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EgoHOS/raw/script/.gitignore

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+*.jpg
+*.png
+
+*.pt
+*.pth
+
+*.zip
+*.tar
+
+# /mmsegmentation
+/data
+/data_to_release
+/testimages
+/testvideos
+
+/mmsegmentation/work_dirs_exp

EgoHOS/raw/script/LICENSE

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+MIT License
+
+Copyright (c) 2022 owenzlz
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

EgoHOS/raw/script/README.md

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+# EgoHOS
+[Project Page](https://www.seas.upenn.edu/~shzhou2/projects/eos_dataset/) |  [Paper](https://arxiv.org/pdf/2208.03826.pdf) | [Bibtex](https://scholar.googleusercontent.com/scholar.bib?q=info:fjdxr9gmTfgJ:scholar.google.com/&output=citation&scisdr=CgVB6GfVENSznnaBOqE:AAGBfm0AAAAAYvmHIqG6dxrrnCPKbcHuv2lyNg5mgWfH&scisig=AAGBfm0AAAAAYvmHIgRZZnzwOLARESMy3LGZV2cCOHLQ&scisf=4&ct=citation&cd=-1&hl=en)
+
+<img src="https://github.com/owenzlz/EgoHOS/blob/main/demo/teaser.gif" style="width:800px;">
+
+**Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications**\
+*European Conference on Computer Vision (ECCV), 2022*\
+[Lingzhi Zhang*](https://owenzlz.github.io/), [Shenghao Zhou*](https://scholar.google.com/citations?user=kWdwbUYAAAAJ&hl=en), [Simon Stent](https://scholar.google.com/citations?user=f3aij5UAAAAJ&hl=en), [Jianbo Shi](https://www.cis.upenn.edu/~jshi/) (* indicates equal contribution)
+
+Our main goal is to provide a tool for better hand-object segmentation on the in-the-wild egocentric videos. 
+
+## Prerequisites
+- Linux
+- Python 3
+- NVIDIA GPU + CUDA CuDNN
+
+**Table of Contents:**<br>
+1. [Setup](#setup) - download pretrained models and resources
+2. [Datasets](#datasets) - download our egocentric hand-object segmentation datasets
+3. [Checkpoints](#checkpoints) - download the checkpoints for all our models
+4. [Inference on Images](#inference_on_images) - quick usage on images
+5. [Inference on Videos](#inference_on_videos) - quick usage on videos<br>
+6. [Other Resources](#other_github) - other resources used in our papers<br>
+
+## Setup
+- Clone this repo:
+```bash
+git clone https://github.com/owenzlz/EgoHOS
+```
+
+- Install dependencies:
+```bash
+pip install -r requirements.txt
+pip install -U openmim
+mim install mmcv-full==1.6.0
+cd mmsegmentation
+pip install -v -e .
+```
+For more information, please refer to MMSegmentation: https://mmsegmentation.readthedocs.io/en/latest/
+
+<a name="datasets"/>
+
+## Datasets
+- Download our dataset using the following command line.
+```bash
+bash download_datasets.sh
+```
+
+After downloading, the dataset is structured as follows: 
+```bash
+- [egohos dataset root]
+    |- train
+        |- image
+        |- label
+        |- contact
+    |- val 
+        |- image
+        |- label
+        |- contact
+    |- test_indomain
+        |- image
+        |- label
+        |- contact
+    |- test_outdomain
+        |- image
+        |- label
+        |- contact
+```
+
+In each label image, the category ids are referred as below. In the contact labels, 'ones' indicate the dense contact region.  
+```bash
+0 -> background
+1 -> left hand
+2 -> right hand
+3 -> 1st order interacting object by left hand
+4 -> 1st order interacting object by right hand
+5 -> 1st order interacting object by both hands
+6 -> 2nd order interacting object by left hand
+7 -> 2nd order interacting object by right hand
+8 -> 2nd order interacting object by both hands
+```
+
+<a name="checkpoints"/>
+
+## Checkpoints
+
+- Download checkponts and config files:
+```bash
+bash download_checkpoints.sh
+```
+
+
+<a name="inference_on_images"/>
+
+## Inference on Images
+
+- Let's first download a few test images for running the demo:
+```bash
+bash download_testimages.sh
+```
+
+Depending on the application scenarios, you may want to use one of these commands to generate the segmentation predictions. Please modify the image directory paths in the bash file if needed. The backen segmentation model is Swin-L backbone with UPerNet head. 
+
+The default of the bash commands run on the images in "./testimages/images", and the results are saved in "./testimages" folder. If you wish to test on your own images, you may either put your images into "./testimages/images" folder or change directories in the bash files.
+
+- Predict two hands, contact boundary, and interacting objects (1st order) sequentially. 
+```bash
+cd mmsegmentation # if you are not in this directory
+bash pred_all_obj1.sh
+```
+
+<img src="https://github.com/owenzlz/EgoHOS/blob/main/demo/twohands_obj1_optimized.gif" style="width:850px;">
+
+- Predict two hands, contact boundary, and interacting objects (1st and 2nd orders) sequentially. 
+```bash
+cd mmsegmentation # if you are not in this directory
+bash pred_all_obj2.sh
+```
+
+<img src="https://github.com/owenzlz/EgoHOS/blob/main/demo/twohands_obj2_optimized.gif" style="width:850px;">
+
+If you only want to predict only hand/contact segmentation, or want to use each module separately, see the commands below. 
+
+- Predict only the left and right hands.
+```bash
+cd mmsegmentation # if you are not in this directory
+bash pred_twohands.sh
+```
+
+<img src="https://github.com/owenzlz/EgoHOS/blob/main/demo/twohands_optimized.gif" style="width:850px;">
+
+- Predict the dense contact boundary. 
+```bash
+cd mmsegmentation # if you are not in this directory
+bash pred_cb.sh
+```
+
+<img src="https://github.com/owenzlz/EgoHOS/blob/main/demo/cb.gif" style="width:850px;">
+
+- Predict the (1st order) interacting objects. 
+```bash
+cd mmsegmentation # if you are not in this directory
+bash pred_obj1.sh
+```
+
+- Predict the (both 1st and 2nd orders) interacting objects. 
+```bash
+cd mmsegmentation
+bash pred_obj2.sh
+```
+
+<a name="inference_on_videos"/>
+
+## Inference on Videos
+
+- Let's first download a few test videos for running the demo:
+```bash
+bash download_testvideos.sh
+```
+
+- Predict hands and (1st order) interacting objects. 
+```bash
+cd mmsegmentation # if you are not in this directory
+bash pred_obj1_video.sh
+```
+
+- Predict hands and (1st and 2nd orders) interacting objects. 
+```bash
+cd mmsegmentation # if you are not in this directory
+bash pred_obj2_video.sh
+```
+
+<a name="other_github"/>
+
+## Other Resouces
+
+We used other resources for the application section, i.e. mesh reconstruction. Please refer to below:
+1. Image Inpainting - LaMa: [https://github.com/saic-mdal/lama](https://github.com/saic-mdal/lama)
+2. Video Inpainting - Flow-edge Guided Video Completion: [https://github.com/vt-vl-lab/FGVC](https://github.com/vt-vl-lab/FGVC)
+3. Mesh Reconstruction of Hand-Object Interaction: [https://github.com/hassony2/homan](https://github.com/hassony2/homan)
+4. Video Recognition - SlowFast Newtork: [https://github.com/epic-kitchens/epic-kitchens-slowfast](https://github.com/epic-kitchens/epic-kitchens-slowfast)
+
+If you wish to generate higher quality mask, you may consider using mask refinement model, i.e. Cascade PSP: [https://github.com/hkchengrex/CascadePSP](https://github.com/hkchengrex/CascadePSP)
+
+### Citation
+If you use this code for your research, please cite our paper:
+```
+@inproceedings{zhang2022fine,
+  title={Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications},
+  author={Zhang, Lingzhi and Zhou, Shenghao and Stent, Simon and Shi, Jianbo},
+  booktitle={European Conference on Computer Vision},
+  pages={127--145},
+  year={2022},
+  organization={Springer}
+}
+```
+
+
+
+

EgoHOS/raw/script/build_env.sh

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+conda create -n open-mmlab python=3.10 -y
+conda activate open-mmlab
+conda install pytorch=1.11.0 torchvision cudatoolkit=11.3 -c pytorch
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11/index.html
+pip install -e .
+
+
+
+

EgoHOS/raw/script/ccda/composite.py

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+from PIL import Image
+import numpy as np 
+from skimage.io import imsave
+import glob
+import pdb
+import os 
+from tqdm import tqdm
+# from sklearn.metrics.pairwise import cosine_similarity
+from scipy.spatial.distance import cosine
+import random
+from shutil import copyfile
+import argparse
+
+parser = argparse.ArgumentParser(description="composite")
+parser.add_argument("--aug_numbers", default=3, type=int)
+parser.add_argument("--top_k", default=10, type=int)
+parser.add_argument("--random_aug", default=True, type=bool)
+parser.add_argument("--composite_hr", default=True, type=bool)
+parser.add_argument("--hr_img_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/image', type=str)
+parser.add_argument("--hr_lbl_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/label', type=str)
+parser.add_argument("--img_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/image_512', type=str)
+parser.add_argument("--lbl_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/label_512', type=str)
+parser.add_argument("--lama_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/lama_512', type=str)
+parser.add_argument("--lama_feat_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/lama_512_feature', type=str)
+parser.add_argument("--aug_img_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/image_ccda', type=str)
+parser.add_argument("--aug_lbl_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/label_ccda', type=str)
+args = parser.parse_args()
+
+os.system('rm -rf ' + args.aug_img_dir); os.makedirs(args.aug_img_dir, exist_ok = True)
+os.system('rm -rf ' + args.aug_lbl_dir); os.makedirs(args.aug_lbl_dir, exist_ok = True)
+
+# Load combine features into a list
+fname_list = []
+feature_list = []
+for file in glob.glob(args.lama_feat_dir + '/*'):
+    fname = os.path.basename(file).split('.')[0]
+    feature = np.load(file)
+    # print(feature.shape)
+    feature_list.append(feature)    
+    fname_list.append(fname)
+
+
+
+# composite images and generate labels
+for file in tqdm(glob.glob(args.lama_feat_dir + '/*')): 
+
+    query_fname = os.path.basename(file).split('.')[0]
+    query_feature = np.load(file)
+
+    score_list = []
+    for feature in feature_list:
+        score = cosine(query_feature, feature)
+        if score != 0.0:
+            score_list.append(score)
+
+    sorted_index_list = np.argsort(np.array(score_list))
+    
+    if args.random_aug:
+        top_k_sorted_index_list = sorted_index_list
+    else:
+        top_k_sorted_index_list = sorted_index_list[:args.top_k]
+    
+    for aug_idx in range(args.aug_numbers):
+        
+        size_match = False
+        while not size_match:
+            sample_idx = random.randint(0, len(top_k_sorted_index_list)-1)
+            index = top_k_sorted_index_list[sample_idx]
+            select_fname = fname_list[index]
+
+            query_img = np.array(Image.open(os.path.join(args.img_dir, query_fname + '.jpg')))
+            query_lbl = np.array(Image.open(os.path.join(args.lbl_dir, query_fname + '.png')))
+            query_msk = np.zeros((query_lbl.shape)); query_msk[query_lbl>0] = 1
+            query_msk = np.repeat(np.expand_dims(query_msk, 2), 3, 2)
+            select_img = np.array(Image.open(os.path.join(args.lama_dir, select_fname + '.jpg')))
+
+            if query_img.shape == select_img.shape:
+                size_match = True
+        
+        if args.composite_hr:
+            hr_query_img = np.array(Image.open(os.path.join(args.hr_img_dir, query_fname + '.jpg')))
+            hr_query_lbl = np.array(Image.open(os.path.join(args.hr_lbl_dir, query_fname + '.png')))
+            hr_query_msk = np.zeros((hr_query_lbl.shape)); hr_query_msk[hr_query_lbl>0] = 1
+            hr_query_msk = np.repeat(np.expand_dims(hr_query_msk, 2), 3, 2)
+            hr_select_img = np.array(Image.open(os.path.join(args.lama_dir, select_fname + '.jpg')).resize((hr_query_img.shape[1], hr_query_img.shape[0])))
+            new_img = hr_query_img * hr_query_msk + hr_select_img * (1 - hr_query_msk)
+        else:
+            new_img = query_img * query_msk + select_img * (1 - query_msk)
+        
+        imsave(os.path.join(args.aug_img_dir, query_fname + '_' + str(aug_idx) + '.jpg'), new_img)
+        src_lbl_file = os.path.join(args.lbl_dir, query_fname + '.png')
+        dst_lbl_file = os.path.join(args.aug_lbl_dir, query_fname + '_' + str(aug_idx) + '.png')
+        copyfile(src_lbl_file, dst_lbl_file)
+    
+    src_ori_img_file = os.path.join(args.img_dir, query_fname + '.jpg')
+    dst_ori_img_file = os.path.join(args.aug_img_dir, query_fname + '.jpg')
+    copyfile(src_ori_img_file, dst_ori_img_file)
+    
+    src_ori_lbl_file = os.path.join(args.lbl_dir, query_fname + '.png')
+    dst_ori_lbl_file = os.path.join(args.aug_lbl_dir, query_fname + '.png')
+    copyfile(src_ori_lbl_file, dst_ori_lbl_file)
+
+
+
+    
+    # pdb.set_trace()

EgoHOS/raw/script/ccda/extract_feature.py

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+from PIL import Image
+import numpy as np 
+from skimage.io import imsave
+import glob
+import pdb
+import os 
+import torchvision
+import torch
+from torchvision.models.feature_extraction import create_feature_extractor
+from torchvision import transforms
+from tqdm import tqdm
+from shutil import copyfile
+
+feat_extractor = torchvision.models.vgg16(pretrained=True).features.cuda()
+
+# pdb.set_trace()
+
+tsfm = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ])
+
+lama_dir = '/mnt/session_space/home/lingzzha/EgoHOS/data/train/lama_512'
+lama_feat_dir = '/mnt/session_space/home/lingzzha/EgoHOS/data/train/lama_512_feature'
+os.makedirs(lama_feat_dir, exist_ok = True)
+
+for file in tqdm(glob.glob(lama_dir + '/*')):
+    fname = os.path.basename(file).split('.')[0]
+    clean_back = tsfm(Image.open(file).convert('RGB').resize((480, 360))).cuda()
+    clean_back_feat = feat_extractor(clean_back.unsqueeze(0)).reshape(-1)
+    clean_back_feat_np = clean_back_feat.cpu().data.numpy()
+    np.save(os.path.join(lama_feat_dir, fname + '.npy'), clean_back_feat_np)
+
+
+
+

EgoHOS/raw/script/commit.sh

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+if [ $# -eq 0 ]
+then
+    message="updated";
+else
+    message=$1;
+fi
+echo "commit with message: $message";
+git add -A --verbose
+git commit -a -m "$message"
+git push --all

EgoHOS/raw/script/data/view?usp=sharing

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draft looks good and covers everything. Finalize 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EgoHOS/raw/script/download_checkpoints.sh

@@ -0,0 +1,5 @@
+pip install gdown
+cd mmsegmentation
+gdown https://drive.google.com/file/d/1DEJBeQ3cR1q7cjjzwDUIQVSoptT-y9U7/view?usp=drive_link
+unzip work_dirs.zip
+rm work_dirs.zip

EgoHOS/raw/script/download_datasets.sh

@@ -0,0 +1,6 @@
+pip install gdown
+mkdir data
+cd data
+gdown https://drive.google.com/file/d/1sk0TVEhZESNF67OW3fz9D5coqpIWkwuK/view?usp=sharing
+unzip egohos_dataset.zip
+rm egohos_dataset.zip

EgoHOS/raw/script/download_testimages.sh

@@ -0,0 +1,4 @@
+pip install gdown
+gdown https://drive.google.com/file/d/1G27in6x25VHdLVbftWv8ddt4kv_lyOmM/view?usp=sharing
+unzip testimages.zip
+rm testimages.zip

EgoHOS/raw/script/download_testvideos.sh

@@ -0,0 +1,4 @@
+pip install gdown
+gdown https://drive.google.com/file/d/1pxm-sT9idU3oa1HC6SY4vrGVvJw_ElVO/view?usp=sharing
+unzip testvideos.zip
+rm testvideos.zip

EgoHOS/raw/script/mmsegmentation/.circleci/config.yml

@@ -0,0 +1,161 @@
+version: 2.1
+
+jobs:
+  lint:
+    docker:
+      - image: cimg/python:3.7.4
+    steps:
+      - checkout
+      - run:
+          name: Install dependencies
+          command: |
+            sudo apt-add-repository ppa:brightbox/ruby-ng -y
+            sudo apt-get update
+            sudo apt-get install -y ruby2.7
+      - run:
+          name: Install pre-commit hook
+          command: |
+            pip install pre-commit
+            pre-commit install
+      - run:
+          name: Linting
+          command: pre-commit run --all-files
+      - run:
+          name: Check docstring coverage
+          command: |
+            pip install interrogate
+            interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 50 mmseg
+
+  build_cpu:
+    parameters:
+      # The python version must match available image tags in
+      # https://circleci.com/developer/images/image/cimg/python
+      python:
+        type: string
+        default: "3.7.4"
+      torch:
+        type: string
+      torchvision:
+        type: string
+    docker:
+      - image: cimg/python:<< parameters.python >>
+    resource_class: large
+    steps:
+      - checkout
+      - run:
+          name: Install Libraries
+          command: |
+            sudo apt-get update
+            sudo apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx libjpeg-dev zlib1g-dev libtinfo-dev libncurses5
+      - run:
+          name: Configure Python & pip
+          command: |
+            python -m pip install --upgrade pip
+            python -m pip install wheel
+      - run:
+          name: Install PyTorch
+          command: |
+            python -V
+            python -m pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - run:
+          name: Install mmseg dependencies
+          command: |
+            python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch<< parameters.torch >>/index.html
+            python -m pip install mmdet
+            python -m pip install -r requirements.txt
+      - run:
+          name: Build and install
+          command: |
+            python -m pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            python -m pip install timm
+            python -m coverage run --branch --source mmseg -m pytest tests/
+            python -m coverage xml
+            python -m coverage report -m
+
+  build_cu101:
+    machine:
+      image: ubuntu-1604-cuda-10.1:201909-23
+    resource_class: gpu.nvidia.small
+    steps:
+      - checkout
+      - run:
+          name: Install Libraries
+          command: |
+            sudo apt-get update
+            sudo apt-get install -y git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx
+      - run:
+          name: Configure Python & pip
+          command: |
+            pyenv global 3.7.0
+            python -m pip install --upgrade pip
+            python -m pip install wheel
+      - run:
+          name: Install PyTorch
+          command: |
+            python -V
+            python -m pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
+      - run:
+          name: Install mmseg dependencies
+          # python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch${{matrix.torch_version}}/index.html
+          command: |
+            python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
+            python -m pip install mmdet
+            python -m pip install -r requirements.txt
+      - run:
+          name: Build and install
+          command: |
+            python setup.py check -m -s
+            TORCH_CUDA_ARCH_LIST=7.0 python -m pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            python -m pip install timm
+            python -m pytest tests/
+
+workflows:
+  unit_tests:
+    jobs:
+      - lint
+      - build_cpu:
+          name: build_cpu_th1.6
+          torch: 1.6.0
+          torchvision: 0.7.0
+          requires:
+            - lint
+      - build_cpu:
+          name: build_cpu_th1.7
+          torch: 1.7.0
+          torchvision: 0.8.1
+          requires:
+            - lint
+      - build_cpu:
+          name: build_cpu_th1.8_py3.9
+          torch: 1.8.0
+          torchvision: 0.9.0
+          python: "3.9.0"
+          requires:
+            - lint
+      - build_cpu:
+          name: build_cpu_th1.9_py3.8
+          torch: 1.9.0
+          torchvision: 0.10.0
+          python: "3.8.0"
+          requires:
+            - lint
+      - build_cpu:
+          name: build_cpu_th1.9_py3.9
+          torch: 1.9.0
+          torchvision: 0.10.0
+          python: "3.9.0"
+          requires:
+            - lint
+      - build_cu101:
+          requires:
+            - build_cpu_th1.6
+            - build_cpu_th1.7
+            - build_cpu_th1.8_py3.9
+            - build_cpu_th1.9_py3.8
+            - build_cpu_th1.9_py3.9

EgoHOS/raw/script/mmsegmentation/.dev/batch_test_list.py

@@ -0,0 +1,133 @@
+# yapf: disable
+# Inference Speed is tested on NVIDIA V100
+hrnet = [
+    dict(
+        config='configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py',
+        checkpoint='fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=33.0),
+    ),
+    dict(
+        config='configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py',
+        checkpoint='fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=76.31),
+    ),
+    dict(
+        config='configs/hrnet/fcn_hr48_512x512_160k_ade20k.py',
+        checkpoint='fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth',
+        eval='mIoU',
+        metric=dict(mIoU=42.02),
+    ),
+    dict(
+        config='configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py',
+        checkpoint='fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=80.65),
+    ),
+]
+pspnet = [
+    dict(
+        config='configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py',
+        checkpoint='pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=78.55),
+    ),
+    dict(
+        config='configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py',
+        checkpoint='pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=79.76),
+    ),
+    dict(
+        config='configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py',
+        checkpoint='pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=44.39),
+    ),
+    dict(
+        config='configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py',
+        checkpoint='pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=42.48),
+    ),
+]
+resnest = [
+    dict(
+        config='configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py',
+        checkpoint='pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=45.44),
+    ),
+    dict(
+        config='configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py',
+        checkpoint='pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=78.57),
+    ),
+]
+fastscnn = [
+    dict(
+        config='configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py',
+        checkpoint='fast_scnn_8x4_160k_lr0.12_cityscapes-0cec9937.pth',
+        eval='mIoU',
+        metric=dict(mIoU=70.96),
+    )
+]
+deeplabv3plus = [
+    dict(
+        config='configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py', # noqa
+        checkpoint='deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=80.98),
+    ),
+    dict(
+        config='configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py', # noqa
+        checkpoint='deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=80.97),
+    ),
+    dict(
+        config='configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py', # noqa
+        checkpoint='deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=80.09),
+    ),
+    dict(
+        config='configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py', # noqa
+        checkpoint='deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=79.83),
+    ),
+]
+vit = [
+    dict(
+        config='configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py',
+        checkpoint='upernet_vit-b16_ln_mln_512x512_160k_ade20k-f444c077.pth',
+        eval='mIoU',
+        metric=dict(mIoU=47.73),
+    ),
+    dict(
+        config='configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py',
+        checkpoint='upernet_deit-s16_ln_mln_512x512_160k_ade20k-c0cd652f.pth',
+        eval='mIoU',
+        metric=dict(mIoU=43.52),
+    ),
+]
+fp16 = [
+    dict(
+        config='configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py', # noqa
+        checkpoint='deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=80.46),
+    )
+]
+swin = [
+    dict(
+        config='configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py', # noqa
+        checkpoint='upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth', # noqa
+        eval='mIoU',
+        metric=dict(mIoU=44.41),
+    )
+]
+# yapf: enable

EgoHOS/raw/script/mmsegmentation/.dev/batch_train_list.txt

@@ -0,0 +1,19 @@
+configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py
+configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py
+configs/hrnet/fcn_hr48_512x512_160k_ade20k.py
+configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py
+configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
+configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
+configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
+configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
+configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py
+configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py
+configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py
+configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
+configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
+configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
+configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
+configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
+configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
+configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py
+configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py

EgoHOS/raw/script/mmsegmentation/.dev/benchmark_evaluation.sh

@@ -0,0 +1,41 @@
+PARTITION=$1
+CHECKPOINT_DIR=$2
+
+echo 'configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fcn_hr18s_512x512_160k_ade20k configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py $CHECKPOINT_DIR/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/fcn_hr18s_512x512_160k_ade20k --cfg-options dist_params.port=28171 &
+echo 'configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fcn_hr18s_512x1024_160k_cityscapes configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py $CHECKPOINT_DIR/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/fcn_hr18s_512x1024_160k_cityscapes --cfg-options dist_params.port=28172 &
+echo 'configs/hrnet/fcn_hr48_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fcn_hr48_512x512_160k_ade20k configs/hrnet/fcn_hr48_512x512_160k_ade20k.py $CHECKPOINT_DIR/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/fcn_hr48_512x512_160k_ade20k --cfg-options dist_params.port=28173 &
+echo 'configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fcn_hr48_512x1024_160k_cityscapes configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py $CHECKPOINT_DIR/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/fcn_hr48_512x1024_160k_cityscapes --cfg-options dist_params.port=28174 &
+echo 'configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION pspnet_r50-d8_512x1024_80k_cityscapes configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py $CHECKPOINT_DIR/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/pspnet_r50-d8_512x1024_80k_cityscapes --cfg-options dist_params.port=28175 &
+echo 'configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION pspnet_r101-d8_512x1024_80k_cityscapes configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py $CHECKPOINT_DIR/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/pspnet_r101-d8_512x1024_80k_cityscapes --cfg-options dist_params.port=28176 &
+echo 'configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION pspnet_r101-d8_512x512_160k_ade20k configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py $CHECKPOINT_DIR/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/pspnet_r101-d8_512x512_160k_ade20k --cfg-options dist_params.port=28177 &
+echo 'configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION pspnet_r50-d8_512x512_160k_ade20k configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py $CHECKPOINT_DIR/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/pspnet_r50-d8_512x512_160k_ade20k --cfg-options dist_params.port=28178 &
+echo 'configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION pspnet_s101-d8_512x512_160k_ade20k configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py $CHECKPOINT_DIR/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/pspnet_s101-d8_512x512_160k_ade20k --cfg-options dist_params.port=28179 &
+echo 'configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION pspnet_s101-d8_512x1024_80k_cityscapes configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py $CHECKPOINT_DIR/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/pspnet_s101-d8_512x1024_80k_cityscapes --cfg-options dist_params.port=28180 &
+echo 'configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fast_scnn_lr0.12_8x4_160k_cityscapes configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py $CHECKPOINT_DIR/fast_scnn_8x4_160k_lr0.12_cityscapes-0cec9937.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/fast_scnn_lr0.12_8x4_160k_cityscapes --cfg-options dist_params.port=28181 &
+echo 'configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION deeplabv3plus_r101-d8_769x769_80k_cityscapes configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py $CHECKPOINT_DIR/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/deeplabv3plus_r101-d8_769x769_80k_cityscapes --cfg-options dist_params.port=28182 &
+echo 'configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION deeplabv3plus_r101-d8_512x1024_80k_cityscapes configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py $CHECKPOINT_DIR/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/deeplabv3plus_r101-d8_512x1024_80k_cityscapes --cfg-options dist_params.port=28183 &
+echo 'configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION deeplabv3plus_r50-d8_512x1024_80k_cityscapes configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py $CHECKPOINT_DIR/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/deeplabv3plus_r50-d8_512x1024_80k_cityscapes --cfg-options dist_params.port=28184 &
+echo 'configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION deeplabv3plus_r50-d8_769x769_80k_cityscapes configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py $CHECKPOINT_DIR/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/deeplabv3plus_r50-d8_769x769_80k_cityscapes --cfg-options dist_params.port=28185 &
+echo 'configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION upernet_vit-b16_ln_mln_512x512_160k_ade20k configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py $CHECKPOINT_DIR/upernet_vit-b16_ln_mln_512x512_160k_ade20k-f444c077.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/upernet_vit-b16_ln_mln_512x512_160k_ade20k --cfg-options dist_params.port=28186 &
+echo 'configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION upernet_deit-s16_ln_mln_512x512_160k_ade20k configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py $CHECKPOINT_DIR/upernet_deit-s16_ln_mln_512x512_160k_ade20k-c0cd652f.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/upernet_deit-s16_ln_mln_512x512_160k_ade20k --cfg-options dist_params.port=28187 &
+echo 'configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py $CHECKPOINT_DIR/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes --cfg-options dist_params.port=28188 &
+echo 'configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py $CHECKPOINT_DIR/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth --eval mIoU --work-dir work_dirs/benchmark_evaluation/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K --cfg-options dist_params.port=28189 &

EgoHOS/raw/script/mmsegmentation/.dev/benchmark_inference.py

@@ -0,0 +1,149 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import hashlib
+import logging
+import os
+import os.path as osp
+import warnings
+from argparse import ArgumentParser
+
+import requests
+from mmcv import Config
+
+from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
+from mmseg.utils import get_root_logger
+
+# ignore warnings when segmentors inference
+warnings.filterwarnings('ignore')
+
+
+def download_checkpoint(checkpoint_name, model_name, config_name, collect_dir):
+    """Download checkpoint and check if hash code is true."""
+    url = f'https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{checkpoint_name}'  # noqa
+
+    r = requests.get(url)
+    assert r.status_code != 403, f'{url} Access denied.'
+
+    with open(osp.join(collect_dir, checkpoint_name), 'wb') as code:
+        code.write(r.content)
+
+    true_hash_code = osp.splitext(checkpoint_name)[0].split('-')[1]
+
+    # check hash code
+    with open(osp.join(collect_dir, checkpoint_name), 'rb') as fp:
+        sha256_cal = hashlib.sha256()
+        sha256_cal.update(fp.read())
+        cur_hash_code = sha256_cal.hexdigest()[:8]
+
+    assert true_hash_code == cur_hash_code, f'{url} download failed, '
+    'incomplete downloaded file or url invalid.'
+
+    if cur_hash_code != true_hash_code:
+        os.remove(osp.join(collect_dir, checkpoint_name))
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint_root', help='Checkpoint file root path')
+    parser.add_argument(
+        '-i', '--img', default='demo/demo.png', help='Image file')
+    parser.add_argument('-a', '--aug', action='store_true', help='aug test')
+    parser.add_argument('-m', '--model-name', help='model name to inference')
+    parser.add_argument(
+        '-s', '--show', action='store_true', help='show results')
+    parser.add_argument(
+        '-d', '--device', default='cuda:0', help='Device used for inference')
+    args = parser.parse_args()
+    return args
+
+
+def inference_model(config_name, checkpoint, args, logger=None):
+    cfg = Config.fromfile(config_name)
+    if args.aug:
+        if 'flip' in cfg.data.test.pipeline[
+                1] and 'img_scale' in cfg.data.test.pipeline[1]:
+            cfg.data.test.pipeline[1].img_ratios = [
+                0.5, 0.75, 1.0, 1.25, 1.5, 1.75
+            ]
+            cfg.data.test.pipeline[1].flip = True
+        else:
+            if logger is not None:
+                logger.error(f'{config_name}: unable to start aug test')
+            else:
+                print(f'{config_name}: unable to start aug test', flush=True)
+
+    model = init_segmentor(cfg, checkpoint, device=args.device)
+    # test a single image
+    result = inference_segmentor(model, args.img)
+
+    # show the results
+    if args.show:
+        show_result_pyplot(model, args.img, result)
+    return result
+
+
+# Sample test whether the inference code is correct
+def main(args):
+    config = Config.fromfile(args.config)
+
+    if not os.path.exists(args.checkpoint_root):
+        os.makedirs(args.checkpoint_root, 0o775)
+
+    # test single model
+    if args.model_name:
+        if args.model_name in config:
+            model_infos = config[args.model_name]
+            if not isinstance(model_infos, list):
+                model_infos = [model_infos]
+            for model_info in model_infos:
+                config_name = model_info['config'].strip()
+                print(f'processing: {config_name}', flush=True)
+                checkpoint = osp.join(args.checkpoint_root,
+                                      model_info['checkpoint'].strip())
+                try:
+                    # build the model from a config file and a checkpoint file
+                    inference_model(config_name, checkpoint, args)
+                except Exception:
+                    print(f'{config_name} test failed!')
+                    continue
+                return
+        else:
+            raise RuntimeError('model name input error.')
+
+    # test all model
+    logger = get_root_logger(
+        log_file='benchmark_inference_image.log', log_level=logging.ERROR)
+
+    for model_name in config:
+        model_infos = config[model_name]
+
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            print('processing: ', model_info['config'], flush=True)
+            config_path = model_info['config'].strip()
+            config_name = osp.splitext(osp.basename(config_path))[0]
+            checkpoint_name = model_info['checkpoint'].strip()
+            checkpoint = osp.join(args.checkpoint_root, checkpoint_name)
+
+            # ensure checkpoint exists
+            try:
+                if not osp.exists(checkpoint):
+                    download_checkpoint(checkpoint_name, model_name,
+                                        config_name.rstrip('.py'),
+                                        args.checkpoint_root)
+            except Exception:
+                logger.error(f'{checkpoint_name} download error')
+                continue
+
+            # test model inference with checkpoint
+            try:
+                # build the model from a config file and a checkpoint file
+                inference_model(config_path, checkpoint, args, logger)
+            except Exception as e:
+                logger.error(f'{config_path} " : {repr(e)}')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)

EgoHOS/raw/script/mmsegmentation/.dev/benchmark_train.sh

@@ -0,0 +1,40 @@
+PARTITION=$1
+
+echo 'configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION fcn_hr18s_512x512_160k_ade20k configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24727 --work-dir work_dirs/hrnet/fcn_hr18s_512x512_160k_ade20k >/dev/null &
+echo 'configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION fcn_hr18s_512x1024_160k_cityscapes configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24728 --work-dir work_dirs/hrnet/fcn_hr18s_512x1024_160k_cityscapes >/dev/null &
+echo 'configs/hrnet/fcn_hr48_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION fcn_hr48_512x512_160k_ade20k configs/hrnet/fcn_hr48_512x512_160k_ade20k.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24729 --work-dir work_dirs/hrnet/fcn_hr48_512x512_160k_ade20k >/dev/null &
+echo 'configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION fcn_hr48_512x1024_160k_cityscapes configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24730 --work-dir work_dirs/hrnet/fcn_hr48_512x1024_160k_cityscapes >/dev/null &
+echo 'configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION pspnet_r50-d8_512x1024_80k_cityscapes configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24731 --work-dir work_dirs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes >/dev/null &
+echo 'configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION pspnet_r101-d8_512x1024_80k_cityscapes configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24732 --work-dir work_dirs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes >/dev/null &
+echo 'configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION pspnet_r101-d8_512x512_160k_ade20k configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24733 --work-dir work_dirs/pspnet/pspnet_r101-d8_512x512_160k_ade20k >/dev/null &
+echo 'configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION pspnet_r50-d8_512x512_160k_ade20k configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24734 --work-dir work_dirs/pspnet/pspnet_r50-d8_512x512_160k_ade20k >/dev/null &
+echo 'configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION pspnet_s101-d8_512x512_160k_ade20k configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24735 --work-dir work_dirs/resnest/pspnet_s101-d8_512x512_160k_ade20k >/dev/null &
+echo 'configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION pspnet_s101-d8_512x1024_80k_cityscapes configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24736 --work-dir work_dirs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes >/dev/null &
+echo 'configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION fast_scnn_lr0.12_8x4_160k_cityscapes configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24737 --work-dir work_dirs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes >/dev/null &
+echo 'configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION deeplabv3plus_r101-d8_769x769_80k_cityscapes configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24738 --work-dir work_dirs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes >/dev/null &
+echo 'configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION deeplabv3plus_r101-d8_512x1024_80k_cityscapes configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24739 --work-dir work_dirs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes >/dev/null &
+echo 'configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION deeplabv3plus_r50-d8_512x1024_80k_cityscapes configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24740 --work-dir work_dirs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes >/dev/null &
+echo 'configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION deeplabv3plus_r50-d8_769x769_80k_cityscapes configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24741 --work-dir work_dirs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes >/dev/null &
+echo 'configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION upernet_vit-b16_ln_mln_512x512_160k_ade20k configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24742 --work-dir work_dirs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k >/dev/null &
+echo 'configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION upernet_deit-s16_ln_mln_512x512_160k_ade20k configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24743 --work-dir work_dirs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k >/dev/null &
+echo 'configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24744 --work-dir work_dirs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes >/dev/null &
+echo 'configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh $PARTITION upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py --cfg-options checkpoint_config.max_keep_ckpts=1 dist_params.port=24745 --work-dir work_dirs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K >/dev/null &

EgoHOS/raw/script/mmsegmentation/.dev/check_urls.py

@@ -0,0 +1,101 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os
+from argparse import ArgumentParser
+
+import requests
+import yaml as yml
+
+from mmseg.utils import get_root_logger
+
+
+def check_url(url):
+    """Check url response status.
+
+    Args:
+        url (str): url needed to check.
+
+    Returns:
+        int, bool: status code and check flag.
+    """
+    flag = True
+    r = requests.head(url)
+    status_code = r.status_code
+    if status_code == 403 or status_code == 404:
+        flag = False
+
+    return status_code, flag
+
+
+def parse_args():
+    parser = ArgumentParser('url valid check.')
+    parser.add_argument(
+        '-m',
+        '--model-name',
+        type=str,
+        help='Select the model needed to check')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    model_name = args.model_name
+
+    # yml path generate.
+    # If model_name is not set, script will check all of the models.
+    if model_name is not None:
+        yml_list = [(model_name, f'configs/{model_name}/{model_name}.yml')]
+    else:
+        # check all
+        yml_list = [(x, f'configs/{x}/{x}.yml') for x in os.listdir('configs/')
+                    if x != '_base_']
+
+    logger = get_root_logger(log_file='url_check.log', log_level=logging.ERROR)
+
+    for model_name, yml_path in yml_list:
+        # Default yaml loader unsafe.
+        model_infos = yml.load(
+            open(yml_path, 'r'), Loader=yml.CLoader)['Models']
+        for model_info in model_infos:
+            config_name = model_info['Name']
+            checkpoint_url = model_info['Weights']
+            # checkpoint url check
+            status_code, flag = check_url(checkpoint_url)
+            if flag:
+                logger.info(f'checkpoint | {config_name} | {checkpoint_url} | '
+                            f'{status_code} valid')
+            else:
+                logger.error(
+                    f'checkpoint | {config_name} | {checkpoint_url} | '
+                    f'{status_code} | error')
+            # log_json check
+            checkpoint_name = checkpoint_url.split('/')[-1]
+            model_time = '-'.join(checkpoint_name.split('-')[:-1]).replace(
+                f'{config_name}_', '')
+            # two style of log_json name
+            # use '_' to link model_time (will be deprecated)
+            log_json_url_1 = f'https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{config_name}_{model_time}.log.json'  # noqa
+            status_code_1, flag_1 = check_url(log_json_url_1)
+            # use '-' to link model_time
+            log_json_url_2 = f'https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{config_name}-{model_time}.log.json'  # noqa
+            status_code_2, flag_2 = check_url(log_json_url_2)
+            if flag_1 or flag_2:
+                if flag_1:
+                    logger.info(
+                        f'log.json | {config_name} | {log_json_url_1} | '
+                        f'{status_code_1} | valid')
+                else:
+                    logger.info(
+                        f'log.json | {config_name} | {log_json_url_2} | '
+                        f'{status_code_2} | valid')
+            else:
+                logger.error(
+                    f'log.json | {config_name} | {log_json_url_1} & '
+                    f'{log_json_url_2} | {status_code_1} & {status_code_2} | '
+                    'error')
+
+
+if __name__ == '__main__':
+    main()

EgoHOS/raw/script/mmsegmentation/.dev/gather_benchmark_evaluation_results.py

@@ -0,0 +1,91 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os.path as osp
+
+import mmcv
+from mmcv import Config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Gather benchmarked model evaluation results')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='benchmark_evaluation_info.json',
+        help='output path of gathered metrics and compared '
+        'results to be stored')
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    root_path = args.root
+    metrics_out = args.out
+    result_dict = {}
+
+    cfg = Config.fromfile(args.config)
+
+    for model_key in cfg:
+        model_infos = cfg[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            previous_metrics = model_info['metric']
+            config = model_info['config'].strip()
+            fname, _ = osp.splitext(osp.basename(config))
+
+            # Load benchmark evaluation json
+            metric_json_dir = osp.join(root_path, fname)
+            if not osp.exists(metric_json_dir):
+                print(f'{metric_json_dir} not existed.')
+                continue
+
+            json_list = glob.glob(osp.join(metric_json_dir, '*.json'))
+            if len(json_list) == 0:
+                print(f'There is no eval json in {metric_json_dir}.')
+                continue
+
+            log_json_path = list(sorted(json_list))[-1]
+            metric = mmcv.load(log_json_path)
+            if config not in metric.get('config', {}):
+                print(f'{config} not included in {log_json_path}')
+                continue
+
+            # Compare between new benchmark results and previous metrics
+            differential_results = dict()
+            new_metrics = dict()
+            for record_metric_key in previous_metrics:
+                if record_metric_key not in metric['metric']:
+                    raise KeyError('record_metric_key not exist, please '
+                                   'check your config')
+                old_metric = previous_metrics[record_metric_key]
+                new_metric = round(metric['metric'][record_metric_key] * 100,
+                                   2)
+
+                differential = new_metric - old_metric
+                flag = '+' if differential > 0 else '-'
+                differential_results[
+                    record_metric_key] = f'{flag}{abs(differential):.2f}'
+                new_metrics[record_metric_key] = new_metric
+
+            result_dict[config] = dict(
+                differential=differential_results,
+                previous=previous_metrics,
+                new=new_metrics)
+
+    if metrics_out:
+        mmcv.dump(result_dict, metrics_out, indent=4)
+    print('===================================')
+    for config_name, metrics in result_dict.items():
+        print(config_name, metrics)
+    print('===================================')

EgoHOS/raw/script/mmsegmentation/.dev/gather_benchmark_train_results.py

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+import argparse
+import glob
+import os.path as osp
+
+import mmcv
+from gather_models import get_final_results
+from mmcv import Config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Gather benchmarked models train results')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='benchmark_train_info.json',
+        help='output path of gathered metrics to be stored')
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    root_path = args.root
+    metrics_out = args.out
+
+    evaluation_cfg = Config.fromfile(args.config)
+
+    result_dict = {}
+    for model_key in evaluation_cfg:
+        model_infos = evaluation_cfg[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            config = model_info['config']
+
+            # benchmark train dir
+            model_name = osp.split(osp.dirname(config))[1]
+            config_name = osp.splitext(osp.basename(config))[0]
+            exp_dir = osp.join(root_path, model_name, config_name)
+            if not osp.exists(exp_dir):
+                print(f'{config} hasn\'t {exp_dir}')
+                continue
+
+            # parse config
+            cfg = mmcv.Config.fromfile(config)
+            total_iters = cfg.runner.max_iters
+            exp_metric = cfg.evaluation.metric
+            if not isinstance(exp_metric, list):
+                exp_metrics = [exp_metric]
+
+            # determine whether total_iters ckpt exists
+            ckpt_path = f'iter_{total_iters}.pth'
+            if not osp.exists(osp.join(exp_dir, ckpt_path)):
+                print(f'{config} hasn\'t {ckpt_path}')
+                continue
+
+            # only the last log json counts
+            log_json_path = list(
+                sorted(glob.glob(osp.join(exp_dir, '*.log.json'))))[-1]
+
+            # extract metric value
+            model_performance = get_final_results(log_json_path, total_iters)
+            if model_performance is None:
+                print(f'log file error: {log_json_path}')
+                continue
+
+            differential_results = dict()
+            old_results = dict()
+            new_results = dict()
+            for metric_key in model_performance:
+                if metric_key in ['mIoU']:
+                    metric = round(model_performance[metric_key] * 100, 2)
+                    old_metric = model_info['metric'][metric_key]
+                    old_results[metric_key] = old_metric
+                    new_results[metric_key] = metric
+                    differential = metric - old_metric
+                    flag = '+' if differential > 0 else '-'
+                    differential_results[
+                        metric_key] = f'{flag}{abs(differential):.2f}'
+            result_dict[config] = dict(
+                differential_results=differential_results,
+                old_results=old_results,
+                new_results=new_results,
+            )
+
+    # 4 save or print results
+    if metrics_out:
+        mmcv.dump(result_dict, metrics_out, indent=4)
+    print('===================================')
+    for config_name, metrics in result_dict.items():
+        print(config_name, metrics)
+    print('===================================')

EgoHOS/raw/script/mmsegmentation/.dev/gather_models.py

@@ -0,0 +1,211 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import hashlib
+import json
+import os
+import os.path as osp
+import shutil
+
+import mmcv
+import torch
+
+# build schedule look-up table to automatically find the final model
+RESULTS_LUT = ['mIoU', 'mAcc', 'aAcc']
+
+
+def calculate_file_sha256(file_path):
+    """calculate file sha256 hash code."""
+    with open(file_path, 'rb') as fp:
+        sha256_cal = hashlib.sha256()
+        sha256_cal.update(fp.read())
+        return sha256_cal.hexdigest()
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    torch.save(checkpoint, out_file)
+    # The hash code calculation and rename command differ on different system
+    # platform.
+    sha = calculate_file_sha256(out_file)
+    final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
+    os.rename(out_file, final_file)
+
+    # Remove prefix and suffix
+    final_file_name = osp.split(final_file)[1]
+    final_file_name = osp.splitext(final_file_name)[0]
+
+    return final_file_name
+
+
+def get_final_iter(config):
+    iter_num = config.split('_')[-2]
+    assert iter_num.endswith('k')
+    return int(iter_num[:-1]) * 1000
+
+
+def get_final_results(log_json_path, iter_num):
+    result_dict = dict()
+    last_iter = 0
+    with open(log_json_path, 'r') as f:
+        for line in f.readlines():
+            log_line = json.loads(line)
+            if 'mode' not in log_line.keys():
+                continue
+
+            # When evaluation, the 'iter' of new log json is the evaluation
+            # steps on single gpu.
+            flag1 = ('aAcc' in log_line) or (log_line['mode'] == 'val')
+            flag2 = (last_iter == iter_num - 50) or (last_iter == iter_num)
+            if flag1 and flag2:
+                result_dict.update({
+                    key: log_line[key]
+                    for key in RESULTS_LUT if key in log_line
+                })
+                return result_dict
+
+            last_iter = log_line['iter']
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Gather benchmarked models')
+    parser.add_argument(
+        '-f', '--config-name', type=str, help='Process the selected config.')
+    parser.add_argument(
+        '-w',
+        '--work-dir',
+        default='work_dirs/',
+        type=str,
+        help='Ckpt storage root folder of benchmarked models to be gathered.')
+    parser.add_argument(
+        '-c',
+        '--collect-dir',
+        default='work_dirs/gather',
+        type=str,
+        help='Ckpt collect root folder of gathered models.')
+    parser.add_argument(
+        '--all', action='store_true', help='whether include .py and .log')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    work_dir = args.work_dir
+    collect_dir = args.collect_dir
+    selected_config_name = args.config_name
+    mmcv.mkdir_or_exist(collect_dir)
+
+    # find all models in the root directory to be gathered
+    raw_configs = list(mmcv.scandir('./configs', '.py', recursive=True))
+
+    # filter configs that is not trained in the experiments dir
+    used_configs = []
+    for raw_config in raw_configs:
+        config_name = osp.splitext(osp.basename(raw_config))[0]
+        if osp.exists(osp.join(work_dir, config_name)):
+            if (selected_config_name is None
+                    or selected_config_name == config_name):
+                used_configs.append(raw_config)
+    print(f'Find {len(used_configs)} models to be gathered')
+
+    # find final_ckpt and log file for trained each config
+    # and parse the best performance
+    model_infos = []
+    for used_config in used_configs:
+        config_name = osp.splitext(osp.basename(used_config))[0]
+        exp_dir = osp.join(work_dir, config_name)
+        # check whether the exps is finished
+        final_iter = get_final_iter(used_config)
+        final_model = 'iter_{}.pth'.format(final_iter)
+        model_path = osp.join(exp_dir, final_model)
+
+        # skip if the model is still training
+        if not osp.exists(model_path):
+            print(f'{used_config} train not finished yet')
+            continue
+
+        # get logs
+        log_json_paths = glob.glob(osp.join(exp_dir, '*.log.json'))
+        log_json_path = log_json_paths[0]
+        model_performance = None
+        for idx, _log_json_path in enumerate(log_json_paths):
+            model_performance = get_final_results(_log_json_path, final_iter)
+            if model_performance is not None:
+                log_json_path = _log_json_path
+                break
+
+        if model_performance is None:
+            print(f'{used_config} model_performance is None')
+            continue
+
+        model_time = osp.split(log_json_path)[-1].split('.')[0]
+        model_infos.append(
+            dict(
+                config_name=config_name,
+                results=model_performance,
+                iters=final_iter,
+                model_time=model_time,
+                log_json_path=osp.split(log_json_path)[-1]))
+
+    # publish model for each checkpoint
+    publish_model_infos = []
+    for model in model_infos:
+        config_name = model['config_name']
+        model_publish_dir = osp.join(collect_dir, config_name)
+
+        publish_model_path = osp.join(model_publish_dir,
+                                      config_name + '_' + model['model_time'])
+        trained_model_path = osp.join(work_dir, config_name,
+                                      'iter_{}.pth'.format(model['iters']))
+        if osp.exists(model_publish_dir):
+            for file in os.listdir(model_publish_dir):
+                if file.endswith('.pth'):
+                    print(f'model {file} found')
+                    model['model_path'] = osp.abspath(
+                        osp.join(model_publish_dir, file))
+                    break
+            if 'model_path' not in model:
+                print(f'dir {model_publish_dir} exists, no model found')
+
+        else:
+            mmcv.mkdir_or_exist(model_publish_dir)
+
+            # convert model
+            final_model_path = process_checkpoint(trained_model_path,
+                                                  publish_model_path)
+            model['model_path'] = final_model_path
+
+        new_json_path = f'{config_name}_{model["log_json_path"]}'
+        # copy log
+        shutil.copy(
+            osp.join(work_dir, config_name, model['log_json_path']),
+            osp.join(model_publish_dir, new_json_path))
+
+        if args.all:
+            new_txt_path = new_json_path.rstrip('.json')
+            shutil.copy(
+                osp.join(work_dir, config_name,
+                         model['log_json_path'].rstrip('.json')),
+                osp.join(model_publish_dir, new_txt_path))
+
+        if args.all:
+            # copy config to guarantee reproducibility
+            raw_config = osp.join('./configs', f'{config_name}.py')
+            mmcv.Config.fromfile(raw_config).dump(
+                osp.join(model_publish_dir, osp.basename(raw_config)))
+
+        publish_model_infos.append(model)
+
+    models = dict(models=publish_model_infos)
+    mmcv.dump(models, osp.join(collect_dir, 'model_infos.json'), indent=4)
+
+
+if __name__ == '__main__':
+    main()

EgoHOS/raw/script/mmsegmentation/.dev/generate_benchmark_evaluation_script.py

@@ -0,0 +1,114 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os.path as osp
+
+from mmcv import Config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert benchmark test model list to script')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('--port', type=int, default=28171, help='dist port')
+    parser.add_argument(
+        '--work-dir',
+        default='work_dirs/benchmark_evaluation',
+        help='the dir to save metric')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='.dev/benchmark_evaluation.sh',
+        help='path to save model benchmark script')
+
+    args = parser.parse_args()
+    return args
+
+
+def process_model_info(model_info, work_dir):
+    config = model_info['config'].strip()
+    fname, _ = osp.splitext(osp.basename(config))
+    job_name = fname
+    checkpoint = model_info['checkpoint'].strip()
+    work_dir = osp.join(work_dir, fname)
+    if not isinstance(model_info['eval'], list):
+        evals = [model_info['eval']]
+    else:
+        evals = model_info['eval']
+    eval = ' '.join(evals)
+    return dict(
+        config=config,
+        job_name=job_name,
+        checkpoint=checkpoint,
+        work_dir=work_dir,
+        eval=eval)
+
+
+def create_test_bash_info(commands, model_test_dict, port, script_name,
+                          partition):
+    config = model_test_dict['config']
+    job_name = model_test_dict['job_name']
+    checkpoint = model_test_dict['checkpoint']
+    work_dir = model_test_dict['work_dir']
+    eval = model_test_dict['eval']
+
+    echo_info = f'\necho \'{config}\' &'
+    commands.append(echo_info)
+    commands.append('\n')
+
+    command_info = f'GPUS=4  GPUS_PER_NODE=4  ' \
+                   f'CPUS_PER_TASK=2 {script_name} '
+
+    command_info += f'{partition} '
+    command_info += f'{job_name} '
+    command_info += f'{config} '
+    command_info += f'$CHECKPOINT_DIR/{checkpoint} '
+
+    command_info += f'--eval {eval} '
+    command_info += f'--work-dir {work_dir} '
+    command_info += f'--cfg-options dist_params.port={port} '
+    command_info += '&'
+
+    commands.append(command_info)
+
+
+def main():
+    args = parse_args()
+    if args.out:
+        out_suffix = args.out.split('.')[-1]
+        assert args.out.endswith('.sh'), \
+            f'Expected out file path suffix is .sh, but get .{out_suffix}'
+
+    commands = []
+    partition_name = 'PARTITION=$1'
+    commands.append(partition_name)
+    commands.append('\n')
+
+    checkpoint_root = 'CHECKPOINT_DIR=$2'
+    commands.append(checkpoint_root)
+    commands.append('\n')
+
+    script_name = osp.join('tools', 'slurm_test.sh')
+    port = args.port
+    work_dir = args.work_dir
+
+    cfg = Config.fromfile(args.config)
+
+    for model_key in cfg:
+        model_infos = cfg[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            print('processing: ', model_info['config'])
+            model_test_dict = process_model_info(model_info, work_dir)
+            create_test_bash_info(commands, model_test_dict, port, script_name,
+                                  '$PARTITION')
+            port += 1
+
+    command_str = ''.join(commands)
+    if args.out:
+        with open(args.out, 'w') as f:
+            f.write(command_str + '\n')
+
+
+if __name__ == '__main__':
+    main()

EgoHOS/raw/script/mmsegmentation/.dev/generate_benchmark_train_script.py

@@ -0,0 +1,91 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os.path as osp
+
+# Default using 4 gpu when training
+config_8gpu_list = [
+    'configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py',  # noqa
+    'configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py',
+    'configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py',
+]
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert benchmark model json to script')
+    parser.add_argument(
+        'txt_path', type=str, help='txt path output by benchmark_filter')
+    parser.add_argument('--port', type=int, default=24727, help='dist port')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='.dev/benchmark_train.sh',
+        help='path to save model benchmark script')
+
+    args = parser.parse_args()
+    return args
+
+
+def create_train_bash_info(commands, config, script_name, partition, port):
+    cfg = config.strip()
+
+    # print cfg name
+    echo_info = f'echo \'{cfg}\' &'
+    commands.append(echo_info)
+    commands.append('\n')
+
+    _, model_name = osp.split(osp.dirname(cfg))
+    config_name, _ = osp.splitext(osp.basename(cfg))
+    # default setting
+    if cfg in config_8gpu_list:
+        command_info = f'GPUS=8  GPUS_PER_NODE=8  ' \
+                        f'CPUS_PER_TASK=2 {script_name} '
+    else:
+        command_info = f'GPUS=4  GPUS_PER_NODE=4  ' \
+                        f'CPUS_PER_TASK=2 {script_name} '
+    command_info += f'{partition} '
+    command_info += f'{config_name} '
+    command_info += f'{cfg} '
+    command_info += f'--cfg-options ' \
+                    f'checkpoint_config.max_keep_ckpts=1 ' \
+                    f'dist_params.port={port} '
+    command_info += f'--work-dir work_dirs/{model_name}/{config_name} '
+    # Let the script shut up
+    command_info += '>/dev/null &'
+
+    commands.append(command_info)
+    commands.append('\n')
+
+
+def main():
+    args = parse_args()
+    if args.out:
+        out_suffix = args.out.split('.')[-1]
+        assert args.out.endswith('.sh'), \
+            f'Expected out file path suffix is .sh, but get .{out_suffix}'
+
+    root_name = './tools'
+    script_name = osp.join(root_name, 'slurm_train.sh')
+    port = args.port
+    partition_name = 'PARTITION=$1'
+
+    commands = []
+    commands.append(partition_name)
+    commands.append('\n')
+    commands.append('\n')
+
+    with open(args.txt_path, 'r') as f:
+        model_cfgs = f.readlines()
+        for i, cfg in enumerate(model_cfgs):
+            create_train_bash_info(commands, cfg, script_name, '$PARTITION',
+                                   port)
+            port += 1
+
+        command_str = ''.join(commands)
+        if args.out:
+            with open(args.out, 'w') as f:
+                f.write(command_str)
+
+
+if __name__ == '__main__':
+    main()

EgoHOS/raw/script/mmsegmentation/.dev/log_collector/example_config.py

@@ -0,0 +1,18 @@
+work_dir = '../../work_dirs'
+metric = 'mIoU'
+
+# specify the log files we would like to collect in `log_items`
+log_items = [
+    'segformer_mit-b5_512x512_160k_ade20k_cnn_lr_with_warmup',
+    'segformer_mit-b5_512x512_160k_ade20k_cnn_no_warmup_lr',
+    'segformer_mit-b5_512x512_160k_ade20k_mit_trans_lr',
+    'segformer_mit-b5_512x512_160k_ade20k_swin_trans_lr'
+]
+# or specify ignore_keywords, then the folders whose name contain
+# `'segformer'` won't be collected
+# ignore_keywords = ['segformer']
+
+# should not include metric
+other_info_keys = ['mAcc']
+markdown_file = 'markdowns/lr_in_trans.json.md'
+json_file = 'jsons/trans_in_cnn.json'

EgoHOS/raw/script/mmsegmentation/.dev/log_collector/log_collector.py

@@ -0,0 +1,143 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import datetime
+import json
+import os
+import os.path as osp
+from collections import OrderedDict
+
+from utils import load_config
+
+# automatically collect all the results
+
+# The structure of the directory:
+#     ├── work-dir
+#     │   ├── config_1
+#     │   │   ├── time1.log.json
+#     │   │   ├── time2.log.json
+#     │   │   ├── time3.log.json
+#     │   │   ├── time4.log.json
+#     │   ├── config_2
+#     │   │   ├── time5.log.json
+#     │   │   ├── time6.log.json
+#     │   │   ├── time7.log.json
+#     │   │   ├── time8.log.json
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='extract info from log.json')
+    parser.add_argument('config_dir')
+    args = parser.parse_args()
+    return args
+
+
+def has_keyword(name: str, keywords: list):
+    for a_keyword in keywords:
+        if a_keyword in name:
+            return True
+    return False
+
+
+def main():
+    args = parse_args()
+    cfg = load_config(args.config_dir)
+    work_dir = cfg['work_dir']
+    metric = cfg['metric']
+    log_items = cfg.get('log_items', [])
+    ignore_keywords = cfg.get('ignore_keywords', [])
+    other_info_keys = cfg.get('other_info_keys', [])
+    markdown_file = cfg.get('markdown_file', None)
+    json_file = cfg.get('json_file', None)
+
+    if json_file and osp.split(json_file)[0] != '':
+        os.makedirs(osp.split(json_file)[0], exist_ok=True)
+    if markdown_file and osp.split(markdown_file)[0] != '':
+        os.makedirs(osp.split(markdown_file)[0], exist_ok=True)
+
+    assert not (log_items and ignore_keywords), \
+        'log_items and ignore_keywords cannot be specified at the same time'
+    assert metric not in other_info_keys, \
+        'other_info_keys should not contain metric'
+
+    if ignore_keywords and isinstance(ignore_keywords, str):
+        ignore_keywords = [ignore_keywords]
+    if other_info_keys and isinstance(other_info_keys, str):
+        other_info_keys = [other_info_keys]
+    if log_items and isinstance(log_items, str):
+        log_items = [log_items]
+
+    if not log_items:
+        log_items = [
+            item for item in sorted(os.listdir(work_dir))
+            if not has_keyword(item, ignore_keywords)
+        ]
+
+    experiment_info_list = []
+    for config_dir in log_items:
+        preceding_path = os.path.join(work_dir, config_dir)
+        log_list = [
+            item for item in os.listdir(preceding_path)
+            if item.endswith('.log.json')
+        ]
+        log_list = sorted(
+            log_list,
+            key=lambda time_str: datetime.datetime.strptime(
+                time_str, '%Y%m%d_%H%M%S.log.json'))
+        val_list = []
+        last_iter = 0
+        for log_name in log_list:
+            with open(os.path.join(preceding_path, log_name), 'r') as f:
+                # ignore the info line
+                f.readline()
+                all_lines = f.readlines()
+                val_list.extend([
+                    json.loads(line) for line in all_lines
+                    if json.loads(line)['mode'] == 'val'
+                ])
+                for index in range(len(all_lines) - 1, -1, -1):
+                    line_dict = json.loads(all_lines[index])
+                    if line_dict['mode'] == 'train':
+                        last_iter = max(last_iter, line_dict['iter'])
+                        break
+
+        new_log_dict = dict(
+            method=config_dir, metric_used=metric, last_iter=last_iter)
+        for index, log in enumerate(val_list, 1):
+            new_ordered_dict = OrderedDict()
+            new_ordered_dict['eval_index'] = index
+            new_ordered_dict[metric] = log[metric]
+            for key in other_info_keys:
+                if key in log:
+                    new_ordered_dict[key] = log[key]
+            val_list[index - 1] = new_ordered_dict
+
+        assert len(val_list) >= 1, \
+            f"work dir {config_dir} doesn't contain any evaluation."
+        new_log_dict['last eval'] = val_list[-1]
+        new_log_dict['best eval'] = max(val_list, key=lambda x: x[metric])
+        experiment_info_list.append(new_log_dict)
+        print(f'{config_dir} is processed')
+
+    if json_file:
+        with open(json_file, 'w') as f:
+            json.dump(experiment_info_list, f, indent=4)
+
+    if markdown_file:
+        lines_to_write = []
+        for index, log in enumerate(experiment_info_list, 1):
+            lines_to_write.append(
+                f"|{index}|{log['method']}|{log['best eval'][metric]}"
+                f"|{log['best eval']['eval_index']}|"
+                f"{log['last eval'][metric]}|"
+                f"{log['last eval']['eval_index']}|{log['last_iter']}|\n")
+        with open(markdown_file, 'w') as f:
+            f.write(f'|exp_num|method|{metric} best|best index|'
+                    f'{metric} last|last index|last iter num|\n')
+            f.write('|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n')
+            f.writelines(lines_to_write)
+
+    print('processed successfully')
+
+
+if __name__ == '__main__':
+    main()

EgoHOS/raw/script/mmsegmentation/.dev/log_collector/readme.md

@@ -0,0 +1,143 @@
+# Log Collector
+
+## Function
+
+Automatically collect logs and write the result in a json file or markdown file.
+
+If there are several `.log.json` files in one folder, Log Collector assumes that the `.log.json` files other than the first one are resume from the preceding `.log.json` file. Log Collector returns the result considering all `.log.json` files.
+
+## Usage:
+
+To use log collector, you need to write a config file to configure the log collector first.
+
+For example:
+
+example_config.py:
+
+```python
+# The work directory that contains folders that contains .log.json files.
+work_dir = '../../work_dirs'
+# The metric used to find the best evaluation.
+metric = 'mIoU'
+
+# **Don't specify the log_items and ignore_keywords at the same time.**
+# Specify the log files we would like to collect in `log_items`.
+# The folders specified should be the subdirectories of `work_dir`.
+log_items = [
+    'segformer_mit-b5_512x512_160k_ade20k_cnn_lr_with_warmup',
+    'segformer_mit-b5_512x512_160k_ade20k_cnn_no_warmup_lr',
+    'segformer_mit-b5_512x512_160k_ade20k_mit_trans_lr',
+    'segformer_mit-b5_512x512_160k_ade20k_swin_trans_lr'
+]
+# Or specify `ignore_keywords`. The folders whose name contain one
+# of the keywords in the `ignore_keywords` list(e.g., `'segformer'`)
+# won't be collected.
+# ignore_keywords = ['segformer']
+
+# Other log items in .log.json that you want to collect.
+# should not include metric.
+other_info_keys = ["mAcc"]
+# The output markdown file's name.
+markdown_file ='markdowns/lr_in_trans.json.md'
+# The output json file's name. (optional)
+json_file = 'jsons/trans_in_cnn.json'
+```
+
+ The structure of the work-dir directory should be like：
+
+```text
+├── work-dir
+│   ├── folder1
+│   │   ├── time1.log.json
+│   │   ├── time2.log.json
+│   │   ├── time3.log.json
+│   │   ├── time4.log.json
+│   ├── folder2
+│   │   ├── time5.log.json
+│   │   ├── time6.log.json
+│   │   ├── time7.log.json
+│   │   ├── time8.log.json
+```
+
+Then , cd to the log collector folder.
+
+Now you can run log_collector.py by using command:
+
+```bash
+python log_collector.py ./example_config.py
+```
+
+The output markdown file is like:
+
+|exp_num|method|mIoU best|best index|mIoU last|last index|last iter num|
+|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+|1|segformer_mit-b5_512x512_160k_ade20k_cnn_lr_with_warmup|0.2776|10|0.2776|10|160000|
+|2|segformer_mit-b5_512x512_160k_ade20k_cnn_no_warmup_lr|0.2802|10|0.2802|10|160000|
+|3|segformer_mit-b5_512x512_160k_ade20k_mit_trans_lr|0.4943|11|0.4943|11|160000|
+|4|segformer_mit-b5_512x512_160k_ade20k_swin_trans_lr|0.4883|11|0.4883|11|160000|
+
+The output json file is like:
+```json
+[
+    {
+        "method": "segformer_mit-b5_512x512_160k_ade20k_cnn_lr_with_warmup",
+        "metric_used": "mIoU",
+        "last_iter": 160000,
+        "last eval": {
+            "eval_index": 10,
+            "mIoU": 0.2776,
+            "mAcc": 0.3779
+        },
+        "best eval": {
+            "eval_index": 10,
+            "mIoU": 0.2776,
+            "mAcc": 0.3779
+        }
+    },
+    {
+        "method": "segformer_mit-b5_512x512_160k_ade20k_cnn_no_warmup_lr",
+        "metric_used": "mIoU",
+        "last_iter": 160000,
+        "last eval": {
+            "eval_index": 10,
+            "mIoU": 0.2802,
+            "mAcc": 0.3764
+        },
+        "best eval": {
+            "eval_index": 10,
+            "mIoU": 0.2802,
+            "mAcc": 0.3764
+        }
+    },
+    {
+        "method": "segformer_mit-b5_512x512_160k_ade20k_mit_trans_lr",
+        "metric_used": "mIoU",
+        "last_iter": 160000,
+        "last eval": {
+            "eval_index": 11,
+            "mIoU": 0.4943,
+            "mAcc": 0.6097
+        },
+        "best eval": {
+            "eval_index": 11,
+            "mIoU": 0.4943,
+            "mAcc": 0.6097
+        }
+    },
+    {
+        "method": "segformer_mit-b5_512x512_160k_ade20k_swin_trans_lr",
+        "metric_used": "mIoU",
+        "last_iter": 160000,
+        "last eval": {
+            "eval_index": 11,
+            "mIoU": 0.4883,
+            "mAcc": 0.6061
+        },
+        "best eval": {
+            "eval_index": 11,
+            "mIoU": 0.4883,
+            "mAcc": 0.6061
+        }
+    }
+]
+```

EgoHOS/raw/script/mmsegmentation/.dev/log_collector/utils.py

@@ -0,0 +1,20 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# modified from https://github.dev/open-mmlab/mmcv
+import os.path as osp
+import sys
+from importlib import import_module
+
+
+def load_config(cfg_dir: str) -> dict:
+    assert cfg_dir.endswith('.py')
+    root_path, file_name = osp.split(cfg_dir)
+    temp_module = osp.splitext(file_name)[0]
+    sys.path.insert(0, root_path)
+    mod = import_module(temp_module)
+    sys.path.pop(0)
+    cfg_dict = {
+        k: v
+        for k, v in mod.__dict__.items() if not k.startswith('__')
+    }
+    del sys.modules[temp_module]
+    return cfg_dict

EgoHOS/raw/script/mmsegmentation/.dev/md2yml.py

@@ -0,0 +1,291 @@
+#!/usr/bin/env python
+
+# Copyright (c) OpenMMLab. All rights reserved.
+# This tool is used to update model-index.yml which is required by MIM, and
+# will be automatically called as a pre-commit hook. The updating will be
+# triggered if any change of model information (.md files in configs/) has been
+# detected before a commit.
+
+import glob
+import os
+import os.path as osp
+import re
+import sys
+
+import mmcv
+from lxml import etree
+
+MMSEG_ROOT = osp.dirname(osp.dirname((osp.dirname(__file__))))
+
+
+def dump_yaml_and_check_difference(obj, filename, sort_keys=False):
+    """Dump object to a yaml file, and check if the file content is different
+    from the original.
+
+    Args:
+        obj (any): The python object to be dumped.
+        filename (str): YAML filename to dump the object to.
+        sort_keys (str); Sort key by dictionary order.
+    Returns:
+        Bool: If the target YAML file is different from the original.
+    """
+
+    str_dump = mmcv.dump(obj, None, file_format='yaml', sort_keys=sort_keys)
+    if osp.isfile(filename):
+        file_exists = True
+        with open(filename, 'r', encoding='utf-8') as f:
+            str_orig = f.read()
+    else:
+        file_exists = False
+        str_orig = None
+
+    if file_exists and str_orig == str_dump:
+        is_different = False
+    else:
+        is_different = True
+        with open(filename, 'w', encoding='utf-8') as f:
+            f.write(str_dump)
+
+    return is_different
+
+
+def parse_md(md_file):
+    """Parse .md file and convert it to a .yml file which can be used for MIM.
+
+    Args:
+        md_file (str): Path to .md file.
+    Returns:
+        Bool: If the target YAML file is different from the original.
+    """
+    collection_name = osp.split(osp.dirname(md_file))[1]
+    configs = os.listdir(osp.dirname(md_file))
+
+    collection = dict(
+        Name=collection_name,
+        Metadata={'Training Data': []},
+        Paper={
+            'URL': '',
+            'Title': ''
+        },
+        README=md_file,
+        Code={
+            'URL': '',
+            'Version': ''
+        })
+    collection.update({'Converted From': {'Weights': '', 'Code': ''}})
+    models = []
+    datasets = []
+    paper_url = None
+    paper_title = None
+    code_url = None
+    code_version = None
+    repo_url = None
+
+    # To avoid re-counting number of backbone model in OpenMMLab,
+    # if certain model in configs folder is backbone whose name is already
+    # recorded in MMClassification, then the `COLLECTION` dict of this model
+    # in MMSegmentation should be deleted, and `In Collection` in `Models`
+    # should be set with head or neck of this config file.
+    is_backbone = None
+
+    with open(md_file, 'r') as md:
+        lines = md.readlines()
+        i = 0
+        current_dataset = ''
+        while i < len(lines):
+            line = lines[i].strip()
+            # In latest README.md the title and url are in the third line.
+            if i == 2:
+                paper_url = lines[i].split('](')[1].split(')')[0]
+                paper_title = lines[i].split('](')[0].split('[')[1]
+            if len(line) == 0:
+                i += 1
+                continue
+            elif line[:3] == '<a ':
+                content = etree.HTML(line)
+                node = content.xpath('//a')[0]
+                if node.text == 'Code Snippet':
+                    code_url = node.get('href', None)
+                    assert code_url is not None, (
+                        f'{collection_name} hasn\'t code snippet url.')
+                    # version extraction
+                    filter_str = r'blob/(.*)/mm'
+                    pattern = re.compile(filter_str)
+                    code_version = pattern.findall(code_url)
+                    assert len(code_version) == 1, (
+                        f'false regular expression ({filter_str}) use.')
+                    code_version = code_version[0]
+                elif node.text == 'Official Repo':
+                    repo_url = node.get('href', None)
+                    assert repo_url is not None, (
+                        f'{collection_name} hasn\'t official repo url.')
+                i += 1
+            elif line[:4] == '### ':
+                datasets.append(line[4:])
+                current_dataset = line[4:]
+                i += 2
+            elif line[:15] == '<!-- [BACKBONE]':
+                is_backbone = True
+                i += 1
+            elif line[0] == '|' and (
+                    i + 1) < len(lines) and lines[i + 1][:3] == '| -':
+                cols = [col.strip() for col in line.split('|')]
+                method_id = cols.index('Method')
+                backbone_id = cols.index('Backbone')
+                crop_size_id = cols.index('Crop Size')
+                lr_schd_id = cols.index('Lr schd')
+                mem_id = cols.index('Mem (GB)')
+                fps_id = cols.index('Inf time (fps)')
+                try:
+                    ss_id = cols.index('mIoU')
+                except ValueError:
+                    ss_id = cols.index('Dice')
+                try:
+                    ms_id = cols.index('mIoU(ms+flip)')
+                except ValueError:
+                    ms_id = False
+                config_id = cols.index('config')
+                download_id = cols.index('download')
+                j = i + 2
+                while j < len(lines) and lines[j][0] == '|':
+                    els = [el.strip() for el in lines[j].split('|')]
+                    config = ''
+                    model_name = ''
+                    weight = ''
+                    for fn in configs:
+                        if fn in els[config_id]:
+                            left = els[download_id].index(
+                                'https://download.openmmlab.com')
+                            right = els[download_id].index('.pth') + 4
+                            weight = els[download_id][left:right]
+                            config = f'configs/{collection_name}/{fn}'
+                            model_name = fn[:-3]
+                    fps = els[fps_id] if els[fps_id] != '-' and els[
+                        fps_id] != '' else -1
+                    mem = els[mem_id].split(
+                        '\\'
+                    )[0] if els[mem_id] != '-' and els[mem_id] != '' else -1
+                    crop_size = els[crop_size_id].split('x')
+                    assert len(crop_size) == 2
+                    method = els[method_id].split()[0].split('-')[-1]
+                    model = {
+                        'Name':
+                        model_name,
+                        'In Collection':
+                        method,
+                        'Metadata': {
+                            'backbone': els[backbone_id],
+                            'crop size': f'({crop_size[0]},{crop_size[1]})',
+                            'lr schd': int(els[lr_schd_id]),
+                        },
+                        'Results': [
+                            {
+                                'Task': 'Semantic Segmentation',
+                                'Dataset': current_dataset,
+                                'Metrics': {
+                                    cols[ss_id]: float(els[ss_id]),
+                                },
+                            },
+                        ],
+                        'Config':
+                        config,
+                        'Weights':
+                        weight,
+                    }
+                    if fps != -1:
+                        try:
+                            fps = float(fps)
+                        except Exception:
+                            j += 1
+                            continue
+                        model['Metadata']['inference time (ms/im)'] = [{
+                            'value':
+                            round(1000 / float(fps), 2),
+                            'hardware':
+                            'V100',
+                            'backend':
+                            'PyTorch',
+                            'batch size':
+                            1,
+                            'mode':
+                            'FP32' if 'fp16' not in config else 'FP16',
+                            'resolution':
+                            f'({crop_size[0]},{crop_size[1]})'
+                        }]
+                    if mem != -1:
+                        model['Metadata']['Training Memory (GB)'] = float(mem)
+                    # Only have semantic segmentation now
+                    if ms_id and els[ms_id] != '-' and els[ms_id] != '':
+                        model['Results'][0]['Metrics'][
+                            'mIoU(ms+flip)'] = float(els[ms_id])
+                    models.append(model)
+                    j += 1
+                i = j
+            else:
+                i += 1
+    flag = (code_url is not None) and (paper_url is not None) and (repo_url
+                                                                   is not None)
+    assert flag, f'{collection_name} readme error'
+    collection['Name'] = method
+    collection['Metadata']['Training Data'] = datasets
+    collection['Code']['URL'] = code_url
+    collection['Code']['Version'] = code_version
+    collection['Paper']['URL'] = paper_url
+    collection['Paper']['Title'] = paper_title
+    collection['Converted From']['Code'] = repo_url
+    # ['Converted From']['Weights] miss
+    # remove empty attribute
+    check_key_list = ['Code', 'Paper', 'Converted From']
+    for check_key in check_key_list:
+        key_list = list(collection[check_key].keys())
+        for key in key_list:
+            if check_key not in collection:
+                break
+            if collection[check_key][key] == '':
+                if len(collection[check_key].keys()) == 1:
+                    collection.pop(check_key)
+                else:
+                    collection[check_key].pop(key)
+    if is_backbone:
+        result = {'Models': models}
+    else:
+        result = {'Collections': [collection], 'Models': models}
+    yml_file = f'{md_file[:-9]}{collection_name}.yml'
+    return dump_yaml_and_check_difference(result, yml_file)
+
+
+def update_model_index():
+    """Update model-index.yml according to model .md files.
+
+    Returns:
+        Bool: If the updated model-index.yml is different from the original.
+    """
+    configs_dir = osp.join(MMSEG_ROOT, 'configs')
+    yml_files = glob.glob(osp.join(configs_dir, '**', '*.yml'), recursive=True)
+    yml_files.sort()
+
+    # add .replace('\\', '/') to avoid Windows Style path
+    model_index = {
+        'Import': [
+            osp.relpath(yml_file, MMSEG_ROOT).replace('\\', '/')
+            for yml_file in yml_files
+        ]
+    }
+    model_index_file = osp.join(MMSEG_ROOT, 'model-index.yml')
+    is_different = dump_yaml_and_check_difference(model_index,
+                                                  model_index_file)
+
+    return is_different
+
+
+if __name__ == '__main__':
+    file_list = [fn for fn in sys.argv[1:] if osp.basename(fn) == 'README.md']
+    if not file_list:
+        sys.exit(0)
+    file_modified = False
+    for fn in file_list:
+        file_modified |= parse_md(fn)
+
+    file_modified |= update_model_index()
+
+    sys.exit(1 if file_modified else 0)

EgoHOS/raw/script/mmsegmentation/.dev/upload_modelzoo.py

@@ -0,0 +1,45 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+import oss2
+
+ACCESS_KEY_ID = os.getenv('OSS_ACCESS_KEY_ID', None)
+ACCESS_KEY_SECRET = os.getenv('OSS_ACCESS_KEY_SECRET', None)
+BUCKET_NAME = 'openmmlab'
+ENDPOINT = 'https://oss-accelerate.aliyuncs.com'
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Upload models to OSS')
+    parser.add_argument('model_zoo', type=str, help='model_zoo input')
+    parser.add_argument(
+        '--dst-folder',
+        type=str,
+        default='mmsegmentation/v0.5',
+        help='destination folder')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    model_zoo = args.model_zoo
+    dst_folder = args.dst_folder
+    bucket = oss2.Bucket(
+        oss2.Auth(ACCESS_KEY_ID, ACCESS_KEY_SECRET), ENDPOINT, BUCKET_NAME)
+
+    for root, dirs, files in os.walk(model_zoo):
+        for file in files:
+            file_path = osp.relpath(osp.join(root, file), model_zoo)
+            print(f'Uploading {file_path}')
+
+            oss2.resumable_upload(bucket, osp.join(dst_folder, file_path),
+                                  osp.join(model_zoo, file_path))
+            bucket.put_object_acl(
+                osp.join(dst_folder, file_path), oss2.OBJECT_ACL_PUBLIC_READ)
+
+
+if __name__ == '__main__':
+    main()

EgoHOS/raw/script/mmsegmentation/.gitignore

@@ -0,0 +1,120 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/en/_build/
+docs/zh_cn/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.DS_Store
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+data
+.vscode
+.idea
+
+# custom
+*.pkl
+*.pkl.json
+*.log.json
+work_dirs/
+mmseg/.mim
+
+# Pytorch
+*.pth

EgoHOS/raw/script/mmsegmentation/.owners.yml

@@ -0,0 +1,10 @@
+assign:
+  strategy:
+    # random
+    # daily-shift-based
+    round-robin
+  assignees:
+    - MeowZheng
+    - MengzhangLI
+    - linfangjian01
+    - xiaoachen98

EgoHOS/raw/script/mmsegmentation/.pre-commit-config.yaml

@@ -0,0 +1,57 @@
+repos:
+  - repo: https://gitlab.com/pycqa/flake8.git
+    rev: 3.8.3
+    hooks:
+      - id: flake8
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.10.1
+    hooks:
+      - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.30.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.1.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://github.com/markdownlint/markdownlint
+    rev: v0.11.0
+    hooks:
+      - id: markdownlint
+        args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034",
+              "-t", "allow_different_nesting"]
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.1.0
+    hooks:
+      - id: codespell
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: local
+    hooks:
+      - id: update-model-index
+        name: update-model-index
+        description: Collect model information and update model-index.yml
+        entry: .dev/md2yml.py
+        additional_dependencies: [mmcv, lxml]
+        language: python
+        files: ^configs/.*\.md$
+        require_serial: true
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0  # Use the rev to fix revision
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmseg", "tools", "tests", "demo"]  # the dir_to_check with expected directory to check

EgoHOS/raw/script/mmsegmentation/.readthedocs.yml

@@ -0,0 +1,9 @@
+version: 2
+
+formats: all
+
+python:
+  version: 3.7
+  install:
+    - requirements: requirements/docs.txt
+    - requirements: requirements/readthedocs.txt

EgoHOS/raw/script/mmsegmentation/CITATION.cff

@@ -0,0 +1,8 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+  - name: "MMSegmentation Contributors"
+title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark"
+date-released: 2020-07-10
+url: "https://github.com/open-mmlab/mmsegmentation"
+license: Apache-2.0

EgoHOS/raw/script/mmsegmentation/LICENSE

@@ -0,0 +1,203 @@
+Copyright 2020 The MMSegmentation Authors. All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
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+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+      "Source" form shall mean the preferred form for making modifications,
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+      "Work" shall mean the work of authorship, whether in Source or
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+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
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+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
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+      PARTICULAR PURPOSE. You are solely responsible for determining the
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+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
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+
+   Copyright 2020 The MMSegmentation Authors.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

EgoHOS/raw/script/mmsegmentation/MANIFEST.in

@@ -0,0 +1,4 @@
+include requirements/*.txt
+include mmseg/.mim/model-index.yml
+recursive-include mmseg/.mim/configs *.py *.yml
+recursive-include mmseg/.mim/tools *.py *.sh

EgoHOS/raw/script/mmsegmentation/README.md

@@ -0,0 +1,230 @@
+<div align="center">
+  <img src="resources/mmseg-logo.png" width="600"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab website</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab platform</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</div>
+
+<br />
+
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmsegmentation)](https://pypi.org/project/mmsegmentation/)
+[![PyPI](https://img.shields.io/pypi/v/mmsegmentation)](https://pypi.org/project/mmsegmentation)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmsegmentation.readthedocs.io/en/latest/)
+[![badge](https://github.com/open-mmlab/mmsegmentation/workflows/build/badge.svg)](https://github.com/open-mmlab/mmsegmentation/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmsegmentation/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmsegmentation)
+[![license](https://img.shields.io/github/license/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
+
+[📘Documentation](https://mmsegmentation.readthedocs.io/en/latest/) |
+[🛠️Installation](https://mmsegmentation.readthedocs.io/en/latest/get_started.html) |
+[👀Model Zoo](https://mmsegmentation.readthedocs.io/en/latest/model_zoo.html) |
+[🆕Update News](https://mmsegmentation.readthedocs.io/en/latest/changelog.html) |
+[🤔Reporting Issues](https://github.com/open-mmlab/mmsegmentation/issues/new/choose)
+
+</div>
+
+<div align="center">
+
+English | [简体中文](README_zh-CN.md)
+
+</div>
+
+## Introduction
+
+MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.
+It is a part of the [OpenMMLab](https://openmmlab.com/) project.
+
+The master branch works with **PyTorch 1.5+**.
+
+![demo image](resources/seg_demo.gif)
+
+<details open>
+<summary>Major features</summary>
+
+- **Unified Benchmark**
+
+  We provide a unified benchmark toolbox for various semantic segmentation methods.
+
+- **Modular Design**
+
+  We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.
+
+- **Support of multiple methods out of box**
+
+  The toolbox directly supports popular and contemporary semantic segmentation frameworks, *e.g.* PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.
+
+- **High efficiency**
+
+  The training speed is faster than or comparable to other codebases.
+
+</details>
+
+## What's New
+
+v0.26.0 was released in 7/1/2022:
+
+- Update New SegFormer models on ADE20K
+- Dedicated MMSegWandbHook for MMSegmentation to use wandb visualization tool
+
+Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
+
+## Installation
+
+Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/dataset_prepare.md#prepare-datasets) for dataset preparation.
+
+## Get Started
+
+Please see [train.md](docs/en/train.md) and [inference.md](docs/en/inference.md) for the basic usage of MMSegmentation.
+There are also tutorials for:
+
+- [customizing dataset](docs/en/tutorials/customize_datasets.md)
+- [designing data pipeline](docs/en/tutorials/data_pipeline.md)
+- [customizing modules](docs/en/tutorials/customize_models.md)
+- [customizing runtime](docs/en/tutorials/customize_runtime.md)
+- [training tricks](docs/en/tutorials/training_tricks.md)
+- [useful tools](docs/en/useful_tools.md)
+
+A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab.
+
+## Benchmark and model zoo
+
+Results and models are available in the [model zoo](docs/en/model_zoo.md).
+
+Supported backbones:
+
+- [x] ResNet (CVPR'2016)
+- [x] ResNeXt (CVPR'2017)
+- [x] [HRNet (CVPR'2019)](configs/hrnet)
+- [x] [ResNeSt (ArXiv'2020)](configs/resnest)
+- [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2)
+- [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3)
+- [x] [Vision Transformer (ICLR'2021)](configs/vit)
+- [x] [Swin Transformer (ICCV'2021)](configs/swin)
+- [x] [Twins (NeurIPS'2021)](configs/twins)
+- [x] [BEiT (ICLR'2022)](configs/beit)
+- [x] [ConvNeXt (CVPR'2022)](configs/convnext)
+- [x] [MAE (CVPR'2022)](configs/mae)
+
+Supported methods:
+
+- [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
+- [x] [ERFNet (T-ITS'2017)](configs/erfnet)
+- [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet)
+- [x] [PSPNet (CVPR'2017)](configs/pspnet)
+- [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
+- [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1)
+- [x] [PSANet (ECCV'2018)](configs/psanet)
+- [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
+- [x] [UPerNet (ECCV'2018)](configs/upernet)
+- [x] [ICNet (ECCV'2018)](configs/icnet)
+- [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net)
+- [x] [EncNet (CVPR'2018)](configs/encnet)
+- [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn)
+- [x] [DANet (CVPR'2019)](configs/danet)
+- [x] [APCNet (CVPR'2019)](configs/apcnet)
+- [x] [EMANet (ICCV'2019)](configs/emanet)
+- [x] [CCNet (ICCV'2019)](configs/ccnet)
+- [x] [DMNet (ICCV'2019)](configs/dmnet)
+- [x] [ANN (ICCV'2019)](configs/ann)
+- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
+- [x] [FastFCN (ArXiv'2019)](configs/fastfcn)
+- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
+- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
+- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
+- [x] [DNLNet (ECCV'2020)](configs/dnlnet)
+- [x] [PointRend (CVPR'2020)](configs/point_rend)
+- [x] [CGNet (TIP'2020)](configs/cgnet)
+- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
+- [x] [STDC (CVPR'2021)](configs/stdc)
+- [x] [SETR (CVPR'2021)](configs/setr)
+- [x] [DPT (ArXiv'2021)](configs/dpt)
+- [x] [Segmenter (ICCV'2021)](configs/segmenter)
+- [x] [SegFormer (NeurIPS'2021)](configs/segformer)
+- [x] [K-Net (NeurIPS'2021)](configs/knet)
+
+Supported datasets:
+
+- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#cityscapes)
+- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-voc)
+- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#ade20k)
+- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-context)
+- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-10k)
+- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-164k)
+- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#chase-db1)
+- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#drive)
+- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#hrf)
+- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#stare)
+- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#dark-zurich)
+- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#nighttime-driving)
+- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#loveda)
+- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-potsdam)
+- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-vaihingen)
+- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isaid)
+
+## FAQ
+
+Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
+
+## Contributing
+
+We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
+
+## Acknowledgement
+
+MMSegmentation is an open source project that welcome any contribution and feedback.
+We wish that the toolbox and benchmark could serve the growing research
+community by providing a flexible as well as standardized toolkit to reimplement existing methods
+and develop their own new semantic segmentation methods.
+
+## Citation
+
+If you find this project useful in your research, please consider cite:
+
+```bibtex
+@misc{mmseg2020,
+    title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
+    author={MMSegmentation Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
+    year={2020}
+}
+```
+
+## License
+
+MMSegmentation is released under the Apache 2.0 license, while some specific features in this library are with other licenses. Please refer to [LICENSES.md](LICENSES.md) for the careful check, if you are using our code for commercial matters.
+
+## Projects in OpenMMLab
+
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
+- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.

EgoHOS/raw/script/mmsegmentation/cleanup_ckpt.py

@@ -0,0 +1,19 @@
+import glob
+import pdb
+import os 
+
+
+ckpt_dir = './work_dirs'
+
+for ckpt_file in glob.glob(ckpt_dir + '/**/*.pth'):
+
+    if 'best' in ckpt_file:
+        print('keep : ', ckpt_file)
+    else:
+        print('remove: ', ckpt_file)
+        os.remove(ckpt_file)
+
+
+
+
+

EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/ade20k.py

@@ -0,0 +1,54 @@
+# dataset settings
+dataset_type = 'ADE20KDataset'
+data_root = 'data/ade/ADEChallengeData2016'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 512)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', reduce_zero_label=True),
+    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 512),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/training',
+        ann_dir='annotations/training',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline))

EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/ade20k_640x640.py

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+# dataset settings
+dataset_type = 'ADE20KDataset'
+data_root = 'data/ade/ADEChallengeData2016'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (640, 640)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', reduce_zero_label=True),
+    dict(type='Resize', img_scale=(2560, 640), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2560, 640),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/training',
+        ann_dir='annotations/training',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline))

EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/chase_db1.py

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+# dataset settings
+dataset_type = 'ChaseDB1Dataset'
+data_root = 'data/CHASE_DB1'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+img_scale = (960, 999)
+crop_size = (128, 128)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=40000,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            img_dir='images/training',
+            ann_dir='annotations/training',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='images/validation',
+        ann_dir='annotations/validation',
+        pipeline=test_pipeline))

EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes.py

@@ -0,0 +1,54 @@
+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 1024)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 1024),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='leftImg8bit/train',
+        ann_dir='gtFine/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='leftImg8bit/val',
+        ann_dir='gtFine/val',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        img_dir='leftImg8bit/val',
+        ann_dir='gtFine/val',
+        pipeline=test_pipeline))

EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_1024x1024.py

@@ -0,0 +1,35 @@
+_base_ = './cityscapes.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (1024, 1024)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 1024),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_768x768.py

@@ -0,0 +1,35 @@
+_base_ = './cityscapes.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (768, 768)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2049, 1025),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))