{
"cells": [
{
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"metadata": {
"id": "3AbTeDP5Tbou"
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"source": [
"# Segmentation\n",
"\n",
"Nous avons déjà appris la détection d'objets, qui nous permet de localiser des objets dans une image en prédisant leurs *boîtes englobantes*. Cependant, pour certaines tâches, nous avons besoin non seulement de boîtes englobantes, mais aussi d'une localisation plus précise des objets. Cette tâche s'appelle **segmentation**.\n",
"\n",
"La segmentation peut être vue comme une **classification de pixels**, où pour **chaque** pixel de l'image, nous devons prédire sa classe (*le fond* étant l'une des classes). Il existe deux principaux algorithmes de segmentation :\n",
"\n",
"* La **segmentation sémantique** indique uniquement la classe des pixels, sans faire de distinction entre différents objets de la même classe.\n",
"* La **segmentation par instance** divise les classes en différentes instances.\n",
"\n",
"Par exemple, pour la segmentation par instance, 10 moutons sont des objets différents, tandis que pour la segmentation sémantique, tous les moutons sont représentés par une seule classe.\n",
"\n",
"![](\"images/instance_vs_semantic.jpeg\")
\n",
"\n",
"> Image tirée de [cet article de blog](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50)\n",
"\n",
"Il existe différentes architectures neuronales pour la segmentation, mais elles ont toutes la même structure :\n",
"\n",
"* **Encodeur** : extrait les caractéristiques de l'image d'entrée.\n",
"* **Décodeur** : transforme ces caractéristiques en une **image masque**, avec la même taille et un nombre de canaux correspondant au nombre de classes.\n",
"\n",
"![](\"images/segm.png\")
\n",
"\n",
"> Image tirée de [cette publication](https://arxiv.org/pdf/2001.05566.pdf)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prérequis\n",
"\n",
"Pour commencer, nous allons importer les bibliothèques nécessaires et vérifier si un GPU est disponible pour l'entraînement.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
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"source": [
"import torch\n",
"import torchvision\n",
"import matplotlib.pyplot as plt\n",
"from torchvision import transforms\n",
"from torch import nn\n",
"from torch import optim\n",
"from tqdm import tqdm\n",
"import numpy as np\n",
"import torch.nn.functional as F\n",
"from skimage.io import imread\n",
"from skimage.transform import resize\n",
"import os\n",
"torch.manual_seed(42)\n",
"np.random.seed(42)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
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"source": [
"device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
"train_size = 0.9\n",
"lr = 1e-3\n",
"weight_decay = 1e-6\n",
"batch_size = 32\n",
"epochs = 30"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D4if75qwFMpJ"
},
"source": [
"## Le jeu de données\n",
"\n",
"Nous utiliserons des images de dermoscopie de nævus humains. Ce jeu de données contient 200 images réparties en trois catégories : nævus typique, nævus atypique et mélanome. Toutes les images sont accompagnées d'un **masque** correspondant qui délimite le nævus.\n",
"\n",
"Le code ci-dessous télécharge le jeu de données depuis son emplacement d'origine et le décompresse. Vous devez avoir l'utilitaire `unrar` installé pour que ce code fonctionne. Vous pouvez l'installer avec `sudo apt-get install unrar` sur Linux, ou en téléchargeant la version en ligne de commande pour Windows [ici](https://www.rarlab.com/rar_add.htm).\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2022-04-08T16:47:25.071036Z",
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"source": [
"#!apt-get install rar\n",
"!wget https://www.dropbox.com/s/k88qukc20ljnbuo/PH2Dataset.rar\n",
"!unrar x -Y PH2Dataset.rar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Maintenant, nous allons définir le code pour charger le jeu de données. Nous transformerons toutes les images en taille 256x256, et diviserons le jeu de données en une partie d'entraînement et une partie de test. Cette fonction retourne les jeux de données d'entraînement et de test, chacun contenant les images originales et les masques délimitant le naevus.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "Rumy9ldAFteW"
},
"outputs": [],
"source": [
"def load_dataset(train_part, root='PH2Dataset'):\n",
" images = []\n",
" masks = []\n",
"\n",
" for root, dirs, files in os.walk(os.path.join(root, 'PH2 Dataset images')):\n",
" if root.endswith('_Dermoscopic_Image'):\n",
" images.append(imread(os.path.join(root, files[0])))\n",
" if root.endswith('_lesion'):\n",
" masks.append(imread(os.path.join(root, files[0])))\n",
"\n",
" size = (256, 256)\n",
" images = torch.permute(torch.FloatTensor(np.array([resize(image, size, mode='constant', anti_aliasing=True,) for image in images])), (0, 3, 1, 2))\n",
" masks = torch.FloatTensor(np.array([resize(mask, size, mode='constant', anti_aliasing=False) > 0.5 for mask in masks])).unsqueeze(1)\n",
"\n",
" indices = np.random.permutation(range(len(images)))\n",
" train_part = int(train_part * len(images))\n",
" train_ind = indices[:train_part]\n",
" test_ind = indices[train_part:]\n",
"\n",
" train_dataset = (images[train_ind, :, :, :], masks[train_ind, :, :, :])\n",
" test_dataset = (images[test_ind, :, :, :], masks[test_ind, :, :, :])\n",
"\n",
" return train_dataset, test_dataset\n",
"\n",
"train_dataset, test_dataset = load_dataset(train_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Traçons maintenant certaines des images du jeu de données pour voir à quoi elles ressemblent :\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
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"source": [
"def plotn(n, data, only_mask=False):\n",
" images, masks = data[0], data[1]\n",
" fig, ax = plt.subplots(1, n)\n",
" fig1, ax1 = plt.subplots(1, n)\n",
" for i, (img, mask) in enumerate(zip(images, masks)):\n",
" if i == n:\n",
" break\n",
" if not only_mask:\n",
" ax[i].imshow(torch.permute(img, (1, 2, 0)))\n",
" else:\n",
" ax[i].imshow(img[0])\n",
" ax1[i].imshow(mask[0])\n",
" ax[i].axis('off')\n",
" ax1[i].axis('off')\n",
" plt.show()\n",
"\n",
"plotn(5, train_dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous aurons également besoin de chargeurs de données pour alimenter les données dans notre réseau neuronal.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
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"outputs": [],
"source": [
"train_dataloader = torch.utils.data.DataLoader(list(zip(train_dataset[0], train_dataset[1])), batch_size=batch_size, shuffle=True)\n",
"test_dataloader = torch.utils.data.DataLoader(list(zip(test_dataset[0], test_dataset[1])), batch_size=1, shuffle=False)\n",
"dataloaders = (train_dataloader, test_dataloader)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ORmas8XhYfS8"
},
"source": [
"## SegNet\n",
"\n",
"L'architecture encodeur-décodeur la plus simple s'appelle **SegNet**. Elle utilise un CNN standard avec des convolutions et des poolings dans l'encodeur, et un CNN de déconvolution qui inclut des convolutions et des suréchantillonnages dans le décodeur. Elle repose également sur la normalisation par lot pour entraîner efficacement un réseau à plusieurs couches.\n",
"\n",
"![](\"images/segnet.png\")
\n",
"\n",
"> Image tirée de cet article : Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n",
"encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2022-04-08T16:03:59.239435Z",
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"source": [
"class SegNet(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.enc_conv0 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(3,3), padding=1)\n",
" self.act0 = nn.ReLU()\n",
" self.bn0 = nn.BatchNorm2d(16)\n",
" self.pool0 = nn.MaxPool2d(kernel_size=(2,2))\n",
"\n",
" self.enc_conv1 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(3,3), padding=1)\n",
" self.act1 = nn.ReLU()\n",
" self.bn1 = nn.BatchNorm2d(32)\n",
" self.pool1 = nn.MaxPool2d(kernel_size=(2,2))\n",
"\n",
" self.enc_conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3,3), padding=1)\n",
" self.act2 = nn.ReLU()\n",
" self.bn2 = nn.BatchNorm2d(64)\n",
" self.pool2 = nn.MaxPool2d(kernel_size=(2,2))\n",
"\n",
" self.enc_conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3,3), padding=1)\n",
" self.act3 = nn.ReLU()\n",
" self.bn3 = nn.BatchNorm2d(128)\n",
" self.pool3 = nn.MaxPool2d(kernel_size=(2,2))\n",
"\n",
" self.bottleneck_conv = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3,3), padding=1)\n",
" \n",
" self.upsample0 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
" self.dec_conv0 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=(3,3), padding=1)\n",
" self.dec_act0 = nn.ReLU()\n",
" self.dec_bn0 = nn.BatchNorm2d(128)\n",
"\n",
" self.upsample1 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
" self.dec_conv1 = nn.Conv2d(in_channels=128, out_channels=64, kernel_size=(3,3), padding=1)\n",
" self.dec_act1 = nn.ReLU()\n",
" self.dec_bn1 = nn.BatchNorm2d(64)\n",
"\n",
" self.upsample2 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
" \n",
" self.dec_conv2 = nn.Conv2d(in_channels=64, out_channels=32, kernel_size=(3,3), padding=1)\n",
" self.dec_act2 = nn.ReLU()\n",
" self.dec_bn2 = nn.BatchNorm2d(32)\n",
"\n",
" self.upsample3 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
" self.dec_conv3 = nn.Conv2d(in_channels=32, out_channels=1, kernel_size=(1,1))\n",
"\n",
" self.sigmoid = nn.Sigmoid()\n",
"\n",
" def forward(self, x):\n",
" e0 = self.pool0(self.bn0(self.act0(self.enc_conv0(x))))\n",
" e1 = self.pool1(self.bn1(self.act1(self.enc_conv1(e0))))\n",
" e2 = self.pool2(self.bn2(self.act2(self.enc_conv2(e1))))\n",
" e3 = self.pool3(self.bn3(self.act3(self.enc_conv3(e2))))\n",
"\n",
" b = self.bottleneck_conv(e3)\n",
"\n",
" d0 = self.dec_bn0(self.dec_act0(self.dec_conv0(self.upsample0(b))))\n",
" d1 = self.dec_bn1(self.dec_act1(self.dec_conv1(self.upsample1(d0))))\n",
" d2 = self.dec_bn2(self.dec_act2(self.dec_conv2(self.upsample2(d1))))\n",
" d3 = self.sigmoid(self.dec_conv3(self.upsample3(d2)))\n",
" return d3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous devrions particulièrement mentionner la fonction de perte utilisée pour la segmentation. Dans les autoencodeurs classiques, nous devons mesurer la similarité entre deux images, et nous pouvons utiliser l'erreur quadratique moyenne pour cela. Dans la segmentation, chaque pixel dans l'image de masque cible représente le numéro de classe (encodé en one-hot le long de la troisième dimension), donc nous devons utiliser des fonctions de perte spécifiques à la classification - la perte d'entropie croisée, moyennée sur tous les pixels. Si le masque est binaire (comme dans notre exemple) - nous utiliserons la **perte d'entropie croisée binaire** (BCE).\n"
]
},
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"cell_type": "code",
"execution_count": null,
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"source": [
"model = SegNet().to(device)\n",
"optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
"loss_fn = nn.BCEWithLogitsLoss()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
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"source": [
"def train(dataloaders, model, loss_fn, optimizer, epochs, device):\n",
" tqdm_iter = tqdm(range(epochs))\n",
" train_dataloader, test_dataloader = dataloaders[0], dataloaders[1]\n",
"\n",
" for epoch in tqdm_iter:\n",
" model.train()\n",
" train_loss = 0.0\n",
" test_loss = 0.0\n",
"\n",
" for batch in train_dataloader:\n",
" imgs, labels = batch\n",
" imgs = imgs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" preds = model(imgs)\n",
" loss = loss_fn(preds, labels)\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" train_loss += loss.item()\n",
"\n",
" model.eval()\n",
" with torch.no_grad():\n",
" for batch in test_dataloader:\n",
" imgs, labels = batch\n",
" imgs = imgs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" preds = model(imgs)\n",
" loss = loss_fn(preds, labels)\n",
"\n",
" test_loss += loss.item()\n",
"\n",
" train_loss /= len(train_dataloader)\n",
" test_loss /= len(test_dataloader)\n",
"\n",
" tqdm_dct = {'train loss:': train_loss, 'test loss:': test_loss}\n",
" tqdm_iter.set_postfix(tqdm_dct, refresh=True)\n",
" tqdm_iter.refresh()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
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{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 30/30 [16:01<00:00, 32.04s/it, train loss:=0.593, test loss:=0.577]\n"
]
}
],
"source": [
"train(dataloaders, model, loss_fn, optimizer, epochs, device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pour évaluer notre modèle, nous allons simplement tracer les masques cibles et les masques prédits pour un certain nombre d'images :\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 203
},
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{
"data": {
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",
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""
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},
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{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"model.eval()\n",
"predictions = []\n",
"image_mask = []\n",
"plots = 5\n",
"images, masks = test_dataset[0], test_dataset[1]\n",
"for i, (img, mask) in enumerate(zip(images, masks)):\n",
" if i == plots:\n",
" break\n",
" img = img.to(device).unsqueeze(0)\n",
" predictions.append((model(img).detach().cpu()[0] > 0.5).float())\n",
" image_mask.append(mask)\n",
"plotn(plots, (predictions, image_mask), only_mask=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il existe également des métriques formelles pour évaluer les performances, que vous pouvez lire [ici](https://towardsdatascience.com/metrics-to-evaluate-your-semantic-segmentation-model-6bcb99639aa2). La plus facile à comprendre est **l'exactitude des pixels** - un pourcentage de pixels classés correctement.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RU5KGWXaTbso"
},
"source": [
"## U-Net\n",
"\n",
"L'architecture SegNet est très intuitive, mais ce n'est pas la plus précise. En effet, nous appliquons d'abord une architecture CNN pyramidale à l'image originale, ce qui réduit la précision spatiale des caractéristiques de l'image. Ensuite, lors de la reconstruction de l'image, nous ne pouvons pas reconstruire correctement les positions des pixels.\n",
"\n",
"Cela nous amène à l'idée des **connexions de saut** entre les couches de convolution dans l'encodeur et le décodeur. Cette architecture est très courante pour la segmentation sémantique et s'appelle **U-Net**. Les connexions de saut à chaque niveau de convolution aident le réseau à ne pas perdre les informations sur les caractéristiques de l'entrée originale à ce niveau.\n",
"\n",
"Nous utiliserons ici une architecture CNN assez simple, mais U-Net peut également utiliser un encodeur plus complexe pour l'extraction des caractéristiques, comme ResNet-50.\n",
"\n",
"![](\"images/unet.png\")
\n",
"\n",
"> Image tirée de l'article : Ronneberger, Olaf, Philipp Fischer, et Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2022-04-08T16:17:45.665392Z",
"iopub.status.busy": "2022-04-08T16:17:45.665102Z",
"iopub.status.idle": "2022-04-08T16:17:45.691051Z",
"shell.execute_reply": "2022-04-08T16:17:45.690314Z",
"shell.execute_reply.started": "2022-04-08T16:17:45.665341Z"
},
"id": "ZLKGrI4YTbs9",
"trusted": true
},
"outputs": [],
"source": [
"class UNet(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.enc_conv0 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(3,3), padding=1)\n",
" self.act0 = nn.ReLU()\n",
" self.bn0 = nn.BatchNorm2d(16)\n",
" self.pool0 = nn.MaxPool2d(kernel_size=(2,2))\n",
"\n",
" self.enc_conv1 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(3,3), padding=1)\n",
" self.act1 = nn.ReLU()\n",
" self.bn1 = nn.BatchNorm2d(32)\n",
" self.pool1 = nn.MaxPool2d(kernel_size=(2,2))\n",
"\n",
" self.enc_conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3,3), padding=1)\n",
" self.act2 = nn.ReLU()\n",
" self.bn2 = nn.BatchNorm2d(64)\n",
" self.pool2 = nn.MaxPool2d(kernel_size=(2,2))\n",
"\n",
" self.enc_conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3,3), padding=1)\n",
" self.act3 = nn.ReLU()\n",
" self.bn3 = nn.BatchNorm2d(128)\n",
" self.pool3 = nn.MaxPool2d(kernel_size=(2,2))\n",
"\n",
" self.bottleneck_conv = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3,3), padding=1)\n",
" \n",
" self.upsample0 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
" self.dec_conv0 = nn.Conv2d(in_channels=384, out_channels=128, kernel_size=(3,3), padding=1)\n",
" self.dec_act0 = nn.ReLU()\n",
" self.dec_bn0 = nn.BatchNorm2d(128)\n",
"\n",
" self.upsample1 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
" self.dec_conv1 = nn.Conv2d(in_channels=192, out_channels=64, kernel_size=(3,3), padding=1)\n",
" self.dec_act1 = nn.ReLU()\n",
" self.dec_bn1 = nn.BatchNorm2d(64)\n",
"\n",
" self.upsample2 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
" self.dec_conv2 = nn.Conv2d(in_channels=96, out_channels=32, kernel_size=(3,3), padding=1)\n",
" self.dec_act2 = nn.ReLU()\n",
" self.dec_bn2 = nn.BatchNorm2d(32)\n",
"\n",
" self.upsample3 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
" self.dec_conv3 = nn.Conv2d(in_channels=48, out_channels=1, kernel_size=(1,1))\n",
"\n",
" self.sigmoid = nn.Sigmoid()\n",
"\n",
" def forward(self, x):\n",
" e0 = self.pool0(self.bn0(self.act0(self.enc_conv0(x))))\n",
" e1 = self.pool1(self.bn1(self.act1(self.enc_conv1(e0))))\n",
" e2 = self.pool2(self.bn2(self.act2(self.enc_conv2(e1))))\n",
" e3 = self.pool3(self.bn3(self.act3(self.enc_conv3(e2))))\n",
"\n",
" cat0 = self.bn0(self.act0(self.enc_conv0(x)))\n",
" cat1 = self.bn1(self.act1(self.enc_conv1(e0)))\n",
" cat2 = self.bn2(self.act2(self.enc_conv2(e1)))\n",
" cat3 = self.bn3(self.act3(self.enc_conv3(e2)))\n",
"\n",
" b = self.bottleneck_conv(e3)\n",
"\n",
" d0 = self.dec_bn0(self.dec_act0(self.dec_conv0(torch.cat((self.upsample0(b), cat3), dim=1))))\n",
" d1 = self.dec_bn1(self.dec_act1(self.dec_conv1(torch.cat((self.upsample1(d0), cat2), dim=1))))\n",
" d2 = self.dec_bn2(self.dec_act2(self.dec_conv2(torch.cat((self.upsample2(d1), cat1), dim=1))))\n",
" d3 = self.sigmoid(self.dec_conv3(torch.cat((self.upsample3(d2), cat0), dim=1)))\n",
" return d3"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2022-04-08T16:17:45.692880Z",
"iopub.status.busy": "2022-04-08T16:17:45.692240Z",
"iopub.status.idle": "2022-04-08T16:17:45.719635Z",
"shell.execute_reply": "2022-04-08T16:17:45.719023Z",
"shell.execute_reply.started": "2022-04-08T16:17:45.692842Z"
},
"id": "15GA_43BTbtI",
"trusted": true
},
"outputs": [],
"source": [
"model = UNet().to(device)\n",
"optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
"loss_fn = nn.BCEWithLogitsLoss()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2022-04-08T16:17:45.721443Z",
"iopub.status.busy": "2022-04-08T16:17:45.721062Z",
"iopub.status.idle": "2022-04-08T16:20:23.193420Z",
"shell.execute_reply": "2022-04-08T16:20:23.191453Z",
"shell.execute_reply.started": "2022-04-08T16:17:45.721410Z"
},
"id": "_dgiuvVVFMpr",
"outputId": "438c570f-9480-48c6-bce6-14fbdf5b2d5f",
"trusted": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 30/30 [29:07<00:00, 58.26s/it, train loss:=0.595, test loss:=0.572] \n"
]
}
],
"source": [
"train(dataloaders, model, loss_fn, optimizer, epochs, device)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 203
},
"id": "iEu5wjuMFMps",
"outputId": "cf76869c-cf86-493f-fa73-a8790d21bf20"
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"model.eval()\n",
"predictions = []\n",
"image_mask = []\n",
"plots = 5\n",
"images, masks = test_dataset[0], test_dataset[1]\n",
"for i, (img, mask) in enumerate(zip(images, masks)):\n",
" if i == plots:\n",
" break\n",
" img = img.to(device).unsqueeze(0)\n",
" predictions.append((model(img).detach().cpu()[0] > 0.5).float())\n",
" image_mask.append(mask)\n",
"plotn(plots, (predictions, image_mask), only_mask=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n---\n\n**Avertissement** : \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de recourir à une traduction professionnelle réalisée par un humain. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "SemanticSegmentation.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
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"name": "python",
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"coopTranslator": {
"original_hash": "c0381c913f8945105ea15a54e55faaa9",
"translation_date": "2025-08-31T14:47:44+00:00",
"source_file": "lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb",
"language_code": "fr"
}
},
"nbformat": 4,
"nbformat_minor": 0
}