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-{
- "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_(1).ipynb b/TD2_Deep_Learning.ipynb
similarity index 77%
rename from TD2_Deep_Learning_(1).ipynb
rename to TD2_Deep_Learning.ipynb
index 8975cd269392150706673fc575ecb8fa1ce23c78..91c1d0c59d5a1eb1a14437d2d32f3d670ca16c2c 100644
--- a/TD2_Deep_Learning_(1).ipynb
+++ b/TD2_Deep_Learning.ipynb
@@ -62,47 +62,47 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "b1950f0a",
- "outputId": "4bc64027-627d-4576-888b-3d6fe0cc23de"
+ "outputId": "4f805dc9-cc98-4cf7-d70b-3819bbf8febe"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
- "tensor([[-0.2028, 2.0237, -1.2843, 0.0230, 1.0070, -0.8287, 2.1567, -1.0386,\n",
- " 0.4017, -0.8005],\n",
- " [-0.9620, 1.8215, 0.6417, -0.6226, -0.5204, 0.0484, 0.6088, 0.1195,\n",
- " 0.1533, -2.0676],\n",
- " [ 0.8341, -0.3921, -1.1890, 0.0912, 0.0578, 0.8995, 0.7551, -1.4050,\n",
- " 0.5272, 0.8045],\n",
- " [ 1.8574, -0.9345, -0.7533, 0.7402, 2.1289, 0.1758, 0.2452, -1.3896,\n",
- " -0.4790, -0.1870],\n",
- " [-0.8667, -0.5722, -0.2892, -0.2629, 1.2258, 0.4263, 0.5919, 1.2951,\n",
- " 0.4660, 0.1756],\n",
- " [ 0.2268, 2.0921, 0.4049, 0.0664, -1.1903, 0.0072, 1.5466, 1.5110,\n",
- " 0.1141, 0.7788],\n",
- " [-1.3739, -0.1683, -0.6034, -0.5225, -0.3639, 0.5341, -1.1496, 1.9338,\n",
- " 0.0659, -0.3923],\n",
- " [ 0.1114, -0.4265, 1.3247, 1.1868, 0.0215, -0.7669, -0.3272, -0.7944,\n",
- " 0.0213, 0.6771],\n",
- " [-0.3311, -0.0580, -1.0317, 0.2543, -0.1310, 1.5036, -0.0574, 1.6840,\n",
- " -0.2404, -1.2480],\n",
- " [ 1.2702, -0.9774, -0.9536, -0.7468, 1.4159, 0.9679, 0.0366, 0.1911,\n",
- " 2.6050, -0.9738],\n",
- " [-0.4592, -0.3593, 0.2656, 0.7353, 1.1558, -1.0067, -0.3048, 0.8037,\n",
- " -0.6275, -0.4056],\n",
- " [-1.5678, -0.2606, 0.3406, 1.4114, 0.4305, 0.3803, -1.0418, -0.3449,\n",
- " -1.1682, 2.8705],\n",
- " [-1.9394, 1.1927, -0.3441, 0.5899, 1.3980, 0.5671, 1.3139, -0.3752,\n",
- " -1.0007, -0.0401],\n",
- " [-1.5236, 0.3370, 0.6458, 0.0757, -0.0781, 0.0312, -2.5394, -1.1414,\n",
- " -0.2234, -1.5022]])\n",
+ "tensor([[-0.5250, -1.5878, 1.2373, -0.4463, -0.9542, 1.5210, 0.3039, -0.1577,\n",
+ " 0.4926, -0.4071],\n",
+ " [-1.3307, -0.8433, -0.7765, -0.1829, 0.7930, -0.7069, -0.5152, -0.4200,\n",
+ " 1.0203, 1.1917],\n",
+ " [-0.1136, -1.2670, 0.5183, 0.3753, 0.3023, -0.2250, 0.2876, 1.6003,\n",
+ " -0.0242, -0.5977],\n",
+ " [-1.2466, 0.8898, 0.8563, -2.7605, 0.4482, -0.4284, 0.5633, 0.5803,\n",
+ " 0.4976, 1.0436],\n",
+ " [-1.1549, -0.1155, 0.1341, -0.4466, 1.1712, 0.5158, 0.1951, -0.2732,\n",
+ " -1.3928, 0.0689],\n",
+ " [-0.9471, -0.3165, -0.9674, -0.5913, 1.6934, -1.0990, 0.3858, 1.2201,\n",
+ " -1.0375, -1.1741],\n",
+ " [ 0.5245, -1.0708, -0.1096, 0.7402, 0.1946, -0.4872, -0.8072, -1.2370,\n",
+ " 1.1297, 0.6642],\n",
+ " [-1.1072, 0.1123, -2.3255, -0.3360, 0.6739, -0.1380, 0.1683, -1.7936,\n",
+ " -1.9374, -0.3841],\n",
+ " [-0.3464, -0.6614, -2.2669, 0.7528, -0.2793, 1.8053, -0.0628, -0.6133,\n",
+ " 0.8094, 0.3893],\n",
+ " [ 2.1503, 1.5026, -0.1787, -2.3691, 0.5555, -0.2884, 0.9710, 0.3432,\n",
+ " -0.0997, 1.4255],\n",
+ " [-0.6184, -1.1084, 0.7993, -1.2145, -0.4672, -0.6829, -0.1484, 0.3961,\n",
+ " 1.5318, -1.8019],\n",
+ " [-0.2495, -0.1458, -1.2062, -1.6310, -0.8753, 1.2997, -0.4681, 0.9760,\n",
+ " 1.1409, 0.4308],\n",
+ " [-1.0811, 0.6437, -0.6477, -0.5390, 0.3499, 1.1290, 0.8379, -1.0099,\n",
+ " -0.2674, -2.1319],\n",
+ " [-0.3605, 0.4578, -0.0146, -0.8141, 1.2869, 0.0233, -0.4747, 0.6892,\n",
+ " 1.4487, 0.2062]])\n",
"AlexNet(\n",
" (features): Sequential(\n",
" (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -177,13 +177,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6e18f2fd",
- "outputId": "3ee0a96e-a3f5-4fd7-f771-377c7ea8cbe3"
+ "outputId": "1caad02a-d607-435d-ddbc-43782a81c17a"
},
"outputs": [
{
@@ -219,13 +219,13 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "462666a2",
- "outputId": "e27c0ab3-32d6-4b33-c9a0-7bf4b99750f3"
+ "outputId": "5c4e0742-9abb-4e19-df69-bfa90c57a472"
},
"outputs": [
{
@@ -239,7 +239,7 @@
"output_type": "stream",
"name": "stderr",
"text": [
- "100%|██████████| 170498071/170498071 [00:03<00:00, 49485448.00it/s]\n"
+ "100%|██████████| 170498071/170498071 [00:12<00:00, 13161528.52it/s]\n"
]
},
{
@@ -998,31 +998,31 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 22,
"metadata": {
"id": "ef623c26",
"colab": {
"base_uri": "https://localhost:8080/"
},
- "outputId": "08c27105-1b6b-4010-d49a-c8f176c58365"
+ "outputId": "693c86b4-17ff-4a7d-94b3-bd8826185aa2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
- "model: fp32 \t Size (KB): 102523.238\n"
+ "model: fp32 \t Size (KB): 46828.292\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
- "102523238"
+ "46828292"
]
},
"metadata": {},
- "execution_count": 11
+ "execution_count": 22
}
],
"source": [
@@ -1330,16 +1330,28 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
"id": "b4d13080",
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 423
+ "height": 547
},
- "outputId": "578776c0-2842-4033-bd8a-ffedda0f7238"
+ "outputId": "29b337ff-b4b9-46ae-9507-c641c81ce66c"
},
"outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-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",
+ "/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=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
+ " warnings.warn(msg)\n",
+ "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
+ "100%|██████████| 97.8M/97.8M [00:00<00:00, 137MB/s]\n"
+ ]
+ },
{
"output_type": "stream",
"name": "stdout",
@@ -1436,7 +1448,7 @@
"outputId": "582d78de-7555-4ced-cbef-13f484377d95"
},
"id": "ofs6wepVIxCo",
- "execution_count": 12,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -1484,7 +1496,7 @@
"outputId": "5cef4ca9-7018-4f71-b459-42ec0c9f89bd"
},
"id": "7W7ZK6sWJamd",
- "execution_count": 13,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -1557,7 +1569,7 @@
"outputId": "a3f2116b-245a-4149-8774-2861617f48bd"
},
"id": "PS0bnfqTJgJA",
- "execution_count": 15,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -1646,7 +1658,7 @@
"outputId": "b942d30c-ffa7-4aba-fb1e-b14a7a5463f7"
},
"id": "Z-_ounggKL_r",
- "execution_count": 16,
+ "execution_count": null,
"outputs": [
{
"output_type": "stream",
@@ -1709,11 +1721,450 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {
- "id": "be2d31f5"
+ "id": "be2d31f5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "outputId": "b9cba17a-fca9-4364-c9a9-06dbdda10402"
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2023-11-22 15:24:10-- https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+ "Resolving download.pytorch.org (download.pytorch.org)... 18.160.200.77, 18.160.200.126, 18.160.200.112, ...\n",
+ "Connecting to download.pytorch.org (download.pytorch.org)|18.160.200.77|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 47286322 (45M) [application/zip]\n",
+ "Saving to: ‘hymenoptera_data.zip’\n",
+ "\n",
+ "hymenoptera_data.zi 100%[===================>] 45.10M 196MB/s in 0.2s \n",
+ "\n",
+ "2023-11-22 15:24:10 (196 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n",
+ "\n",
+ "Archive: hymenoptera_data.zip\n",
+ " creating: hymenoptera_data/\n",
+ " creating: hymenoptera_data/train/\n",
+ " creating: hymenoptera_data/train/ants/\n",
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+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
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\n"
+ },
+ "metadata": {}
+ }
+ ],
"source": [
"import os\n",
"\n",
@@ -1722,6 +2173,7 @@
"import torch\n",
"import torchvision\n",
"from torchvision import datasets, transforms\n",
+ "os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'\n",
"\n",
"# Data augmentation and normalization for training\n",
"# Just normalization for validation\n",
@@ -1749,16 +2201,21 @@
"}\n",
"\n",
"data_dir = \"hymenoptera_data\"\n",
+ "\n",
+ "!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+ "!unzip hymenoptera_data.zip\n",
+ "\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",
+ " for x in [\"train\", \"val\"] if x != \".ipynb_checkpoints\"\n",
"}\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",
+ " for x in [\"train\", \"val\"] if x != \".ipynb_checkpoints\"\n",
"}\n",
"dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
"class_names = image_datasets[\"train\"].classes\n",
@@ -1805,16 +2262,110 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {
- "id": "572d824c"
+ "id": "572d824c",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "7fdf8046-aee1-4dff-bbee-cb174d510636"
},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(_create_warning_msg(\n",
+ "/usr/local/lib/python3.10/dist-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",
+ "/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=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"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/10\n",
+ "----------\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-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"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "train Loss: 0.6672 Acc: 0.6393\n",
+ "val Loss: 0.4174 Acc: 0.7908\n",
+ "\n",
+ "Epoch 2/10\n",
+ "----------\n",
+ "train Loss: 0.5774 Acc: 0.7459\n",
+ "val Loss: 0.1873 Acc: 0.9542\n",
+ "\n",
+ "Epoch 3/10\n",
+ "----------\n",
+ "train Loss: 0.3908 Acc: 0.8525\n",
+ "val Loss: 0.2271 Acc: 0.9085\n",
+ "\n",
+ "Epoch 4/10\n",
+ "----------\n",
+ "train Loss: 0.3549 Acc: 0.8156\n",
+ "val Loss: 0.1635 Acc: 0.9477\n",
+ "\n",
+ "Epoch 5/10\n",
+ "----------\n",
+ "train Loss: 0.4418 Acc: 0.8197\n",
+ "val Loss: 0.3405 Acc: 0.8627\n",
+ "\n",
+ "Epoch 6/10\n",
+ "----------\n",
+ "train Loss: 0.5072 Acc: 0.7787\n",
+ "val Loss: 0.4774 Acc: 0.8105\n",
+ "\n",
+ "Epoch 7/10\n",
+ "----------\n",
+ "train Loss: 0.6458 Acc: 0.7746\n",
+ "val Loss: 0.1821 Acc: 0.9477\n",
+ "\n",
+ "Epoch 8/10\n",
+ "----------\n",
+ "train Loss: 0.3293 Acc: 0.8443\n",
+ "val Loss: 0.1823 Acc: 0.9477\n",
+ "\n",
+ "Epoch 9/10\n",
+ "----------\n",
+ "train Loss: 0.2703 Acc: 0.8852\n",
+ "val Loss: 0.2138 Acc: 0.9346\n",
+ "\n",
+ "Epoch 10/10\n",
+ "----------\n",
+ "train Loss: 0.3470 Acc: 0.8648\n",
+ "val Loss: 0.2350 Acc: 0.9281\n",
+ "\n",
+ "Training complete in 0m 33s\n",
+ "Best val Acc: 0.954248\n",
+ "Test loss: 0.1476 \n",
+ " Test acc: 0.9592\n"
+ ]
+ }
+ ],
"source": [
"import copy\n",
"import os\n",
"import time\n",
"\n",
+ "import random\n",
+ "import shutil\n",
+ "\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import torch\n",
@@ -1847,21 +2398,79 @@
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
" ]\n",
" ),\n",
+ " \"test\": 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",
+ "#Création du dossier test dans hymenoptera_data\n",
+ "\n",
+ "# Chemin des dossiers sources\n",
+ "dossier_source_train_ants = 'hymenoptera_data/train/ants'\n",
+ "dossier_source_val_ants = 'hymenoptera_data/val/ants'\n",
+ "dossier_source_train_bees = 'hymenoptera_data/train/bees'\n",
+ "dossier_source_val_bees = 'hymenoptera_data/val/bees'\n",
+ "\n",
+ "# Chemin du dossier de destination\n",
+ "dossier_destination_test_ants = 'hymenoptera_data/test/ants'\n",
+ "dossier_destination_test_bees = 'hymenoptera_data/test/bees'\n",
+ "\n",
+ "# Nombre de fichiers à copier\n",
+ "nombre_de_fichiers_a_copier = 10\n",
+ "\n",
+ "# Sélection aléatoire de 10 fichiers parmi la liste\n",
+ "fichiers_aleatoires_train_ants = random.sample(os.listdir(dossier_source_train_ants), nombre_de_fichiers_a_copier)\n",
+ "fichiers_aleatoires_val_ants = random.sample(os.listdir(dossier_source_val_ants), nombre_de_fichiers_a_copier)\n",
+ "fichiers_aleatoires_train_bees = random.sample(os.listdir(dossier_source_train_bees), nombre_de_fichiers_a_copier)\n",
+ "fichiers_aleatoires_val_bees = random.sample(os.listdir(dossier_source_val_bees), nombre_de_fichiers_a_copier)\n",
+ "\n",
+ "# Création du dossier de destination s'il n'existe pas\n",
+ "if not os.path.exists(dossier_destination_test_ants):\n",
+ " os.makedirs(dossier_destination_test_ants)\n",
+ "\n",
+ "if not os.path.exists(dossier_destination_test_bees):\n",
+ " os.makedirs(dossier_destination_test_bees)\n",
+ "\n",
+ "# Copie des fichiers sélectionnés dans le dossier de destination\n",
+ "for fichier in fichiers_aleatoires_train_ants:\n",
+ " chemin_source = os.path.join(dossier_source_train_ants, fichier)\n",
+ " chemin_destination = os.path.join(dossier_destination_test_ants, fichier)\n",
+ " shutil.copy(chemin_source, chemin_destination)\n",
+ "\n",
+ "for fichier in fichiers_aleatoires_train_bees:\n",
+ " chemin_source = os.path.join(dossier_source_train_bees, fichier)\n",
+ " chemin_destination = os.path.join(dossier_destination_test_bees, fichier)\n",
+ " shutil.copy(chemin_source, chemin_destination)\n",
+ "\n",
+ "for fichier in fichiers_aleatoires_val_ants:\n",
+ " chemin_source = os.path.join(dossier_source_val_ants, fichier)\n",
+ " chemin_destination = os.path.join(dossier_destination_test_ants, fichier)\n",
+ " shutil.copy(chemin_source, chemin_destination)\n",
+ "\n",
+ "for fichier in fichiers_aleatoires_val_bees:\n",
+ " chemin_source = os.path.join(dossier_source_val_bees, fichier)\n",
+ " chemin_destination = os.path.join(dossier_destination_test_bees, fichier)\n",
+ " shutil.copy(chemin_source, chemin_destination)\n",
+ "\n",
"data_dir = \"hymenoptera_data\"\n",
+ "\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",
+ " for x in [\"train\", \"val\",\"test\"]\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",
+ " for x in [\"train\", \"val\",\"test\"]\n",
"}\n",
- "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+ "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\",\"test\"]}\n",
"class_names = image_datasets[\"train\"].classes\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
@@ -1968,6 +2577,34 @@
" model.load_state_dict(best_model_wts)\n",
" return model, epoch_time\n",
"\n",
+ "def eval_model(model,criterion):\n",
+ " phase = \"test\"\n",
+ "\n",
+ " model.eval()\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",
+ " # 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",
+ " # Statistics\n",
+ " running_loss += loss.item() * inputs.size(0)\n",
+ " running_corrects += torch.sum(preds == labels.data)\n",
+ "\n",
+ " test_loss = running_loss / dataset_sizes[phase]\n",
+ " test_acc = running_corrects.double() / dataset_sizes[phase]\n",
+ "\n",
+ " print(\"Test loss: {:.4f} \\n Test acc: {:.4f}\".format( test_loss, test_acc))\n",
"\n",
"# Download a pre-trained ResNet18 model and freeze its weights\n",
"model = torchvision.models.resnet18(pretrained=True)\n",
@@ -1988,10 +2625,23 @@
"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"
+ ")\n",
+ "\n",
+ "eval_model(model,criterion)"
],
"id": "572d824c"
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Sous Google Colab, j'ai dû télécharger et dézipper informatiquement le dossier de hymenoptera_data afin de pouvoir lancer le programme fourni dans le BE. Ainsi, je dois également créer informatiquement le dossier test sur lequel évaluer mon modèle. C'est à cela que serve les lignes de codes créant les dossier demandés, à base des dossiers val et train. On remarque que le test Accuracy est de 0.9592, et le test loss de 0.15 ce qui est très bon. Le modèle est donc très performant.\n",
+ " A noter également que j'ai choisi de prendre 10 images de chaques tests, cela peut-être modifié mais les résultats restent relativement similaires."
+ ],
+ "metadata": {
+ "id": "se9QqWVx5WHy"
+ },
+ "id": "se9QqWVx5WHy"
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -2010,6 +2660,402 @@
],
"id": "mq4MnH6i0AKj"
},
+ {
+ "cell_type": "code",
+ "source": [
+ "import copy\n",
+ "import os\n",
+ "import time\n",
+ "\n",
+ "import random\n",
+ "import shutil\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",
+ " \"test\": 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",
+ "\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\",\"test\"]\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\",\"test\"]\n",
+ "}\n",
+ "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\",\"test\"]}\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",
+ "def eval_model(model,criterion):\n",
+ " phase = \"test\"\n",
+ "\n",
+ " model.eval()\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",
+ " # 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",
+ " # Statistics\n",
+ " running_loss += loss.item() * inputs.size(0)\n",
+ " running_corrects += torch.sum(preds == labels.data)\n",
+ "\n",
+ " test_loss = running_loss / dataset_sizes[phase]\n",
+ " test_acc = running_corrects.double() / dataset_sizes[phase]\n",
+ "\n",
+ " print(\"Test loss: {:.4f} \\n Test acc: {:.4f}\".format( test_loss, test_acc))\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.Sequential(\n",
+ " nn.Linear(num_ftrs, 10),\n",
+ " nn.ReLU(),\n",
+ " nn.Dropout(0.4),\n",
+ " nn.Linear(10, 2),\n",
+ " nn.Dropout(0.4)\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",
+ "\n",
+ "eval_model(model,criterion)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "c1ht4_RuwAPX",
+ "outputId": "f708027e-cd27-473c-f6f0-04c20b3b1f4c"
+ },
+ "id": "c1ht4_RuwAPX",
+ "execution_count": 31,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-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",
+ "/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=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"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/10\n",
+ "----------\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-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"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "train Loss: 0.7073 Acc: 0.5041\n",
+ "val Loss: 0.5622 Acc: 0.8693\n",
+ "\n",
+ "Epoch 2/10\n",
+ "----------\n",
+ "train Loss: 0.6581 Acc: 0.5943\n",
+ "val Loss: 0.5268 Acc: 0.9281\n",
+ "\n",
+ "Epoch 3/10\n",
+ "----------\n",
+ "train Loss: 0.6319 Acc: 0.6107\n",
+ "val Loss: 0.4690 Acc: 0.7386\n",
+ "\n",
+ "Epoch 4/10\n",
+ "----------\n",
+ "train Loss: 0.5654 Acc: 0.6844\n",
+ "val Loss: 0.3571 Acc: 0.9216\n",
+ "\n",
+ "Epoch 5/10\n",
+ "----------\n",
+ "train Loss: 0.6048 Acc: 0.6230\n",
+ "val Loss: 0.4651 Acc: 0.9346\n",
+ "\n",
+ "Epoch 6/10\n",
+ "----------\n",
+ "train Loss: 0.6198 Acc: 0.6230\n",
+ "val Loss: 0.3884 Acc: 0.9346\n",
+ "\n",
+ "Epoch 7/10\n",
+ "----------\n",
+ "train Loss: 0.5611 Acc: 0.6680\n",
+ "val Loss: 0.3740 Acc: 0.9608\n",
+ "\n",
+ "Epoch 8/10\n",
+ "----------\n",
+ "train Loss: 0.5563 Acc: 0.6557\n",
+ "val Loss: 0.3353 Acc: 0.9608\n",
+ "\n",
+ "Epoch 9/10\n",
+ "----------\n",
+ "train Loss: 0.5847 Acc: 0.6475\n",
+ "val Loss: 0.3512 Acc: 0.9608\n",
+ "\n",
+ "Epoch 10/10\n",
+ "----------\n",
+ "train Loss: 0.5557 Acc: 0.6803\n",
+ "val Loss: 0.3597 Acc: 0.9542\n",
+ "\n",
+ "Training complete in 0m 34s\n",
+ "Best val Acc: 0.960784\n",
+ "Test loss: 0.3782 \n",
+ " Test acc: 0.9796\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.cpu()\n",
+ "\n",
+ "# Quantification du modèle\n",
+ "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+ "\n",
+ "#Affichage des tailles de modèle\n",
+ "print(\"Taille du modèle de base\")\n",
+ "print_size_of_model(model, \"int8\")\n",
+ "print(\"Taille du modèle quantifié\")\n",
+ "print_size_of_model(quantized_model, \"int8\")\n",
+ "\n",
+ "#Evaluation du modèle\n",
+ "eval_model(quantized_model,criterion)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 443
+ },
+ "id": "yXnOvUFIHACu",
+ "outputId": "f1d8724e-d989-4a53-a160-d7b0cdd09e25"
+ },
+ "id": "yXnOvUFIHACu",
+ "execution_count": 32,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Taille du modèle de base\n",
+ "model: int8 \t Size (KB): 44797.562\n",
+ "Taille du modèle quantifié\n",
+ "model: int8 \t Size (KB): 44783.654\n"
+ ]
+ },
+ {
+ "output_type": "error",
+ "ename": "RuntimeError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-32-7f902b5b4046>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m#Evaluation du modèle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0meval_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquantized_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m<ipython-input-31-8abf47fa7235>\u001b[0m in \u001b[0;36meval_model\u001b[0;34m(model, criterion)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;31m# Track history if only in training phase\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"train\"\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--> 186\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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 187\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\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 188\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\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_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\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;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1517\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\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 1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1520\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[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 1525\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 1526\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\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 1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\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/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 284\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[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\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 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_forward_impl\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[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\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 269\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\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[1;32m 270\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\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[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\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;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1517\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\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 1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1520\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[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 1525\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 1526\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\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 1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\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/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 459\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[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\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 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\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/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 455\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 456\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 457\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mRuntimeError\u001b[0m: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "On remarque que le test loss est moins bon qu'avec le modèle précédent, l'accuracy n'a quant à lui pas changé."
+ ],
+ "metadata": {
+ "id": "aolhPVBi-W89"
+ },
+ "id": "aolhPVBi-W89"
+ },
{
"cell_type": "markdown",
"metadata": {