{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "3AbTeDP5Tbou" }, "source": [ "# Segmentierung\n", "\n", "Wir haben bereits über Objekterkennung gelernt, die es uns ermöglicht, Objekte in einem Bild zu lokalisieren, indem ihre *begrenzenden Boxen* vorhergesagt werden. Für einige Aufgaben benötigen wir jedoch nicht nur begrenzende Boxen, sondern auch eine präzisere Objektlokalisierung. Diese Aufgabe nennt man **Segmentierung**.\n", "\n", "Segmentierung kann als **Pixelklassifikation** betrachtet werden, wobei für **jedes** Pixel des Bildes seine Klasse vorhergesagt werden muss (*Hintergrund* ist eine der Klassen). Es gibt zwei Hauptalgorithmen für die Segmentierung:\n", "\n", "* **Semantische Segmentierung** gibt nur die Pixelklasse an und unterscheidet nicht zwischen verschiedenen Objekten derselben Klasse.\n", "* **Instanzsegmentierung** teilt Klassen in verschiedene Instanzen auf.\n", "\n", "Bei der Instanzsegmentierung sind 10 Schafe unterschiedliche Objekte, bei der semantischen Segmentierung werden alle Schafe durch eine Klasse repräsentiert.\n", "\n", "\n", "\n", "> Bild aus [diesem Blogbeitrag](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50)\n", "\n", "Es gibt verschiedene neuronale Architekturen für die Segmentierung, aber sie haben alle die gleiche Struktur:\n", "\n", "* **Encoder** extrahiert Merkmale aus dem Eingabebild.\n", "* **Decoder** transformiert diese Merkmale in das **Maskenbild**, mit derselben Größe und einer Anzahl von Kanälen, die der Anzahl der Klassen entspricht.\n", "\n", "\n", "\n", "> Bild aus [dieser Publikation](https://arxiv.org/pdf/2001.05566.pdf)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Voraussetzungen\n", "\n", "Zunächst importieren wir die benötigten Bibliotheken und überprüfen, ob eine GPU für das Training verfügbar ist.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2022-04-08T15:48:06.861688Z", "iopub.status.busy": "2022-04-08T15:48:06.861254Z", "iopub.status.idle": "2022-04-08T15:48:06.868219Z", "shell.execute_reply": "2022-04-08T15:48:06.867567Z", "shell.execute_reply.started": "2022-04-08T15:48:06.861644Z" }, "id": "tv1T3XemFMpE", "trusted": true }, "outputs": [], "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": { "iopub.execute_input": "2022-04-08T16:03:32.933989Z", "iopub.status.busy": "2022-04-08T16:03:32.933733Z", "iopub.status.idle": "2022-04-08T16:03:32.937996Z", "shell.execute_reply": "2022-04-08T16:03:32.937366Z", "shell.execute_reply.started": "2022-04-08T16:03:32.933959Z" }, "id": "xhSEogpDFMpK", "trusted": true }, "outputs": [], "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": [ "## Der Datensatz\n", "\n", "Wir verwenden Dermoskopie-Bilder von menschlichen Nävi. Dieser Datensatz enthält 200 Bilder, die in drei Klassen unterteilt sind: typischer Nävus, atypischer Nävus und Melanom. Alle Bilder enthalten außerdem entsprechende **Masken**, die den Nävus umreißen.\n", "\n", "Der folgende Code lädt den Datensatz von der Originalquelle herunter und entpackt ihn. Damit dieser Code funktioniert, muss das `unrar`-Tool installiert sein. Sie können es unter Linux mit `sudo apt-get install unrar` installieren oder die Kommandozeilen-Version für Windows [hier](https://www.rarlab.com/rar_add.htm) herunterladen.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-04-08T16:47:25.071036Z", "iopub.status.busy": "2022-04-08T16:47:25.070746Z", "iopub.status.idle": "2022-04-08T16:47:32.612115Z", "shell.execute_reply": "2022-04-08T16:47:32.611253Z", "shell.execute_reply.started": "2022-04-08T16:47:25.071005Z" }, "id": "TkuYGLRKFMpK", "trusted": true }, "outputs": [], "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": [ "Nun werden wir den Code definieren, um den Datensatz zu laden. Wir werden alle Bilder in die Größe 256x256 umwandeln und den Datensatz in einen Trainings- und einen Testteil aufteilen. Diese Funktion gibt Trainings- und Testdatensätze zurück, die jeweils die Originalbilder und Masken enthalten, die das Muttermal umreißen.\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": [ "Lassen Sie uns nun einige der Bilder aus dem Datensatz plotten, um zu sehen, wie sie aussehen:\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2022-04-08T16:03:58.259127Z", "iopub.status.busy": "2022-04-08T16:03:58.258819Z", "iopub.status.idle": "2022-04-08T16:03:58.266433Z", "shell.execute_reply": "2022-04-08T16:03:58.265489Z", "shell.execute_reply.started": "2022-04-08T16:03:58.259090Z" }, "id": "jP2_-AjIFMpO", "trusted": true }, "outputs": [], "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": [ "Wir werden auch Dataloader benötigen, um die Daten in unser neuronales Netzwerk einzuspeisen.\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2022-04-08T16:03:58.247264Z", "iopub.status.busy": "2022-04-08T16:03:58.246853Z", "iopub.status.idle": "2022-04-08T16:03:58.256661Z", "shell.execute_reply": "2022-04-08T16:03:58.255964Z", "shell.execute_reply.started": "2022-04-08T16:03:58.247222Z" }, "id": "rUGDAa61FMpN", "trusted": true }, "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", "Die einfachste Encoder-Decoder-Architektur wird **SegNet** genannt. Sie verwendet ein standardmäßiges CNN mit Convolutions und Poolings im Encoder sowie ein Deconvolution-CNN, das Convolutions und Upsamplings im Decoder umfasst. Außerdem setzt sie auf Batch-Normalisierung, um ein erfolgreiches Training des mehrschichtigen Netzwerks zu ermöglichen.\n", "\n", "\n", "\n", "> Bild aus diesem Paper: 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", "iopub.status.busy": "2022-04-08T16:03:59.239186Z", "iopub.status.idle": "2022-04-08T16:03:59.257319Z", "shell.execute_reply": "2022-04-08T16:03:59.256388Z", "shell.execute_reply.started": "2022-04-08T16:03:59.239401Z" }, "id": "QDsSrmbeTbp9", "trusted": true }, "outputs": [], "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": [ "Wir sollten insbesondere die Verlustfunktion erwähnen, die für die Segmentierung verwendet wird. In klassischen Autoencodern müssen wir die Ähnlichkeit zwischen zwei Bildern messen, und dafür können wir den mittleren quadratischen Fehler verwenden. Bei der Segmentierung repräsentiert jedes Pixel im Zielmaskenbild die Klassennummer (one-hot-codiert entlang der dritten Dimension), daher müssen wir verlustspezifische Funktionen für die Klassifikation verwenden - Kreuzentropieverlust, gemittelt über alle Pixel. Wenn die Maske binär ist (wie in unserem Beispiel) - verwenden wir den **binären Kreuzentropieverlust** (BCE).\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2022-04-08T16:03:59.259154Z", "iopub.status.busy": "2022-04-08T16:03:59.258856Z", "iopub.status.idle": "2022-04-08T16:03:59.284201Z", "shell.execute_reply": "2022-04-08T16:03:59.283559Z", "shell.execute_reply.started": "2022-04-08T16:03:59.259108Z" }, "id": "2GoR8-huFMpn", "trusted": true }, "outputs": [], "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": [ "Der Trainingsloop wird auf die übliche Weise definiert:\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2022-04-08T16:03:59.286008Z", "iopub.status.busy": "2022-04-08T16:03:59.285826Z", "iopub.status.idle": "2022-04-08T16:03:59.298404Z", "shell.execute_reply": "2022-04-08T16:03:59.297656Z", "shell.execute_reply.started": "2022-04-08T16:03:59.285986Z" }, "id": "HQ_UFglyTbqM", "trusted": true }, "outputs": [], "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/" }, "execution": { "iopub.execute_input": "2022-04-08T16:03:59.302207Z", "iopub.status.busy": "2022-04-08T16:03:59.301979Z", "iopub.status.idle": "2022-04-08T16:17:44.792683Z", "shell.execute_reply": "2022-04-08T16:17:44.791975Z", "shell.execute_reply.started": "2022-04-08T16:03:59.302184Z" }, "id": "MsgM_kZRFMpo", "outputId": "8ff2de4a-ad8d-4b57-bdeb-ecaa90f742e2", "trusted": true }, "outputs": [ { "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": [ "Um unser Modell zu bewerten, werden wir einfach Zielmasken und vorhergesagte Masken für eine Anzahl von Bildern plotten:\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 203 }, "execution": { "iopub.execute_input": "2022-04-08T16:17:44.795590Z", "iopub.status.busy": "2022-04-08T16:17:44.794942Z", "iopub.status.idle": "2022-04-08T16:17:45.663916Z", "shell.execute_reply": "2022-04-08T16:17:45.663160Z", "shell.execute_reply.started": "2022-04-08T16:17:44.795550Z" }, "id": "FXvR7P1FFMpo", "outputId": "16b1f56d-93e0-48a0-d0c2-a8214b537741", "trusted": true }, "outputs": [ { "data": { "image/png": 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", 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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": [ "Es gibt auch einige formale Metriken zur Bewertung der Leistung, über die Sie [hier](https://towardsdatascience.com/metrics-to-evaluate-your-semantic-segmentation-model-6bcb99639aa2) lesen können. Die einfachste zu verstehende ist die **Pixelgenauigkeit** - ein Prozentsatz der korrekt klassifizierten Pixel.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "RU5KGWXaTbso" }, "source": [ "## U-Net\n", "\n", "Die SegNet-Architektur ist sehr intuitiv, aber nicht die genaueste. Tatsächlich wenden wir zunächst eine pyramidale CNN-Architektur auf das Originalbild an, was die räumliche Genauigkeit der Bildmerkmale verringert. Wenn wir dann das Bild rekonstruieren, können wir die Pixelpositionen nicht korrekt wiederherstellen.\n", "\n", "Dies führt uns zur Idee der **Skip-Verbindungen** zwischen den Convolution-Schichten im Encoder und Decoder. Diese Architektur ist sehr verbreitet in der semantischen Segmentierung und wird als **U-Net** bezeichnet. Skip-Verbindungen auf jeder Convolution-Ebene helfen dem Netzwerk, keine Informationen über Merkmale aus dem ursprünglichen Input auf dieser Ebene zu verlieren.\n", "\n", "Wir werden hier eine recht einfache CNN-Architektur verwenden, aber U-Net kann auch komplexere Encoder für die Merkmalsextraktion nutzen, wie zum Beispiel ResNet-50.\n", "\n", "\n", "\n", "> Bild aus der Publikation: Ronneberger, Olaf, Philipp Fischer und 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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", 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", 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" ] }, "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**Haftungsausschluss**: \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, beachten Sie bitte, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben.\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 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "coopTranslator": { "original_hash": "c0381c913f8945105ea15a54e55faaa9", "translation_date": "2025-08-31T16:16:00+00:00", "source_file": "lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb", "language_code": "de" } }, "nbformat": 4, "nbformat_minor": 0 }