microsoft--ai-for-beginners
wehub-resource-sync
782 行
59 KiB
Plaintext
782 行
59 KiB
Plaintext
{
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"cells": [
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"metadata": {
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"id": "3AbTeDP5Tbou"
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},
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"source": [
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"# Segmentacja\n",
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"\n",
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"Już nauczyliśmy się o Wykrywaniu Obiektów, które pozwala nam lokalizować obiekty na obrazie poprzez przewidywanie ich *ramki ograniczającej* (bounding boxes). Jednak w niektórych zadaniach potrzebujemy nie tylko ramek ograniczających, ale także bardziej precyzyjnej lokalizacji obiektów. To zadanie nazywa się **segmentacją**.\n",
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"\n",
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"Segmentację można postrzegać jako **klasyfikację pikseli**, gdzie dla **każdego** piksela obrazu musimy przewidzieć jego klasę (*tło* jest jedną z klas). Istnieją dwa główne algorytmy segmentacji:\n",
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"\n",
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"* **Segmentacja semantyczna** określa jedynie klasę piksela i nie rozróżnia różnych obiektów tej samej klasy\n",
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"* **Segmentacja instancji** dzieli klasy na różne instancje.\n",
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"\n",
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"Na przykład w segmentacji instancji 10 owiec to różne obiekty, podczas gdy w segmentacji semantycznej wszystkie owce są reprezentowane przez jedną klasę.\n",
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"\n",
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"<img src=\"images/instance_vs_semantic.jpeg\" width=\"50%\">\n",
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"\n",
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"> Obraz z [tego wpisu na blogu](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50)\n",
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"\n",
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"Istnieją różne architektury sieci neuronowych do segmentacji, ale wszystkie mają tę samą strukturę:\n",
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"\n",
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"* **Enkoder** wyodrębnia cechy z obrazu wejściowego\n",
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"* **Dekoder** przekształca te cechy w **obraz maski**, o tym samym rozmiarze i liczbie kanałów odpowiadającej liczbie klas.\n",
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"\n",
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"<img src=\"images/segm.png\" width=\"80%\">\n",
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"\n",
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"> Obraz z [tej publikacji](https://arxiv.org/pdf/2001.05566.pdf)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Wymagania wstępne\n",
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"\n",
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"Na początek zaimportujemy wymagane biblioteki i sprawdzimy, czy dostępny jest GPU do treningu.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-04-08T15:48:06.861688Z",
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"id": "tv1T3XemFMpE",
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"trusted": true
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torchvision\n",
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"import matplotlib.pyplot as plt\n",
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"from torchvision import transforms\n",
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"from torch import nn\n",
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"from torch import optim\n",
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"from tqdm import tqdm\n",
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"import numpy as np\n",
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"import torch.nn.functional as F\n",
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"from skimage.io import imread\n",
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"from skimage.transform import resize\n",
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"import os\n",
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"torch.manual_seed(42)\n",
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"np.random.seed(42)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-04-08T16:03:32.933989Z",
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"trusted": true
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"outputs": [],
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"source": [
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"device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
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"train_size = 0.9\n",
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"lr = 1e-3\n",
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"weight_decay = 1e-6\n",
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"batch_size = 32\n",
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"epochs = 30"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "D4if75qwFMpJ"
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},
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"source": [
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"## Zbiór danych\n",
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"\n",
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"Wykorzystamy zbiór obrazów dermoskopowych ludzkich znamion. Ten zbiór danych zawiera 200 obrazów podzielonych na trzy klasy: typowe znamiona, atypowe znamiona oraz czerniak. Wszystkie obrazy posiadają również odpowiadające im **maski**, które wyznaczają obszar znamienia.\n",
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"\n",
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"Poniższy kod pobiera zbiór danych z oryginalnej lokalizacji i rozpakowuje go. Aby kod działał, musisz mieć zainstalowane narzędzie `unrar`. Możesz je zainstalować za pomocą `sudo apt-get install unrar` na systemie Linux lub pobierając wersję wiersza poleceń dla systemu Windows [tutaj](https://www.rarlab.com/rar_add.htm).\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-04-08T16:47:25.071036Z",
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"trusted": true
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"source": [
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"#!apt-get install rar\n",
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"!wget https://www.dropbox.com/s/k88qukc20ljnbuo/PH2Dataset.rar\n",
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"!unrar x -Y PH2Dataset.rar"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Teraz zdefiniujemy kod do wczytania zbioru danych. Przekształcimy wszystkie obrazy na rozmiar 256x256 i podzielimy zbiór danych na część treningową i testową. Ta funkcja zwraca zbiory danych treningowych i testowych, z których każdy zawiera oryginalne obrazy oraz maski zaznaczające znamię.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"id": "Rumy9ldAFteW"
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},
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"outputs": [],
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"source": [
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"def load_dataset(train_part, root='PH2Dataset'):\n",
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" images = []\n",
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" masks = []\n",
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"\n",
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" for root, dirs, files in os.walk(os.path.join(root, 'PH2 Dataset images')):\n",
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" if root.endswith('_Dermoscopic_Image'):\n",
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" images.append(imread(os.path.join(root, files[0])))\n",
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" if root.endswith('_lesion'):\n",
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" masks.append(imread(os.path.join(root, files[0])))\n",
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"\n",
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" size = (256, 256)\n",
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" images = torch.permute(torch.FloatTensor(np.array([resize(image, size, mode='constant', anti_aliasing=True,) for image in images])), (0, 3, 1, 2))\n",
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" masks = torch.FloatTensor(np.array([resize(mask, size, mode='constant', anti_aliasing=False) > 0.5 for mask in masks])).unsqueeze(1)\n",
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"\n",
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" indices = np.random.permutation(range(len(images)))\n",
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" train_part = int(train_part * len(images))\n",
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" train_ind = indices[:train_part]\n",
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" test_ind = indices[train_part:]\n",
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"\n",
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" train_dataset = (images[train_ind, :, :, :], masks[train_ind, :, :, :])\n",
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" test_dataset = (images[test_ind, :, :, :], masks[test_ind, :, :, :])\n",
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"\n",
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" return train_dataset, test_dataset\n",
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"\n",
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"train_dataset, test_dataset = load_dataset(train_size)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Teraz narysujmy kilka obrazów z zestawu danych, aby zobaczyć, jak wyglądają:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution": {
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"id": "jP2_-AjIFMpO",
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"trusted": true
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"outputs": [],
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"source": [
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"def plotn(n, data, only_mask=False):\n",
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" images, masks = data[0], data[1]\n",
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" fig, ax = plt.subplots(1, n)\n",
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" fig1, ax1 = plt.subplots(1, n)\n",
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" for i, (img, mask) in enumerate(zip(images, masks)):\n",
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" if i == n:\n",
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" break\n",
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" if not only_mask:\n",
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" ax[i].imshow(torch.permute(img, (1, 2, 0)))\n",
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" else:\n",
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" ax[i].imshow(img[0])\n",
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" ax1[i].imshow(mask[0])\n",
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" ax[i].axis('off')\n",
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" ax1[i].axis('off')\n",
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" plt.show()\n",
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"\n",
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"plotn(5, train_dataset)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Będziemy również potrzebować ładowarek danych do przekazywania danych do naszej sieci neuronowej.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"execution": {
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"trusted": true
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"outputs": [],
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"source": [
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"train_dataloader = torch.utils.data.DataLoader(list(zip(train_dataset[0], train_dataset[1])), batch_size=batch_size, shuffle=True)\n",
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"test_dataloader = torch.utils.data.DataLoader(list(zip(test_dataset[0], test_dataset[1])), batch_size=1, shuffle=False)\n",
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"dataloaders = (train_dataloader, test_dataloader)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ORmas8XhYfS8"
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},
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"source": [
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"## SegNet\n",
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"\n",
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"Najprostsza architektura typu encoder-decoder nazywa się **SegNet**. Wykorzystuje standardową CNN z konwolucjami i warstwami pooling w encoderze oraz dekonwolucyjną CNN, która zawiera konwolucje i operacje upsampling w decoderze. Dodatkowo opiera się na normalizacji wsadowej, aby skutecznie trenować wielowarstwową sieć.\n",
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"\n",
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"<img src=\"images/segnet.png\" width=\"80%\">\n",
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"\n",
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"> Obraz pochodzi z tego artykułu: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n",
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"encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n"
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]
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},
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"trusted": true
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"outputs": [],
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"source": [
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"class SegNet(nn.Module):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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" self.enc_conv0 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(3,3), padding=1)\n",
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" self.act0 = nn.ReLU()\n",
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" self.bn0 = nn.BatchNorm2d(16)\n",
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" self.pool0 = nn.MaxPool2d(kernel_size=(2,2))\n",
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"\n",
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" self.enc_conv1 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(3,3), padding=1)\n",
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" self.act1 = nn.ReLU()\n",
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" self.bn1 = nn.BatchNorm2d(32)\n",
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" self.pool1 = nn.MaxPool2d(kernel_size=(2,2))\n",
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"\n",
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" self.enc_conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3,3), padding=1)\n",
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" self.act2 = nn.ReLU()\n",
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" self.bn2 = nn.BatchNorm2d(64)\n",
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" self.pool2 = nn.MaxPool2d(kernel_size=(2,2))\n",
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"\n",
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" self.enc_conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3,3), padding=1)\n",
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" self.act3 = nn.ReLU()\n",
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" self.bn3 = nn.BatchNorm2d(128)\n",
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" self.pool3 = nn.MaxPool2d(kernel_size=(2,2))\n",
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"\n",
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" self.bottleneck_conv = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3,3), padding=1)\n",
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" \n",
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" self.upsample0 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
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" self.dec_conv0 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=(3,3), padding=1)\n",
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" self.dec_act0 = nn.ReLU()\n",
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" self.dec_bn0 = nn.BatchNorm2d(128)\n",
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"\n",
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" self.upsample1 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
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" self.dec_conv1 = nn.Conv2d(in_channels=128, out_channels=64, kernel_size=(3,3), padding=1)\n",
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" self.dec_act1 = nn.ReLU()\n",
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" self.dec_bn1 = nn.BatchNorm2d(64)\n",
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"\n",
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" self.upsample2 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
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" \n",
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" self.dec_conv2 = nn.Conv2d(in_channels=64, out_channels=32, kernel_size=(3,3), padding=1)\n",
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" self.dec_act2 = nn.ReLU()\n",
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" self.dec_bn2 = nn.BatchNorm2d(32)\n",
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"\n",
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" self.upsample3 = nn.UpsamplingBilinear2d(scale_factor=2)\n",
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" self.dec_conv3 = nn.Conv2d(in_channels=32, out_channels=1, kernel_size=(1,1))\n",
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"\n",
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" self.sigmoid = nn.Sigmoid()\n",
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"\n",
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" def forward(self, x):\n",
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" e0 = self.pool0(self.bn0(self.act0(self.enc_conv0(x))))\n",
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" e1 = self.pool1(self.bn1(self.act1(self.enc_conv1(e0))))\n",
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" e2 = self.pool2(self.bn2(self.act2(self.enc_conv2(e1))))\n",
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" e3 = self.pool3(self.bn3(self.act3(self.enc_conv3(e2))))\n",
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"\n",
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" b = self.bottleneck_conv(e3)\n",
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"\n",
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" d0 = self.dec_bn0(self.dec_act0(self.dec_conv0(self.upsample0(b))))\n",
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" d1 = self.dec_bn1(self.dec_act1(self.dec_conv1(self.upsample1(d0))))\n",
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" d2 = self.dec_bn2(self.dec_act2(self.dec_conv2(self.upsample2(d1))))\n",
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" d3 = self.sigmoid(self.dec_conv3(self.upsample3(d2)))\n",
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" return d3"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Powinniśmy szczególnie wspomnieć o funkcji straty używanej do segmentacji. W klasycznych autoenkoderach musimy zmierzyć podobieństwo między dwoma obrazami i możemy do tego użyć średniego błędu kwadratowego. W segmentacji każdy piksel w docelowym obrazie maski reprezentuje numer klasy (zakodowany w formie one-hot wzdłuż trzeciego wymiaru), więc musimy używać funkcji straty specyficznych dla klasyfikacji - straty krzyżowo-entropijnej, uśrednionej dla wszystkich pikseli. Jeśli maska jest binarna (jak w naszym przykładzie) - użyjemy **straty krzyżowo-entropijnej binarnej** (BCE).\n"
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]
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},
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{
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"cell_type": "code",
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"iopub.status.busy": "2022-04-08T16:03:59.258856Z",
|
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"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": [
|
|
"Pętla treningowa jest zdefiniowana w zwykły sposób:\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": [
|
|
"Aby ocenić nasz model, po prostu przedstawimy maski docelowe i maski przewidywane dla kilku obrazów:\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": {
|
|
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",
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"text/plain": [
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"<Figure size 432x288 with 5 Axes>"
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]
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},
|
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"metadata": {
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"needs_background": "light"
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},
|
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"output_type": "display_data"
|
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},
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{
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"data": {
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|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 5 Axes>"
|
|
]
|
|
},
|
|
"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": [
|
|
"Istnieją również formalne metryki do oceny wydajności, o których możesz przeczytać [tutaj](https://towardsdatascience.com/metrics-to-evaluate-your-semantic-segmentation-model-6bcb99639aa2). Najłatwiejszą do zrozumienia jest **dokładność pikseli** - procent pikseli sklasyfikowanych poprawnie.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "RU5KGWXaTbso"
|
|
},
|
|
"source": [
|
|
"## U-Net\n",
|
|
"\n",
|
|
"Architektura SegNet jest bardzo intuicyjna, ale nie jest najdokładniejsza. W rzeczywistości najpierw stosujemy architekturę piramidalną CNN do oryginalnego obrazu, co zmniejsza przestrzenną dokładność cech obrazu. Następnie, podczas rekonstrukcji obrazu, nie możemy poprawnie odtworzyć pozycji pikseli.\n",
|
|
"\n",
|
|
"To prowadzi nas do pomysłu **połączeń przeskakujących** między warstwami konwolucyjnymi w enkoderze i dekoderze. Ta architektura jest bardzo powszechna w segmentacji semantycznej i nazywa się **U-Net**. Połączenia przeskakujące na każdym poziomie konwolucji pomagają sieci nie tracić informacji o cechach z oryginalnego wejścia na tym poziomie.\n",
|
|
"\n",
|
|
"Tutaj użyjemy dość prostej architektury CNN, ale U-Net może również korzystać z bardziej złożonego enkodera do ekstrakcji cech, takiego jak ResNet-50.\n",
|
|
"\n",
|
|
"<img src=\"images/unet.png\" width=\"70%\">\n",
|
|
"\n",
|
|
"> Obraz z artykułu: Ronneberger, Olaf, Philipp Fischer i 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": [
|
|
"<Figure size 432x288 with 5 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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",
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"text/plain": [
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"<Figure size 432x288 with 5 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"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)"
|
|
]
|
|
},
|
|
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n---\n\n**Zastrzeżenie**: \nTen dokument został przetłumaczony za pomocą usługi tłumaczenia AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dokładamy wszelkich starań, aby tłumaczenie było precyzyjne, prosimy pamiętać, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w jego rodzimym języku powinien być uznawany za wiarygodne źródło. W przypadku informacji o kluczowym znaczeniu zaleca się skorzystanie z profesjonalnego tłumaczenia przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z użycia tego tłumaczenia.\n"
|
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]
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}
|
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],
|
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"metadata": {
|
|
"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"name": "SemanticSegmentation.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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},
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"coopTranslator": {
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"original_hash": "c0381c913f8945105ea15a54e55faaa9",
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"translation_date": "2025-08-31T13:06:23+00:00",
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"source_file": "lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb",
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"language_code": "pl"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |