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mmsegmentation/demo/MMSegmentation_Tutorial.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/main/demo/MMSegmentation_Tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FVmnaxFJvsb8"
},
"source": [
"# MMSegmentation Tutorial\n",
"Welcome to MMSegmentation! \n",
"\n",
"In this tutorial, we demo\n",
"* How to do inference with MMSeg trained weight\n",
"* How to train on your own dataset and visualize the results. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QS8YHrEhbpas"
},
"source": [
"## Install MMSegmentation\n",
"This step may take several minutes. \n",
"\n",
"We use PyTorch 1.12 and CUDA 11.3 for this tutorial. You may install other versions by change the version number in pip install command. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UWyLrLYaNEaL",
"outputId": "32a47fe3-f10d-47a1-f6b9-b7c235abdab1"
},
"outputs": [],
"source": [
"# Check nvcc version\n",
"!nvcc -V\n",
"# Check GCC version\n",
"!gcc --version"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ki3WUBjKbutg",
"outputId": "14bd14b0-4d8c-4fa9-e3f9-da35c0efc0d5"
},
"outputs": [],
"source": [
"# Install PyTorch\n",
"!conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch\n",
"# Install mim\n",
"!pip install -U openmim\n",
"# Install mmengine\n",
"!mim install mmengine\n",
"# Install MMCV\n",
"!mim install 'mmcv >= 2.0.0rc1'\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nR-hHRvbNJJZ",
"outputId": "10c3b131-d4db-458c-fc10-b94b1c6ed546"
},
"outputs": [],
"source": [
"!rm -rf mmsegmentation\n",
"!git clone -b main https://github.com/open-mmlab/mmsegmentation.git \n",
"%cd mmsegmentation\n",
"!pip install -e ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mAE_h7XhPT7d",
"outputId": "83bf0f8e-fc69-40b1-f9fe-0025724a217c"
},
"outputs": [],
"source": [
"# Check Pytorch installation\n",
"import torch, torchvision\n",
"print(torch.__version__, torch.cuda.is_available())\n",
"\n",
"# Check MMSegmentation installation\n",
"import mmseg\n",
"print(mmseg.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ta51clKX4cwM"
},
"source": [
"## Finetune a semantic segmentation model on a new dataset\n",
"\n",
"To finetune on a customized dataset, the following steps are necessary. \n",
"1. Add a new dataset class. \n",
"2. Create a config file accordingly. \n",
"3. Perform training and evaluation. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AcZg6x_K5Zs3"
},
"source": [
"### Add a new dataset\n",
"\n",
"Datasets in MMSegmentation require image and semantic segmentation maps to be placed in folders with the same prefix. To support a new dataset, we may need to modify the original file structure. \n",
"\n",
"In this tutorial, we give an example of converting the dataset. You may refer to [docs](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/tutorials/customize_datasets.md#customize-datasets-by-reorganizing-data) for details about dataset reorganization. \n",
"\n",
"We use [Stanford Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) as an example. The dataset contains 715 images chosen from existing public datasets [LabelMe](http://labelme.csail.mit.edu), [MSRC](http://research.microsoft.com/en-us/projects/objectclassrecognition), [PASCAL VOC](http://pascallin.ecs.soton.ac.uk/challenges/VOC) and [Geometric Context](http://www.cs.illinois.edu/homes/dhoiem/). Images from these datasets are mainly outdoor scenes, each containing approximately 320-by-240 pixels. \n",
"In this tutorial, we use the region annotations as labels. There are 8 classes in total, i.e. sky, tree, road, grass, water, building, mountain, and foreground object. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TFIt7MHq5Wls",
"outputId": "74a126e4-c8a4-4d2f-a910-b58b71843a23"
},
"outputs": [],
"source": [
"# download and unzip\n",
"!wget http://dags.stanford.edu/data/iccv09Data.tar.gz -O stanford_background.tar.gz\n",
"!tar xf stanford_background.tar.gz"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 377
},
"id": "78LIci7F9WWI",
"outputId": "c432ddac-5a50-47b1-daac-5a26b07afea2"
},
"outputs": [],
"source": [
"# Let's take a look at the dataset\n",
"import mmcv\n",
"import mmengine\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"img = mmcv.imread('iccv09Data/images/6000124.jpg')\n",
"plt.figure(figsize=(8, 6))\n",
"plt.imshow(mmcv.bgr2rgb(img))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L5mNQuc2GsVE"
},
"source": [
"We need to convert the annotation into semantic map format as an image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WnGZfribFHCx"
},
"outputs": [],
"source": [
"# define dataset root and directory for images and annotations\n",
"data_root = 'iccv09Data'\n",
"img_dir = 'images'\n",
"ann_dir = 'labels'\n",
"# define class and palette for better visualization\n",
"classes = ('sky', 'tree', 'road', 'grass', 'water', 'bldg', 'mntn', 'fg obj')\n",
"palette = [[128, 128, 128], [129, 127, 38], [120, 69, 125], [53, 125, 34], \n",
" [0, 11, 123], [118, 20, 12], [122, 81, 25], [241, 134, 51]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WnGZfribFHCx"
},
"outputs": [],
"source": [
"import os.path as osp\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"# convert dataset annotation to semantic segmentation map\n",
"for file in mmengine.scandir(osp.join(data_root, ann_dir), suffix='.regions.txt'):\n",
" seg_map = np.loadtxt(osp.join(data_root, ann_dir, file)).astype(np.uint8)\n",
" seg_img = Image.fromarray(seg_map).convert('P')\n",
" seg_img.putpalette(np.array(palette, dtype=np.uint8))\n",
" seg_img.save(osp.join(data_root, ann_dir, file.replace('.regions.txt', \n",
" '.png')))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 377
},
"id": "5MCSS9ABfSks",
"outputId": "92b9bafc-589e-48fc-c9e9-476f125d6522"
},
"outputs": [],
"source": [
"# Let's take a look at the segmentation map we got\n",
"import matplotlib.patches as mpatches\n",
"img = Image.open('iccv09Data/labels/6000124.png')\n",
"plt.figure(figsize=(8, 6))\n",
"im = plt.imshow(np.array(img.convert('RGB')))\n",
"\n",
"# create a patch (proxy artist) for every color \n",
"patches = [mpatches.Patch(color=np.array(palette[i])/255., \n",
" label=classes[i]) for i in range(8)]\n",
"# put those patched as legend-handles into the legend\n",
"plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., \n",
" fontsize='large')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WbeLYCp2k5hl"
},
"outputs": [],
"source": [
"# split train/val set randomly\n",
"split_dir = 'splits'\n",
"mmengine.mkdir_or_exist(osp.join(data_root, split_dir))\n",
"filename_list = [osp.splitext(filename)[0] for filename in mmengine.scandir(\n",
" osp.join(data_root, ann_dir), suffix='.png')]\n",
"with open(osp.join(data_root, split_dir, 'train.txt'), 'w') as f:\n",
" # select first 4/5 as train set\n",
" train_length = int(len(filename_list)*4/5)\n",
" f.writelines(line + '\\n' for line in filename_list[:train_length])\n",
"with open(osp.join(data_root, split_dir, 'val.txt'), 'w') as f:\n",
" # select last 1/5 as train set\n",
" f.writelines(line + '\\n' for line in filename_list[train_length:])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HchvmGYB_rrO"
},
"source": [
"After downloading the data, we need to implement `load_annotations` function in the new dataset class `StanfordBackgroundDataset`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LbsWOw62_o-X"
},
"outputs": [],
"source": [
"from mmseg.registry import DATASETS\n",
"from mmseg.datasets import BaseSegDataset\n",
"\n",
"\n",
"@DATASETS.register_module()\n",
"class StanfordBackgroundDataset(BaseSegDataset):\n",
" METAINFO = dict(classes = classes, palette = palette)\n",
" def __init__(self, **kwargs):\n",
" super().__init__(img_suffix='.jpg', seg_map_suffix='.png', **kwargs)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yUVtmn3Iq3WA"
},
"source": [
"### Create a config file\n",
"In the next step, we need to modify the config for the training. To accelerate the process, we finetune the model from trained weights."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Download config and checkpoint files\n",
"!mim download mmsegmentation --config pspnet_r50-d8_4xb2-40k_cityscapes-512x1024 --dest ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Wwnj9tRzqX_A"
},
"outputs": [],
"source": [
"from mmengine import Config\n",
"cfg = Config.fromfile('configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py')\n",
"print(f'Config:\\n{cfg.pretty_text}')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1y2oV5w97jQo"
},
"source": [
"Since the given config is used to train PSPNet on the cityscapes dataset, we need to modify it accordingly for our new dataset. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eyKnYC1Z7iCV",
"outputId": "6195217b-187f-4675-994b-ba90d8bb3078"
},
"outputs": [],
"source": [
"# Since we use only one GPU, BN is used instead of SyncBN\n",
"cfg.norm_cfg = dict(type='BN', requires_grad=True)\n",
"cfg.crop_size = (256, 256)\n",
"cfg.model.data_preprocessor.size = cfg.crop_size\n",
"cfg.model.backbone.norm_cfg = cfg.norm_cfg\n",
"cfg.model.decode_head.norm_cfg = cfg.norm_cfg\n",
"cfg.model.auxiliary_head.norm_cfg = cfg.norm_cfg\n",
"# modify num classes of the model in decode/auxiliary head\n",
"cfg.model.decode_head.num_classes = 8\n",
"cfg.model.auxiliary_head.num_classes = 8\n",
"\n",
"# Modify dataset type and path\n",
"cfg.dataset_type = 'StanfordBackgroundDataset'\n",
"cfg.data_root = data_root\n",
"\n",
"cfg.train_dataloader.batch_size = 8\n",
"\n",
"cfg.train_pipeline = [\n",
" dict(type='LoadImageFromFile'),\n",
" dict(type='LoadAnnotations'),\n",
" dict(type='RandomResize', scale=(320, 240), ratio_range=(0.5, 2.0), keep_ratio=True),\n",
" dict(type='RandomCrop', crop_size=cfg.crop_size, cat_max_ratio=0.75),\n",
" dict(type='RandomFlip', prob=0.5),\n",
" dict(type='PackSegInputs')\n",
"]\n",
"\n",
"cfg.test_pipeline = [\n",
" dict(type='LoadImageFromFile'),\n",
" dict(type='Resize', scale=(320, 240), keep_ratio=True),\n",
" # add loading annotation after ``Resize`` because ground truth\n",
" # does not need to do resize data transform\n",
" dict(type='LoadAnnotations'),\n",
" dict(type='PackSegInputs')\n",
"]\n",
"\n",
"\n",
"cfg.train_dataloader.dataset.type = cfg.dataset_type\n",
"cfg.train_dataloader.dataset.data_root = cfg.data_root\n",
"cfg.train_dataloader.dataset.data_prefix = dict(img_path=img_dir, seg_map_path=ann_dir)\n",
"cfg.train_dataloader.dataset.pipeline = cfg.train_pipeline\n",
"cfg.train_dataloader.dataset.ann_file = 'splits/train.txt'\n",
"\n",
"cfg.val_dataloader.dataset.type = cfg.dataset_type\n",
"cfg.val_dataloader.dataset.data_root = cfg.data_root\n",
"cfg.val_dataloader.dataset.data_prefix = dict(img_path=img_dir, seg_map_path=ann_dir)\n",
"cfg.val_dataloader.dataset.pipeline = cfg.test_pipeline\n",
"cfg.val_dataloader.dataset.ann_file = 'splits/val.txt'\n",
"\n",
"cfg.test_dataloader = cfg.val_dataloader\n",
"\n",
"\n",
"# Load the pretrained weights\n",
"cfg.load_from = 'pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'\n",
"\n",
"# Set up working dir to save files and logs.\n",
"cfg.work_dir = './work_dirs/tutorial'\n",
"\n",
"cfg.train_cfg.max_iters = 200\n",
"cfg.train_cfg.val_interval = 200\n",
"cfg.default_hooks.logger.interval = 10\n",
"cfg.default_hooks.checkpoint.interval = 200\n",
"\n",
"# Set seed to facilitate reproducing the result\n",
"cfg['randomness'] = dict(seed=0)\n",
"\n",
"# Let's have a look at the final config used for training\n",
"print(f'Config:\\n{cfg.pretty_text}')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QWuH14LYF2gQ"
},
"source": [
"### Train and Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jYKoSfdMF12B",
"outputId": "422219ca-d7a5-4890-f09f-88c959942e64"
},
"outputs": [],
"source": [
"from mmengine.runner import Runner\n",
"\n",
"runner = Runner.from_cfg(cfg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# start training\n",
"runner.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DEkWOP-NMbc_"
},
"source": [
"Inference with trained model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 645
},
"id": "ekG__UfaH_OU",
"outputId": "1437419c-869a-4902-df86-d4f6f8b2597a"
},
"outputs": [],
"source": [
"from mmseg.apis import init_model, inference_model, show_result_pyplot\n",
"\n",
"# Init the model from the config and the checkpoint\n",
"checkpoint_path = './work_dirs/tutorial/iter_200.pth'\n",
"model = init_model(cfg, checkpoint_path, 'cuda:0')\n",
"\n",
"img = mmcv.imread('iccv09Data/images/6000124.jpg')\n",
"result = inference_model(model, img)\n",
"plt.figure(figsize=(8, 6))\n",
"vis_result = show_result_pyplot(model, img, result)\n",
"plt.imshow(mmcv.bgr2rgb(vis_result))\n"
]
}
],
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