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Deep_Learning_with_PyTorch_ImageSegmentation.ipynb
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "view-in-github", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"<a href=\"https://colab.research.google.com/gist/liutiming/e1e6b75ab26bd0d6b7e9b5a20f94889a/deep_learning_with_pytorch_imagesegmentation-1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "RK_xW9gR6bta", | |
"metadata": { | |
"id": "RK_xW9gR6bta" | |
}, | |
"source": [ | |
"# Task 1 : Set up colab gpu runtime environment" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "Trj2m5ohqPY8", | |
"metadata": { | |
"id": "Trj2m5ohqPY8", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "50c93a62-9592-44a0-9b75-a03051c5f025" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
"Collecting segmentation-models-pytorch\n", | |
" Downloading segmentation_models_pytorch-0.3.0-py3-none-any.whl (97 kB)\n", | |
"\u001b[K |████████████████████████████████| 97 kB 3.9 MB/s \n", | |
"\u001b[?25hRequirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from segmentation-models-pytorch) (7.1.2)\n", | |
"Collecting timm==0.4.12\n", | |
" Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n", | |
"\u001b[K |████████████████████████████████| 376 kB 25.5 MB/s \n", | |
"\u001b[?25hCollecting pretrainedmodels==0.7.4\n", | |
" Downloading pretrainedmodels-0.7.4.tar.gz (58 kB)\n", | |
"\u001b[K |████████████████████████████████| 58 kB 6.7 MB/s \n", | |
"\u001b[?25hCollecting efficientnet-pytorch==0.7.1\n", | |
" Downloading efficientnet_pytorch-0.7.1.tar.gz (21 kB)\n", | |
"Requirement already satisfied: torchvision>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from segmentation-models-pytorch) (0.13.1+cu113)\n", | |
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from segmentation-models-pytorch) (4.64.1)\n", | |
"Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from efficientnet-pytorch==0.7.1->segmentation-models-pytorch) (1.12.1+cu113)\n", | |
"Collecting munch\n", | |
" Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n", | |
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet-pytorch==0.7.1->segmentation-models-pytorch) (4.1.1)\n", | |
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.5.0->segmentation-models-pytorch) (2.23.0)\n", | |
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.5.0->segmentation-models-pytorch) (1.21.6)\n", | |
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation-models-pytorch) (1.15.0)\n", | |
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision>=0.5.0->segmentation-models-pytorch) (1.24.3)\n", | |
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision>=0.5.0->segmentation-models-pytorch) (3.0.4)\n", | |
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision>=0.5.0->segmentation-models-pytorch) (2.10)\n", | |
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision>=0.5.0->segmentation-models-pytorch) (2022.6.15)\n", | |
"Building wheels for collected packages: efficientnet-pytorch, pretrainedmodels\n", | |
" Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
" Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.7.1-py3-none-any.whl size=16446 sha256=b43a53d5674110862a6bc0f3d3c0cc8f775a0e350c579fba86e75b72f9545d9d\n", | |
" Stored in directory: /root/.cache/pip/wheels/0e/cc/b2/49e74588263573ff778da58cc99b9c6349b496636a7e165be6\n", | |
" Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
" Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-py3-none-any.whl size=60965 sha256=6fe9c9cfb5d501f5b8b8322a21c7d71b57f01d0ea033106da7d3c3347db085f9\n", | |
" Stored in directory: /root/.cache/pip/wheels/ed/27/e8/9543d42de2740d3544db96aefef63bda3f2c1761b3334f4873\n", | |
"Successfully built efficientnet-pytorch pretrainedmodels\n", | |
"Installing collected packages: munch, timm, pretrainedmodels, efficientnet-pytorch, segmentation-models-pytorch\n", | |
"Successfully installed efficientnet-pytorch-0.7.1 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.3.0 timm-0.4.12\n", | |
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
"Collecting git+https://github.com/albumentations-team/albumentations\n", | |
" Cloning https://github.com/albumentations-team/albumentations to /tmp/pip-req-build-chm_85s4\n", | |
" Running command git clone -q https://github.com/albumentations-team/albumentations /tmp/pip-req-build-chm_85s4\n", | |
"Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.1) (1.21.6)\n", | |
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.1) (1.7.3)\n", | |
"Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.1) (0.18.3)\n", | |
"Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.1) (6.0)\n", | |
"Requirement already satisfied: qudida>=0.0.4 in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.1) (0.0.4)\n", | |
"Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.1) (4.6.0.66)\n", | |
"Requirement already satisfied: opencv-python-headless>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from qudida>=0.0.4->albumentations==1.2.1) (4.6.0.66)\n", | |
"Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from qudida>=0.0.4->albumentations==1.2.1) (1.0.2)\n", | |
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from qudida>=0.0.4->albumentations==1.2.1) (4.1.1)\n", | |
"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==1.2.1) (7.1.2)\n", | |
"Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==1.2.1) (2021.11.2)\n", | |
"Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==1.2.1) (2.6.3)\n", | |
"Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==1.2.1) (1.3.0)\n", | |
"Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==1.2.1) (3.2.2)\n", | |
"Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations==1.2.1) (2.9.0)\n", | |
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==1.2.1) (3.0.9)\n", | |
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==1.2.1) (1.4.4)\n", | |
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==1.2.1) (0.11.0)\n", | |
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==1.2.1) (2.8.2)\n", | |
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations==1.2.1) (1.15.0)\n", | |
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.2.1) (3.1.0)\n", | |
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.2.1) (1.1.0)\n", | |
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
"Requirement already satisfied: opencv-contrib-python in /usr/local/lib/python3.7/dist-packages (4.6.0.66)\n", | |
"Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-contrib-python) (1.21.6)\n" | |
] | |
} | |
], | |
"source": [ | |
"!pip install segmentation-models-pytorch\n", | |
"!pip install -U git+https://github.com/albumentations-team/albumentations\n", | |
"!pip install --upgrade opencv-contrib-python" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "7kVJz-wpTERY", | |
"metadata": { | |
"id": "7kVJz-wpTERY" | |
}, | |
"source": [ | |
"# Download Dataset\n", | |
"\n", | |
"original author of the dataset :\n", | |
"https://github.com/VikramShenoy97/Human-Segmentation-Dataset\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "jKFIZ8IlTDgT", | |
"metadata": { | |
"id": "jKFIZ8IlTDgT", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "732c8602-bf4d-44ba-eea4-a712801f3874" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"Cloning into 'Human-Segmentation-Dataset-master'...\n", | |
"remote: Enumerating objects: 592, done.\u001b[K\n", | |
"remote: Counting objects: 100% (592/592), done.\u001b[K\n", | |
"remote: Compressing objects: 100% (591/591), done.\u001b[K\n", | |
"remote: Total 592 (delta 3), reused 587 (delta 1), pack-reused 0\u001b[K\n", | |
"Receiving objects: 100% (592/592), 13.60 MiB | 28.54 MiB/s, done.\n", | |
"Resolving deltas: 100% (3/3), done.\n" | |
] | |
} | |
], | |
"source": [ | |
"!git clone https://github.com/parth1620/Human-Segmentation-Dataset-master.git" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "NXcstKkYxaEm", | |
"metadata": { | |
"id": "NXcstKkYxaEm" | |
}, | |
"source": [ | |
"" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "compliant-gossip", | |
"metadata": { | |
"id": "compliant-gossip" | |
}, | |
"source": [ | |
"# Some Common Imports" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "0F7rW6SNTn5r", | |
"metadata": { | |
"id": "0F7rW6SNTn5r" | |
}, | |
"outputs": [], | |
"source": [ | |
"import sys\n", | |
"sys.path.append('Human-Segmentation-Dataset-master')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "unlikely-winner", | |
"metadata": { | |
"id": "unlikely-winner" | |
}, | |
"outputs": [], | |
"source": [ | |
"import torch \n", | |
"import cv2\n", | |
"\n", | |
"import numpy as np \n", | |
"import pandas as pd\n", | |
"import matplotlib.pyplot as plt \n", | |
"\n", | |
"from sklearn.model_selection import train_test_split\n", | |
"from tqdm import tqdm\n", | |
"\n", | |
"import helper" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "61ff586d-4005-4ac9-9eeb-f53904a7f0e7", | |
"metadata": { | |
"id": "61ff586d-4005-4ac9-9eeb-f53904a7f0e7" | |
}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "moved-bottle", | |
"metadata": { | |
"id": "moved-bottle" | |
}, | |
"source": [ | |
"# Task : 2 Setup Configurations" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "interim-grant", | |
"metadata": { | |
"id": "interim-grant" | |
}, | |
"outputs": [], | |
"source": [ | |
"CSV_FILE = \"/content/Human-Segmentation-Dataset-master/train.csv\"\n", | |
"DATA_DIR = \"/content/\"\n", | |
"\n", | |
"DEVICE = \"cuda\"\n", | |
"\n", | |
"EPOCHS = 25\n", | |
"LR = 0.003\n", | |
"IMG_SIZE = 320\n", | |
"BATCH_SIZE = 4\n", | |
"\n", | |
"ENCODER = \"timm-efficientnet-b0\"\n", | |
"WEIGHTS = \"imagenet\"" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "cooked-stranger", | |
"metadata": { | |
"id": "cooked-stranger" | |
}, | |
"outputs": [], | |
"source": [ | |
"df = pd.read_csv(CSV_FILE)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "anonymous-conducting", | |
"metadata": { | |
"id": "anonymous-conducting" | |
}, | |
"outputs": [], | |
"source": [ | |
"row = df.iloc[4]\n", | |
"\n", | |
"image_path = row.images\n", | |
"mask_path = row.masks\n", | |
"\n", | |
"image = cv2.imread(image_path)\n", | |
"image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", | |
"\n", | |
"mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) / 255.0" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "superb-belle", | |
"metadata": { | |
"id": "superb-belle", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 251 | |
}, | |
"outputId": "fc4c12ed-cda3-4268-f83f-4eeb0838c956" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"<matplotlib.image.AxesImage at 0x7f9dc806a610>" | |
] | |
}, | |
"metadata": {}, | |
"execution_count": 11 | |
}, | |
{ | |
"output_type": "display_data", | |
"data": { | |
"text/plain": [ | |
"<Figure size 720x360 with 2 Axes>" | |
], | |
"image/png": 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\n" | |
}, | |
"metadata": { | |
"needs_background": "light" | |
} | |
} | |
], | |
"source": [ | |
"f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n", | |
" \n", | |
"ax1.set_title('IMAGE')\n", | |
"ax1.imshow(image)\n", | |
"\n", | |
"ax2.set_title('GROUND TRUTH')\n", | |
"ax2.imshow(mask,cmap = 'gray')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "fabulous-peripheral", | |
"metadata": { | |
"id": "fabulous-peripheral" | |
}, | |
"outputs": [], | |
"source": [ | |
"train_df, valid_df = train_test_split(df, test_size = 0.2, random_state = 42)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "fancy-knock", | |
"metadata": { | |
"id": "fancy-knock" | |
}, | |
"source": [ | |
"# Task 3 : Augmentation Functions" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "further-vietnamese", | |
"metadata": { | |
"id": "further-vietnamese" | |
}, | |
"source": [ | |
"albumentation documentation : https://albumentations.ai/docs/" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "rocky-pavilion", | |
"metadata": { | |
"id": "rocky-pavilion" | |
}, | |
"outputs": [], | |
"source": [ | |
"import albumentations as A\n", | |
"\n", | |
"def get_train_augs():\n", | |
" return A.Compose([\n", | |
" A.Resize(IMG_SIZE, IMG_SIZE), \n", | |
" A.HorizontalFlip(p = 0.5),\n", | |
" A.VerticalFlip(p = 0.5)\n", | |
" ])\n", | |
"\n", | |
"\n", | |
"def get_valid_augs():\n", | |
" return A.Compose([\n", | |
" A.Resize(IMG_SIZE, IMG_SIZE), \n", | |
" ])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "possible-cinema", | |
"metadata": { | |
"id": "possible-cinema" | |
}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "ceramic-closer", | |
"metadata": { | |
"id": "ceramic-closer" | |
}, | |
"source": [ | |
"# Task 4 : Create Custom Dataset " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "anonymous-wagon", | |
"metadata": { | |
"id": "anonymous-wagon" | |
}, | |
"outputs": [], | |
"source": [ | |
"from torch.utils.data import Dataset" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "rubber-humanitarian", | |
"metadata": { | |
"id": "rubber-humanitarian" | |
}, | |
"outputs": [], | |
"source": [ | |
"class SegmentationDataset(Dataset):\n", | |
" def __init__(self, df, augmentations):\n", | |
" self.df = df\n", | |
" self.augmentations = augmentations \n", | |
" \n", | |
" def __len__(self):\n", | |
" return len(self.df)\n", | |
" \n", | |
" def __getitem__(self, idx):\n", | |
" row = self.df.iloc[idx]\n", | |
" \n", | |
" image_path = row.images \n", | |
" mask_path = row.masks\n", | |
" \n", | |
" image = cv2.imread(image_path)\n", | |
" image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", | |
"\n", | |
" mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) \n", | |
" mask = np.expand_dims(mask, axis = -1)\n", | |
" \n", | |
" if self.augmentations:\n", | |
" data = self.augmentations(image = image, mask = mask)\n", | |
" image = data[\"image\"]\n", | |
" mask = data[\"mask\"]\n", | |
" \n", | |
" image = np.transpose(image, (2, 0, 1)).astype(np.float32)\n", | |
" mask = np.transpose(mask, (2, 0, 1)).astype(np.float32)\n", | |
" \n", | |
" image = torch.Tensor(image)/255.0\n", | |
" mask = torch.round(torch.Tensor(mask)/255.0)\n", | |
" \n", | |
" return image, mask" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "looking-lightning", | |
"metadata": { | |
"id": "looking-lightning" | |
}, | |
"outputs": [], | |
"source": [ | |
"trainset = SegmentationDataset(train_df, get_train_augs())\n", | |
"validset = SegmentationDataset(valid_df, get_valid_augs())" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "gothic-extreme", | |
"metadata": { | |
"id": "gothic-extreme", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "a953a871-dee9-4784-9980-c6b3293e31f3" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"Size of Trainset : 232\n", | |
"Size of Validset : 58\n" | |
] | |
}, | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"<__main__.SegmentationDataset at 0x7f9dbc2151d0>" | |
] | |
}, | |
"metadata": {}, | |
"execution_count": 17 | |
} | |
], | |
"source": [ | |
"print(f\"Size of Trainset : {len(trainset)}\")\n", | |
"print(f\"Size of Validset : {len(validset)}\")\n", | |
"\n", | |
"trainset" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "aging-being", | |
"metadata": { | |
"id": "aging-being", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 318 | |
}, | |
"outputId": "84ecfa52-c596-45d0-ad06-3a85d1159075" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"text/plain": [ | |
"<Figure size 720x360 with 2 Axes>" | |
], | |
"image/png": 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\n" | |
}, | |
"metadata": { | |
"needs_background": "light" | |
} | |
} | |
], | |
"source": [ | |
"image, mask = trainset[24]\n", | |
"helper.show_image(image, mask)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "impossible-literature", | |
"metadata": { | |
"id": "impossible-literature" | |
}, | |
"source": [ | |
"# Task 5 : Load dataset into batches" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "honey-paraguay", | |
"metadata": { | |
"id": "honey-paraguay" | |
}, | |
"outputs": [], | |
"source": [ | |
"from torch.utils.data import DataLoader" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "lesbian-terror", | |
"metadata": { | |
"id": "lesbian-terror" | |
}, | |
"outputs": [], | |
"source": [ | |
"trainloader = DataLoader(trainset, batch_size = BATCH_SIZE, shuffle = True)\n", | |
"validloader = DataLoader(validset, batch_size = BATCH_SIZE)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "saved-blend", | |
"metadata": { | |
"id": "saved-blend", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "c36f122a-0ee2-4e3a-a880-53b7111b93e5" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"15" | |
] | |
}, | |
"metadata": {}, | |
"execution_count": 21 | |
} | |
], | |
"source": [ | |
"len(trainloader)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "heard-nightmare", | |
"metadata": { | |
"id": "heard-nightmare" | |
}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "fiscal-genome", | |
"metadata": { | |
"id": "fiscal-genome" | |
}, | |
"source": [ | |
"# Task 6 : Create Segmentation Model" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "opening-benefit", | |
"metadata": { | |
"id": "opening-benefit" | |
}, | |
"source": [ | |
"segmentation_models_pytorch documentation : https://smp.readthedocs.io/en/latest/" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "protected-emphasis", | |
"metadata": { | |
"id": "protected-emphasis" | |
}, | |
"outputs": [], | |
"source": [ | |
"from torch import nn\n", | |
"import segmentation_models_pytorch as smp\n", | |
"from segmentation_models_pytorch.losses import DiceLoss" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "stopped-treaty", | |
"metadata": { | |
"id": "stopped-treaty" | |
}, | |
"outputs": [], | |
"source": [ | |
"class SegmentationModel(nn.Module):\n", | |
" def __init__(self):\n", | |
" super(SegmentationModel, self).__init__()\n", | |
" \n", | |
" self.arc = smp.Unet(\n", | |
" encoder_name = ENCODER,\n", | |
" encoder_weights = WEIGHTS, \n", | |
" in_channels = 3, \n", | |
" classes = 1,\n", | |
" activation = None\n", | |
" )\n", | |
" \n", | |
" def forward(self, images, masks = None):\n", | |
" logits = self.arc(images)\n", | |
" \n", | |
" if masks != None:\n", | |
" loss1 = DiceLoss(mode = \"binary\")(logits, masks)\n", | |
" loss2 = nn.BCEWithLogitsLoss()(logits, masks)\n", | |
" return logits, loss1 + loss2\n", | |
" \n", | |
" return logits" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "christian-settlement", | |
"metadata": { | |
"id": "christian-settlement", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "7f65f55e-017c-401c-fd0e-c4900ee23fb5" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"SegmentationModel(\n", | |
" (arc): Unet(\n", | |
" (encoder): EfficientNetEncoder(\n", | |
" (conv_stem): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (blocks): Sequential(\n", | |
" (0): Sequential(\n", | |
" (0): DepthwiseSeparableConv(\n", | |
" (conv_dw): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n", | |
" (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(8, 32, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pw): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Identity()\n", | |
" )\n", | |
" )\n", | |
" (1): Sequential(\n", | |
" (0): InvertedResidual(\n", | |
" (conv_pw): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n", | |
" (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(96, 4, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(4, 96, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" (1): InvertedResidual(\n", | |
" (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n", | |
" (bn2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" )\n", | |
" (2): Sequential(\n", | |
" (0): InvertedResidual(\n", | |
" (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(144, 144, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=144, bias=False)\n", | |
" (bn2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" (1): InvertedResidual(\n", | |
" (conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)\n", | |
" (bn2): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" )\n", | |
" (3): Sequential(\n", | |
" (0): InvertedResidual(\n", | |
" (conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False)\n", | |
" (bn2): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" (1): InvertedResidual(\n", | |
" (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n", | |
" (bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" (2): InvertedResidual(\n", | |
" (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n", | |
" (bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" )\n", | |
" (4): Sequential(\n", | |
" (0): InvertedResidual(\n", | |
" (conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(480, 480, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=480, bias=False)\n", | |
" (bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" (1): InvertedResidual(\n", | |
" (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n", | |
" (bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" (2): InvertedResidual(\n", | |
" (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n", | |
" (bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" )\n", | |
" (5): Sequential(\n", | |
" (0): InvertedResidual(\n", | |
" (conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False)\n", | |
" (bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" (1): InvertedResidual(\n", | |
" (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n", | |
" (bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" (2): InvertedResidual(\n", | |
" (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n", | |
" (bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" (3): InvertedResidual(\n", | |
" (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n", | |
" (bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" )\n", | |
" (6): Sequential(\n", | |
" (0): InvertedResidual(\n", | |
" (conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act1): Swish()\n", | |
" (conv_dw): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)\n", | |
" (bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (se): SqueezeExcite(\n", | |
" (conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (act1): Swish()\n", | |
" (conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n", | |
" (gate): Sigmoid()\n", | |
" )\n", | |
" (conv_pwl): Conv2d(1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" )\n", | |
" )\n", | |
" )\n", | |
" (conv_head): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
" (bn2): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (act2): Swish()\n", | |
" (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n", | |
" )\n", | |
" (decoder): UnetDecoder(\n", | |
" (center): Identity()\n", | |
" (blocks): ModuleList(\n", | |
" (0): DecoderBlock(\n", | |
" (conv1): Conv2dReLU(\n", | |
" (0): Conv2d(432, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention1): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" (conv2): Conv2dReLU(\n", | |
" (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention2): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" )\n", | |
" (1): DecoderBlock(\n", | |
" (conv1): Conv2dReLU(\n", | |
" (0): Conv2d(296, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention1): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" (conv2): Conv2dReLU(\n", | |
" (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention2): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" )\n", | |
" (2): DecoderBlock(\n", | |
" (conv1): Conv2dReLU(\n", | |
" (0): Conv2d(152, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention1): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" (conv2): Conv2dReLU(\n", | |
" (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention2): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" )\n", | |
" (3): DecoderBlock(\n", | |
" (conv1): Conv2dReLU(\n", | |
" (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention1): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" (conv2): Conv2dReLU(\n", | |
" (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention2): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" )\n", | |
" (4): DecoderBlock(\n", | |
" (conv1): Conv2dReLU(\n", | |
" (0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention1): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" (conv2): Conv2dReLU(\n", | |
" (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
" (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" )\n", | |
" (attention2): Attention(\n", | |
" (attention): Identity()\n", | |
" )\n", | |
" )\n", | |
" )\n", | |
" )\n", | |
" (segmentation_head): SegmentationHead(\n", | |
" (0): Conv2d(16, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", | |
" (1): Identity()\n", | |
" (2): Activation(\n", | |
" (activation): Identity()\n", | |
" )\n", | |
" )\n", | |
" )\n", | |
")" | |
] | |
}, | |
"metadata": {}, | |
"execution_count": 32 | |
} | |
], | |
"source": [ | |
"model = SegmentationModel()\n", | |
"model.to(DEVICE)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "threaded-bracket", | |
"metadata": { | |
"id": "threaded-bracket" | |
}, | |
"source": [ | |
"# Task 7 : Create Train and Validation Function " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "alone-voltage", | |
"metadata": { | |
"id": "alone-voltage" | |
}, | |
"outputs": [], | |
"source": [ | |
"def train_fn(data_loader, model, optimizer):\n", | |
" model.train()\n", | |
" total_loss = 0.0\n", | |
"\n", | |
" for images, masks in tqdm(data_loader):\n", | |
" images = images.to(DEVICE)\n", | |
" masks = masks.to(DEVICE)\n", | |
"\n", | |
" optimizer.zero_grad()\n", | |
"\n", | |
" logits, loss = model(images, masks)\n", | |
"\n", | |
" loss.backward()\n", | |
"\n", | |
" total_loss += loss.item()\n", | |
"\n", | |
" return total_loss/len(data_loader)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "whole-musician", | |
"metadata": { | |
"id": "whole-musician" | |
}, | |
"outputs": [], | |
"source": [ | |
"def eval_fn(data_loader, model):\n", | |
" model.eval()\n", | |
" total_loss = 0.0\n", | |
"\n", | |
" with torch.no_grad():\n", | |
" for images, masks in tqdm(data_loader):\n", | |
" images = images.to(DEVICE)\n", | |
" masks = masks.to(DEVICE)\n", | |
"\n", | |
" logits, loss = model(images, masks)\n", | |
"\n", | |
"\n", | |
" total_loss += loss.item()\n", | |
"\n", | |
" return total_loss/len(data_loader)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "primary-variance", | |
"metadata": { | |
"id": "primary-variance" | |
}, | |
"source": [ | |
"# Task 8 : Train Model" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "floral-france", | |
"metadata": { | |
"id": "floral-france" | |
}, | |
"outputs": [], | |
"source": [ | |
"optimizer = torch.optim.Adam(model.parameters(), lr = LR)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "mounted-sword", | |
"metadata": { | |
"id": "mounted-sword", | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"outputId": "66979554-3fe1-4a36-aade-7b377430538e" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 3.68it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 8.98it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"saved model\n", | |
"EPOCH: 1, train_loss: 1.3635244780573352, valid loss: 1.2902666181325912\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.07it/s]\n", | |
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] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 2, train_loss: 1.366169839069761, valid loss: 1.3329655677080154\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:06<00:00, 4.16it/s]\n", | |
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] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 3, train_loss: 1.3599485652200107, valid loss: 1.3274962902069092\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
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] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 4, train_loss: 1.36952019559926, valid loss: 1.328959345817566\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.09it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 8.92it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 5, train_loss: 1.3656974660939183, valid loss: 1.3263712972402573\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.01it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.57it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 6, train_loss: 1.350389686124078, valid loss: 1.3229997903108597\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.02it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.09it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 7, train_loss: 1.3610765399604008, valid loss: 1.3345502465963364\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.02it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.47it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 8, train_loss: 1.3665051295839508, valid loss: 1.3354982733726501\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.07it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.31it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 9, train_loss: 1.3580118253313262, valid loss: 1.3252924978733063\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.06it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.32it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 10, train_loss: 1.364902294915298, valid loss: 1.3260292261838913\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 3.99it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 8.87it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 11, train_loss: 1.3704976829989204, valid loss: 1.3297546952962875\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.00it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 8.96it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 12, train_loss: 1.3638903601416226, valid loss: 1.3257688134908676\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 3.96it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.28it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 13, train_loss: 1.3665032880059604, valid loss: 1.3278229236602783\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.05it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.51it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 14, train_loss: 1.3640607102163906, valid loss: 1.3263511955738068\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.04it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.45it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 15, train_loss: 1.3619224079724015, valid loss: 1.335459589958191\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 3.85it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 8.92it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 16, train_loss: 1.3611075384863491, valid loss: 1.3162650167942047\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 3.92it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.20it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 17, train_loss: 1.3629307582460601, valid loss: 1.3267879784107208\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 3.98it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 8.96it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 18, train_loss: 1.3698403424230114, valid loss: 1.3260602355003357\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 3.97it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.57it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 19, train_loss: 1.3600868521065548, valid loss: 1.329484611749649\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.07it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.33it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 20, train_loss: 1.367086620166384, valid loss: 1.3298315405845642\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.05it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.55it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 21, train_loss: 1.3620125507486278, valid loss: 1.3214455097913742\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.01it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 8.76it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 22, train_loss: 1.3635328021542779, valid loss: 1.3275495320558548\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 3.98it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.05it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 23, train_loss: 1.3541699319050229, valid loss: 1.3270032107830048\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 3.92it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 8.66it/s]\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 24, train_loss: 1.3581038055748775, valid loss: 1.3252092003822327\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"100%|██████████| 29/29 [00:07<00:00, 4.05it/s]\n", | |
"100%|██████████| 8/8 [00:00<00:00, 9.46it/s]" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"EPOCH: 25, train_loss: 1.3570278800767044, valid loss: 1.327903687953949\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"\n" | |
] | |
} | |
], | |
"source": [ | |
"best_valid_loss = np.Inf\n", | |
"\n", | |
"for i in range(EPOCHS):\n", | |
" train_loss = train_fn(trainloader, model, optimizer)\n", | |
" valid_loss = eval_fn(validloader, model)\n", | |
"\n", | |
" if valid_loss < best_valid_loss:\n", | |
" torch.save(model.state_dict(), \"best_model.pt\")\n", | |
" print(\"saved model\")\n", | |
" best_valid_loss = valid_loss\n", | |
"\n", | |
" print(f\"EPOCH: {i+1}, train_loss: {train_loss}, valid loss: {valid_loss}\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "CVVzhk0HupfA", | |
"metadata": { | |
"id": "CVVzhk0HupfA" | |
}, | |
"source": [ | |
"# Task 9 : Inference" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "seventh-seating", | |
"metadata": { | |
"id": "seventh-seating", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 228 | |
}, | |
"outputId": "963f4acc-30ba-4b82-d7dd-b54c7053dc4d" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"text/plain": [ | |
"<Figure size 720x360 with 3 Axes>" | |
], | |
"image/png": 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\n" | |
}, | |
"metadata": { | |
"needs_background": "light" | |
} | |
} | |
], | |
"source": [ | |
"idx = 10\n", | |
"model.load_state_dict(torch.load(\"/content/best_model.pt\"))\n", | |
"\n", | |
"image, mask = validset[idx]\n", | |
"\n", | |
"logits_mask = model(image.to(DEVICE).unsqueeze(0))\n", | |
"\n", | |
"pred_mask = torch.sigmoid(logits_mask)\n", | |
"pred_mask = (pred_mask > 0.5) *1.0\n", | |
"\n", | |
"helper.show_image(image, mask, pred_mask.detach().cpu().squeeze(0))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "divided-mustang", | |
"metadata": { | |
"id": "divided-mustang", | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 228 | |
}, | |
"outputId": "fcc2b450-3cff-4e6d-9f06-5869c5f86507" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"text/plain": [ | |
"<Figure size 720x360 with 3 Axes>" | |
], |
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