only in training phase\n", + " with torch.set_grad_enabled(phase == \"train\"):\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " # backward + optimize only if in training phase\n", + " if phase == \"train\":\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # Statistics\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + "\n", + " epoch_loss = running_loss / dataset_sizes[phase]\n", + " epoch_acc = running_corrects.double() / dataset_sizes[phase]\n", + "\n", + " print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n", + "\n", + " # Deep copy the model\n", + " if phase == \"val\" and epoch_acc > best_acc:\n", + " best_acc = epoch_acc\n", + " best_model_wts = copy.deepcopy(model.state_dict())\n", + "\n", + " # Add the epoch time\n", + " t_epoch = time.time() - epoch_start\n", + " epoch_time.append(t_epoch)\n", + " print()\n", + "\n", + " time_elapsed = time.time() - since\n", + " print(\n", + " \"Training complete in {:.0f}m {:.0f}s\".format(\n", + " time_elapsed // 60, time_elapsed % 60\n", + " )\n", + " )\n", + " print(\"Best val Acc: {:4f}\".format(best_acc))\n", + "\n", + " # Load best model weights\n", + " model.load_state_dict(best_model_wts)\n", + " return model, epoch_time\n", + "\n", + "\n", + "# Download a pre-trained ResNet18 model and freeze its weights\n", + "model = torchvision.models.resnet18(pretrained=True)\n", + "for param in model.parameters():\n", + " param.requires_grad = False\n", + "\n", + "# Replace the final fully connected layer\n", + "# Parameters of newly constructed modules have requires_grad=True by default\n", + "\n", + "num_ftrs = model.fc.in_features\n", + "\n", + "# model.fc = nn.Linear(num_ftrs, 2) # OLD LAST LAYER\n", + "\n", + "model.fc = nn.Sequential(\n", + " nn.Linear(num_ftrs, 512),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.2),\n", + " nn.Linear(512, 2),\n", + " #nn.ReLU(), j'ai enlevé car je ne comprend pas l'interret en sortie ? un softmax serait surement plus approprié\n", + ") # NEW LAST LAYER\n", + "\n", + "\n", + "\n", + "\n", + "# Send the model to the GPU\n", + "model = model.to(device)\n", + "# Set the loss function\n", + "criterion = nn.CrossEntropyLoss()\n", + "\n", + "# Observe that only the parameters of the final layer are being optimized\n", + "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n", + "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n", + "model, epoch_time = train_model(\n", + " model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Acc: 0.9608\n" + ] + } + ], + "source": [ + "def eval_model(model):\n", + " model.eval()\n", + " running_corrects = 0\n", + " # Iterate over data.\n", + " for inputs, labels in dataloaders[\"val\"]:\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device)\n", + "\n", + " # Forward\n", + " # Track history if only in training phase\n", + " with torch.set_grad_enabled(False):\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + "\n", + " # Statistics\n", + " running_corrects += torch.sum(preds == labels.data)\n", + "\n", + " epoch_acc = running_corrects.double() / dataset_sizes[\"val\"]\n", + "\n", + " print(\"Acc: {:.4f}\".format(epoch_acc))\n", + "\n", + "eval_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model: fp32 \t Size (KB): 45831.61\n", + "model: int8 \t Size (KB): 45043.622\n", + "Acc: 0.9477\n" + ] + } + ], + "source": [ + "import torch.quantization\n", + "\n", + "def print_size_of_model(model_cpu, label=\"\"):\n", + " torch.save(model_cpu.state_dict(), \"temp.p\")\n", + " size = os.path.getsize(\"temp.p\")\n", + " print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n", + " os.remove(\"temp.p\")\n", + " return size\n", + "\n", + "print_size_of_model(model,\"fp32\")\n", + "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n", + "print_size_of_model(quantized_model, \"int8\")\n", + "\n", + "\n", + "eval_model(quantized_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Acc = 0.9542 for first model (with only one layer)\n", + "\n", + "Acc = 0.9608 for second one and we also see that it's more stable\n", + "\n", + "Acc after quantization = 0.9477\n", + "\n", + "***\n", + "\n", + "Ici la taille du modèle ne se réduit pas autant que précédemment, car on ne quantifie pas le modèle entier, mais seulement les dernières couches. On peut voir que la précision est légèrement réduite, mais pas de beaucoup. On peut donc quantifier le modèle sans perdre trop de précision." + ] + }, + { + "cell_type": "markdown", + "id": "xnwWFaEBc_R7", + "metadata": { + "id": "xnwWFaEBc_R7" + }, + "source": [ + "Experiments:\n", + "Study the code and the results obtained.\n", + "\n", + "\n", + "***\n", + "Here it's important to note that we should not do random crops in training, because some images, when you crop them, you will lose the important part of the image. So do the same as validation : resize to 256, then center crop to 224.\n", + "***\n", + "\n", + "Modify the code and add an \"eval_model\" function to allow\n", + "the evaluation of the model on a test set (different from the learning and validation sets used during the learning phase). Study the results obtained.\n", + "\n", + "***\n", + "As the validation set is never used to tweak hyperparameters. We can use it as a test set. So that's what I did with the eval_model function.\n", + "***\n", + "\n", + "\n", + "Now modify the code to replace the current classification layer with a set of two layers using a \"relu\" activation function for the middle layer, and the \"dropout\" mechanism for both layers. Renew the experiments and study the results obtained.\n", + "\n", + "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy." + ] + }, + { + "cell_type": "markdown", + "id": "04a263f0", + "metadata": { + "id": "04a263f0" + }, + "source": [ + "## Optional\n", + " \n", + "Try this at home!!\n", + "\n", + "\n", + "Pytorch offers a framework to export a given CNN to your selfphone (either android or iOS). Have a look at the tutorial https://pytorch.org/mobile/home/\n", + "\n", + "The Exercise consists in deploying the CNN of Exercise 4 in your phone and then test it on live.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "fe954ce4", + "metadata": { + "id": "fe954ce4" + }, + "source": [ + "## Author\n", + "\n", + "Alberto BOSIO - Ph. D." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "vscode": { + "interpreter": { + "hash": "9e3efbebb05da2d4a1968abe9a0645745f54b63feb7a85a514e4da0495be97eb" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/plane.png b/plane.png new file mode 100644 index 0000000000000000000000000000000000000000..3eb8cac38538b33f9694817130eba21ee1aa61b2 Binary files /dev/null and b/plane.png differ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..118630e15fad1de3736b69dbe018d09e5822ccbe --- /dev/null +++ b/requirements.txt @@ 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