diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb index 1a3c2bb1e615055fc35477ee398d67d05f645fc4..545cb7b4f34d42e2f4baba7c68f4da78bc34728c 100644 --- a/TD2 Deep Learning.ipynb +++ b/TD2 Deep Learning.ipynb @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 125, "id": "6e18f2fd", "metadata": {}, "outputs": [ @@ -191,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 126, "id": "462666a2", "metadata": {}, "outputs": [ @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 127, "id": "317bf070", "metadata": {}, "outputs": [ @@ -1011,7 +1011,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The accuracy of the second model is better !" + "#### Comments :\n", + "\n", + "The accuracy of the second model is better !\n", + "\n", + "We have an overall accuracy of 71% compared to 61% with the first model.\n", + "\n", + "When we compare that to the model defined in TP_1, we can conclude that those model are way better. The accuracy was arround 34% for the model using KNN and arround 20% for the model using a simple perceptron with a single hidden layer. " ] }, { @@ -1031,7 +1037,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 128, "id": "ef623c26", "metadata": {}, "outputs": [ @@ -1048,7 +1054,7 @@ "251278" ] }, - "execution_count": 12, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -1078,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 129, "id": "c4c65d4b", "metadata": {}, "outputs": [ @@ -1095,7 +1101,7 @@ "76522" ] }, - "execution_count": 13, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -1112,6 +1118,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "#### Comments:\n", + "\n", "The quantize model has 251278/76522 = 3.28 time less parameter than the original model and is 3.28 time lighter than the original model" ] }, @@ -1317,7 +1325,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The quantized_model is as efficient as the first model !" + "#### Comments:\n", + "The quantized_model is bit weaker for the bird, cat and horse category (1% difference) while it performs slightly better for the frog and truck model (1% difference). Those differences of accuracy are negligible. In the end the overall accuracy are exaclty the same for both model.\n", + "\n", + "We can conclude by saying that the the quantized_model is as efficient as the first model !" ] }, { @@ -1332,7 +1343,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Quantization aware training" + "### Quantization aware training (à faire)" ] }, { @@ -1463,7 +1474,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The study of the code is in the comment of the code" + "(comments) The study of the code is in the comments of the code" ] }, { @@ -1528,7 +1539,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "4 out of 6 are good predictions" + "#### Comments:\n", + "4 out of 6 of the new images are good predictions.\n", + "\n", + "The model had problem to recognize the tiger_cat(predicted a leopard) and the boxer(Rhodesian Ridgeback). Those errors of classification may be due to the fact that those species are really similar to each other." ] }, { @@ -1660,6 +1674,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "#### Comments :\n", + "\n", "The models work the same with the quantize version (however we note that the quantize version for that model the reduction of size is not that impressive as for the reduction in excercice 2)" ] }, @@ -1681,13 +1697,13 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 7, "id": "be2d31f5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1784,11 +1800,95 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "572d824c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/coco/project/env/lib/python3.10/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", + "/home/coco/project/env/lib/python3.10/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": [ + "/home/coco/project/env/lib/python3.10/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: 0.6199 Acc: 0.6434\n", + "val Loss: 0.4458 Acc: 0.7843\n", + "\n", + "Epoch 2/10\n", + "----------\n", + "train Loss: 0.5062 Acc: 0.7541\n", + "val Loss: 0.2676 Acc: 0.9020\n", + "\n", + "Epoch 3/10\n", + "----------\n", + "train Loss: 0.5261 Acc: 0.7910\n", + "val Loss: 0.4625 Acc: 0.8170\n", + "\n", + "Epoch 4/10\n", + "----------\n", + "train Loss: 0.4325 Acc: 0.8279\n", + "val Loss: 0.2048 Acc: 0.9346\n", + "\n", + "Epoch 5/10\n", + "----------\n", + "train Loss: 0.4354 Acc: 0.8197\n", + "val Loss: 0.2067 Acc: 0.9412\n", + "\n", + "Epoch 6/10\n", + "----------\n", + "train Loss: 0.4120 Acc: 0.7992\n", + "val Loss: 0.1780 Acc: 0.9608\n", + "\n", + "Epoch 7/10\n", + "----------\n", + "train Loss: 0.4088 Acc: 0.7992\n", + "val Loss: 0.2020 Acc: 0.9477\n", + "\n", + "Epoch 8/10\n", + "----------\n", + "train Loss: 0.4200 Acc: 0.8074\n", + "val Loss: 0.1677 Acc: 0.9542\n", + "\n", + "Epoch 9/10\n", + "----------\n", + "train Loss: 0.3774 Acc: 0.8361\n", + "val Loss: 0.1734 Acc: 0.9542\n", + "\n", + "Epoch 10/10\n", + "----------\n", + "train Loss: 0.3216 Acc: 0.8525\n", + "val Loss: 0.1778 Acc: 0.9477\n", + "\n", + "Training complete in 1m 14s\n", + "Best val Acc: 0.960784\n" + ] + } + ], "source": [ + "#Import the necessary modules\n", "import copy\n", "import os\n", "import time\n", @@ -1802,6 +1902,9 @@ "from torch.optim import lr_scheduler\n", "from torchvision import datasets, transforms\n", "\n", + "\n", + "# Basically, preparation of the data\n", + "\n", "# Data augmentation and normalization for training\n", "# Just normalization for validation\n", "data_transforms = {\n", @@ -1827,15 +1930,21 @@ " ),\n", "}\n", "\n", + "# Get the images\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", + " # datasets.ImageFolder charger des données d'image organisées dans une structure de dossiers spécifique juste en donannt le dossier ou sont chargées les catégories d'images.\n", + " x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) # data_transforms[x] prepare les images selon le type\n", " for x in [\"train\", \"val\"]\n", "}\n", "dataloaders = {\n", + " # : Spécifie la taille de chaque mini-lot. \n", + " # Ici, chaque itération du DataLoader fournira 4 échantillons.\n", + " #shuffle melange le dataset original\n", + " #num_workers 4 processus en paralèlle\n", " x: torch.utils.data.DataLoader(\n", - " image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n", + " image_datasets[x], batch_size=4, shuffle=True, num_workers=4 #shuffle le dataset original\n", " )\n", " for x in [\"train\", \"val\"]\n", "}\n", @@ -1846,14 +1955,14 @@ "# Helper function for displaying images\n", "def imshow(inp, title=None):\n", " \"\"\"Imshow for Tensor.\"\"\"\n", - " inp = inp.numpy().transpose((1, 2, 0))\n", + " inp = inp.numpy().transpose((1, 2, 0)) #[224x224x3]? a quoi sert la transpose ici ?\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", + " inp = np.clip(inp, 0, 1) # contraindre les valeurs d'un tableau NumPy (inp dans ce cas) à un certain intervalle\n", " plt.imshow(inp)\n", " if title is not None:\n", " plt.title(title)\n", @@ -1890,7 +1999,7 @@ " scheduler.step()\n", " model.train() # Set model to training mode\n", " else:\n", - " model.eval() # Set model to evaluate mode\n", + " model.eval() # Set model to evaluate mode, on se place on inférence, on teste sans entrainer\n", "\n", " running_loss = 0.0\n", " running_corrects = 0\n", @@ -1924,7 +2033,7 @@ "\n", " print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n", "\n", - " # Deep copy the model\n", + " # Deep copy the model, on garde le modele avec la meilleure accuracy\n", " if phase == \"val\" and epoch_acc > best_acc:\n", " best_acc = epoch_acc\n", " best_model_wts = copy.deepcopy(model.state_dict())\n", @@ -1942,7 +2051,7 @@ " )\n", " print(\"Best val Acc: {:4f}\".format(best_acc))\n", "\n", - " # Load best model weights\n", + " # Load best model weights, va venir ecraser le modele pour garder uniquement le modele avec les meilleurs poids qui donneront la meilleur accuracy\n", " model.load_state_dict(best_model_wts)\n", " return model, epoch_time\n", "\n", @@ -1950,12 +2059,21 @@ "# 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", + " param.requires_grad = False #PAS DE GRADIENT POUR NE PAS TOUCHER A TOUTES LES COUCHES SAUF LA DERNIERE COUCHE(définie après)\n", + " #ces paramètres resteront figés et ne seront pas modifiés lorsque on entraînere le modèle avec de nouvelles données.\n", "\n", - "# Replace the final fully connected layer\n", + "# Replace the final fully connected layer ON REMPLACE JUSTE LA DERNIERE\n", "# Parameters of newly constructed modules have requires_grad=True by default\n", + "\n", + "# Cela récupère le nombre d'entités en entrée (ou le nombre de neurones) de la dernière couche entièrement connectée (fc) du modèle.\n", + "#Cette valeur correspond au nombre de caractéristiques en sortie de la dernière \n", + "# couche convolutionnelle ou de regroupement avant la couche entièrement connectée. \n", + "# nécéssaire pour savoir combien de neurones sont nécessaires en entrée pour la nouvelle couche entièrement connectée.\n", "num_ftrs = model.fc.in_features\n", - "model.fc = nn.Linear(num_ftrs, 2)\n", + "\n", + "#Ces lignes remplacent la dernière couche entièrement connectée (fc) du modèle par une nouvelle couche linéaire (nn.Linear) qui a num_ftrs en entrée \n", + "# (c'est-à-dire le même nombre de caractéristiques que la couche précédente) et 2 en sortie. Ici deux pour notre modèle de classification binaire sur les ants et les bees\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", @@ -1966,7 +2084,27 @@ "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", + "#On rajoute une ligne pour sauvegarder le modèle (cela sera utile lors de l'inférence)\n", + "torch.save(model.state_dict(), \"ex_4_transfert_learning_model1.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comments : \n", + "In summary, these lines of code load a pre-trained ResNet-18 model and prevent all model parameters from being updated during subsequent training, thereby preserving the previously learned weights.\n", + "\n", + "A layer is added at the end of the network that will be specialized in recongnising ants and bees. This is the only layers that will be trained during the training phase (i.e. the only layer that gets their weights upddated)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Experiments" ] }, { @@ -1976,13 +2114,713 @@ "source": [ "Experiments:\n", "Study the code and the results obtained.\n", - "\n", + "(Comments: the study of the code is in the comments of the code)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Eval function on a different test set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "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", + "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." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On a telechargé des images d'abeilles et de fourmis sur Kaggle pour construire un nouveau jeu de données aux liens suivants:\n", + "\n", + "https://www.kaggle.com/datasets/saurabhshahane/ant-detection/data\n", + "\n", + "https://www.kaggle.com/datasets/jerzydziewierz/bee-vs-wasp" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "def eval_model(model, data_dir):\n", + " #on place le modèle en inférence\n", + " model.eval()\n", + "\n", + " #on prépare la donnée\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)), #resize the image to a 224x224 standard size for training\n", + " transforms.ToTensor(), #convert to a tensor \n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), #normalization x-x_mean/std_dev; stabilze the training process by bringing the input data to a similar scale across different channels.\n", + " ]\n", + " )\n", + "\n", + " \n", + " # Get the images\n", + " image_datasets_test = datasets.ImageFolder(data_dir, data_transform) # data_transform prepare les images\n", + " dataset_sizes = len(image_datasets_test)\n", + " class_names = image_datasets_test.classes\n", + "\n", + "\n", + " #initialisation de l'accuracy\n", + " running_corrects = 0\n", + " for image, label in tqdm(image_datasets_test):\n", + " pred = model(image.unsqueeze(0))\n", + " running_corrects += torch.sum(pred.argmax() == label) \n", + " \n", + " accuracy = running_corrects/dataset_sizes\n", + " return(f\"The accuracy of the model is: {accuracy*100} %\")" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/3040 [00:00<?, ?it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3040/3040 [01:30<00:00, 33.66it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "'The accuracy of the model is: 98.125 %'" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_model(model,\"ex_4_data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comments :\n", + "We can see that the model adapts to new data !" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modification of the model " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/coco/project/env/lib/python3.10/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", + "/home/coco/project/env/lib/python3.10/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": [ + "/home/coco/project/env/lib/python3.10/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: 0.7185 Acc: 0.5369\n", + "val Loss: 0.4142 Acc: 0.9412\n", + "\n", + "Epoch 2/10\n", + "----------\n", + "train Loss: 0.5729 Acc: 0.6762\n", + "val Loss: 0.2826 Acc: 0.9346\n", + "\n", + "Epoch 3/10\n", + "----------\n", + "train Loss: 0.5389 Acc: 0.7008\n", + "val Loss: 0.2887 Acc: 0.9085\n", + "\n", + "Epoch 4/10\n", + "----------\n", + "train Loss: 0.5928 Acc: 0.6680\n", + "val Loss: 0.2641 Acc: 0.9346\n", + "\n", + "Epoch 5/10\n", + "----------\n", + "train Loss: 0.6002 Acc: 0.6598\n", + "val Loss: 0.2765 Acc: 0.9346\n", + "\n", + "Epoch 6/10\n", + "----------\n", + "train Loss: 0.4815 Acc: 0.7172\n", + "val Loss: 0.2449 Acc: 0.9346\n", + "\n", + "Epoch 7/10\n", + "----------\n", + "train Loss: 0.4691 Acc: 0.7541\n", + "val Loss: 0.2206 Acc: 0.9477\n", + "\n", + "Epoch 8/10\n", + "----------\n", + "train Loss: 0.5404 Acc: 0.7172\n", + "val Loss: 0.2578 Acc: 0.9281\n", + "\n", + "Epoch 9/10\n", + "----------\n", + "train Loss: 0.4763 Acc: 0.7336\n", + "val Loss: 0.2228 Acc: 0.9542\n", + "\n", + "Epoch 10/10\n", + "----------\n", + "train Loss: 0.4962 Acc: 0.7623\n", + "val Loss: 0.2387 Acc: 0.9346\n", + "\n", + "Training complete in 1m 32s\n", + "Best val Acc: 0.954248\n" + ] + } + ], + "source": [ + "#Import the necessary modules\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 torch.optim import lr_scheduler\n", + "from torchvision import datasets, transforms\n", + "\n", + "import torch.nn.functional as F\n", + "\n", "\n", - "Now modify the code to replace the current classification layer with a set of two layers using a \"relu\" activation function for the middle layer, and the \"dropout\" mechanism for both layers. Renew the experiments and study the results obtained.\n", + "# Basically, preparation of the data\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", - "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy." + "# Get the images\n", + "data_dir = \"hymenoptera_data\"\n", + "# Create train and validation datasets and loaders\n", + "image_datasets = {\n", + " # datasets.ImageFolder charger des données d'image organisées dans une structure de dossiers spécifique juste en donannt le dossier ou sont chargées les catégories d'images.\n", + " x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) # data_transforms[x] prepare les images selon le type\n", + " for x in [\"train\", \"val\"]\n", + "}\n", + "dataloaders = {\n", + " # : Spécifie la taille de chaque mini-lot. \n", + " # Ici, chaque itération du DataLoader fournira 4 échantillons.\n", + " #shuffle melange le dataset original\n", + " #num_workers 4 processus en paralèlle\n", + " x: torch.utils.data.DataLoader(\n", + " image_datasets[x], batch_size=4, shuffle=True, num_workers=4 #shuffle le dataset original\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)) #[224x224x3]? a quoi sert la transpose ici ?\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) # contraindre les valeurs d'un tableau NumPy (inp dans ce cas) à un certain intervalle\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, on se place on inférence, on teste sans entrainer\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, on garde le modele avec la meilleure accuracy\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, va venir ecraser le modele pour garder uniquement le modele avec les meilleurs poids qui donneront la meilleur accuracy\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 #PAS DE GRADIENT POUR NE PAS TOUCHER A TOUTES LES COUCHES SAUF LA DERNIERE COUCHE(définie après)\n", + " #ces paramètres resteront figés et ne seront pas modifiés lorsque on entraînere le modèle avec de nouvelles données.\n", + "\n", + "# Replace the final fully connected layer ON REMPLACE JUSTE LA DERNIERE\n", + "# Parameters of newly constructed modules have requires_grad=True by default\n", + "\n", + "# Cela récupère le nombre d'entités en entrée (ou le nombre de neurones) de la dernière couche entièrement connectée (fc) du modèle.\n", + "#Cette valeur correspond au nombre de caractéristiques en sortie de la dernière \n", + "# couche convolutionnelle ou de regroupement avant la couche entièrement connectée. \n", + "# nécéssaire pour savoir combien de neurones sont nécessaires en entrée pour la nouvelle couche entièrement connectée.\n", + "num_ftrs = model.fc.in_features\n", + "\n", + "#Ces lignes remplacent permettent de coder le nouveau modèle qui est composé de\n", + "#deux couches utilisant:\n", + "# -une fonction d'activation \"relu\" pour la couche du milieu\n", + "# et le \"dropout\" pour les deux couches.\n", + "model.fc = nn.Sequential(\n", + " nn.Linear(num_ftrs, num_ftrs//2),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.5), # Adding dropout to prevent overfitting\n", + " nn.Linear(num_ftrs//2, 2), # Output layer for binary classification with dropout\n", + " nn.Dropout(0.5) \n", + ")\n", + "\n", + "# Send the model to the GPU\n", + "model = model.to(device)\n", + "# Set the loss function\n", + "criterion = nn.CrossEntropyLoss()\n", + "\n", + "# Observe that only the parameters of the final layer are being optimized\n", + "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n", + "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n", + "model, epoch_time = train_model(\n", + " model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n", + ")\n", + "\n", + "#On rajoute une ligne pour sauvegarder le modèle (cela sera utile lors de l'inférence)\n", + "torch.save(model.state_dict(), \"ex_4_transfert_learning_model2.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3040/3040 [01:29<00:00, 33.84it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "'The accuracy of the model is: 98.58552551269531 %'" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_model(model,\"ex_4_data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comments: \n", + "The new model is slightly better than the first one, 98.58% of accuracy vs 98.125%" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apply their quantization (post and quantization aware) and evaluate impact on model size and accuracy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Quantization of model 1 and 2 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Model 1" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<All keys matched successfully>" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Download a pre-trained ResNet18 model and freeze its weights\n", + "model1 = torchvision.models.resnet18(pretrained=True)\n", + "for param in model.parameters():\n", + " param.requires_grad = False \n", + "num_ftrs = model1.fc.in_features\n", + "model1.fc = nn.Linear(num_ftrs, 2) \n", + "\n", + "model1.load_state_dict(torch.load(\"./ex_4_transfert_learning_model1.pt\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model: fp32 \t Size (KB): 44780.42\n", + "model: int8 \t Size (KB): 44778.17\n" + ] + }, + { + "data": { + "text/plain": [ + "44778170" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch.quantization\n", + "\n", + "quantized_model1 = torch.quantization.quantize_dynamic(model1, dtype=torch.qint8)\n", + "print_size_of_model(model1, \"fp32\")\n", + "print_size_of_model(quantized_model1, \"int8\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comments:\n", + "Etrangement, le modele quantized1 n'est que très peu réduit en taille...\n", + "\n", + "Regardons les performances" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3040/3040 [01:37<00:00, 31.22it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "'The accuracy of the model is: 98.09210205078125 %'" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_model(quantized_model1,\"ex_4_data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les performance sont un peu moins bonne que le modèle original, cependant la différence est négligeable " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Modèle 2" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/coco/project/env/lib/python3.10/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", + "/home/coco/project/env/lib/python3.10/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" + ] + }, + { + "data": { + "text/plain": [ + "<All keys matched successfully>" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model2 = torchvision.models.resnet18(pretrained=True)\n", + "for param in model2.parameters():\n", + " param.requires_grad = False\n", + "num_ftrs = model2.fc.in_features\n", + "model2.fc = nn.Sequential(\n", + " nn.Linear(num_ftrs, num_ftrs//2),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.5), # Adding dropout to prevent overfitting\n", + " nn.Linear(num_ftrs//2, 2), # Output layer for binary classification with dropout\n", + " nn.Dropout(0.5) \n", + ")\n", + "model2.load_state_dict(torch.load(\"./ex_4_transfert_learning_model2.pt\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model: fp32 \t Size (KB): 45304.25\n", + "model: int8 \t Size (KB): 44911.014\n" + ] + }, + { + "data": { + "text/plain": [ + "44911014" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch.quantization\n", + "\n", + "quantized_model2 = torch.quantization.quantize_dynamic(model2, dtype=torch.qint8)\n", + "print_size_of_model(model2, \"fp32\")\n", + "print_size_of_model(quantized_model2, \"int8\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comments:\n", + "De même que pour le premiere modèle, le modèle quantized2 n'est que très peu réduit en taille...\n", + "\n", + "Regardons les performances" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3040/3040 [01:32<00:00, 32.79it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "'The accuracy of the model is: 98.5526351928711 %'" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_model(quantized_model2,\"ex_4_data\")" ] }, {