diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb index b5ec160aef93252e09f5434df7f2770757bde6ed..80f3e2e9f745ac2698e143588fb78b13b33a7fbc 100644 --- a/TD2 Deep Learning.ipynb +++ b/TD2 Deep Learning.ipynb @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "id": "6e18f2fd", "metadata": {}, "outputs": [ @@ -875,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 41, "id": "ef623c26", "metadata": {}, "outputs": [], @@ -1667,111 +1667,40 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "be2d31f5", + "execution_count": 4, + "id": "20e31c0a", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ + "# Import all necessary libraries for this section\n", + "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 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" + "from torch.optim import lr_scheduler\n", + "from torchvision import datasets, transforms" ] }, { "cell_type": "markdown", - "id": "bbd48800", + "id": "728fb32f", "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." + "### 1. Forming the Test Set\n", + "\n", + "The available dataset 'hymenoptera_data' is uniquely composed of a training and validation set. A first step is to sample 20% of both the training and validation sets to compose a test set." ] }, { "cell_type": "code", - "execution_count": 1, - "id": "6b92bf6a", + "execution_count": null, + "id": "43ca9125", "metadata": {}, "outputs": [], "source": [ @@ -1779,7 +1708,7 @@ "import shutil\n", "import random\n", "\n", - "data_dir = 'hymenoptera_data' \n", + "data_dir = 'hymenoptera_data/' \n", "train_dir = os.path.join(data_dir, 'train')\n", "val_dir = os.path.join(data_dir, 'val')\n", "test_dir = os.path.join(data_dir, 'test')\n", @@ -1787,8 +1716,8 @@ "os.makedirs(os.path.join(test_dir, 'ants'), exist_ok=True)\n", "os.makedirs(os.path.join(test_dir, 'bees'), exist_ok=True)\n", "\n", - "\n", - "portion_test = 0.2 \n", + "classes = ['ants', 'bees']\n", + "portion_test = 0.2 # 20% samples of both train and val will be moved to test\n", "\n", "def move_sample_images(source_dir, dest_dir, portion):\n", " images = os.listdir(source_dir)\n", @@ -1800,59 +1729,47 @@ " dest_path = os.path.join(dest_dir, image)\n", " shutil.move(src_path, dest_path) \n", "\n", + "# Make it for each class\n", + "for cls in classes:\n", "\n", - "for cl in ['ants', 'bees']:\n", + " train_class_dir = os.path.join(train_dir, cls)\n", + " val_class_dir = os.path.join(val_dir, cls)\n", + " test_class_dir = os.path.join(test_dir, cls)\n", "\n", - " train_class_dir = os.path.join(train_dir, cl)\n", - " val_class_dir = os.path.join(val_dir, cl)\n", - " test_class_dir = os.path.join(test_dir, cl)\n", - " \n", - " move_sample_images(train_class_dir, test_class_dir, portion_test)\n", - " \n", - " move_sample_images(val_class_dir, test_class_dir, portion_test)\n" + " move_sample_images(train_class_dir, test_class_dir, portion_test) # move 20% of train to test\n", + " move_sample_images(val_class_dir, test_class_dir, portion_test) # move 20% of val to test\n" ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "ce217f88", + "cell_type": "markdown", + "id": "153bd66a", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\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" + "### 2. Forming data loaders " ] }, { "cell_type": "code", - "execution_count": 19, - "id": "572d824c", + "execution_count": 21, + "id": "be2d31f5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Data augmentation and normalization for training\n", - "# Just normalization for validation\n", + "# Just normalization for validation and test (test has been added to the data loader)\n", "data_transforms = {\n", - " \"train\": transforms.Compose(\n", + " 'train': transforms.Compose(\n", " [\n", " transforms.RandomResizedCrop(\n", " 224\n", @@ -1864,7 +1781,7 @@ " ), # ImageNet models expect this norm\n", " ]\n", " ),\n", - " \"val\": transforms.Compose(\n", + " 'val': transforms.Compose(\n", " [\n", " transforms.Resize(256),\n", " transforms.CenterCrop(224),\n", @@ -1872,7 +1789,7 @@ " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", " ]\n", " ),\n", - " \"test\": transforms.Compose(\n", + " 'test': transforms.Compose(\n", " [\n", " transforms.Resize(256),\n", " transforms.CenterCrop(224),\n", @@ -1895,8 +1812,8 @@ " 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", + "class_names = image_datasets['train'].classes # ['ants', 'bees']\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", @@ -1915,18 +1832,41 @@ " 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", + "# Get a the first 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", + "out = torchvision.utils.make_grid(inputs)\n", "\n", - "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n", + "imshow(out, title=[class_names[x] for x in classes])\n" + ] + }, + { + "cell_type": "markdown", + "id": "293b4ca8", + "metadata": {}, + "source": [ + "### 3. Train function" + ] + }, + { + "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": [ + "# Train a model based on data loaders\n", + "def train_model(model, dataloaders, criterion, optimizer, scheduler, num_epochs=25):\n", + " # Keep track of the time needed for training\n", " since = time.time()\n", "\n", " best_model_wts = copy.deepcopy(model.state_dict())\n", @@ -1935,6 +1875,8 @@ " epoch_time = [] # we'll keep track of the time needed for each epoch\n", "\n", " for epoch in range(num_epochs):\n", + " \n", + " # Keep track of the time needed for each epoch\n", " epoch_start = time.time()\n", " print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n", " print(\"-\" * 10)\n", @@ -1942,7 +1884,7 @@ " # Each epoch has a training and validation phase\n", " for phase in [\"train\", \"val\"]:\n", " if phase == \"train\":\n", - " scheduler.step()\n", + " scheduler.step() # Update learning rate during training\n", " model.train() # Set model to training mode\n", " else:\n", " model.eval() # Set model to evaluate mode\n", @@ -1955,7 +1897,7 @@ " inputs = inputs.to(device)\n", " labels = labels.to(device)\n", "\n", - " # zero the parameter gradients\n", + " # Zero the parameter gradients\n", " optimizer.zero_grad()\n", "\n", " # Forward\n", @@ -1967,19 +1909,19 @@ "\n", " # backward + optimize only if in training phase\n", " if phase == \"train\":\n", - " loss.backward()\n", - " optimizer.step()\n", + " loss.backward() # Compute the gradient\n", + " optimizer.step() # Update the weights\n", "\n", " # Statistics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", + " running_loss += loss.item() * inputs.size(0) \n", + " running_corrects += torch.sum(preds == labels.data) # Number of correct predictions\n", "\n", - " epoch_loss = running_loss / dataset_sizes[phase]\n", + " epoch_loss = running_loss / dataset_sizes[phase] # Average loss over all batches\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", + " # Deep copy the model if it's the best one so far\n", " if phase == \"val\" and epoch_acc > best_acc:\n", " best_acc = epoch_acc\n", " best_model_wts = copy.deepcopy(model.state_dict())\n", @@ -1989,16 +1931,20 @@ " epoch_time.append(t_epoch)\n", " print()\n", "\n", + " # Print the time needed for the entire training\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", + "\n", + " # Print the best accuracy on validation set\n", " print(\"Best val Acc: {:4f}\".format(best_acc))\n", "\n", " # Load best model weights\n", " model.load_state_dict(best_model_wts)\n", + "\n", " return model, epoch_time" ] }, @@ -2007,57 +1953,53 @@ "id": "80361eac", "metadata": {}, "source": [ - "### Evaluate & Comparaison Function" - ] - }, - { - "cell_type": "markdown", - "id": "1e7c404d", - "metadata": {}, - "source": [ - "Il faut dans un premier temps récuperer un dataset de test labellisé (ants-bees) pour évaluer l'accuracy des différents modèles." + "### 4. Evaluation and Comparison Functions\n", + "\n", + "In this section, we define a function (eval_model) to assess the accuracy of a model over a test loader. We also define a function (compare_evaluation_model) to evaluate the accuracy gain or loss between a model and its quantized version." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 36, "id": "6b24abd4", "metadata": {}, "outputs": [], "source": [ + "# New classes to predict\n", "classes = [\n", " \"ants\",\n", " \"bees\"\n", "]\n", "\n", - "# function to allow the evaluation of the model on a test set\n", - "def eval_model(model, test_loader):\n", + "# Function to evaluate the model on the test set\n", + "def eval_model(model, test_loader, classes):\n", " \n", - " # track test loss\n", - " test_loss = 0.0\n", + " # Test accuracy for each class (ants, bees)\n", " class_correct = [0, 0]\n", " class_total = [0, 0]\n", "\n", " model.eval()\n", - " # iterate over test data\n", + " # Iterate over test batches\n", " for data, target in test_loader:\n", - " # forward pass: compute predicted outputs by passing inputs to the model\n", + " # Forward pass: compute predicted outputs by passing inputs to the model\n", " output = model(data)\n", - " # convert output probabilities to predicted class\n", + " # Convert output probabilities to predicted class\n", " _, pred = torch.max(output, 1)\n", - " # compare predictions to true label\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(target.shape[0]):\n", + " # Calculate test accuracy for each object class\n", + " batch_size = target.shape[0]\n", + " for i in range(batch_size):\n", " label = target.data[i]\n", - " class_correct[label] += correct[i].item() # add one to the label class if the prediction is correct\n", + " class_correct[label] += correct[i].item() # Add one to the correct class count if the prediction is correct\n", " class_total[label] += 1\n", "\n", + " # Display test accuracy for each class\n", " for i in range(2):\n", " if class_total[i] > 0:\n", " print(\n", @@ -2072,6 +2014,7 @@ " else:\n", " print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n", "\n", + " # Display overall test accuracy\n", " print(\n", " \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n", " % (\n", @@ -2084,53 +2027,58 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 37, "id": "ed5e580e", "metadata": {}, "outputs": [], "source": [ - "# track test accuracy\n", - "class_correct = [0, 0]\n", - "qat_class_correct = [0, 0]\n", - "class_total = [0, 0]\n", + "# Compare test accuracy of a model and its quantized version\n", + "def compare_evaluation_model(model, quantized_model, classes, test_loader):\n", "\n", - "# Let's compare accuracies between a quantized model and non quantized model \n", - "def compare_evaluation_model(model, qat_model, test_loader):\n", - " model.eval(); qat_model.eval()\n", - " # iterate over test data\n", + " # Test accuracy for each class (ants, bees)\n", + " class_correct = [0, 0] # for model\n", + " quantized_class_correct = [0, 0] # for quantized model\n", + " class_total = [0, 0]\n", + "\n", + " model.eval(); quantized_model.eval()\n", + " # Iterate over test batches\n", " for data, target in test_loader:\n", - " # forward pass: compute predicted outputs by passing inputs to the model\n", + "\n", + " # Forward pass: compute predicted outputs by passing inputs to the model\n", " output = model(data)\n", - " qat_output = qat_model(data)\n", - " # convert output probabilities to predicted class\n", + " quantized_output = quantized_model(data)\n", + "\n", + " # Convert output probabilities to predicted class\n", " _, pred = torch.max(output, 1)\n", - " _, qat_pred = torch.max(qat_output, 1)\n", - " # compare predictions to true label\n", - " correct_tensor = pred.eq(target.data.view_as(pred))\n", - " qat_correct_tensor = qat_pred.eq(target.data.view_as(qat_pred))\n", + " _, quantized_pred = torch.max(quantized_output, 1)\n", "\n", + " # Compare predictions to true label\n", + " correct_tensor = pred.eq(target.data.view_as(pred))\n", + " quantized_correct_tensor = quantized_pred.eq(target.data.view_as(quantized_pred))\n", " correct = (\n", " np.squeeze(correct_tensor.numpy())\n", " )\n", - " qat_correct = (\n", - " np.squeeze(qat_correct_tensor.numpy())\n", + " quantized_correct = (\n", + " np.squeeze(quantized_correct_tensor.numpy())\n", " )\n", "\n", - " # calculate test accuracy for each object class\n", - " for i in range(target.shape[0]):\n", - " label = target.data[i]\n", - " class_correct[label] += correct[i].item() \n", - " qat_class_correct[label] += qat_correct[i].item()\n", + " # Calculate test accuracy for each object class\n", + " batch_size = target.shape[0]\n", + " for i in range(batch_size):\n", + " label = target.data[i] # Index of the class\n", + " class_correct[label] += correct[i].item() # Add one to the correct class count if the prediction is correct for model\n", + " quantized_class_correct[label] += quantized_correct[i].item() # Add one to the correct class count if the prediction is correct for quantized model\n", " class_total[label] += 1\n", "\n", + " # Display accuracy gain/loss for each class and overall between model and quantized model\n", " for i in range(2):\n", " if class_total[i] > 0:\n", " print(\n", - " \"Test Accuracy difference of %5s between no quantized and quantized_model: %2d instance(s) (%.2f%%)\"\n", + " \"Test Accuracy difference of %5s between model and its quantized version: %2d instance(s) (%.2f%%)\"\n", " % (\n", " classes[i],\n", - " class_correct[i] - qat_class_correct[i],\n", - " 100 * (class_correct[i] - qat_class_correct[i]) / class_total[i],\n", + " class_correct[i] - quantized_class_correct[i],\n", + " 100 * (class_correct[i] - quantized_class_correct[i]) / class_correct[i],\n", "\n", " )\n", " )\n", @@ -2138,10 +2086,10 @@ " print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n", "\n", " print(\n", - " \"\\nTest Accuracy (Overall) difference between no quantized and quantized_model: %2d instance(s) (%.2f%%)\"\n", + " \"\\nTest Accuracy (Overall) difference between model and its quantized version: %2d instance(s) (%.2f%%)\"\n", " % (\n", - " np.sum(class_correct) - np.sum(qat_class_correct),\n", - " 100.0 * (np.sum(class_correct) - np.sum(qat_class_correct)) / np.sum(class_total)\n", + " np.sum(class_correct) - np.sum(quantized_class_correct),\n", + " 100.0 * (np.sum(class_correct) - np.sum(quantized_class_correct)) / np.sum(class_correct)\n", " )\n", " )" ] @@ -2151,7 +2099,7 @@ "id": "8302134d", "metadata": {}, "source": [ - "### ResNet18 - Model1" + "### 5. Model1 (a first fine tuning of ResNet18)" ] }, { @@ -2159,126 +2107,56 @@ "id": "b3356654", "metadata": {}, "source": [ - "#### => Model1 non quantisé" + "##### 5.1 No quantized model1" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 55, + "id": "c17f897f", + "metadata": {}, + "outputs": [], + "source": [ + "# Create function to instanciate the 1st fine tuned ResNet18 model\n", + "def instantiate_model1():\n", + " model1 = torchvision.models.resnet18(pretrained=True)\n", + " for param in model1.parameters():\n", + " param.requires_grad = False\n", + "\n", + " num_ftrs = model1.fc.in_features\n", + " model1.fc = nn.Linear(num_ftrs, 2)\n", + "\n", + " return model1" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "9e154207", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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: 0.7045 Acc: 0.5738\n", - "val Loss: 0.6174 Acc: 0.6667\n", - "\n", - "Epoch 2/10\n", - "----------\n", - "train Loss: 0.4214 Acc: 0.7910\n", - "val Loss: 0.2446 Acc: 0.8954\n", - "\n", - "Epoch 3/10\n", - "----------\n", - "train Loss: 0.4302 Acc: 0.8033\n", - "val Loss: 0.2280 Acc: 0.9020\n", - "\n", - "Epoch 4/10\n", - "----------\n", - "train Loss: 0.5210 Acc: 0.7705\n", - "val Loss: 0.1614 Acc: 0.9542\n", - "\n", - "Epoch 5/10\n", - "----------\n", - "train Loss: 0.7569 Acc: 0.6885\n", - "val Loss: 0.1732 Acc: 0.9477\n", - "\n", - "Epoch 6/10\n", - "----------\n", - "train Loss: 0.5961 Acc: 0.7992\n", - "val Loss: 0.1825 Acc: 0.9346\n", - "\n", - "Epoch 7/10\n", - "----------\n", - "train Loss: 0.3902 Acc: 0.8402\n", - "val Loss: 0.2513 Acc: 0.9216\n", - "\n", - "Epoch 8/10\n", - "----------\n", - "train Loss: 0.4861 Acc: 0.7623\n", - "val Loss: 0.1720 Acc: 0.9477\n", - "\n", - "Epoch 9/10\n", - "----------\n", - "train Loss: 0.3405 Acc: 0.8525\n", - "val Loss: 0.1966 Acc: 0.9346\n", - "\n", - "Epoch 10/10\n", - "----------\n", - "train Loss: 0.3727 Acc: 0.8443\n", - "val Loss: 0.2243 Acc: 0.9281\n", - "\n", - "Training complete in 3m 40s\n", - "Best val Acc: 0.954248\n" - ] - } - ], + "outputs": [], "source": [ - "# 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", + "# ------------- Train model1 phase -------------\n", + "\n", + "# Instantiate model1\n", + "model1 = instantiate_model1()\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", + "criterion = nn.CrossEntropyLoss() # Binary Cross Entropy Loss\n", + "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9) # Only parameters of final layer are being optimized\n", + "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1) # Decay LR by a factor of 0.1 every 7 epochs\n", + "\n", + "model1, epoch_time = train_model(\n", + " model1, criterion, optimizer_conv, exp_lr_scheduler, classes, num_epochs=10\n", ")\n", "\n", - "torch.save(model.state_dict(), 'resnet18_model_1.pt')" + "torch.save(model1.state_dict(), 'model1.pt')" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 57, "id": "cf8d3f16", "metadata": {}, "outputs": [ @@ -2304,18 +2182,13 @@ } ], "source": [ - "test_loader = dataloaders['test']\n", + "# ------------- Test model1 phase -------------\n", "\n", - "model = torchvision.models.resnet18(pretrained=True)\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", - "\n", - "model.load_state_dict(torch.load('resnet18_model_1.pt'))\n", + "model1 = instantiate_model1()\n", + "test_loader = dataloaders['test']\n", + "model1.load_state_dict(torch.load('model1.pt'))\n", "\n", - "eval_model(model, test_loader)" + "eval_model(model1, test_loader, classes)" ] }, { @@ -2323,46 +2196,54 @@ "id": "64a7f5a6", "metadata": {}, "source": [ - "#### => Model1 quantisé post training" + "#### 5.2 Post Training Quantized Model1" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 59, "id": "71cbf48e", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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": [ "model: fp32 \t Size (KB): 44780.42\n", "model: int8 \t Size (KB): 44778.17\n", - "Size of model after quantization (newNet): 99.99 % of the original model size\n", - "Test Accuracy difference of ants between no quantized and quantized_model: 0 instance(s) (0.00%)\n", - "Test Accuracy difference of bees between no quantized and quantized_model: 0 instance(s) (0.00%)\n", + "Size of PTQ model1 : 99.99 % of the original model1 size\n", + "Test Accuracy difference of ants between model and its quantized version: 0 instance(s) (0.00%)\n", + "Test Accuracy difference of bees between model and its quantized version: 0 instance(s) (0.00%)\n", "\n", - "Test Accuracy (Overall) difference between no quantized and quantized_model: 0 instance(s) (0.00%)\n" + "Test Accuracy (Overall) difference between model and its quantized version: 0 instance(s) (0.00%)\n" ] } ], "source": [ - "model = torchvision.models.resnet18(pretrained=True)\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", - "\n", - "model.load_state_dict(torch.load('resnet18_model_1.pt'))\n", + "# ------------- Quantize model1 (PTQ) & compare accuracy -------------\n", "\n", - "post_training_quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n", + "# Instantiate model1\n", + "model1 = instantiate_model1()\n", + "model1.load_state_dict(torch.load('model1.pt'))\n", + "post_training_quantized_model1 = torch.quantization.quantize_dynamic(model1, dtype=torch.qint8)\n", "\n", - "model_size = print_size_of_model(model, \"fp32\")\n", - "post_training_quantized_model_size = print_size_of_model(post_training_quantized_model, \"int8\")\n", - "print(\"Size of model after quantization (newNet): \",round(100 * post_training_quantized_model_size/ model_size, 2), \"% of the original model size\")\n", + "# Evaluation compression rate\n", + "model1_size = print_size_of_model(model1, \"fp32\")\n", + "post_training_quantized_model1_size = print_size_of_model(post_training_quantized_model1, \"int8\")\n", + "print(\"Size of PTQ model1 : \",round(100 * post_training_quantized_model1_size/ model1_size, 2), \"% of the original model1 size\")\n", "\n", - "compare_evaluation_model(model, post_training_quantized_model, test_loader)" + "# Compare accuracy between model1 and its quantized version\n", + "compare_evaluation_model(model1, post_training_quantized_model1, classes, test_loader)" ] }, { @@ -2370,158 +2251,113 @@ "id": "1ea4be31", "metadata": {}, "source": [ - "#### => Model1 quantisé QAT" + "#### 5.3 Quantization Aware Training Model1" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "cdd21828", + "execution_count": 60, + "id": "fe4b2a01", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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", - "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\ao\\quantization\\observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.\n", - " warnings.warn(\n", - "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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": [ - "Epoch 1/10\n", - "----------\n", - "train Loss: 0.6348 Acc: 0.6516\n", - "val Loss: 0.3329 Acc: 0.8627\n", - "\n", - "Epoch 2/10\n", - "----------\n", - "train Loss: 0.4914 Acc: 0.7828\n", - "val Loss: 0.1980 Acc: 0.9281\n", - "\n", - "Epoch 3/10\n", - "----------\n", - "train Loss: 0.4627 Acc: 0.7828\n", - "val Loss: 0.2073 Acc: 0.9346\n", - "\n", - "Epoch 4/10\n", - "----------\n", - "train Loss: 0.4517 Acc: 0.7951\n", - "val Loss: 0.1748 Acc: 0.9346\n", - "\n", - "Epoch 5/10\n", - "----------\n", - "train Loss: 0.4063 Acc: 0.8033\n", - "val Loss: 0.1690 Acc: 0.9412\n", - "\n", - "Epoch 6/10\n", - "----------\n", - "train Loss: 0.4445 Acc: 0.8074\n", - "val Loss: 0.2293 Acc: 0.9085\n", - "\n", - "Epoch 7/10\n", - "----------\n", - "train Loss: 0.3835 Acc: 0.8402\n", - "val Loss: 0.1949 Acc: 0.9346\n", - "\n", - "Epoch 8/10\n", - "----------\n", - "train Loss: 0.3291 Acc: 0.8443\n", - "val Loss: 0.1920 Acc: 0.9412\n", - "\n", - "Epoch 9/10\n", - "----------\n", - "train Loss: 0.3748 Acc: 0.8156\n", - "val Loss: 0.1823 Acc: 0.9412\n", - "\n", - "Epoch 10/10\n", - "----------\n", - "train Loss: 0.2983 Acc: 0.8607\n", - "val Loss: 0.1803 Acc: 0.9477\n", - "\n", - "Training complete in 4m 34s\n", - "Best val Acc: 0.947712\n" - ] - } - ], + "outputs": [], "source": [ - "# Download a pre-trained ResNet18 model and freeze its weights\n", - "qat_model = torchvision.models.resnet18(pretrained=True)\n", - "for param in qat_model.parameters():\n", - " param.requires_grad = False\n", - " \n", - "qat_model.fc = nn.Sequential(\n", - " torch.quantization.QuantStub(), \n", - " nn.Linear(num_ftrs, 2), \n", - " torch.quantization.DeQuantStub() \n", - ")\n", + "# Create function to instanciate the 1st QAT fine tuned ResNet18 model\n", + "def instantiate_qat_model1():\n", "\n", - "# Appliquer QAT seulement à la dernière couche\n", - "qat_model.fc.qconfig = torch.quantization.get_default_qat_qconfig('x86') # recommended in pytorch documentation\n", - "torch.quantization.prepare_qat(qat_model.fc, inplace=True) # add fake quantization modules to simulate quantization during training\n", + " # Download a pre-trained ResNet18 model and freeze its weights\n", + " qat_model1 = torchvision.models.resnet18(pretrained=True)\n", + " for param in qat_model1.parameters():\n", + " param.requires_grad = False\n", + " \n", + " num_ftrs = qat_model1.fc.in_features\n", + " qat_model1.fc = nn.Sequential(\n", + " torch.quantization.QuantStub(), \n", + " nn.Linear(num_ftrs, 2), \n", + " torch.quantization.DeQuantStub() \n", + " )\n", "\n", + " # Only apply QAT to the last layer\n", + " qat_model1.fc.qconfig = torch.quantization.get_default_qat_qconfig('x86') # recommended in pytorch documentation\n", + " torch.quantization.prepare_qat(qat_model1.fc, inplace=True) # add fake quantization modules to simulate quantization during training\n", + "\n", + " return qat_model1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdd21828", + "metadata": {}, + "outputs": [], + "source": [ + "# ------------- Train qat_model1 phase -------------\n", + "\n", + "# Instantiate qat_model1\n", + "qat_model1 = instantiate_qat_model1()\n", "# Send the model to the GPU\n", "qat_model = qat_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(qat_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", - "qat_model, epoch_time = train_model(\n", - " qat_model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n", + "\n", + "criterion = nn.CrossEntropyLoss() # Binary Cross Entropy Loss\n", + "optimizer_conv = optim.SGD(qat_model1.fc.parameters(), lr=0.001, momentum=0.9) # Only parameters of final layer are being optimized\n", + "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1) # Decay LR by a factor of 0.1 every 7 epochs\n", + "\n", + "# Let's train the model!\n", + "qat_model1, epoch_time = train_model(\n", + " qat_model1, criterion, optimizer_conv, exp_lr_scheduler, classes, num_epochs=10\n", ")\n", "\n", - "torch.save(qat_model.state_dict(), 'resnet18_model_1_qat.pt')" + "torch.save(qat_model1.state_dict(), 'qat_model1.pt')" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 64, "id": "078d9bbb", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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", + "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\ao\\quantization\\observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.\n", + " warnings.warn(\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "model: fp32 \t Size (KB): 45831.61\n", + "model: fp32 \t Size (KB): 44780.42\n", "model: int8 \t Size (KB): 44779.366\n", - "Size of model after quantization (newNet): 97.7 % of the original model size\n", - "Test Accuracy difference of ants between no quantized and quantized_model: -4 instance(s) (-3.51%)\n", - "Test Accuracy difference of bees between no quantized and quantized_model: 1 instance(s) (0.83%)\n", + "Size of qat_model1 after quantization: 100.0 % of the original model1 size\n", + "Test Accuracy difference of ants between model and its quantized version: -2 instance(s) (-5.71%)\n", + "Test Accuracy difference of bees between model and its quantized version: 1 instance(s) (2.56%)\n", "\n", - "Test Accuracy (Overall) difference between no quantized and quantized_model: -3 instance(s) (-1.28%)\n" + "Test Accuracy (Overall) difference between model and its quantized version: -1 instance(s) (-1.35%)\n" ] } ], "source": [ - "qat_model = torchvision.models.resnet18(pretrained=True)\n", - "qat_model.fc = nn.Sequential(\n", - " torch.quantization.QuantStub(), \n", - " nn.Linear(num_ftrs, 2), \n", - " torch.quantization.DeQuantStub() \n", - ")\n", + "# -------------Compare qat_model2 accuracy to model2 ------------\n", "\n", - "qat_model.fc.qconfig = torch.quantization.get_default_qat_qconfig('x86') # recommended in pytorch documentation\n", - "torch.quantization.prepare_qat(qat_model.fc, inplace=True) # add fake quantization modules to simulate quantization during training\n", - "qat_model.load_state_dict(torch.load('resnet18_model_1_qat.pt'))\n", + "# Instantiate qat_model1\n", + "qat_model1 = instantiate_qat_model1()\n", + "# Load the model\n", + "qat_model1.load_state_dict(torch.load('qat_model1.pt'))\n", "\n", - "qat_model = torch.quantization.convert(qat_model, inplace=True) # convert the qat_model to a real quantized model in int8 format by default\n", + "qat_model1 = torch.quantization.convert(qat_model1, inplace=True) # Qauantize qat_model1\n", "\n", - "model_size = print_size_of_model(model, \"fp32\")\n", - "qat_model_size = print_size_of_model(qat_model, \"int8\")\n", - "print(\"Size of model after quantization (newNet): \",round(100 * qat_model_size/ model_size, 2), \"% of the original model size\")\n", + "# Evaluation compression rate\n", + "model1_size = print_size_of_model(model1, \"fp32\")\n", + "qat_model1_size = print_size_of_model(qat_model1, \"int8\")\n", + "print(\"Size of qat_model1 after quantization: \",round(100 * qat_model1_size/ model1_size, 2), \"% of the original model1 size\")\n", "\n", - "compare_evaluation_model(model, qat_model, test_loader)" + "# Compare accuracy between model1 and its QAT quantized version\n", + "compare_evaluation_model(model1, qat_model1, classes, test_loader)" ] }, { @@ -2529,7 +2365,7 @@ "id": "32c13c41", "metadata": {}, "source": [ - "### ResNet18 - Model2" + "### 6. Model2 (a second fine tuning of ResNet18)" ] }, { @@ -2537,92 +2373,62 @@ "id": "2cb42fd1", "metadata": {}, "source": [ - "#### => Model2 non quantisé" + "##### 6.1 No quantized model2" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 31, + "id": "998ff2dd", + "metadata": {}, + "outputs": [], + "source": [ + "# Create function to instanciate the 2nd fine tuned ResNet18 model\n", + "def instantiate_model2():\n", + " model2 = torchvision.models.resnet18(pretrained=True)\n", + " for param in model2.parameters():\n", + " param.requires_grad = False\n", + "\n", + " num_ftrs = model2.fc.in_features\n", + " model2.fc = nn.Sequential(\n", + " nn.Linear(num_ftrs, 512),\n", + " nn.ReLU(),\n", + " nn.Dropout(p=0.5),\n", + " nn.Linear(512, 2),\n", + " nn.Dropout(p=0.5)\n", + " )\n", + "\n", + " return model2" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "a37aebc1", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\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" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[24], line 28\u001b[0m\n\u001b[0;32m 26\u001b[0m optimizer_conv \u001b[38;5;241m=\u001b[39m optim\u001b[38;5;241m.\u001b[39mSGD(model\u001b[38;5;241m.\u001b[39mfc\u001b[38;5;241m.\u001b[39mparameters(), lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.001\u001b[39m, momentum\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.9\u001b[39m)\n\u001b[0;32m 27\u001b[0m exp_lr_scheduler \u001b[38;5;241m=\u001b[39m lr_scheduler\u001b[38;5;241m.\u001b[39mStepLR(optimizer_conv, step_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m7\u001b[39m, gamma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m)\n\u001b[1;32m---> 28\u001b[0m model, epoch_time \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer_conv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexp_lr_scheduler\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\n\u001b[0;32m 30\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 32\u001b[0m torch\u001b[38;5;241m.\u001b[39msave(model\u001b[38;5;241m.\u001b[39mstate_dict(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresnet18_model_2.pt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "Cell \u001b[1;32mIn[18], line 109\u001b[0m, in \u001b[0;36mtrain_model\u001b[1;34m(model, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[0;32m 107\u001b[0m \u001b[38;5;66;03m# Iterate over data.\u001b[39;00m\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m inputs, labels \u001b[38;5;129;01min\u001b[39;00m dataloaders[phase]:\n\u001b[1;32m--> 109\u001b[0m inputs \u001b[38;5;241m=\u001b[39m \u001b[43minputs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 110\u001b[0m labels \u001b[38;5;241m=\u001b[39m labels\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m 112\u001b[0m \u001b[38;5;66;03m# zero the parameter gradients\u001b[39;00m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ - "# 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", - "\n", - "# Replace fc layer by a set of two layers using a \"relu\" activation function for the middle layer,\n", - "# and the \"dropout\" mechanism for both layers.\n", - "model.fc = nn.Sequential(\n", - " nn.Linear(num_ftrs, 512), \n", - " nn.ReLU(), \n", - " nn.Dropout(p=0.5), \n", - " nn.Linear(512, 2), \n", - " nn.Dropout(p=0.5) \n", - ")\n", + "# ------------- Train model2 phase -------------\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", + "# Instantiate the model1\n", + "model2 = instantiate_model2()\n", + "# Send the model to the CPU (for my case)\n", + "model2 = model2.to(device)\n", + "criterion = nn.CrossEntropyLoss() # Binary Cross Entropy Loss (2 classes only)\n", + "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9) # Only the parameters of the final layer are being optimized\n", + "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1) # Decay LR by a factor of 0.1 every 7 epochs\n", "\n", - "torch.save(model.state_dict(), 'resnet18_model_2.pt')" + "# Let's train the model!\n", + "model2, epoch_time = train_model(\n", + " model2, dataloaders, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10)\n", + "\n", + "# Save the model\n", + "torch.save(model2.state_dict(), 'model2.pt')" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 38, "id": "81f265ea", "metadata": {}, "outputs": [ @@ -2638,25 +2444,13 @@ } ], "source": [ - "model = torchvision.models.resnet18(pretrained=True)\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", - "\n", - "# Replace fc layer by a set of two layers using a \"relu\" activation function for the middle layer,\n", - "# and the \"dropout\" mechanism for both layers.\n", - "model.fc = nn.Sequential(\n", - " nn.Linear(num_ftrs, 512), \n", - " nn.ReLU(), \n", - " nn.Dropout(p=0.5), \n", - " nn.Linear(512, 2), \n", - " nn.Dropout(p=0.5) \n", - ")\n", + "# ------------- Test model2 phase -------------\n", "\n", - "model.load_state_dict(torch.load('resnet18_model_2.pt'))\n", + "model2 =instantiate_model2()\n", + "test_loader = dataloaders['test']\n", + "model2.load_state_dict(torch.load('model2.pt'))\n", "\n", - "eval_model(model, test_loader)" + "eval_model(model2, test_loader, classes)" ] }, { @@ -2664,12 +2458,12 @@ "id": "8573d8be", "metadata": {}, "source": [ - "#### => Model2 quantisé post training" + "#### 6.2 Post Training Quantized Model2" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 52, "id": "f54f9301", "metadata": {}, "outputs": [ @@ -2679,40 +2473,28 @@ "text": [ "model: fp32 \t Size (KB): 45831.61\n", "model: int8 \t Size (KB): 45043.622\n", - "Size of model after quantization (newNet): 98.28 % of the original model size\n", - "Test Accuracy difference of ants between no quantized and quantized_model: 0 instance(s) (0.00%)\n", - "Test Accuracy difference of bees between no quantized and quantized_model: 0 instance(s) (0.00%)\n", + "Size of PTQ model2: 98.28 % of the no quantized model2 size\n", + "Test Accuracy difference of ants between model and its quantized version: 0 instance(s) (0.00%)\n", + "Test Accuracy difference of bees between model and its quantized version: 0 instance(s) (0.00%)\n", "\n", - "Test Accuracy (Overall) difference between no quantized and quantized_model: 0 instance(s) (0.00%)\n" + "Test Accuracy (Overall) difference between model and its quantized version: 0 instance(s) (0.00%)\n" ] } ], "source": [ - "model = torchvision.models.resnet18(pretrained=True)\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", - "\n", - "# Replace fc layer by a set of two layers using a \"relu\" activation function for the middle layer,\n", - "# and the \"dropout\" mechanism for both layers.\n", - "model.fc = nn.Sequential(\n", - " nn.Linear(num_ftrs, 512), \n", - " nn.ReLU(), \n", - " nn.Dropout(p=0.5), \n", - " nn.Linear(512, 2), \n", - " nn.Dropout(p=0.5) \n", - ")\n", - "\n", - "model.load_state_dict(torch.load('resnet18_model_2.pt'))\n", + "# ------------- Quantize model2 (PTQ) & compare accuracy -------------\n", + "post_training_quantized_model2 = instantiate_model2()\n", + "post_training_quantized_model2.load_state_dict(torch.load('model2.pt'))\n", "\n", - "post_training_quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n", + "post_training_quantized_model2 = torch.quantization.quantize_dynamic(post_training_quantized_model2, dtype=torch.qint8)\n", "\n", - "model_size = print_size_of_model(model, \"fp32\")\n", - "post_training_quantized_model_size = print_size_of_model(post_training_quantized_model, \"int8\")\n", - "print(\"Size of model after quantization (newNet): \",round(100 * post_training_quantized_model_size/ model_size, 2), \"% of the original model size\")\n", + "# Estimate the percentage of compression\n", + "model2_size = print_size_of_model(model2, \"fp32\")\n", + "post_training_quantized_model2_size = print_size_of_model(post_training_quantized_model2, \"int8\")\n", + "print(\"Size of PTQ model2: \",round(100 * post_training_quantized_model2_size/ model2_size, 2), \"% of the no quantized model2 size\")\n", "\n", - "compare_evaluation_model(model, post_training_quantized_model, test_loader)" + "# Compare the accuracy of the model2 to its PTQ version\n", + "compare_evaluation_model(model2, post_training_quantized_model2, classes, test_loader)" ] }, { @@ -2720,120 +2502,71 @@ "id": "db729e87", "metadata": {}, "source": [ - "#### => Model2 quantisé QAT" + "#### 6.3 Quantization Aware Training Model2" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 48, "id": "c4553729", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\Danie\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\ao\\quantization\\observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "----------\n", - "train Loss: 0.7195 Acc: 0.5246\n", - "val Loss: 0.5958 Acc: 0.6405\n", - "\n", - "Epoch 2/10\n", - "----------\n", - "train Loss: 0.7818 Acc: 0.4754\n", - "val Loss: 0.6377 Acc: 0.4771\n", - "\n", - "Epoch 3/10\n", - "----------\n", - "train Loss: 0.7714 Acc: 0.5738\n", - "val Loss: 0.2763 Acc: 0.9346\n", - "\n", - "Epoch 4/10\n", - "----------\n", - "train Loss: 0.7687 Acc: 0.6352\n", - "val Loss: 0.3743 Acc: 0.8301\n", - "\n", - "Epoch 5/10\n", - "----------\n", - "train Loss: 0.5976 Acc: 0.6639\n", - "val Loss: 0.2245 Acc: 0.9412\n", - "\n", - "Epoch 6/10\n", - "----------\n", - "train Loss: 0.5488 Acc: 0.7213\n", - "val Loss: 0.2397 Acc: 0.9281\n", - "\n", - "Epoch 7/10\n", - "----------\n", - "train Loss: 0.4806 Acc: 0.7295\n", - "val Loss: 0.2331 Acc: 0.9412\n", - "\n", - "Epoch 8/10\n", - "----------\n", - "train Loss: 0.5356 Acc: 0.6967\n", - "val Loss: 0.2250 Acc: 0.9412\n", - "\n", - "Epoch 9/10\n", - "----------\n", - "train Loss: 0.4575 Acc: 0.7459\n", - "val Loss: 0.2131 Acc: 0.9542\n", - "\n", - "Epoch 10/10\n", - "----------\n", - "train Loss: 0.5185 Acc: 0.7254\n", - "val Loss: 0.2014 Acc: 0.9608\n", - "\n", - "Training complete in 4m 55s\n", - "Best val Acc: 0.960784\n" - ] - } - ], + "outputs": [], "source": [ - "# Download a pre-trained ResNet18 model and freeze its weights\n", - "qat_model = torchvision.models.resnet18(pretrained=True)\n", - "for param in qat_model.parameters():\n", - " param.requires_grad = False\n", - " \n", - "qat_model.fc = nn.Sequential(\n", - " torch.quantization.QuantStub(), \n", - " nn.Linear(num_ftrs, 512), \n", - " nn.ReLU(), \n", - " nn.Dropout(p=0.5), \n", - " nn.Linear(512, 2), \n", - " nn.Dropout(p=0.5), \n", - " torch.quantization.DeQuantStub() \n", - ")\n", + "# Create function to instanciate the 2nd QAT fine tuned ResNet18 model\n", + "def instantiate_qat_model2():\n", "\n", - "# Appliquer QAT seulement à la dernière couche\n", - "qat_model.fc.qconfig = torch.quantization.get_default_qat_qconfig('x86') # recommended in pytorch documentation\n", - "torch.quantization.prepare_qat(qat_model.fc, inplace=True) # add fake quantization modules to simulate quantization during training\n", + " # Download a pre-trained ResNet18 model and freeze its weights\n", + " qat_model2 = torchvision.models.resnet18(pretrained=True)\n", + " for param in qat_model2.parameters():\n", + " param.requires_grad = False\n", + " \n", + " num_ftrs = qat_model2.fc.in_features\n", + " qat_model2.fc = nn.Sequential(\n", + " torch.quantization.QuantStub(), \n", + " nn.Linear(num_ftrs, 512), \n", + " nn.ReLU(), \n", + " nn.Dropout(p=0.5), \n", + " nn.Linear(512, 2), \n", + " nn.Dropout(p=0.5), \n", + " torch.quantization.DeQuantStub() \n", + " )\n", "\n", - "# Send the model to the GPU\n", - "qat_model = qat_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(qat_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", - "qat_model, epoch_time = train_model(\n", - " qat_model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n", + " # Only apply QAT to the last layer\n", + " qat_model2.fc.qconfig = torch.quantization.get_default_qat_qconfig('x86') # recommended in pytorch documentation\n", + " torch.quantization.prepare_qat(qat_model2.fc, inplace=True) # add fake quantization modules to simulate quantization during training\n", + "\n", + " return qat_model2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8a59604", + "metadata": {}, + "outputs": [], + "source": [ + "# ------------- Train qat_model2 phase -------------\n", + "\n", + "# Instantiate the qat_model2\n", + "qat_model2 = instantiate_qat_model2()\n", + "# Send the model to the GPU (CPU for my case)\n", + "qat_model2 = qat_model2.to(device)\n", + "\n", + "\n", + "criterion = nn.CrossEntropyLoss() # Binary Cross Entropy Loss (2 classes only)\n", + "optimizer_conv = optim.SGD(qat_model2.fc.parameters(), lr=0.001, momentum=0.9) # Only the parameters of the final layer are being optimized\n", + "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1) # Decay LR by a factor of 0.1 every 7 epochs\n", + "\n", + "qat_model2, epoch_time = train_model(\n", + " qat_model2, criterion, optimizer_conv, exp_lr_scheduler, classes, num_epochs=10\n", ")\n", "\n", - "torch.save(qat_model.state_dict(), 'resnet18_model_2_qat.pt')" + "torch.save(qat_model2.state_dict(), 'qat_model2.pt')" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 51, "id": "1cf47022", "metadata": {}, "outputs": [ @@ -2843,39 +2576,28 @@ "text": [ "model: fp32 \t Size (KB): 45831.61\n", "model: int8 \t Size (KB): 45053.384\n", - "Size of model after quantization (newNet): 98.3 % of the original model size\n", - "Test Accuracy difference of ants between no quantized and quantized_model: -7 instance(s) (-4.61%)\n", - "Test Accuracy difference of bees between no quantized and quantized_model: 1 instance(s) (0.62%)\n", + "Size of qat_model2 after quantization: 98.3 % of the original model2 size\n", + "Test Accuracy difference of ants between model and its quantized version: -3 instance(s) (-9.09%)\n", + "Test Accuracy difference of bees between model and its quantized version: 0 instance(s) (0.00%)\n", "\n", - "Test Accuracy (Overall) difference between no quantized and quantized_model: -6 instance(s) (-1.92%)\n" + "Test Accuracy (Overall) difference between model and its quantized version: -3 instance(s) (-4.17%)\n" ] } ], "source": [ - "qat_model = torchvision.models.resnet18(pretrained=True)\n", - " \n", - "qat_model.fc = nn.Sequential(\n", - " torch.quantization.QuantStub(), \n", - " nn.Linear(num_ftrs, 512), \n", - " nn.ReLU(), \n", - " nn.Dropout(p=0.5), \n", - " nn.Linear(512, 2), \n", - " nn.Dropout(p=0.5), \n", - " torch.quantization.DeQuantStub() \n", - ")\n", + "# -------------Compare qat_model2 accuracy to model2 -------------\n", + "qat_model2 = instantiate_qat_model2()\n", + "qat_model2.load_state_dict(torch.load('qat_model2.pt'))\n", "\n", - "qat_model.fc.qconfig = torch.quantization.get_default_qat_qconfig('x86') # recommended in pytorch documentation\n", - "torch.quantization.prepare_qat(qat_model.fc, inplace=True) # add fake quantization modules to simulate quantization during training\n", - "qat_model.load_state_dict(torch.load('resnet18_model_2_qat.pt'))\n", + "qat_model2 = torch.quantization.convert(qat_model2, inplace=True) # Qauntize qat_model2\n", "\n", - "qat_model = torch.quantization.convert(qat_model, inplace=True) # convert the qat_model to a real quantized model in int8 format by default\n", + "# Estimate the percentage of compression\n", + "model2_size = print_size_of_model(model2, \"fp32\")\n", + "qat_model2_size = print_size_of_model(qat_model2, \"int8\")\n", + "print(\"Size of qat_model2 after quantization: \",round(100 * qat_model2_size/ model2_size, 2), \"% of the original model2 size\")\n", "\n", - "model_size = print_size_of_model(model, \"fp32\")\n", - "qat_model_size = print_size_of_model(qat_model, \"int8\")\n", - "print(\"Size of model after quantization (newNet): \",round(100 * qat_model_size/ model_size, 2), \"% of the original model size\")\n", - "\n", - "\n", - "compare_evaluation_model(model, qat_model, test_loader)" + "# Compare the accuracy of the model2 to its QAT quantized version\n", + "compare_evaluation_model(model2, qat_model2, classes, test_loader)" ] }, { @@ -2902,9 +2624,7 @@ "**Commentaires**\n", "\n", "\n", - "Ce code permet de télecharger le réseau pre-entrainé ResNet18 et de figer ses poids. On remplace ensuite la dernière couche du modèle par une fully connected layer pour de la classification binaire (entre ants et bees). Les paramètres de cette dernière couche peuvent être modifiés permettant un fine tuning sur la base hymenoptera. Avant fine tuning, la dernière couche a model.fc.in_features neuronnes d'entrée et 1000 neuronnes de sortie (pour toutes les classes d'InageNet). On la modifie ici pour avoir uniquement 2 neuronnes en sortie (uniquement pour les 2 classes à discriminer)\n", - "\n", - "\n" + "This code allows you to download the pre-trained ResNet18 network and freeze its weights. It then replaces the final layer of the model with a fully connected layer for binary classification (between ants and bees). The parameters of this final layer can be modified, allowing for fine-tuning on the Hymenoptera dataset. Before fine-tuning, the final layer has model.fc.in_features input neurons and 1000 output neurons (for all ImageNet classes). Here, we modify it to have only 2 output neurons (specifically for the 2 classes to be discriminated)." ] }, {