diff --git a/EgoHOS/raw/script/.gitignore b/EgoHOS/raw/script/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..bfbaa9d01de84710fdd7c0d3db6b869da84bafef --- /dev/null +++ b/EgoHOS/raw/script/.gitignore @@ -0,0 +1,16 @@ +*.jpg +*.png + +*.pt +*.pth + +*.zip +*.tar + +# /mmsegmentation +/data +/data_to_release +/testimages +/testvideos + +/mmsegmentation/work_dirs_exp diff --git a/EgoHOS/raw/script/LICENSE b/EgoHOS/raw/script/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..1f8e5c6e643932db1a57f394644c44ed5940b6fa --- /dev/null +++ b/EgoHOS/raw/script/LICENSE @@ -0,0 +1,21 @@ +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. diff --git a/EgoHOS/raw/script/README.md b/EgoHOS/raw/script/README.md new file mode 100644 index 0000000000000000000000000000000000000000..517f153725157b084af754a0c13fa5964a661795 --- /dev/null +++ b/EgoHOS/raw/script/README.md @@ -0,0 +1,200 @@ +# 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) + + + +**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:**
+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
+6. [Other Resources](#other_github) - other resources used in our papers
+ +## 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/ + + + +## 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 +``` + + + +## Checkpoints + +- Download checkponts and config files: +```bash +bash download_checkpoints.sh +``` + + + + +## 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 +``` + + + +- 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 +``` + + + +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 +``` + + + +- Predict the dense contact boundary. +```bash +cd mmsegmentation # if you are not in this directory +bash pred_cb.sh +``` + + + +- 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 +``` + + + +## 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 +``` + + + +## 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} +} +``` + + + + diff --git a/EgoHOS/raw/script/build_env.sh b/EgoHOS/raw/script/build_env.sh new file mode 100644 index 0000000000000000000000000000000000000000..e2eab9e8f5be2ba55b29baad035153ef370502b5 --- /dev/null +++ b/EgoHOS/raw/script/build_env.sh @@ -0,0 +1,9 @@ +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 . + + + + diff --git a/EgoHOS/raw/script/ccda/composite.py b/EgoHOS/raw/script/ccda/composite.py new file mode 100644 index 0000000000000000000000000000000000000000..729cd5cd2a372f75ad8d49e10bc00a44dc15c6c6 --- /dev/null +++ b/EgoHOS/raw/script/ccda/composite.py @@ -0,0 +1,106 @@ +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() \ No newline at end of file diff --git a/EgoHOS/raw/script/ccda/extract_feature.py b/EgoHOS/raw/script/ccda/extract_feature.py new file mode 100644 index 0000000000000000000000000000000000000000..f6ff9c57cdbbfc0068a31db7aced1f913d16136c --- /dev/null +++ b/EgoHOS/raw/script/ccda/extract_feature.py @@ -0,0 +1,36 @@ +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) + + + + diff --git a/EgoHOS/raw/script/commit.sh b/EgoHOS/raw/script/commit.sh new file mode 100644 index 0000000000000000000000000000000000000000..b258aa7d946ea588b448dd78f46f8638cee9cb87 --- /dev/null +++ b/EgoHOS/raw/script/commit.sh @@ -0,0 +1,10 @@ +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 \ No newline at end of file diff --git a/EgoHOS/raw/script/data/view?usp=sharing b/EgoHOS/raw/script/data/view?usp=sharing new file mode 100644 index 0000000000000000000000000000000000000000..9a84e5f2729af438e667ef0614902dbf43ff4683 --- /dev/null +++ b/EgoHOS/raw/script/data/view?usp=sharing @@ -0,0 +1,3 @@ +data.zip - Google 雲端硬碟
登入
\ No newline at end of file diff --git a/EgoHOS/raw/script/demo/cb.gif b/EgoHOS/raw/script/demo/cb.gif new file mode 100644 index 0000000000000000000000000000000000000000..fc6f944de310d95868cc2317d8a751e695bbb2c6 --- /dev/null +++ b/EgoHOS/raw/script/demo/cb.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b34e96f284412adf2afaa8f5526c0766c6317f32c549236616dc78399aa5ba08 +size 8054330 diff --git a/EgoHOS/raw/script/demo/teaser.gif b/EgoHOS/raw/script/demo/teaser.gif new file mode 100644 index 0000000000000000000000000000000000000000..0b3da06bafbae1f35e4d075cdcac6192c77605cb --- /dev/null +++ b/EgoHOS/raw/script/demo/teaser.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29d43c94ab62cd8b29c90d6c8e400988d753edb780e6fba281b80f2c657c9119 +size 8765238 diff --git a/EgoHOS/raw/script/demo/twohands_obj1_optimized.gif b/EgoHOS/raw/script/demo/twohands_obj1_optimized.gif new file mode 100644 index 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b/EgoHOS/raw/script/demo/twohands_optimized.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fd83a0dd2304f311debfda3e34567f1e502852abaee89b409fa81d8d2ebdba65 +size 13719763 diff --git a/EgoHOS/raw/script/download_checkpoints.sh b/EgoHOS/raw/script/download_checkpoints.sh new file mode 100644 index 0000000000000000000000000000000000000000..a944ece8aad29787c763646006888860434d7f66 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/EgoHOS/raw/script/download_datasets.sh b/EgoHOS/raw/script/download_datasets.sh new file mode 100644 index 0000000000000000000000000000000000000000..cf7d57d32b5c1d6f0028fcefbc50fb0ba6ed84b1 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/EgoHOS/raw/script/download_testimages.sh b/EgoHOS/raw/script/download_testimages.sh new file mode 100644 index 0000000000000000000000000000000000000000..5a68759db0b07b89543254cbcb5fa90af39bdeed --- /dev/null +++ b/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 \ No newline at end of file diff --git a/EgoHOS/raw/script/download_testvideos.sh b/EgoHOS/raw/script/download_testvideos.sh new file mode 100644 index 0000000000000000000000000000000000000000..548f997c3c45bee5288e252bac18cb891855c328 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/EgoHOS/raw/script/mmsegmentation/.circleci/config.yml b/EgoHOS/raw/script/mmsegmentation/.circleci/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..9456918aacf55a9e66d298aa91141d33f922b656 --- /dev/null +++ b/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 diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/batch_test_list.py b/EgoHOS/raw/script/mmsegmentation/.dev/batch_test_list.py new file mode 100644 index 0000000000000000000000000000000000000000..c4fd8f97e44414566f1894befb3d0036322e5881 --- /dev/null +++ b/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 diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/batch_train_list.txt b/EgoHOS/raw/script/mmsegmentation/.dev/batch_train_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..17d19932e60b126f9c5f014a528ae63de34245e1 --- /dev/null +++ b/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 diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/benchmark_evaluation.sh b/EgoHOS/raw/script/mmsegmentation/.dev/benchmark_evaluation.sh new file mode 100644 index 0000000000000000000000000000000000000000..68dc272dad254cdcca6fe34384fc299843bf9270 --- /dev/null +++ b/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 & diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/benchmark_inference.py b/EgoHOS/raw/script/mmsegmentation/.dev/benchmark_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..5124811036f1e75b79b7650998dc8dd165b27ada --- /dev/null +++ b/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) diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/benchmark_train.sh b/EgoHOS/raw/script/mmsegmentation/.dev/benchmark_train.sh new file mode 100644 index 0000000000000000000000000000000000000000..cde47a0a579ac7bb74e6293ba36f907f0ccae243 --- /dev/null +++ b/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 & diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/check_urls.py b/EgoHOS/raw/script/mmsegmentation/.dev/check_urls.py new file mode 100644 index 0000000000000000000000000000000000000000..42b64745de6a73f240c5d182492fac19b1b765a5 --- /dev/null +++ b/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() diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/gather_benchmark_evaluation_results.py b/EgoHOS/raw/script/mmsegmentation/.dev/gather_benchmark_evaluation_results.py new file mode 100644 index 0000000000000000000000000000000000000000..47b557a1056fe90a585f0d38ae304b15e602d133 --- /dev/null +++ b/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('===================================') diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/gather_benchmark_train_results.py b/EgoHOS/raw/script/mmsegmentation/.dev/gather_benchmark_train_results.py new file mode 100644 index 0000000000000000000000000000000000000000..8aff2c4228734a5eac5d9115c3099c4e757d9414 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/.dev/gather_benchmark_train_results.py @@ -0,0 +1,100 @@ +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('===================================') diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/gather_models.py b/EgoHOS/raw/script/mmsegmentation/.dev/gather_models.py new file mode 100644 index 0000000000000000000000000000000000000000..3eedf6110ba754550d2b38535354d85a280944b7 --- /dev/null +++ b/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() diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/generate_benchmark_evaluation_script.py b/EgoHOS/raw/script/mmsegmentation/.dev/generate_benchmark_evaluation_script.py new file mode 100644 index 0000000000000000000000000000000000000000..d86e94bc8f4a7fc272b079a2070ae75f17cae692 --- /dev/null +++ b/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() diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/generate_benchmark_train_script.py b/EgoHOS/raw/script/mmsegmentation/.dev/generate_benchmark_train_script.py new file mode 100644 index 0000000000000000000000000000000000000000..6e8a0ae31135a07005ed991925e239c2be451fd6 --- /dev/null +++ b/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() diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/log_collector/example_config.py b/EgoHOS/raw/script/mmsegmentation/.dev/log_collector/example_config.py new file mode 100644 index 0000000000000000000000000000000000000000..bc2b4d6c1d0d2d4e741c24c4e16ef9e09c03e882 --- /dev/null +++ b/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' diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/log_collector/log_collector.py b/EgoHOS/raw/script/mmsegmentation/.dev/log_collector/log_collector.py new file mode 100644 index 0000000000000000000000000000000000000000..d0f4080877915d9ba1cbd451663cd702e798b057 --- /dev/null +++ b/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() diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/log_collector/readme.md b/EgoHOS/raw/script/mmsegmentation/.dev/log_collector/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..41ea23539548e0ff5cee74be7345f4cb47085429 --- /dev/null +++ b/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 + } + } +] +``` diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/log_collector/utils.py b/EgoHOS/raw/script/mmsegmentation/.dev/log_collector/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..848516a783b1f2c569993a29b7199c3a8785b2a3 --- /dev/null +++ b/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 diff --git a/EgoHOS/raw/script/mmsegmentation/.dev/md2yml.py b/EgoHOS/raw/script/mmsegmentation/.dev/md2yml.py new file mode 100644 index 0000000000000000000000000000000000000000..0fa41dcc18ea5a23f70d330b1c94664afd5f2137 --- /dev/null +++ b/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] == ' + +
 
+
+ OpenMMLab website + + + HOT + + +      + OpenMMLab platform + + + TRY IT OUT + + +
+
 
+ +
+ +[![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) + + + +
+ +English | [简体中文](README_zh-CN.md) + +
+ +## 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) + +
+Major features + +- **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. + +
+ +## 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. diff --git a/EgoHOS/raw/script/mmsegmentation/cleanup_ckpt.py b/EgoHOS/raw/script/mmsegmentation/cleanup_ckpt.py new file mode 100644 index 0000000000000000000000000000000000000000..2c5fa3859aa6cb3cb79704dd289b3504fd01a2fe --- /dev/null +++ b/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) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..efc8b4bb20c981f3db6df7eb52b3dc0744c94cc0 --- /dev/null +++ b/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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/ade20k_640x640.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/ade20k_640x640.py new file mode 100644 index 0000000000000000000000000000000000000000..14a4bb092f1db24eb871cd33fd98555cd814ef9c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/ade20k_640x640.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 = (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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/chase_db1.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/chase_db1.py new file mode 100644 index 0000000000000000000000000000000000000000..298594ea925f87f22b37094a2ec50e370aec96a0 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/chase_db1.py @@ -0,0 +1,59 @@ +# 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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..f21867c63e1835f6fceb61f066e802fd8fd2a735 --- /dev/null +++ b/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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_1024x1024.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_1024x1024.py new file mode 100644 index 0000000000000000000000000000000000000000..f98d929723b4539323ba6c9db867dfa4b01ffb22 --- /dev/null +++ b/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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_768x768.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_768x768.py new file mode 100644 index 0000000000000000000000000000000000000000..fde9d7c7d8076dabff081fce0989eec6a6f5ff07 --- /dev/null +++ b/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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_769x769.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_769x769.py new file mode 100644 index 0000000000000000000000000000000000000000..336c7b254fe392b4703039fec86a83acdbd2e1a5 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_769x769.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 = (769, 769) +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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_832x832.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_832x832.py new file mode 100644 index 0000000000000000000000000000000000000000..b9325cc0087dffb0d3f51fe8a1ebcc45eb76b30d --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/cityscapes_832x832.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 = (832, 832) +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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/coco-stuff10k.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/coco-stuff10k.py new file mode 100644 index 0000000000000000000000000000000000000000..ec0496928b9464406a4013023c553cf3e7da526b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/coco-stuff10k.py @@ -0,0 +1,57 @@ +# dataset settings +dataset_type = 'COCOStuffDataset' +data_root = 'data/coco_stuff10k' +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, + reduce_zero_label=True, + img_dir='images/train2014', + ann_dir='annotations/train2014', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + reduce_zero_label=True, + img_dir='images/test2014', + ann_dir='annotations/test2014', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + reduce_zero_label=True, + img_dir='images/test2014', + ann_dir='annotations/test2014', + pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/coco-stuff164k.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/coco-stuff164k.py new file mode 100644 index 0000000000000000000000000000000000000000..a6a38f2ac4d1a39d4a89bb101462d3ec805c3aff --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/coco-stuff164k.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'COCOStuffDataset' +data_root = 'data/coco_stuff164k' +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'), + 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/train2017', + ann_dir='annotations/train2017', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/val2017', + ann_dir='annotations/val2017', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='images/val2017', + ann_dir='annotations/val2017', + pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/drive.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/drive.py new file mode 100644 index 0000000000000000000000000000000000000000..06e8ff606e0d2a4514ec8b7d2c6c436a32efcbf4 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/drive.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'DRIVEDataset' +data_root = 'data/DRIVE' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +img_scale = (584, 565) +crop_size = (64, 64) +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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_cb.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_cb.py new file mode 100644 index 0000000000000000000000000000000000000000..da2e5c3dc80af154185873925462380d2db24f87 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_cb.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), ratio_range=(1.0, 1.0)), + # dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=1.0), + dict(type='RandomFlip', prob=0.0), + 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image', + ann_dir='train/label_contact', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label_contact', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label_contact', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_cb_ccda.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_cb_ccda.py new file mode 100644 index 0000000000000000000000000000000000000000..990768d41079011665173651e6e70aee9b8cccf7 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_cb_ccda.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), ratio_range=(1.0, 1.0)), + # dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=1.0), + dict(type='RandomFlip', prob=0.0), + 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image_ccda', + ann_dir='train/label_contact_ccda', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label_contact', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label_contact', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj1.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj1.py new file mode 100644 index 0000000000000000000000000000000000000000..c15d173c48acd0c44ed8c65fc040d45f92dace64 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj1.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image', + ann_dir='train/label_handobj1', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_outdomain/image', + ann_dir='test_outdomain/label_handobj1', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label_handobj1', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj1_ccda.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj1_ccda.py new file mode 100644 index 0000000000000000000000000000000000000000..d930425698319d9ab8da0b7f4a5d79785e281d32 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj1_ccda.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image_ccda', + ann_dir='train/label_handobj1_ccda', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj2.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj2.py new file mode 100644 index 0000000000000000000000000000000000000000..28806b483fdede1f99acc819887ba7f3901d804c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj2.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image', + ann_dir='train/label', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj2_ccda.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj2_ccda.py new file mode 100644 index 0000000000000000000000000000000000000000..23c23d937e61f7ac3cb26ea13f38dd20aea5dcc6 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj2_ccda.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image_ccda', + ann_dir='train/label_handobj2_ccda', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj2_subset.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj2_subset.py new file mode 100644 index 0000000000000000000000000000000000000000..f9ebfe36fd24552496a05a27616b7df793f9a6f8 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_handobj2_subset.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image_200', + ann_dir='train/label_200', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj1.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj1.py new file mode 100644 index 0000000000000000000000000000000000000000..af0690d60a0fd2c5511e9d885b15ab050eedc5ce --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj1.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), ratio_range=(1.0, 1.0)), + # dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=1.0), + dict(type='RandomFlip', prob=0.0), + 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image', + ann_dir='train/label_obj1', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label_obj1', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label_obj1', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj1_ccda.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj1_ccda.py new file mode 100644 index 0000000000000000000000000000000000000000..0d6eee54dbdc344581e20056d62ae8be5f4cecee --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj1_ccda.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), ratio_range=(1.0, 1.0)), + # dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=1.0), + dict(type='RandomFlip', prob=0.0), + 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image_ccda', + ann_dir='train/label_obj1_ccda', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label_obj1', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label_obj1', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj2.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj2.py new file mode 100644 index 0000000000000000000000000000000000000000..a48cf2e3e6419e1d08a39f26a93e47430fb906e9 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj2.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), ratio_range=(1.0, 1.0)), + # dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=1.0), + dict(type='RandomFlip', prob=0.0), + 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image', + ann_dir='train/label_obj2', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label_obj2', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label_obj2', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj2_ccda.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj2_ccda.py new file mode 100644 index 0000000000000000000000000000000000000000..7685028e0993ff39a5a5c68e207c703acd21d6a9 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_obj2_ccda.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), ratio_range=(1.0, 1.0)), + # dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=1.0), + dict(type='RandomFlip', prob=0.0), + 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image_ccda', + ann_dir='train/label_obj2_ccda', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label_obj2', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label_obj2', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_twohands.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_twohands.py new file mode 100644 index 0000000000000000000000000000000000000000..d8a09e1a6ab14ac5e001af5f2fa0f959c9ba1f22 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_twohands.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image', + ann_dir='train/label_twohands', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label_twohands', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label_twohands', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_twohands_ccda.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_twohands_ccda.py new file mode 100644 index 0000000000000000000000000000000000000000..756215ad74cf9929f5b20df69593fa22a59ec83c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/egohos_twohands_ccda.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'EgoHOSDataset' +data_root = '/mnt/session_space/home/lingzzha/EgoHOS/data' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (360, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(360, 480), 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=(360, 480), + # 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=16, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='train/image_ccda', + ann_dir='train/label_twohands_ccda', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='val/image', + ann_dir='val/label', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='test_indomain/image', + ann_dir='test_indomain/label', + pipeline=test_pipeline)) + + + + + diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/hrf.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/hrf.py new file mode 100644 index 0000000000000000000000000000000000000000..242d790eb1b83e75cf6b7eaa7a35c674099311ad --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/hrf.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'HRFDataset' +data_root = 'data/HRF' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +img_scale = (2336, 3504) +crop_size = (256, 256) +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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/isaid.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/isaid.py new file mode 100644 index 0000000000000000000000000000000000000000..8e4c26abb703e07b0361eca68a862d7d869409da --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/isaid.py @@ -0,0 +1,62 @@ +# dataset settings +dataset_type = 'iSAIDDataset' +data_root = 'data/iSAID' + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +""" +This crop_size setting is followed by the implementation of +`PointFlow: Flowing Semantics Through Points for Aerial Image +Segmentation `_. +""" + +crop_size = (896, 896) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='Resize', img_scale=(896, 896), 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=(896, 896), + # 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='img_dir/train', + ann_dir='ann_dir/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='img_dir/val', + ann_dir='ann_dir/val', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='img_dir/val', + ann_dir='ann_dir/val', + pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/loveda.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/loveda.py new file mode 100644 index 0000000000000000000000000000000000000000..e5533569594601f52a4a4f164ba361dd0ed16da7 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/loveda.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'LoveDADataset' +data_root = 'data/loveDA' +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=(1024, 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=4, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_root=data_root, + img_dir='img_dir/train', + ann_dir='ann_dir/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='img_dir/val', + ann_dir='ann_dir/val', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='img_dir/val', + ann_dir='ann_dir/val', + pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_context.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_context.py new file mode 100644 index 0000000000000000000000000000000000000000..ff65bad1b86d7e3a5980bb5b9fc55798dc8df5f4 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_context.py @@ -0,0 +1,60 @@ +# dataset settings +dataset_type = 'PascalContextDataset' +data_root = 'data/VOCdevkit/VOC2010/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +img_scale = (520, 520) +crop_size = (480, 480) + +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], + 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='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/train.txt', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/val.txt', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/val.txt', + pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_context_59.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_context_59.py new file mode 100644 index 0000000000000000000000000000000000000000..37585abab89834b95cd5bdd993b994fca1db65f6 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_context_59.py @@ -0,0 +1,60 @@ +# dataset settings +dataset_type = 'PascalContextDataset59' +data_root = 'data/VOCdevkit/VOC2010/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +img_scale = (520, 520) +crop_size = (480, 480) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + 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], + 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='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/train.txt', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/val.txt', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClassContext', + split='ImageSets/SegmentationContext/val.txt', + pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_voc12.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_voc12.py new file mode 100644 index 0000000000000000000000000000000000000000..ba1d42d0c5781f56dc177d860d856bb34adce555 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_voc12.py @@ -0,0 +1,57 @@ +# dataset settings +dataset_type = 'PascalVOCDataset' +data_root = 'data/VOCdevkit/VOC2012' +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'), + 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='JPEGImages', + ann_dir='SegmentationClass', + split='ImageSets/Segmentation/train.txt', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClass', + split='ImageSets/Segmentation/val.txt', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='JPEGImages', + ann_dir='SegmentationClass', + split='ImageSets/Segmentation/val.txt', + pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_voc12_aug.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_voc12_aug.py new file mode 100644 index 0000000000000000000000000000000000000000..3f23b6717d53ad29f02dd15046802a2631a5076b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/pascal_voc12_aug.py @@ -0,0 +1,9 @@ +_base_ = './pascal_voc12.py' +# dataset settings +data = dict( + train=dict( + ann_dir=['SegmentationClass', 'SegmentationClassAug'], + split=[ + 'ImageSets/Segmentation/train.txt', + 'ImageSets/Segmentation/aug.txt' + ])) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/potsdam.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/potsdam.py new file mode 100644 index 0000000000000000000000000000000000000000..f74c4a56c35c38e3290890ac2500e8cc6102cd50 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/potsdam.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'PotsdamDataset' +data_root = 'data/potsdam' +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=(512, 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=(512, 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='img_dir/train', + ann_dir='ann_dir/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='img_dir/val', + ann_dir='ann_dir/val', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='img_dir/val', + ann_dir='ann_dir/val', + pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/stare.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/stare.py new file mode 100644 index 0000000000000000000000000000000000000000..3f71b25488cc11a6b4d582ac52b5a24e1ad1cf8e --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/stare.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'STAREDataset' +data_root = 'data/STARE' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +img_scale = (605, 700) +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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/vaihingen.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/vaihingen.py new file mode 100644 index 0000000000000000000000000000000000000000..c0df282c4a236e2e65318d2d08a0402f96569b6e --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/datasets/vaihingen.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'ISPRSDataset' +data_root = 'data/vaihingen' +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=(512, 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=(512, 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='img_dir/train', + ann_dir='ann_dir/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_root=data_root, + img_dir='img_dir/val', + ann_dir='ann_dir/val', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_root=data_root, + img_dir='img_dir/val', + ann_dir='ann_dir/val', + pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/default_runtime.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/default_runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..b564cc4e7e7d9a67dacaaddecb100e4d8f5c005b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/default_runtime.py @@ -0,0 +1,14 @@ +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook', by_epoch=False), + # dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] +cudnn_benchmark = True diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ann_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ann_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..a2cb653827e44e6015b3b83bc578003e614a6aa1 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ann_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='ANNHead', + in_channels=[1024, 2048], + in_index=[2, 3], + channels=512, + project_channels=256, + query_scales=(1, ), + key_pool_scales=(1, 3, 6, 8), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/apcnet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/apcnet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..c8f5316cbcf3896ba9de7ca2c801eba512f01d5e --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/apcnet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='APCHead', + in_channels=2048, + in_index=3, + channels=512, + pool_scales=(1, 2, 3, 6), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=dict(type='SyncBN', requires_grad=True), + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/bisenetv1_r18-d32.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/bisenetv1_r18-d32.py new file mode 100644 index 0000000000000000000000000000000000000000..40698644ba77d479412ba7d81e7d60196b93235d --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/bisenetv1_r18-d32.py @@ -0,0 +1,68 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='BiSeNetV1', + in_channels=3, + context_channels=(128, 256, 512), + spatial_channels=(64, 64, 64, 128), + out_indices=(0, 1, 2), + out_channels=256, + backbone_cfg=dict( + type='ResNet', + in_channels=3, + depth=18, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=None), + decode_head=dict( + type='FCNHead', + in_channels=256, + in_index=0, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=64, + num_convs=1, + num_classes=19, + in_index=1, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + dict( + type='FCNHead', + in_channels=128, + channels=64, + num_convs=1, + num_classes=19, + in_index=2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/bisenetv2.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/bisenetv2.py new file mode 100644 index 0000000000000000000000000000000000000000..f8fffeecad989c43b51dd2c19c381b9521e6dcba --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/bisenetv2.py @@ -0,0 +1,80 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='BiSeNetV2', + detail_channels=(64, 64, 128), + semantic_channels=(16, 32, 64, 128), + semantic_expansion_ratio=6, + bga_channels=128, + out_indices=(0, 1, 2, 3, 4), + init_cfg=None, + align_corners=False), + decode_head=dict( + type='FCNHead', + in_channels=128, + in_index=0, + channels=1024, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=16, + channels=16, + num_convs=2, + num_classes=19, + in_index=1, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + dict( + type='FCNHead', + in_channels=32, + channels=64, + num_convs=2, + num_classes=19, + in_index=2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + dict( + type='FCNHead', + in_channels=64, + channels=256, + num_convs=2, + num_classes=19, + in_index=3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + dict( + type='FCNHead', + in_channels=128, + channels=1024, + num_convs=2, + num_classes=19, + in_index=4, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ccnet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ccnet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..794148f576b9e215c3c6963e73dffe98204b7717 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ccnet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='CCHead', + in_channels=2048, + in_index=3, + channels=512, + recurrence=2, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/cgnet.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/cgnet.py new file mode 100644 index 0000000000000000000000000000000000000000..eff8d9458c877c5db894957e0b1b4597e40da6ab --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/cgnet.py @@ -0,0 +1,35 @@ +# model settings +norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='CGNet', + norm_cfg=norm_cfg, + in_channels=3, + num_channels=(32, 64, 128), + num_blocks=(3, 21), + dilations=(2, 4), + reductions=(8, 16)), + decode_head=dict( + type='FCNHead', + in_channels=256, + in_index=2, + channels=256, + num_convs=0, + concat_input=False, + dropout_ratio=0, + num_classes=19, + norm_cfg=norm_cfg, + loss_decode=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + class_weight=[ + 2.5959933, 6.7415504, 3.5354059, 9.8663225, 9.690899, 9.369352, + 10.289121, 9.953208, 4.3097677, 9.490387, 7.674431, 9.396905, + 10.347791, 6.3927646, 10.226669, 10.241062, 10.280587, + 10.396974, 10.055647 + ])), + # model training and testing settings + train_cfg=dict(sampler=None), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/danet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/danet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..2c934939fac48525f22ad86f489a041dd7db7d09 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/danet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DAHead', + in_channels=2048, + in_index=3, + channels=512, + pam_channels=64, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/deeplabv3_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/deeplabv3_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..d7a43bee01422ad4795dd27874e0cd4bb6cbfecf --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/deeplabv3_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='ASPPHead', + in_channels=2048, + in_index=3, + channels=512, + dilations=(1, 12, 24, 36), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/deeplabv3_unet_s5-d16.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/deeplabv3_unet_s5-d16.py new file mode 100644 index 0000000000000000000000000000000000000000..0cd262999d8b2cb8e14a5c32190ae73f479d8e81 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/deeplabv3_unet_s5-d16.py @@ -0,0 +1,50 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='UNet', + in_channels=3, + base_channels=64, + num_stages=5, + strides=(1, 1, 1, 1, 1), + enc_num_convs=(2, 2, 2, 2, 2), + dec_num_convs=(2, 2, 2, 2), + downsamples=(True, True, True, True), + enc_dilations=(1, 1, 1, 1, 1), + dec_dilations=(1, 1, 1, 1), + with_cp=False, + conv_cfg=None, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + upsample_cfg=dict(type='InterpConv'), + norm_eval=False), + decode_head=dict( + type='ASPPHead', + in_channels=64, + in_index=4, + channels=16, + dilations=(1, 12, 24, 36), + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=128, + in_index=3, + channels=64, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=256, stride=170)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/deeplabv3plus_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/deeplabv3plus_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..050e39e091d816df9028d23aa3ecf9db74e441e1 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/deeplabv3plus_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DepthwiseSeparableASPPHead', + in_channels=2048, + in_index=3, + channels=512, + dilations=(1, 12, 24, 36), + c1_in_channels=256, + c1_channels=48, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/dmnet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/dmnet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..d22ba52640bebd805b3b8d07025e276dfb023759 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/dmnet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DMHead', + in_channels=2048, + in_index=3, + channels=512, + filter_sizes=(1, 3, 5, 7), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=dict(type='SyncBN', requires_grad=True), + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/dnl_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/dnl_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..edb4c174c51e34c103737ba39bfc48bf831e561d --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/dnl_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DNLHead', + in_channels=2048, + in_index=3, + channels=512, + dropout_ratio=0.1, + reduction=2, + use_scale=True, + mode='embedded_gaussian', + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/dpt_vit-b16.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/dpt_vit-b16.py new file mode 100644 index 0000000000000000000000000000000000000000..dfd48a95f84c3aa7ec5cc21b40c28a9fb8333c09 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/dpt_vit-b16.py @@ -0,0 +1,31 @@ +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='pretrain/vit-b16_p16_224-80ecf9dd.pth', # noqa + backbone=dict( + type='VisionTransformer', + img_size=224, + embed_dims=768, + num_layers=12, + num_heads=12, + out_indices=(2, 5, 8, 11), + final_norm=False, + with_cls_token=True, + output_cls_token=True), + decode_head=dict( + type='DPTHead', + in_channels=(768, 768, 768, 768), + channels=256, + embed_dims=768, + post_process_channels=[96, 192, 384, 768], + num_classes=150, + readout_type='project', + input_transform='multiple_select', + in_index=(0, 1, 2, 3), + norm_cfg=norm_cfg, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=None, + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) # yapf: disable diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/emanet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/emanet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..26adcd430926de0862204a71d345f2543167f27b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/emanet_r50-d8.py @@ -0,0 +1,47 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='EMAHead', + in_channels=2048, + in_index=3, + channels=256, + ema_channels=512, + num_bases=64, + num_stages=3, + momentum=0.1, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/encnet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/encnet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..be777123a886503172a95fe0719e956a147bbd68 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/encnet_r50-d8.py @@ -0,0 +1,48 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='EncHead', + in_channels=[512, 1024, 2048], + in_index=(1, 2, 3), + channels=512, + num_codes=32, + use_se_loss=True, + add_lateral=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_se_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/erfnet_fcn.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/erfnet_fcn.py new file mode 100644 index 0000000000000000000000000000000000000000..7f2e9bff8d1917ca436da8da49c085c599bdec87 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/erfnet_fcn.py @@ -0,0 +1,32 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='ERFNet', + in_channels=3, + enc_downsample_channels=(16, 64, 128), + enc_stage_non_bottlenecks=(5, 8), + enc_non_bottleneck_dilations=(2, 4, 8, 16), + enc_non_bottleneck_channels=(64, 128), + dec_upsample_channels=(64, 16), + dec_stages_non_bottleneck=(2, 2), + dec_non_bottleneck_channels=(64, 16), + dropout_ratio=0.1, + init_cfg=None), + decode_head=dict( + type='FCNHead', + in_channels=16, + channels=128, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fast_scnn.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fast_scnn.py new file mode 100644 index 0000000000000000000000000000000000000000..8e89d911dde26b02a21197b1608f1c862d6a51f5 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fast_scnn.py @@ -0,0 +1,57 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='FastSCNN', + downsample_dw_channels=(32, 48), + global_in_channels=64, + global_block_channels=(64, 96, 128), + global_block_strides=(2, 2, 1), + global_out_channels=128, + higher_in_channels=64, + lower_in_channels=128, + fusion_out_channels=128, + out_indices=(0, 1, 2), + norm_cfg=norm_cfg, + align_corners=False), + decode_head=dict( + type='DepthwiseSeparableFCNHead', + in_channels=128, + channels=128, + concat_input=False, + num_classes=19, + in_index=-1, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1)), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=19, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=19, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fastfcn_r50-d32_jpu_psp.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fastfcn_r50-d32_jpu_psp.py new file mode 100644 index 0000000000000000000000000000000000000000..9dc8609aebc5dddfe5ff7a97778870156b739edd --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fastfcn_r50-d32_jpu_psp.py @@ -0,0 +1,53 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + dilations=(1, 1, 2, 4), + strides=(1, 2, 2, 2), + out_indices=(1, 2, 3), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + neck=dict( + type='JPU', + in_channels=(512, 1024, 2048), + mid_channels=512, + start_level=0, + end_level=-1, + dilations=(1, 2, 4, 8), + align_corners=False, + norm_cfg=norm_cfg), + decode_head=dict( + type='PSPHead', + in_channels=2048, + in_index=2, + channels=512, + pool_scales=(1, 2, 3, 6), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=1, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fcn_hr18.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fcn_hr18.py new file mode 100644 index 0000000000000000000000000000000000000000..c3e299bc89ada56ca14bbffcbdb08a586b8ed9e9 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fcn_hr18.py @@ -0,0 +1,52 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + type='HRNet', + norm_cfg=norm_cfg, + norm_eval=False, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(18, 36)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(18, 36, 72)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(18, 36, 72, 144)))), + decode_head=dict( + type='FCNHead', + in_channels=[18, 36, 72, 144], + in_index=(0, 1, 2, 3), + channels=sum([18, 36, 72, 144]), + input_transform='resize_concat', + kernel_size=1, + num_convs=1, + concat_input=False, + dropout_ratio=-1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fcn_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fcn_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..5e98f6cc918b6146fc6d613c6918e825ef1355c3 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fcn_r50-d8.py @@ -0,0 +1,45 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='FCNHead', + in_channels=2048, + in_index=3, + channels=512, + num_convs=2, + concat_input=True, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fcn_unet_s5-d16.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fcn_unet_s5-d16.py new file mode 100644 index 0000000000000000000000000000000000000000..a33e7972877f902d0e7d18401ca675e3e4e60a18 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fcn_unet_s5-d16.py @@ -0,0 +1,51 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='UNet', + in_channels=3, + base_channels=64, + num_stages=5, + strides=(1, 1, 1, 1, 1), + enc_num_convs=(2, 2, 2, 2, 2), + dec_num_convs=(2, 2, 2, 2), + downsamples=(True, True, True, True), + enc_dilations=(1, 1, 1, 1, 1), + dec_dilations=(1, 1, 1, 1), + with_cp=False, + conv_cfg=None, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + upsample_cfg=dict(type='InterpConv'), + norm_eval=False), + decode_head=dict( + type='FCNHead', + in_channels=64, + in_index=4, + channels=64, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=128, + in_index=3, + channels=64, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=256, stride=170)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fpn_r50.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fpn_r50.py new file mode 100644 index 0000000000000000000000000000000000000000..86ab327db92e44c14822d65f1c9277cb007f17c1 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/fpn_r50.py @@ -0,0 +1,36 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=4), + decode_head=dict( + type='FPNHead', + in_channels=[256, 256, 256, 256], + in_index=[0, 1, 2, 3], + feature_strides=[4, 8, 16, 32], + channels=128, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/gcnet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/gcnet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2ad69f5c22adfe79d5fdabf920217628987166 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/gcnet_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='GCHead', + in_channels=2048, + in_index=3, + channels=512, + ratio=1 / 4., + pooling_type='att', + fusion_types=('channel_add', ), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/icnet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/icnet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..d7273cd28ec6cdd033bf5767359e27888c53b178 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/icnet_r50-d8.py @@ -0,0 +1,74 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='ICNet', + backbone_cfg=dict( + type='ResNetV1c', + in_channels=3, + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + in_channels=3, + layer_channels=(512, 2048), + light_branch_middle_channels=32, + psp_out_channels=512, + out_channels=(64, 256, 256), + norm_cfg=norm_cfg, + align_corners=False, + ), + neck=dict( + type='ICNeck', + in_channels=(64, 256, 256), + out_channels=128, + norm_cfg=norm_cfg, + align_corners=False), + decode_head=dict( + type='FCNHead', + in_channels=128, + channels=128, + num_convs=1, + in_index=2, + dropout_ratio=0, + num_classes=19, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=128, + num_convs=1, + num_classes=19, + in_index=0, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=128, + channels=128, + num_convs=1, + num_classes=19, + in_index=1, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/isanet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/isanet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..c0221a371ddc06b503a8722789a7a82a7442d41f --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/isanet_r50-d8.py @@ -0,0 +1,45 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='ISAHead', + in_channels=2048, + in_index=3, + channels=512, + isa_channels=256, + down_factor=(8, 8), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/lraspp_m-v3-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/lraspp_m-v3-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..93258242a90695cc94a7c6bd41562d6a75988771 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/lraspp_m-v3-d8.py @@ -0,0 +1,25 @@ +# model settings +norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='MobileNetV3', + arch='large', + out_indices=(1, 3, 16), + norm_cfg=norm_cfg), + decode_head=dict( + type='LRASPPHead', + in_channels=(16, 24, 960), + in_index=(0, 1, 2), + channels=128, + input_transform='multiple_select', + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/nonlocal_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/nonlocal_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..5674a39854cafd1f2e363bac99c58ccae62f24da --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/nonlocal_r50-d8.py @@ -0,0 +1,46 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='NLHead', + in_channels=2048, + in_index=3, + channels=512, + dropout_ratio=0.1, + reduction=2, + use_scale=True, + mode='embedded_gaussian', + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ocrnet_hr18.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ocrnet_hr18.py new file mode 100644 index 0000000000000000000000000000000000000000..c60f62a7cdf3f5c5096a7a7e725e8268fddcb057 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ocrnet_hr18.py @@ -0,0 +1,68 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='CascadeEncoderDecoder', + num_stages=2, + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + type='HRNet', + norm_cfg=norm_cfg, + norm_eval=False, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(18, 36)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(18, 36, 72)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(18, 36, 72, 144)))), + decode_head=[ + dict( + type='FCNHead', + in_channels=[18, 36, 72, 144], + channels=sum([18, 36, 72, 144]), + in_index=(0, 1, 2, 3), + input_transform='resize_concat', + kernel_size=1, + num_convs=1, + concat_input=False, + dropout_ratio=-1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='OCRHead', + in_channels=[18, 36, 72, 144], + in_index=(0, 1, 2, 3), + input_transform='resize_concat', + channels=512, + ocr_channels=256, + dropout_ratio=-1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ocrnet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ocrnet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..615aa3ff703942b6c22b2d6e9642504dd3e41ebd --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/ocrnet_r50-d8.py @@ -0,0 +1,47 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='CascadeEncoderDecoder', + num_stages=2, + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=[ + dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='OCRHead', + in_channels=2048, + in_index=3, + channels=512, + ocr_channels=256, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)) + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/pointrend_r50.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/pointrend_r50.py new file mode 100644 index 0000000000000000000000000000000000000000..9d323dbf9466d41e0800aa57ef84045f3d874bdf --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/pointrend_r50.py @@ -0,0 +1,56 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='CascadeEncoderDecoder', + num_stages=2, + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=4), + decode_head=[ + dict( + type='FPNHead', + in_channels=[256, 256, 256, 256], + in_index=[0, 1, 2, 3], + feature_strides=[4, 8, 16, 32], + channels=128, + dropout_ratio=-1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + dict( + type='PointHead', + in_channels=[256], + in_index=[0], + channels=256, + num_fcs=3, + coarse_pred_each_layer=True, + dropout_ratio=-1, + num_classes=19, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)) + ], + # model training and testing settings + train_cfg=dict( + num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75), + test_cfg=dict( + mode='whole', + subdivision_steps=2, + subdivision_num_points=8196, + scale_factor=2)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/psanet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/psanet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..689513fa9d2a40f14bf0ae4ae61f38f0dcc1b3da --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/psanet_r50-d8.py @@ -0,0 +1,49 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='PSAHead', + in_channels=2048, + in_index=3, + channels=512, + mask_size=(97, 97), + psa_type='bi-direction', + compact=False, + shrink_factor=2, + normalization_factor=1.0, + psa_softmax=True, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/pspnet_r50-d8.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/pspnet_r50-d8.py new file mode 100644 index 0000000000000000000000000000000000000000..f451e08ad2eb0732dcb806b1851eb978d4acf136 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/pspnet_r50-d8.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='PSPHead', + in_channels=2048, + in_index=3, + channels=512, + pool_scales=(1, 2, 3, 6), + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/pspnet_unet_s5-d16.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/pspnet_unet_s5-d16.py new file mode 100644 index 0000000000000000000000000000000000000000..fcff9ec4f41fad158344ecd77313dc14564f3682 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/pspnet_unet_s5-d16.py @@ -0,0 +1,50 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='UNet', + in_channels=3, + base_channels=64, + num_stages=5, + strides=(1, 1, 1, 1, 1), + enc_num_convs=(2, 2, 2, 2, 2), + dec_num_convs=(2, 2, 2, 2), + downsamples=(True, True, True, True), + enc_dilations=(1, 1, 1, 1, 1), + dec_dilations=(1, 1, 1, 1), + with_cp=False, + conv_cfg=None, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + upsample_cfg=dict(type='InterpConv'), + norm_eval=False), + decode_head=dict( + type='PSPHead', + in_channels=64, + in_index=4, + channels=16, + pool_scales=(1, 2, 3, 6), + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=128, + in_index=3, + channels=64, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=256, stride=170)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/segformer_mit-b0.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/segformer_mit-b0.py new file mode 100644 index 0000000000000000000000000000000000000000..5b3e07331d0b42961e2969b75ca364f7b535b43f --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/segformer_mit-b0.py @@ -0,0 +1,34 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='MixVisionTransformer', + in_channels=3, + embed_dims=32, + num_stages=4, + num_layers=[2, 2, 2, 2], + num_heads=[1, 2, 5, 8], + patch_sizes=[7, 3, 3, 3], + sr_ratios=[8, 4, 2, 1], + out_indices=(0, 1, 2, 3), + mlp_ratio=4, + qkv_bias=True, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.1), + decode_head=dict( + type='SegformerHead', + in_channels=[32, 64, 160, 256], + in_index=[0, 1, 2, 3], + channels=256, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/segmenter_vit-b16_mask.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/segmenter_vit-b16_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..622f1228a4988bc64f783dee936ab8c37ed2a46b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/segmenter_vit-b16_mask.py @@ -0,0 +1,36 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_base_p16_384_20220308-96dfe169.pth' # noqa +# model settings +backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=checkpoint, + backbone=dict( + type='VisionTransformer', + img_size=(512, 512), + patch_size=16, + in_channels=3, + embed_dims=768, + num_layers=12, + num_heads=12, + drop_path_rate=0.1, + attn_drop_rate=0.0, + drop_rate=0.0, + final_norm=True, + norm_cfg=backbone_norm_cfg, + with_cls_token=True, + interpolate_mode='bicubic', + ), + decode_head=dict( + type='SegmenterMaskTransformerHead', + in_channels=768, + channels=768, + num_classes=150, + num_layers=2, + num_heads=12, + embed_dims=768, + dropout_ratio=0.0, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + ), + test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(480, 480)), +) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/setr_mla.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/setr_mla.py new file mode 100644 index 0000000000000000000000000000000000000000..af4ba2492a7c8d0868810968939de7ff25c26ca6 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/setr_mla.py @@ -0,0 +1,95 @@ +# model settings +backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth', + backbone=dict( + type='VisionTransformer', + img_size=(768, 768), + patch_size=16, + in_channels=3, + embed_dims=1024, + num_layers=24, + num_heads=16, + out_indices=(5, 11, 17, 23), + drop_rate=0.1, + norm_cfg=backbone_norm_cfg, + with_cls_token=False, + interpolate_mode='bilinear', + ), + neck=dict( + type='MLANeck', + in_channels=[1024, 1024, 1024, 1024], + out_channels=256, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + ), + decode_head=dict( + type='SETRMLAHead', + in_channels=(256, 256, 256, 256), + channels=512, + in_index=(0, 1, 2, 3), + dropout_ratio=0, + mla_channels=128, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=256, + channels=256, + in_index=0, + dropout_ratio=0, + num_convs=0, + kernel_size=1, + concat_input=False, + num_classes=19, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=256, + channels=256, + in_index=1, + dropout_ratio=0, + num_convs=0, + kernel_size=1, + concat_input=False, + num_classes=19, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=256, + channels=256, + in_index=2, + dropout_ratio=0, + num_convs=0, + kernel_size=1, + concat_input=False, + num_classes=19, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=256, + channels=256, + in_index=3, + dropout_ratio=0, + num_convs=0, + kernel_size=1, + concat_input=False, + num_classes=19, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + ], + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/setr_naive.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/setr_naive.py new file mode 100644 index 0000000000000000000000000000000000000000..0c330ea2d19d6cd8214ae342147cc34915f619a7 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/setr_naive.py @@ -0,0 +1,80 @@ +# model settings +backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth', + backbone=dict( + type='VisionTransformer', + img_size=(768, 768), + patch_size=16, + in_channels=3, + embed_dims=1024, + num_layers=24, + num_heads=16, + out_indices=(9, 14, 19, 23), + drop_rate=0.1, + norm_cfg=backbone_norm_cfg, + with_cls_token=True, + interpolate_mode='bilinear', + ), + decode_head=dict( + type='SETRUPHead', + in_channels=1024, + channels=256, + in_index=3, + num_classes=19, + dropout_ratio=0, + norm_cfg=norm_cfg, + num_convs=1, + up_scale=4, + kernel_size=1, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=[ + dict( + type='SETRUPHead', + in_channels=1024, + channels=256, + in_index=0, + num_classes=19, + dropout_ratio=0, + norm_cfg=norm_cfg, + num_convs=1, + up_scale=4, + kernel_size=1, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='SETRUPHead', + in_channels=1024, + channels=256, + in_index=1, + num_classes=19, + dropout_ratio=0, + norm_cfg=norm_cfg, + num_convs=1, + up_scale=4, + kernel_size=1, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='SETRUPHead', + in_channels=1024, + channels=256, + in_index=2, + num_classes=19, + dropout_ratio=0, + norm_cfg=norm_cfg, + num_convs=1, + up_scale=4, + kernel_size=1, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)) + ], + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/setr_pup.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/setr_pup.py new file mode 100644 index 0000000000000000000000000000000000000000..8e5f23b9c99d029df677f8d97d372eb609460e97 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/setr_pup.py @@ -0,0 +1,80 @@ +# model settings +backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth', + backbone=dict( + type='VisionTransformer', + img_size=(768, 768), + patch_size=16, + in_channels=3, + embed_dims=1024, + num_layers=24, + num_heads=16, + out_indices=(9, 14, 19, 23), + drop_rate=0.1, + norm_cfg=backbone_norm_cfg, + with_cls_token=True, + interpolate_mode='bilinear', + ), + decode_head=dict( + type='SETRUPHead', + in_channels=1024, + channels=256, + in_index=3, + num_classes=19, + dropout_ratio=0, + norm_cfg=norm_cfg, + num_convs=4, + up_scale=2, + kernel_size=3, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=[ + dict( + type='SETRUPHead', + in_channels=1024, + channels=256, + in_index=0, + num_classes=19, + dropout_ratio=0, + norm_cfg=norm_cfg, + num_convs=1, + up_scale=4, + kernel_size=3, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='SETRUPHead', + in_channels=1024, + channels=256, + in_index=1, + num_classes=19, + dropout_ratio=0, + norm_cfg=norm_cfg, + num_convs=1, + up_scale=4, + kernel_size=3, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='SETRUPHead', + in_channels=1024, + channels=256, + in_index=2, + num_classes=19, + dropout_ratio=0, + norm_cfg=norm_cfg, + num_convs=1, + up_scale=4, + kernel_size=3, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + ], + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/stdc.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/stdc.py new file mode 100644 index 0000000000000000000000000000000000000000..341a4ec58939ceb1cf81957947bcb9fd720a169d --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/stdc.py @@ -0,0 +1,83 @@ +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='STDCContextPathNet', + backbone_cfg=dict( + type='STDCNet', + stdc_type='STDCNet1', + in_channels=3, + channels=(32, 64, 256, 512, 1024), + bottleneck_type='cat', + num_convs=4, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + with_final_conv=False), + last_in_channels=(1024, 512), + out_channels=128, + ffm_cfg=dict(in_channels=384, out_channels=256, scale_factor=4)), + decode_head=dict( + type='FCNHead', + in_channels=256, + channels=256, + num_convs=1, + num_classes=19, + in_index=3, + concat_input=False, + dropout_ratio=0.1, + norm_cfg=norm_cfg, + align_corners=True, + sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000), + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=64, + num_convs=1, + num_classes=19, + in_index=2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000), + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + dict( + type='FCNHead', + in_channels=128, + channels=64, + num_convs=1, + num_classes=19, + in_index=1, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000), + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + dict( + type='STDCHead', + in_channels=256, + channels=64, + num_convs=1, + num_classes=2, + boundary_threshold=0.1, + in_index=0, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=True, + loss_decode=[ + dict( + type='CrossEntropyLoss', + loss_name='loss_ce', + use_sigmoid=True, + loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=1.0) + ]), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/twins_pcpvt-s_fpn.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/twins_pcpvt-s_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..0f4488a75ac5f6dbde86e14c4ef4f4b7b1aac78c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/twins_pcpvt-s_fpn.py @@ -0,0 +1,45 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_small_20220308-e638c41c.pth' # noqa + +# model settings +backbone_norm_cfg = dict(type='LN') +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='PCPVT', + init_cfg=dict(type='Pretrained', checkpoint=checkpoint), + in_channels=3, + embed_dims=[64, 128, 320, 512], + num_heads=[1, 2, 5, 8], + patch_sizes=[4, 2, 2, 2], + strides=[4, 2, 2, 2], + mlp_ratios=[8, 8, 4, 4], + out_indices=(0, 1, 2, 3), + qkv_bias=True, + norm_cfg=backbone_norm_cfg, + depths=[3, 4, 6, 3], + sr_ratios=[8, 4, 2, 1], + norm_after_stage=False, + drop_rate=0.0, + attn_drop_rate=0., + drop_path_rate=0.2), + neck=dict( + type='FPN', + in_channels=[64, 128, 320, 512], + out_channels=256, + num_outs=4), + decode_head=dict( + type='FPNHead', + in_channels=[256, 256, 256, 256], + in_index=[0, 1, 2, 3], + feature_strides=[4, 8, 16, 32], + channels=128, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/twins_pcpvt-s_upernet.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/twins_pcpvt-s_upernet.py new file mode 100644 index 0000000000000000000000000000000000000000..14a74b988d38e61ea4cb899b95fab4c5735696d7 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/twins_pcpvt-s_upernet.py @@ -0,0 +1,53 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_small_20220308-e638c41c.pth' # noqa + +# model settings +backbone_norm_cfg = dict(type='LN') +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='PCPVT', + init_cfg=dict(type='Pretrained', checkpoint=checkpoint), + in_channels=3, + embed_dims=[64, 128, 320, 512], + num_heads=[1, 2, 5, 8], + patch_sizes=[4, 2, 2, 2], + strides=[4, 2, 2, 2], + mlp_ratios=[8, 8, 4, 4], + out_indices=(0, 1, 2, 3), + qkv_bias=True, + norm_cfg=backbone_norm_cfg, + depths=[3, 4, 6, 3], + sr_ratios=[8, 4, 2, 1], + norm_after_stage=False, + drop_rate=0.0, + attn_drop_rate=0., + drop_path_rate=0.2), + decode_head=dict( + type='UPerHead', + in_channels=[64, 128, 320, 512], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=512, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=320, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_beit.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_beit.py new file mode 100644 index 0000000000000000000000000000000000000000..9c5bfa3310671b82a076ecafe65d158551c42c46 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_beit.py @@ -0,0 +1,50 @@ +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='BEiT', + img_size=(640, 640), + patch_size=16, + in_channels=3, + embed_dims=768, + num_layers=12, + num_heads=12, + mlp_ratio=4, + out_indices=(3, 5, 7, 11), + qv_bias=True, + attn_drop_rate=0.0, + drop_path_rate=0.1, + norm_cfg=dict(type='LN', eps=1e-6), + act_cfg=dict(type='GELU'), + norm_eval=False, + init_values=0.1), + neck=dict(type='Feature2Pyramid', embed_dim=768, rescales=[4, 2, 1, 0.5]), + decode_head=dict( + type='UPerHead', + in_channels=[768, 768, 768, 768], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=768, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=768, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_convnext.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_convnext.py new file mode 100644 index 0000000000000000000000000000000000000000..36b882f683789cfcc025ec45a9418dcb968dbd25 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_convnext.py @@ -0,0 +1,44 @@ +norm_cfg = dict(type='SyncBN', requires_grad=True) +custom_imports = dict(imports='mmcls.models', allow_failed_imports=False) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_32xb128-noema_in1k_20220301-2a0ee547.pth' # noqa +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='mmcls.ConvNeXt', + arch='base', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + type='UPerHead', + in_channels=[128, 256, 512, 1024], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=512, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=384, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_mae.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_mae.py new file mode 100644 index 0000000000000000000000000000000000000000..1e0da708277764a9038c0e85a773ea346f3bb2db --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_mae.py @@ -0,0 +1,49 @@ +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='MAE', + img_size=(640, 640), + patch_size=16, + in_channels=3, + embed_dims=768, + num_layers=12, + num_heads=12, + mlp_ratio=4, + out_indices=(3, 5, 7, 11), + attn_drop_rate=0.0, + drop_path_rate=0.1, + norm_cfg=dict(type='LN', eps=1e-6), + act_cfg=dict(type='GELU'), + norm_eval=False, + init_values=0.1), + neck=dict(type='Feature2Pyramid', embed_dim=768, rescales=[4, 2, 1, 0.5]), + decode_head=dict( + type='UPerHead', + in_channels=[384, 384, 384, 384], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=512, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=384, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_r50.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_r50.py new file mode 100644 index 0000000000000000000000000000000000000000..10974962fdd7136031fd06de1700f497d355ceaa --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_r50.py @@ -0,0 +1,44 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='UPerHead', + in_channels=[256, 512, 1024, 2048], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=512, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_swin.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_swin.py new file mode 100644 index 0000000000000000000000000000000000000000..71b51629e04fb9c92bc8cde10ffe3a67c5ca1efe --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_swin.py @@ -0,0 +1,54 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +backbone_norm_cfg = dict(type='LN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='SwinTransformer', + pretrain_img_size=224, + embed_dims=96, + patch_size=4, + window_size=7, + mlp_ratio=4, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + strides=(4, 2, 2, 2), + out_indices=(0, 1, 2, 3), + qkv_bias=True, + qk_scale=None, + patch_norm=True, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + use_abs_pos_embed=False, + act_cfg=dict(type='GELU'), + norm_cfg=backbone_norm_cfg), + decode_head=dict( + type='UPerHead', + in_channels=[96, 192, 384, 768], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=512, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=384, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_vit-b16_ln_mln.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_vit-b16_ln_mln.py new file mode 100644 index 0000000000000000000000000000000000000000..cd6587dfe0ed5454b02f4ea153027dc220e22d8e --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/models/upernet_vit-b16_ln_mln.py @@ -0,0 +1,57 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained='pretrain/jx_vit_base_p16_224-80ecf9dd.pth', + backbone=dict( + type='VisionTransformer', + img_size=(512, 512), + patch_size=16, + in_channels=3, + embed_dims=768, + num_layers=12, + num_heads=12, + mlp_ratio=4, + out_indices=(2, 5, 8, 11), + qkv_bias=True, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.0, + with_cls_token=True, + norm_cfg=dict(type='LN', eps=1e-6), + act_cfg=dict(type='GELU'), + norm_eval=False, + interpolate_mode='bicubic'), + neck=dict( + type='MultiLevelNeck', + in_channels=[768, 768, 768, 768], + out_channels=768, + scales=[4, 2, 1, 0.5]), + decode_head=dict( + type='UPerHead', + in_channels=[768, 768, 768, 768], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=512, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=768, + in_index=3, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=19, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) # yapf: disable diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_160k.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_160k.py new file mode 100644 index 0000000000000000000000000000000000000000..39630f215bd5b952407f8bbf1f0e6f91038767a5 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_160k.py @@ -0,0 +1,9 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=160000) +checkpoint_config = dict(by_epoch=False, interval=16000) +evaluation = dict(interval=16000, metric='mIoU', pre_eval=True) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_20k.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_20k.py new file mode 100644 index 0000000000000000000000000000000000000000..73c7021972bb8e955440b5afc8eaf0a4853b98a7 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_20k.py @@ -0,0 +1,9 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=20000) +checkpoint_config = dict(by_epoch=False, interval=2000) +evaluation = dict(interval=2000, metric='mIoU', pre_eval=True) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_320k.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_320k.py new file mode 100644 index 0000000000000000000000000000000000000000..a0b230626f638fc2072dbc78cca834edce3fdc23 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_320k.py @@ -0,0 +1,9 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=320000) +checkpoint_config = dict(by_epoch=False, interval=32000) +evaluation = dict(interval=32000, metric='mIoU') diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_40k.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_40k.py new file mode 100644 index 0000000000000000000000000000000000000000..d2c502325944a1c5aa894283f28c610d67ab4da8 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_40k.py @@ -0,0 +1,9 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=40000) +checkpoint_config = dict(by_epoch=False, interval=4000) +evaluation = dict(interval=4000, metric='mIoU', pre_eval=True) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_80k.py b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_80k.py new file mode 100644 index 0000000000000000000000000000000000000000..8365a878e9e19fff1080b0268ee26405eea34e43 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/_base_/schedules/schedule_80k.py @@ -0,0 +1,9 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=80000) +checkpoint_config = dict(by_epoch=False, interval=8000) +evaluation = dict(interval=8000, metric='mIoU', pre_eval=True) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/README.md b/EgoHOS/raw/script/mmsegmentation/configs/ann/README.md new file mode 100644 index 0000000000000000000000000000000000000000..30a59c3c3e6f99dcd2e859bffcf7de589b559320 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/README.md @@ -0,0 +1,69 @@ +# ANN + +[Asymmetric Non-local Neural Networks for Semantic Segmentation](https://arxiv.org/abs/1908.07678) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation, which has two prominent components: Asymmetric Pyramid Non-local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB). APNB leverages a pyramid sampling module into the non-local block to largely reduce the computation and memory consumption without sacrificing the performance. AFNB is adapted from APNB to fuse the features of different levels under a sufficient consideration of long range dependencies and thus considerably improves the performance. Extensive experiments on semantic segmentation benchmarks demonstrate the effectiveness and efficiency of our work. In particular, we report the state-of-the-art performance of 81.3 mIoU on the Cityscapes test set. For a 256x128 input, APNB is around 6 times faster than a non-local block on GPU while 28 times smaller in GPU running memory occupation. Code is available at: [this https URL](https://github.com/MendelXu/ANN). + + +
+ +
+ + +## Citation + +```bibtex +@inproceedings{zhu2019asymmetric, + title={Asymmetric non-local neural networks for semantic segmentation}, + author={Zhu, Zhen and Xu, Mengde and Bai, Song and Huang, Tengteng and Bai, Xiang}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + pages={593--602}, + year={2019} +} +``` + + +## Results and models + +### Cityscapes + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| ANN | R-50-D8 | 512x1024 | 40000 | 6 | 3.71 | 77.40 | 78.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211.log.json) | +| ANN | R-101-D8 | 512x1024 | 40000 | 9.5 | 2.55 | 76.55 | 78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243.log.json) | +| ANN | R-50-D8 | 769x769 | 40000 | 6.8 | 1.70 | 78.89 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712.log.json) | +| ANN | R-101-D8 | 769x769 | 40000 | 10.7 | 1.15 | 79.32 | 80.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720.log.json) | +| ANN | R-50-D8 | 512x1024 | 80000 | - | - | 77.34 | 78.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911.log.json) | +| ANN | R-101-D8 | 512x1024 | 80000 | - | - | 77.14 | 78.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728.log.json) | +| ANN | R-50-D8 | 769x769 | 80000 | - | - | 78.88 | 80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426.log.json) | +| ANN | R-101-D8 | 769x769 | 80000 | - | - | 78.80 | 80.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713.log.json) | + +### ADE20K + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| ANN | R-50-D8 | 512x512 | 80000 | 9.1 | 21.01 | 41.01 | 42.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818.log.json) | +| ANN | R-101-D8 | 512x512 | 80000 | 12.5 | 14.12 | 42.94 | 44.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818.log.json) | +| ANN | R-50-D8 | 512x512 | 160000 | - | - | 41.74 | 42.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733.log.json) | +| ANN | R-101-D8 | 512x512 | 160000 | - | - | 42.94 | 44.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733.log.json) | + +### Pascal VOC 2012 + Aug + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| ANN | R-50-D8 | 512x512 | 20000 | 6 | 20.92 | 74.86 | 76.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246.log.json) | +| ANN | R-101-D8 | 512x512 | 20000 | 9.5 | 13.94 | 77.47 | 78.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246.log.json) | +| ANN | R-50-D8 | 512x512 | 40000 | - | - | 76.56 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314.log.json) | +| ANN | R-101-D8 | 512x512 | 40000 | - | - | 76.70 | 78.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314.log.json) | diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann.yml b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann.yml new file mode 100644 index 0000000000000000000000000000000000000000..ff6bea653b231ce98f79263b24d9912ca23a5eee --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann.yml @@ -0,0 +1,305 @@ +Collections: +- Name: ANN + Metadata: + Training Data: + - Cityscapes + - ADE20K + - Pascal VOC 2012 + Aug + Paper: + URL: https://arxiv.org/abs/1908.07678 + Title: Asymmetric Non-local Neural Networks for Semantic Segmentation + README: configs/ann/README.md + Code: + URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185 + Version: v0.17.0 + Converted From: + Code: https://github.com/MendelXu/ANN +Models: +- Name: ann_r50-d8_512x1024_40k_cityscapes + In Collection: ANN + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 269.54 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 6.0 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.4 + mIoU(ms+flip): 78.57 + Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth +- Name: ann_r101-d8_512x1024_40k_cityscapes + In Collection: ANN + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 392.16 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 9.5 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.55 + mIoU(ms+flip): 78.85 + Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth +- Name: ann_r50-d8_769x769_40k_cityscapes + In Collection: ANN + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 588.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 6.8 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.89 + mIoU(ms+flip): 80.46 + Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth +- Name: ann_r101-d8_769x769_40k_cityscapes + In Collection: ANN + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 869.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 10.7 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.32 + mIoU(ms+flip): 80.94 + Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth +- Name: ann_r50-d8_512x1024_80k_cityscapes + In Collection: ANN + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.34 + mIoU(ms+flip): 78.65 + Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth +- Name: ann_r101-d8_512x1024_80k_cityscapes + In Collection: ANN + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.14 + mIoU(ms+flip): 78.81 + Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth +- Name: ann_r50-d8_769x769_80k_cityscapes + In Collection: ANN + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.88 + mIoU(ms+flip): 80.57 + Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth +- Name: ann_r101-d8_769x769_80k_cityscapes + In Collection: ANN + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.8 + mIoU(ms+flip): 80.34 + Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth +- Name: ann_r50-d8_512x512_80k_ade20k + In Collection: ANN + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 47.6 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 9.1 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.01 + mIoU(ms+flip): 42.3 + Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth +- Name: ann_r101-d8_512x512_80k_ade20k + In Collection: ANN + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 70.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 12.5 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.94 + mIoU(ms+flip): 44.18 + Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth +- Name: ann_r50-d8_512x512_160k_ade20k + In Collection: ANN + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.74 + mIoU(ms+flip): 42.62 + Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth +- Name: ann_r101-d8_512x512_160k_ade20k + In Collection: ANN + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.94 + mIoU(ms+flip): 44.06 + Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth +- Name: ann_r50-d8_512x512_20k_voc12aug + In Collection: ANN + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 47.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 6.0 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 74.86 + mIoU(ms+flip): 76.13 + Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth +- Name: ann_r101-d8_512x512_20k_voc12aug + In Collection: ANN + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 71.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 9.5 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.47 + mIoU(ms+flip): 78.7 + Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth +- Name: ann_r50-d8_512x512_40k_voc12aug + In Collection: ANN + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.56 + mIoU(ms+flip): 77.51 + Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth +- Name: ann_r101-d8_512x512_40k_voc12aug + In Collection: ANN + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.7 + mIoU(ms+flip): 78.06 + Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..d494e07333217e0c6830d36d1bb58fa78b03cfb0 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_512x1024_40k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..1eeff0b030cf1db8c6ec9740fa65db44b2026d58 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_512x1024_80k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..9e43af541f6e3df3f36479e736bb0c03fc916970 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_160k_ade20k.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_512x512_160k_ade20k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..d854f2e4223731f443369febc500dbccdc524d9d --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_512x512_20k_voc12aug.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..893c53b1ca4bf9788e4d94f0f53cfe92a93f48ce --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_512x512_40k_voc12aug.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_80k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..a64dac670ed4d4632e7b9791ec5f8a334dcea78e --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_512x512_80k_ade20k.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_512x512_80k_ade20k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..59508248490b3edbac1c46b4fcc7891f99655b9b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_769x769_40k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..a9c712d1ccfd62ddf6f12ff01ea347ca1995013b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_769x769_80k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..00b2594ba8a1c9edc90cca7a6d7c3334fa209edc --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/ann_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..ef7b369dd9e12b2282a30da14f99dd4547c53a7b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/ann_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..ca6bb248ac867d463c274f975c884aa80a57730f --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_160k_ade20k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/ann_r50-d8.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +model = dict( + decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_20k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_20k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..071f190261c4e8f4a80a5da12a88e0cfcdfef0d8 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_20k_voc12aug.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/ann_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +model = dict( + decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_40k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_40k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..82a1c9386c51fb0ada436e51702beb961a534b26 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_40k_voc12aug.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/ann_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +model = dict( + decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_80k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..5e04aa7c6ac050d119e07b715e2082f692e1a1de --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_512x512_80k_ade20k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/ann_r50-d8.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +model = dict( + decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_769x769_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..4912bdb9fb298518ae084eb7df0ad22d3e4ff84f --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_769x769_40k_cityscapes.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/ann_r50-d8.py', + '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +model = dict( + decode_head=dict(align_corners=True), + auxiliary_head=dict(align_corners=True), + test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_769x769_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..d1cc072b152986102286f503e3d7b92999bf414c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ann/ann_r50-d8_769x769_80k_cityscapes.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/ann_r50-d8.py', + '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_80k.py' +] +model = dict( + decode_head=dict(align_corners=True), + auxiliary_head=dict(align_corners=True), + test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/README.md b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5e1fd6b4201622df972e0cb120925a6856b0c672 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/README.md @@ -0,0 +1,58 @@ +# APCNet + +[Adaptive Pyramid Context Network for Semantic Segmentation](https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks. Current context based segmentation methods differ with each other in how to construct context features and perform differently in practice. This paper firstly introduces three desirable properties of context features in segmentation task. Specially, we find that Global-guided Local Affinity (GLA) can play a vital role in constructing effective context features, while this property has been largely ignored in previous works. Based on this analysis, this paper proposes Adaptive Pyramid Context Network (APCNet)for semantic segmentation. APCNet adaptively constructs multi-scale contextual representations with multiple welldesigned Adaptive Context Modules (ACMs). Specifically, each ACM leverages a global image representation as a guidance to estimate the local affinity coefficients for each sub-region, and then calculates a context vector with these affinities. We empirically evaluate our APCNet on three semantic segmentation and scene parsing datasets, including PASCAL VOC 2012, Pascal-Context, and ADE20K dataset. Experimental results show that APCNet achieves state-ofthe-art performance on all three benchmarks, and obtains a new record 84.2% on PASCAL VOC 2012 test set without MS COCO pre-trained and any post-processing. + + +
+ +
+ +## Citation + +```bibtex +@InProceedings{He_2019_CVPR, +author = {He, Junjun and Deng, Zhongying and Zhou, Lei and Wang, Yali and Qiao, Yu}, +title = {Adaptive Pyramid Context Network for Semantic Segmentation}, +booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, +month = {June}, +year = {2019} +} +``` + +## Results and models + +### Cityscapes + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| APCNet | R-50-D8 | 512x1024 | 40000 | 7.7 | 3.57 | 78.02 | 79.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json) | +| APCNet | R-101-D8 | 512x1024 | 40000 | 11.2 | 2.15 | 79.08 | 80.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) | +| APCNet | R-50-D8 | 769x769 | 40000 | 8.7 | 1.52 | 77.89 | 79.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json) | +| APCNet | R-101-D8 | 769x769 | 40000 | 12.7 | 1.03 | 77.96 | 79.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json) | +| APCNet | R-50-D8 | 512x1024 | 80000 | - | - | 78.96 | 79.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json) | +| APCNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.64 | 80.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) | +| APCNet | R-50-D8 | 769x769 | 80000 | - | - | 78.79 | 80.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json) | +| APCNet | R-101-D8 | 769x769 | 80000 | - | - | 78.45 | 79.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json) | + +### ADE20K + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| APCNet | R-50-D8 | 512x512 | 80000 | 10.1 | 19.61 | 42.20 | 43.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json) | +| APCNet | R-101-D8 | 512x512 | 80000 | 13.6 | 13.10 | 45.54 | 46.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json) | +| APCNet | R-50-D8 | 512x512 | 160000 | - | - | 43.40 | 43.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json) | +| APCNet | R-101-D8 | 512x512 | 160000 | - | - | 45.41 | 46.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) | diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet.yml b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet.yml new file mode 100644 index 0000000000000000000000000000000000000000..7a453a36072ed7b3df6fdfbf8db884a783d055e7 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet.yml @@ -0,0 +1,232 @@ +Collections: +- Name: APCNet + Metadata: + Training Data: + - Cityscapes + - ADE20K + Paper: + URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html + Title: Adaptive Pyramid Context Network for Semantic Segmentation + README: configs/apcnet/README.md + Code: + URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 + Version: v0.17.0 + Converted From: + Code: https://github.com/Junjun2016/APCNet +Models: +- Name: apcnet_r50-d8_512x1024_40k_cityscapes + In Collection: APCNet + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 280.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 7.7 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.02 + mIoU(ms+flip): 79.26 + Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth +- Name: apcnet_r101-d8_512x1024_40k_cityscapes + In Collection: APCNet + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 465.12 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 11.2 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.08 + mIoU(ms+flip): 80.34 + Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth +- Name: apcnet_r50-d8_769x769_40k_cityscapes + In Collection: APCNet + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 657.89 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 8.7 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.89 + mIoU(ms+flip): 79.75 + Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth +- Name: apcnet_r101-d8_769x769_40k_cityscapes + In Collection: APCNet + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 970.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 12.7 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.96 + mIoU(ms+flip): 79.24 + Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth +- Name: apcnet_r50-d8_512x1024_80k_cityscapes + In Collection: APCNet + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.96 + mIoU(ms+flip): 79.94 + Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth +- Name: apcnet_r101-d8_512x1024_80k_cityscapes + In Collection: APCNet + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.64 + mIoU(ms+flip): 80.61 + Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth +- Name: apcnet_r50-d8_769x769_80k_cityscapes + In Collection: APCNet + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.79 + mIoU(ms+flip): 80.35 + Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth +- Name: apcnet_r101-d8_769x769_80k_cityscapes + In Collection: APCNet + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.45 + mIoU(ms+flip): 79.91 + Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth +- Name: apcnet_r50-d8_512x512_80k_ade20k + In Collection: APCNet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 50.99 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 10.1 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.2 + mIoU(ms+flip): 43.3 + Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth +- Name: apcnet_r101-d8_512x512_80k_ade20k + In Collection: APCNet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 76.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 13.6 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.54 + mIoU(ms+flip): 46.65 + Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth +- Name: apcnet_r50-d8_512x512_160k_ade20k + In Collection: APCNet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.4 + mIoU(ms+flip): 43.94 + Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth +- Name: apcnet_r101-d8_512x512_160k_ade20k + In Collection: APCNet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.41 + mIoU(ms+flip): 46.63 + Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..1e1cec67355abae33d518417eb96eae111f16d2b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_512x1024_40k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..04cb006ba146268e1d3278151bc6ea00a4fb1bfe --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_512x1024_80k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..1ce2279a0fbfd6fcc7cd20e3f552b1a39f47d943 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_512x512_160k_ade20k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..8f10b98406c88256c66d3bbe241c149791d68feb --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_512x512_80k_ade20k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..5c44ebcaf36075e67208c5f033d1e5f9a78dda4e --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_769x769_40k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..616984575dda73a13fc5870f60ae6ffa30d6b01b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_769x769_80k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..99c61a942e4868315ce4a9404d113f73fed4a4ea --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..62a0627ae2e9bb17974068e56ee660093e944e0d --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..f7821c559d2f92d23b28e07e040a54cfc425eefc --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +model = dict( + decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..daafa5fbc12c3ed6c10b5234d520166f774e0f94 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +model = dict( + decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..3db6140cb97da1d202fd464d01f793276effa629 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/apcnet_r50-d8.py', + '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +model = dict( + decode_head=dict(align_corners=True), + auxiliary_head=dict(align_corners=True), + test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..9cac4254f37bc3755bff869a10eb3cb75db4d943 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/apcnet_r50-d8.py', + '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_80k.py' +] +model = dict( + decode_head=dict(align_corners=True), + auxiliary_head=dict(align_corners=True), + test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/beit/README.md b/EgoHOS/raw/script/mmsegmentation/configs/beit/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e0286f2e4b39ab3c71d80beb4665d74b98060f63 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/beit/README.md @@ -0,0 +1,84 @@ +# BEiT + +[BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image patches (such as 16x16 pixels), and visual tokens (i.e., discrete tokens). We first "tokenize" the original image into visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder. Experimental results on image classification and semantic segmentation show that our model achieves competitive results with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K, significantly outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains 86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%). The code and pretrained models are available at [this https URL](https://github.com/microsoft/unilm/tree/master/beit). + + +
+ +
+ +## Citation + +```bibtex +@inproceedings{beit, + title={{BEiT}: {BERT} Pre-Training of Image Transformers}, + author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei}, + booktitle={International Conference on Learning Representations}, + year={2022}, + url={https://openreview.net/forum?id=p-BhZSz59o4} +} +``` + +## Usage + +To use other repositories' pre-trained models, it is necessary to convert keys. + +We provide a script [`beit2mmseg.py`](../../tools/model_converters/beit2mmseg.py) in the tools directory to convert the key of models from [the official repo](https://github.com/microsoft/unilm/tree/master/beit/semantic_segmentation) to MMSegmentation style. + +```shell +python tools/model_converters/beit2mmseg.py ${PRETRAIN_PATH} ${STORE_PATH} +``` + +E.g. + +```shell +python tools/model_converters/beit2mmseg.py https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22k.pth pretrain/beit_base_patch16_224_pt22k_ft22k.pth +``` + +This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`. + +In our default setting, pretrained models could be defined below: + + | pretrained models | original models | + | ------ | -------- | + |BEiT_base.pth | ['BEiT_base'](https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22k.pth) | + |BEiT_large.pth | ['BEiT_large'](https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth) | + +Verify the single-scale results of the model: + +```shell +sh tools/dist_test.sh \ +configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py \ +upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth $GPUS --eval mIoU +``` + +Since relative position embedding requires the input length and width to be equal, the sliding window is adopted for multi-scale inference. So we set min_size=640, that is, the shortest edge is 640. So the multi-scale inference of config is performed separately, instead of '--aug-test'. For multi-scale inference: + +```shell +sh tools/dist_test.sh \ +configs/beit/upernet_beit-large_fp16_640x640_160k_ade20k_ms.py \ +upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth $GPUS --eval mIoU +``` + +## Results and models + +### ADE20K + +| Method | Backbone | Crop Size | pretrain | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ---------- | ------- | -------- | --- | --- | -------------- | ----- | ------------: | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| UperNet | BEiT-B | 640x640 | ImageNet-22K | 224x224 | 16 | 160000 | 15.88 | 2.00 | 53.08 | 53.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k.log.json) | +| UperNet | BEiT-L | 640x640 | ImageNet-22K | 224x224 | 8 | 320000 | 22.64 | 0.96 | 56.33 | 56.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.log.json) | diff --git a/EgoHOS/raw/script/mmsegmentation/configs/beit/beit.yml b/EgoHOS/raw/script/mmsegmentation/configs/beit/beit.yml new file mode 100644 index 0000000000000000000000000000000000000000..6f3cee3eddfba1b4c6582760df4cbdfae9522227 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/beit/beit.yml @@ -0,0 +1,45 @@ +Models: +- Name: upernet_beit-base_8x2_640x640_160k_ade20k + In Collection: UperNet + Metadata: + backbone: BEiT-B + crop size: (640,640) + lr schd: 160000 + inference time (ms/im): + - value: 500.0 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (640,640) + Training Memory (GB): 15.88 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 53.08 + mIoU(ms+flip): 53.84 + Config: configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth +- Name: upernet_beit-large_fp16_8x1_640x640_160k_ade20k + In Collection: UperNet + Metadata: + backbone: BEiT-L + crop size: (640,640) + lr schd: 320000 + inference time (ms/im): + - value: 1041.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (640,640) + Training Memory (GB): 22.64 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 56.33 + mIoU(ms+flip): 56.84 + Config: configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-base_640x640_160k_ade20k_ms.py b/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-base_640x640_160k_ade20k_ms.py new file mode 100644 index 0000000000000000000000000000000000000000..f764c92c11e0bfac26fdce782287138a5676a68c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-base_640x640_160k_ade20k_ms.py @@ -0,0 +1,24 @@ +_base_ = './upernet_beit-base_8x2_640x640_160k_ade20k.py' + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +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=True, + transforms=[ + dict(type='Resize', keep_ratio=True, min_size=640), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), + samples_per_gpu=2) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..b36adc3c0d58a4e4ec66fbc22baad5ea570f048b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py @@ -0,0 +1,30 @@ +_base_ = [ + '../_base_/models/upernet_beit.py', '../_base_/datasets/ade20k_640x640.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] + +model = dict( + pretrained='pretrain/beit_base_patch16_224_pt22k_ft22k.pth', + test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(426, 426))) + +optimizer = dict( + _delete_=True, + type='AdamW', + lr=3e-5, + betas=(0.9, 0.999), + weight_decay=0.05, + constructor='LayerDecayOptimizerConstructor', + paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.9)) + +lr_config = dict( + _delete_=True, + policy='poly', + warmup='linear', + warmup_iters=1500, + warmup_ratio=1e-6, + power=1.0, + min_lr=0.0, + by_epoch=False) + +# By default, models are trained on 8 GPUs with 2 images per GPU +data = dict(samples_per_gpu=2) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-large_fp16_640x640_160k_ade20k_ms.py b/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-large_fp16_640x640_160k_ade20k_ms.py new file mode 100644 index 0000000000000000000000000000000000000000..fd4d9477d4b7b60ee7a14d10143ecac5a92527f5 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-large_fp16_640x640_160k_ade20k_ms.py @@ -0,0 +1,22 @@ +_base_ = './upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py' + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +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=True, + transforms=[ + dict(type='Resize', keep_ratio=True, min_size=640), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..e6247b7352ba0c3f428744e12abb5cd1c6ea3727 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py @@ -0,0 +1,47 @@ +_base_ = [ + '../_base_/models/upernet_beit.py', '../_base_/datasets/ade20k_640x640.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_320k.py' +] + +model = dict( + pretrained='pretrain/beit_large_patch16_224_pt22k_ft22k.pth', + backbone=dict( + type='BEiT', + embed_dims=1024, + num_layers=24, + num_heads=16, + mlp_ratio=4, + qv_bias=True, + init_values=1e-6, + drop_path_rate=0.2, + out_indices=[7, 11, 15, 23]), + neck=dict(embed_dim=1024, rescales=[4, 2, 1, 0.5]), + decode_head=dict( + in_channels=[1024, 1024, 1024, 1024], num_classes=150, channels=1024), + auxiliary_head=dict(in_channels=1024, num_classes=150), + test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(426, 426))) + +optimizer = dict( + _delete_=True, + type='AdamW', + lr=2e-5, + betas=(0.9, 0.999), + weight_decay=0.05, + constructor='LayerDecayOptimizerConstructor', + paramwise_cfg=dict(num_layers=24, layer_decay_rate=0.95)) + +lr_config = dict( + _delete_=True, + policy='poly', + warmup='linear', + warmup_iters=3000, + warmup_ratio=1e-6, + power=1.0, + min_lr=0.0, + by_epoch=False) + +data = dict(samples_per_gpu=1) +optimizer_config = dict( + type='GradientCumulativeFp16OptimizerHook', cumulative_iters=2) + +fp16 = dict() diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/README.md b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/README.md new file mode 100644 index 0000000000000000000000000000000000000000..75ac37c1c1842be4f430126095ecf1347b9c3f61 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/README.md @@ -0,0 +1,63 @@ +# BiSeNetV1 + +[BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048x1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance. + + +
+ +
+ +## Citation + +```bibtex +@inproceedings{yu2018bisenet, + title={Bisenet: Bilateral segmentation network for real-time semantic segmentation}, + author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong}, + booktitle={Proceedings of the European conference on computer vision (ECCV)}, + pages={325--341}, + year={2018} +} +``` + +## Results and models + +### Cityscapes + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| BiSeNetV1 (No Pretrain) | R-18-D32 | 1024x1024 | 160000 | 5.69 | 31.77 | 74.44 | 77.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239.log.json) | +| BiSeNetV1| R-18-D32 | 1024x1024 | 160000 | 5.69 | 31.77 | 74.37 | 76.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251.log.json) | +| BiSeNetV1 (4x8) | R-18-D32 | 1024x1024 | 160000 | 11.17 | 31.77 | 75.16 | 77.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322.log.json) | +| BiSeNetV1 (No Pretrain) | R-50-D32 | 1024x1024 | 160000 | 15.39 | 7.71 | 76.92 | 78.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639.log.json) | +| BiSeNetV1 | R-50-D32 | 1024x1024 | 160000 | 15.39 | 7.71 | 77.68 | 79.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628.log.json) | + +### COCO-Stuff 164k + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| BiSeNetV1 (No Pretrain) | R-18-D32 | 512x512 | 160000 | - | - | 25.45 | 26.15 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328.log.json) | +| BiSeNetV1| R-18-D32 | 512x512 | 160000 | 6.33 | 74.24 | 28.55 | 29.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100.log.json) | +| BiSeNetV1 (No Pretrain) | R-50-D32 | 512x512 | 160000 | - | - | 29.82 | 30.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616.log.json) | +| BiSeNetV1 | R-50-D32 | 512x512 | 160000 | 9.28 | 32.60 | 34.88 | 35.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932.log.json) | +| BiSeNetV1 (No Pretrain) | R-101-D32 | 512x512 | 160000 | - | - | 31.14 | 31.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json) | +| BiSeNetV1 | R-101-D32 | 512x512 | 160000 | 10.36 | 25.25 | 37.38 | 37.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220.log.json) | + +Note: + +- `4x8`: Using 4 GPUs with 8 samples per GPU in training. +- For BiSeNetV1 on Cityscapes dataset, default setting is 4 GPUs with 4 samples per GPU in training. +- `No Pretrain` means the model is trained from scratch. diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1.yml b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1.yml new file mode 100644 index 0000000000000000000000000000000000000000..61f264b0568cadde5045ba3b3721ee412e68e19f --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1.yml @@ -0,0 +1,234 @@ +Collections: +- Name: BiSeNetV1 + Metadata: + Training Data: + - Cityscapes + - COCO-Stuff 164k + Paper: + URL: https://arxiv.org/abs/1808.00897 + Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' + README: configs/bisenetv1/README.md + Code: + URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266 + Version: v0.18.0 + Converted From: + Code: https://github.com/ycszen/TorchSeg/tree/master/model/bisenet +Models: +- Name: bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes + In Collection: BiSeNetV1 + Metadata: + backbone: R-18-D32 + crop size: (1024,1024) + lr schd: 160000 + inference time (ms/im): + - value: 31.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (1024,1024) + Training Memory (GB): 5.69 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 74.44 + mIoU(ms+flip): 77.05 + Config: configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth +- Name: bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes + In Collection: BiSeNetV1 + Metadata: + backbone: R-18-D32 + crop size: (1024,1024) + lr schd: 160000 + inference time (ms/im): + - value: 31.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (1024,1024) + Training Memory (GB): 5.69 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 74.37 + mIoU(ms+flip): 76.91 + Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth +- Name: bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes + In Collection: BiSeNetV1 + Metadata: + backbone: R-18-D32 + crop size: (1024,1024) + lr schd: 160000 + inference time (ms/im): + - value: 31.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (1024,1024) + Training Memory (GB): 11.17 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.16 + mIoU(ms+flip): 77.24 + Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth +- Name: bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes + In Collection: BiSeNetV1 + Metadata: + backbone: R-50-D32 + crop size: (1024,1024) + lr schd: 160000 + inference time (ms/im): + - value: 129.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (1024,1024) + Training Memory (GB): 15.39 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.92 + mIoU(ms+flip): 78.87 + Config: configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth +- Name: bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes + In Collection: BiSeNetV1 + Metadata: + backbone: R-50-D32 + crop size: (1024,1024) + lr schd: 160000 + inference time (ms/im): + - value: 129.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (1024,1024) + Training Memory (GB): 15.39 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.68 + mIoU(ms+flip): 79.57 + Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth +- Name: bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: BiSeNetV1 + Metadata: + backbone: R-18-D32 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 25.45 + mIoU(ms+flip): 26.15 + Config: configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth +- Name: bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: BiSeNetV1 + Metadata: + backbone: R-18-D32 + crop size: (512,512) + lr schd: 160000 + inference time (ms/im): + - value: 13.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 6.33 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 28.55 + mIoU(ms+flip): 29.26 + Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth +- Name: bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: BiSeNetV1 + Metadata: + backbone: R-50-D32 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 29.82 + mIoU(ms+flip): 30.33 + Config: configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth +- Name: bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: BiSeNetV1 + Metadata: + backbone: R-50-D32 + crop size: (512,512) + lr schd: 160000 + inference time (ms/im): + - value: 30.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 9.28 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 34.88 + mIoU(ms+flip): 35.37 + Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth +- Name: bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: BiSeNetV1 + Metadata: + backbone: R-101-D32 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 31.14 + mIoU(ms+flip): 31.76 + Config: configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth +- Name: bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: BiSeNetV1 + Metadata: + backbone: R-101-D32 + crop size: (512,512) + lr schd: 160000 + inference time (ms/im): + - value: 39.6 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 10.36 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 37.38 + mIoU(ms+flip): 37.99 + Config: configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000000000000000000000000000000000..c3fe21597d6dfab1d27669674926b5e761466cce --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,6 @@ +_base_ = './bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py' +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000000000000000000000000000000000..b1e1c3e86363cfb187efb69006486146dabb10c9 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/bisenetv1_r18-d32.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +model = dict( + backbone=dict( + context_channels=(512, 1024, 2048), + spatial_channels=(256, 256, 256, 512), + out_channels=1024, + backbone_cfg=dict(type='ResNet', depth=101)), + decode_head=dict(in_channels=1024, channels=1024, num_classes=171), + auxiliary_head=[ + dict(in_channels=512, channels=256, num_classes=171), + dict(in_channels=512, channels=256, num_classes=171), + ]) +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.005) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..f4019e930e54e034ac4592b6b31af6a0fd391ca2 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/bisenetv1_r18-d32.py', + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.025) +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, +) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..ef061a16bde683828bb118b0540adb2814b3009d --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py @@ -0,0 +1,16 @@ +_base_ = [ + '../_base_/models/bisenetv1_r18-d32.py', + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet18_v1c')))) +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.025) +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, +) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..f4b9f6d2c9559f136bb50948e57f47a2c420d6de --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py @@ -0,0 +1,5 @@ +_base_ = './bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py' +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, +) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000000000000000000000000000000000..c6d93049e28826e8038cbc16d5241a260f4714eb --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,6 @@ +_base_ = './bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py' +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))), ) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000000000000000000000000000000000..78d7fea62963e08d152477bb1309c1629572de8c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/bisenetv1_r18-d32.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +model = dict( + decode_head=dict(num_classes=171), + auxiliary_head=[ + dict(num_classes=171), + dict(num_classes=171), + ]) +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.005) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..7cadd503c40639c40731e39ab0ad77745102be6a --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py @@ -0,0 +1,42 @@ +_base_ = [ + '../_base_/models/bisenetv1_r18-d32.py', + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + type='BiSeNetV1', + context_channels=(512, 1024, 2048), + spatial_channels=(256, 256, 256, 512), + out_channels=1024, + backbone_cfg=dict(type='ResNet', depth=50)), + decode_head=dict( + type='FCNHead', in_channels=1024, in_index=0, channels=1024), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=512, + channels=256, + num_convs=1, + num_classes=19, + in_index=1, + norm_cfg=norm_cfg, + concat_input=False), + dict( + type='FCNHead', + in_channels=512, + channels=256, + num_convs=1, + num_classes=19, + in_index=2, + norm_cfg=norm_cfg, + concat_input=False), + ]) +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.05) +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, +) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..5625a76c0873ca7ddb6dcfea2f273da4114c421d --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py @@ -0,0 +1,7 @@ +_base_ = './bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py' +model = dict( + type='EncoderDecoder', + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000000000000000000000000000000000..f0fea69f2f01c68f2df774839c650774a43e97f5 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,7 @@ +_base_ = './bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py' + +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000000000000000000000000000000000..dbbccc69d80923a2fe43879094d25d7221e716ab --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/bisenetv1_r18-d32.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +model = dict( + backbone=dict( + context_channels=(512, 1024, 2048), + spatial_channels=(256, 256, 256, 512), + out_channels=1024, + backbone_cfg=dict(type='ResNet', depth=50)), + decode_head=dict(in_channels=1024, channels=1024, num_classes=171), + auxiliary_head=[ + dict(in_channels=512, channels=256, num_classes=171), + dict(in_channels=512, channels=256, num_classes=171), + ]) +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.005) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/README.md b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1bc74249073eb7a372af869e4c171fb8c0402b91 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/README.md @@ -0,0 +1,53 @@ +# BiSeNetV2 + +[Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://arxiv.org/abs/2004.02147) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +The low-level details and high-level semantics are both essential to the semantic segmentation task. However, to speed up the model inference, current approaches almost always sacrifice the low-level details, which leads to a considerable accuracy decrease. We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for realtime semantic segmentation. To this end, we propose an efficient and effective architecture with a good trade-off between speed and accuracy, termed Bilateral Segmentation Network (BiSeNet V2). This architecture involves: (i) a Detail Branch, with wide channels and shallow layers to capture low-level details and generate high-resolution feature representation; (ii) a Semantic Branch, with narrow channels and deep layers to obtain high-level semantic context. The Semantic Branch is lightweight due to reducing the channel capacity and a fast-downsampling strategy. Furthermore, we design a Guided Aggregation Layer to enhance mutual connections and fuse both types of feature representation. Besides, a booster training strategy is designed to improve the segmentation performance without any extra inference cost. Extensive quantitative and qualitative evaluations demonstrate that the proposed architecture performs favourably against a few state-of-the-art real-time semantic segmentation approaches. Specifically, for a 2,048x1,024 input, we achieve 72.6% Mean IoU on the Cityscapes test set with a speed of 156 FPS on one NVIDIA GeForce GTX 1080 Ti card, which is significantly faster than existing methods, yet we achieve better segmentation accuracy. + + +
+ +
+ +## Citation + +```bibtex +@article{yu2021bisenet, + title={Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation}, + author={Yu, Changqian and Gao, Changxin and Wang, Jingbo and Yu, Gang and Shen, Chunhua and Sang, Nong}, + journal={International Journal of Computer Vision}, + pages={1--18}, + year={2021}, + publisher={Springer} +} +``` + + +## Results and models + +### Cityscapes + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| BiSeNetV2 | BiSeNetV2 | 1024x1024 | 160000 | 7.64 | 31.77 | 73.21 | 75.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551.log.json) | +| BiSeNetV2 (OHEM) | BiSeNetV2 | 1024x1024 | 160000 | 7.64 | - | 73.57 | 75.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947.log.json) | +| BiSeNetV2 (4x8) | BiSeNetV2 | 1024x1024 | 160000 | 15.05 | - | 75.76 | 77.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032.log.json) | +| BiSeNetV2 (FP16) | BiSeNetV2 | 1024x1024 | 160000 | 5.77 | 36.65 | 73.07 | 75.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942.log.json) | + +Note: + +- `OHEM` means Online Hard Example Mining (OHEM) is adopted in training. +- `FP16` means Mixed Precision (FP16) is adopted in training. +- `4x8` means 4 GPUs with 8 samples per GPU in training. diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2.yml b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2.yml new file mode 100644 index 0000000000000000000000000000000000000000..455fa6c4796d987584b07698fb2563da4a962849 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2.yml @@ -0,0 +1,88 @@ +Collections: +- Name: BiSeNetV2 + Metadata: + Training Data: + - Cityscapes + Paper: + URL: https://arxiv.org/abs/2004.02147 + Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic + Segmentation' + README: configs/bisenetv2/README.md + Code: + URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545 + Version: v0.18.0 +Models: +- Name: bisenetv2_fcn_4x4_1024x1024_160k_cityscapes + In Collection: BiSeNetV2 + Metadata: + backbone: BiSeNetV2 + crop size: (1024,1024) + lr schd: 160000 + inference time (ms/im): + - value: 31.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (1024,1024) + Training Memory (GB): 7.64 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 73.21 + mIoU(ms+flip): 75.74 + Config: configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth +- Name: bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes + In Collection: BiSeNetV2 + Metadata: + backbone: BiSeNetV2 + crop size: (1024,1024) + lr schd: 160000 + Training Memory (GB): 7.64 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 73.57 + mIoU(ms+flip): 75.8 + Config: configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth +- Name: bisenetv2_fcn_4x8_1024x1024_160k_cityscapes + In Collection: BiSeNetV2 + Metadata: + backbone: BiSeNetV2 + crop size: (1024,1024) + lr schd: 160000 + Training Memory (GB): 15.05 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 75.76 + mIoU(ms+flip): 77.79 + Config: configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth +- Name: bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes + In Collection: BiSeNetV2 + Metadata: + backbone: BiSeNetV2 + crop size: (1024,1024) + lr schd: 160000 + inference time (ms/im): + - value: 27.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (1024,1024) + Training Memory (GB): 5.77 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 73.07 + mIoU(ms+flip): 75.13 + Config: configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..1248bd87aadd56cc41c4e9d95409a7387cb445fb --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/bisenetv2.py', + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.05) +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, +) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..5e93bea8be06bfec390ec718531b79b01b1e04bf --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/bisenetv2.py', + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.05) +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, +) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..0196214b7dfe21ad8408218d81b300c3dc7f2967 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py @@ -0,0 +1,5 @@ +_base_ = './bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py' +# fp16 settings +optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.) +# fp16 placeholder +fp16 = dict() diff --git a/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..f14e528130b1587db51ba8db2a5c3da4c5968866 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/bisenetv2.py', + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +sampler = dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000) +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.05) +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, +) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/README.md b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9cefcf023e4da1a63666e3890ea0c2d0579eae2c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/README.md @@ -0,0 +1,67 @@ +# CCNet + +[CCNet: Criss-Cross Attention for Semantic Segmentation](https://arxiv.org/abs/1811.11721) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a Criss-Cross Network (CCNet) for obtaining full-image contextual information in a very effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Besides, a category consistent loss is proposed to enforce the criss-cross attention module to produce more discriminative features. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85% of the non-local block. 3) The state-of-the-art performance. We conduct extensive experiments on semantic segmentation benchmarks including Cityscapes, ADE20K, human parsing benchmark LIP, instance segmentation benchmark COCO, video segmentation benchmark CamVid. In particular, our CCNet achieves the mIoU scores of 81.9%, 45.76% and 55.47% on the Cityscapes test set, the ADE20K validation set and the LIP validation set respectively, which are the new state-of-the-art results. The source codes are available at [this https URL](https://github.com/speedinghzl/CCNet). + + +
+ +
+ +## Citation + +```bibtex +@article{huang2018ccnet, + title={CCNet: Criss-Cross Attention for Semantic Segmentation}, + author={Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu}, + booktitle={ICCV}, + year={2019} +} +``` + + +## Results and models + +### Cityscapes + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| CCNet | R-50-D8 | 512x1024 | 40000 | 6 | 3.32 | 77.76 | 78.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517.log.json) | +| CCNet | R-101-D8 | 512x1024 | 40000 | 9.5 | 2.31 | 76.35 | 78.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540.log.json) | +| CCNet | R-50-D8 | 769x769 | 40000 | 6.8 | 1.43 | 78.46 | 79.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125.log.json) | +| CCNet | R-101-D8 | 769x769 | 40000 | 10.7 | 1.01 | 76.94 | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428.log.json) | +| CCNet | R-50-D8 | 512x1024 | 80000 | - | - | 79.03 | 80.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421.log.json) | +| CCNet | R-101-D8 | 512x1024 | 80000 | - | - | 78.87 | 79.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935.log.json) | +| CCNet | R-50-D8 | 769x769 | 80000 | - | - | 79.29 | 81.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421.log.json) | +| CCNet | R-101-D8 | 769x769 | 80000 | - | - | 79.45 | 80.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502.log.json) | + +### ADE20K + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| CCNet | R-50-D8 | 512x512 | 80000 | 8.8 | 20.89 | 41.78 | 42.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848.log.json) | +| CCNet | R-101-D8 | 512x512 | 80000 | 12.2 | 14.11 | 43.97 | 45.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848.log.json) | +| CCNet | R-50-D8 | 512x512 | 160000 | - | - | 42.08 | 43.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435.log.json) | +| CCNet | R-101-D8 | 512x512 | 160000 | - | - | 43.71 | 45.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644.log.json) | + +### Pascal VOC 2012 + Aug + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| CCNet | R-50-D8 | 512x512 | 20000 | 6 | 20.45 | 76.17 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212.log.json) | +| CCNet | R-101-D8 | 512x512 | 20000 | 9.5 | 13.64 | 77.27 | 79.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212.log.json) | +| CCNet | R-50-D8 | 512x512 | 40000 | - | - | 75.96 | 77.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127.log.json) | +| CCNet | R-101-D8 | 512x512 | 40000 | - | - | 77.87 | 78.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127.log.json) | diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet.yml b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet.yml new file mode 100644 index 0000000000000000000000000000000000000000..b264f2e6c20f7eef1924ee4a63dc0d92dd3bab71 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet.yml @@ -0,0 +1,305 @@ +Collections: +- Name: CCNet + Metadata: + Training Data: + - Cityscapes + - ADE20K + - Pascal VOC 2012 + Aug + Paper: + URL: https://arxiv.org/abs/1811.11721 + Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' + README: configs/ccnet/README.md + Code: + URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 + Version: v0.17.0 + Converted From: + Code: https://github.com/speedinghzl/CCNet +Models: +- Name: ccnet_r50-d8_512x1024_40k_cityscapes + In Collection: CCNet + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 301.2 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 6.0 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.76 + mIoU(ms+flip): 78.87 + Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth +- Name: ccnet_r101-d8_512x1024_40k_cityscapes + In Collection: CCNet + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 432.9 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 9.5 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.35 + mIoU(ms+flip): 78.19 + Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth +- Name: ccnet_r50-d8_769x769_40k_cityscapes + In Collection: CCNet + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 699.3 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 6.8 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.46 + mIoU(ms+flip): 79.93 + Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth +- Name: ccnet_r101-d8_769x769_40k_cityscapes + In Collection: CCNet + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 990.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 10.7 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.94 + mIoU(ms+flip): 78.62 + Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth +- Name: ccnet_r50-d8_512x1024_80k_cityscapes + In Collection: CCNet + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.03 + mIoU(ms+flip): 80.16 + Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth +- Name: ccnet_r101-d8_512x1024_80k_cityscapes + In Collection: CCNet + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.87 + mIoU(ms+flip): 79.9 + Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth +- Name: ccnet_r50-d8_769x769_80k_cityscapes + In Collection: CCNet + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.29 + mIoU(ms+flip): 81.08 + Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth +- Name: ccnet_r101-d8_769x769_80k_cityscapes + In Collection: CCNet + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.45 + mIoU(ms+flip): 80.66 + Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth +- Name: ccnet_r50-d8_512x512_80k_ade20k + In Collection: CCNet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 47.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 8.8 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.78 + mIoU(ms+flip): 42.98 + Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth +- Name: ccnet_r101-d8_512x512_80k_ade20k + In Collection: CCNet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 70.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 12.2 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.97 + mIoU(ms+flip): 45.13 + Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth +- Name: ccnet_r50-d8_512x512_160k_ade20k + In Collection: CCNet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.08 + mIoU(ms+flip): 43.13 + Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth +- Name: ccnet_r101-d8_512x512_160k_ade20k + In Collection: CCNet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.71 + mIoU(ms+flip): 45.04 + Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth +- Name: ccnet_r50-d8_512x512_20k_voc12aug + In Collection: CCNet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 48.9 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 6.0 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.17 + mIoU(ms+flip): 77.51 + Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth +- Name: ccnet_r101-d8_512x512_20k_voc12aug + In Collection: CCNet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 73.31 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 9.5 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.27 + mIoU(ms+flip): 79.02 + Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth +- Name: ccnet_r50-d8_512x512_40k_voc12aug + In Collection: CCNet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 75.96 + mIoU(ms+flip): 77.04 + Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth +- Name: ccnet_r101-d8_512x512_40k_voc12aug + In Collection: CCNet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.87 + mIoU(ms+flip): 78.9 + Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..d2bac38ca6760af6441ede5a04409ed495ef87f3 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_512x1024_40k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..989928ab7f98da86e291451040ff85669a9fbddb --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_512x1024_80k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..c32bf48751f0a18983bff0d99310870b71801663 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_512x512_160k_ade20k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..53eb77c0cd6690668ee7c2a666bd85b9a5f7e73b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_512x512_20k_voc12aug.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..d7eb668f39bbd22a1f42628428bc19d1645e9865 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_512x512_40k_voc12aug.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..029c1d525b809b61dc8e548ebe4fb26e5c68a8be --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_512x512_80k_ade20k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..43f05fab05ee4e20c3509a923118fe9818543cbd --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_769x769_40k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..654f377b6f6152c9bd98d33824a39a41d7510c3f --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_769x769_80k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..6a4316dde57206fe369e72fa0d32a529fe1a1932 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..16e34356e9f8566ec73e3c25c771e281d3eeb975 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..1ad94d8988bb822c1571816255464126d9d5b95d --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +model = dict( + decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..bbcd29ccea8dcf9f67f1cd198dacd5dab380b265 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/ccnet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +model = dict( + decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..947b8ac8ce1ddf7906ad39788c6992df3b506d29 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/ccnet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +model = dict( + decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..1a1f49cf6b112afdadf1841571f51b98c010ddf8 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +model = dict( + decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..580d59ca6995ea95a9345ef3ea574ea5b57e9cfb --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/ccnet_r50-d8.py', + '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +model = dict( + decode_head=dict(align_corners=True), + auxiliary_head=dict(align_corners=True), + test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..c6dac64377bb3f73fdf5c836fa9c38757f75ff76 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/ccnet_r50-d8.py', + '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_80k.py' +] +model = dict( + decode_head=dict(align_corners=True), + auxiliary_head=dict(align_corners=True), + test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/cgnet/README.md b/EgoHOS/raw/script/mmsegmentation/configs/cgnet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fefb291404732d03ba6c25ca0b7875e1a937d497 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/cgnet/README.md @@ -0,0 +1,45 @@ +# CGNet + +[CGNet: A Light-weight Context Guided Network for Semantic Segmentation](https://arxiv.org/abs/1811.08201) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification network and ignore the inherent characteristic of semantic segmentation. To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight and efficient network for semantic segmentation. We first propose the Context Guided (CG) block, which learns the joint feature of both local feature and surrounding context, and further improves the joint feature with the global context. Based on the CG block, we develop CGNet which captures contextual information in all stages of the network and is specially tailored for increasing segmentation accuracy. CGNet is also elaborately designed to reduce the number of parameters and save memory footprint. Under an equivalent number of parameters, the proposed CGNet significantly outperforms existing segmentation networks. Extensive experiments on Cityscapes and CamVid datasets verify the effectiveness of the proposed approach. Specifically, without any post-processing and multi-scale testing, the proposed CGNet achieves 64.8% mean IoU on Cityscapes with less than 0.5 M parameters. The source code for the complete system can be found at [this https URL](https://github.com/wutianyiRosun/CGNet). + + +
+ +
+ +## Citation + +```bibtext +@article{wu2020cgnet, + title={Cgnet: A light-weight context guided network for semantic segmentation}, + author={Wu, Tianyi and Tang, Sheng and Zhang, Rui and Cao, Juan and Zhang, Yongdong}, + journal={IEEE Transactions on Image Processing}, + volume={30}, + pages={1169--1179}, + year={2020}, + publisher={IEEE} +} +``` + +## Results and models + +### Cityscapes + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| CGNet | M3N21 | 680x680 | 60000 | 7.5 | 30.51 | 65.63 | 68.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/cgnet/cgnet_680x680_60k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes-20201101_110253.log.json) | +| CGNet | M3N21 | 512x1024 | 60000 | 8.3 | 31.14 | 68.27 | 70.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/cgnet/cgnet_512x1024_60k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes-20201101_110254.log.json) | diff --git a/EgoHOS/raw/script/mmsegmentation/configs/cgnet/cgnet.yml b/EgoHOS/raw/script/mmsegmentation/configs/cgnet/cgnet.yml new file mode 100644 index 0000000000000000000000000000000000000000..bcd6d89c1bdbfb63e94faa4a65f14e8373cf752c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/cgnet/cgnet.yml @@ -0,0 +1,59 @@ +Collections: +- Name: CGNet + Metadata: + Training Data: + - Cityscapes + Paper: + URL: https://arxiv.org/abs/1811.08201 + Title: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation' + README: configs/cgnet/README.md + Code: + URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/cgnet.py#L187 + Version: v0.17.0 + Converted From: + Code: https://github.com/wutianyiRosun/CGNet +Models: +- Name: cgnet_680x680_60k_cityscapes + In Collection: CGNet + Metadata: + backbone: M3N21 + crop size: (680,680) + lr schd: 60000 + inference time (ms/im): + - value: 32.78 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (680,680) + Training Memory (GB): 7.5 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 65.63 + mIoU(ms+flip): 68.04 + Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth +- Name: cgnet_512x1024_60k_cityscapes + In Collection: CGNet + Metadata: + backbone: M3N21 + crop size: (512,1024) + lr schd: 60000 + inference time (ms/im): + - value: 32.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 8.3 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 68.27 + mIoU(ms+flip): 70.33 + Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/cgnet/cgnet_512x1024_60k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/cgnet/cgnet_512x1024_60k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..6efc4cd5c8604d355bf1cd7d02c5528874b858a4 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/cgnet/cgnet_512x1024_60k_cityscapes.py @@ -0,0 +1,66 @@ +_base_ = ['../_base_/models/cgnet.py', '../_base_/default_runtime.py'] + +# optimizer +optimizer = dict(type='Adam', lr=0.001, eps=1e-08, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +total_iters = 60000 +checkpoint_config = dict(by_epoch=False, interval=4000) +evaluation = dict(interval=4000, metric='mIoU') + +# dataset settings +dataset_type = 'CityscapesDataset' +data_root = 'data/cityscapes/' +img_norm_cfg = dict( + mean=[72.39239876, 82.90891754, 73.15835921], std=[1, 1, 1], 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', flip_ratio=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=8, + workers_per_gpu=4, + 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)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/cgnet/cgnet_680x680_60k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/cgnet/cgnet_680x680_60k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..7d4ebe136533fc35e05e0c514511d041f092cfd2 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/cgnet/cgnet_680x680_60k_cityscapes.py @@ -0,0 +1,50 @@ +_base_ = [ + '../_base_/models/cgnet.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py' +] + +# optimizer +optimizer = dict(type='Adam', lr=0.001, eps=1e-08, weight_decay=0.0005) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) +# runtime settings +total_iters = 60000 +checkpoint_config = dict(by_epoch=False, interval=4000) +evaluation = dict(interval=4000, metric='mIoU') + +img_norm_cfg = dict( + mean=[72.39239876, 82.90891754, 73.15835921], std=[1, 1, 1], to_rgb=True) +crop_size = (680, 680) +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), + dict(type='RandomFlip', flip_ratio=0.5), + 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=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/convnext/README.md b/EgoHOS/raw/script/mmsegmentation/configs/convnext/README.md new file mode 100644 index 0000000000000000000000000000000000000000..48c37afb049d6c0e12da22860064ec637615e335 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/convnext/README.md @@ -0,0 +1,71 @@ +# ConvNeXt + +[A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets. + + +
+ +
+ +```bibtex +@article{liu2022convnet, + title={A ConvNet for the 2020s}, + author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining}, + journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2022} +} +``` + +### Usage + +- ConvNeXt backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks. + +```shell +pip install mmcls>=0.20.1 +``` + +### Pre-trained Models + +The pre-trained models on ImageNet-1k or ImageNet-21k are used to fine-tune on the downstream tasks. + +| Model | Training Data | Params(M) | Flops(G) | Download | +|:--------------:|:-------------:|:---------:|:--------:|:--------:| +| ConvNeXt-T\* | ImageNet-1k | 28.59 | 4.46 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth) | +| ConvNeXt-S\* | ImageNet-1k | 50.22 | 8.69 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth) | +| ConvNeXt-B\* | ImageNet-1k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_32xb128-noema_in1k_20220301-2a0ee547.pth) | +| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_in21k_20220301-262fd037.pth) | +| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-large_3rdparty_in21k_20220301-e6e0ea0a.pth) | +| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-xlarge_3rdparty_in21k_20220301-08aa5ddc.pth) | + +*Models with \* are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt/tree/main/semantic_segmentation#results-and-fine-tuned-models).* + +## Results and models + +### ADE20K + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ---------- | ------- | -------- | --- | --- | -------------- | ----- | +| UperNet | ConvNeXt-T | 512x512 | 160000 | 4.23 | 19.90 | 46.11 | 46.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553.log.json) | +| UperNet | ConvNeXt-S | 512x512 | 160000 | 5.16 | 15.18 | 48.56 | 49.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208.log.json) | +| UperNet | ConvNeXt-B | 512x512 | 160000 | 6.33 | 14.41 | 48.71 | 49.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227.log.json) | +| UperNet | ConvNeXt-B |640x640 | 160000 | 8.53 | 10.88 | 52.13 | 52.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859.log.json) | +| UperNet | ConvNeXt-L |640x640 | 160000 | 12.08 | 7.69 | 53.16 | 53.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532.log.json) | +| UperNet | ConvNeXt-XL |640x640 | 160000 | 26.16\* | 6.33 | 53.58 | 54.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344.log.json) | + +Note: + +- `Mem (GB)` with \* is collected when `cudnn_benchmark=True`, and hardware is V100. diff --git a/EgoHOS/raw/script/mmsegmentation/configs/convnext/convnext.yml b/EgoHOS/raw/script/mmsegmentation/configs/convnext/convnext.yml new file mode 100644 index 0000000000000000000000000000000000000000..3e521eff3e67a0dc4f4e9560bb7dfa7baf091d6b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/convnext/convnext.yml @@ -0,0 +1,133 @@ +Models: +- Name: upernet_convnext_tiny_fp16_512x512_160k_ade20k + In Collection: UperNet + Metadata: + backbone: ConvNeXt-T + crop size: (512,512) + lr schd: 160000 + inference time (ms/im): + - value: 50.25 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (512,512) + Training Memory (GB): 4.23 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 46.11 + mIoU(ms+flip): 46.62 + Config: configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth +- Name: upernet_convnext_small_fp16_512x512_160k_ade20k + In Collection: UperNet + Metadata: + backbone: ConvNeXt-S + crop size: (512,512) + lr schd: 160000 + inference time (ms/im): + - value: 65.88 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (512,512) + Training Memory (GB): 5.16 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 48.56 + mIoU(ms+flip): 49.02 + Config: configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth +- Name: upernet_convnext_base_fp16_512x512_160k_ade20k + In Collection: UperNet + Metadata: + backbone: ConvNeXt-B + crop size: (512,512) + lr schd: 160000 + inference time (ms/im): + - value: 69.4 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (512,512) + Training Memory (GB): 6.33 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 48.71 + mIoU(ms+flip): 49.54 + Config: configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth +- Name: upernet_convnext_base_fp16_640x640_160k_ade20k + In Collection: UperNet + Metadata: + backbone: ConvNeXt-B + crop size: (640,640) + lr schd: 160000 + inference time (ms/im): + - value: 91.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (640,640) + Training Memory (GB): 8.53 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 52.13 + mIoU(ms+flip): 52.66 + Config: configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth +- Name: upernet_convnext_large_fp16_640x640_160k_ade20k + In Collection: UperNet + Metadata: + backbone: ConvNeXt-L + crop size: (640,640) + lr schd: 160000 + inference time (ms/im): + - value: 130.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (640,640) + Training Memory (GB): 12.08 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 53.16 + mIoU(ms+flip): 53.38 + Config: configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth +- Name: upernet_convnext_xlarge_fp16_640x640_160k_ade20k + In Collection: UperNet + Metadata: + backbone: ConvNeXt-XL + crop size: (640,640) + lr schd: 160000 + inference time (ms/im): + - value: 157.98 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (640,640) + Training Memory (GB): 26.16 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 53.58 + mIoU(ms+flip): 54.11 + Config: configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..7bf35b2f1ab1b2cb2205d8ad80b4198e555bfa2a --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py @@ -0,0 +1,40 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +model = dict( + decode_head=dict(in_channels=[128, 256, 512, 1024], num_classes=150), + auxiliary_head=dict(in_channels=512, num_classes=150), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optimizer = dict( + constructor='LearningRateDecayOptimizerConstructor', + _delete_=True, + type='AdamW', + lr=0.0001, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 12 + }) + +lr_config = dict( + _delete_=True, + policy='poly', + warmup='linear', + warmup_iters=1500, + warmup_ratio=1e-6, + power=1.0, + min_lr=0.0, + by_epoch=False) + +# By default, models are trained on 8 GPUs with 2 images per GPU +data = dict(samples_per_gpu=2) +# fp16 settings +optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic') +# fp16 placeholder +fp16 = dict() diff --git a/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..8d2c0c26d014771c4e2d3126073d102138f2cd88 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', + '../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 640) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_in21k_20220301-262fd037.pth' # noqa +model = dict( + backbone=dict( + type='mmcls.ConvNeXt', + arch='base', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[128, 256, 512, 1024], + num_classes=150, + ), + auxiliary_head=dict(in_channels=512, num_classes=150), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(426, 426)), +) + +optimizer = dict( + constructor='LearningRateDecayOptimizerConstructor', + _delete_=True, + type='AdamW', + lr=0.0001, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 12 + }) + +lr_config = dict( + _delete_=True, + policy='poly', + warmup='linear', + warmup_iters=1500, + warmup_ratio=1e-6, + power=1.0, + min_lr=0.0, + by_epoch=False) + +# By default, models are trained on 8 GPUs with 2 images per GPU +data = dict(samples_per_gpu=2) +# fp16 settings +optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic') +# fp16 placeholder +fp16 = dict() diff --git a/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..7527ed51fee3c134c7257b56bd2810fdcffd98d3 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', + '../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 640) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-large_3rdparty_in21k_20220301-e6e0ea0a.pth' # noqa +model = dict( + backbone=dict( + type='mmcls.ConvNeXt', + arch='large', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[192, 384, 768, 1536], + num_classes=150, + ), + auxiliary_head=dict(in_channels=768, num_classes=150), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(426, 426)), +) + +optimizer = dict( + constructor='LearningRateDecayOptimizerConstructor', + _delete_=True, + type='AdamW', + lr=0.0001, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 12 + }) + +lr_config = dict( + _delete_=True, + policy='poly', + warmup='linear', + warmup_iters=1500, + warmup_ratio=1e-6, + power=1.0, + min_lr=0.0, + by_epoch=False) + +# By default, models are trained on 8 GPUs with 2 images per GPU +data = dict(samples_per_gpu=2) +# fp16 settings +optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic') +# fp16 placeholder +fp16 = dict() diff --git a/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..2e95f3af990dbc5f441dd2f6bbcbfab86517885c --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth' # noqa +model = dict( + backbone=dict( + type='mmcls.ConvNeXt', + arch='small', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.3, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=150, + ), + auxiliary_head=dict(in_channels=384, num_classes=150), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optimizer = dict( + constructor='LearningRateDecayOptimizerConstructor', + _delete_=True, + type='AdamW', + lr=0.0001, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 12 + }) + +lr_config = dict( + _delete_=True, + policy='poly', + warmup='linear', + warmup_iters=1500, + warmup_ratio=1e-6, + power=1.0, + min_lr=0.0, + by_epoch=False) + +# By default, models are trained on 8 GPUs with 2 images per GPU +data = dict(samples_per_gpu=2) +# fp16 settings +optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic') +# fp16 placeholder +fp16 = dict() diff --git a/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..35c72a8d9945fb80e86a98859a90f8940cec3f67 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + backbone=dict( + type='mmcls.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=150, + ), + auxiliary_head=dict(in_channels=384, num_classes=150), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optimizer = dict( + constructor='LearningRateDecayOptimizerConstructor', + _delete_=True, + type='AdamW', + lr=0.0001, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }) + +lr_config = dict( + _delete_=True, + policy='poly', + warmup='linear', + warmup_iters=1500, + warmup_ratio=1e-6, + power=1.0, + min_lr=0.0, + by_epoch=False) + +# By default, models are trained on 8 GPUs with 2 images per GPU +data = dict(samples_per_gpu=2) +# fp16 settings +optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic') +# fp16 placeholder +fp16 = dict() diff --git a/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..0e2f38ebbd7015153c6e4a2d1fad499bc47de4a0 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', + '../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 640) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-xlarge_3rdparty_in21k_20220301-08aa5ddc.pth' # noqa +model = dict( + backbone=dict( + type='mmcls.ConvNeXt', + arch='xlarge', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[256, 512, 1024, 2048], + num_classes=150, + ), + auxiliary_head=dict(in_channels=1024, num_classes=150), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(426, 426)), +) + +optimizer = dict( + constructor='LearningRateDecayOptimizerConstructor', + _delete_=True, + type='AdamW', + lr=0.00008, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 12 + }) + +lr_config = dict( + _delete_=True, + policy='poly', + warmup='linear', + warmup_iters=1500, + warmup_ratio=1e-6, + power=1.0, + min_lr=0.0, + by_epoch=False) + +# By default, models are trained on 8 GPUs with 2 images per GPU +data = dict(samples_per_gpu=2) +# fp16 settings +optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic') +# fp16 placeholder +fp16 = dict() diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/README.md b/EgoHOS/raw/script/mmsegmentation/configs/danet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..411c5956212fba5c351e385a57b23942a8946eeb --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/README.md @@ -0,0 +1,66 @@ +# DANet + +[Dual Attention Network for Scene Segmentation](https://arxiv.org/abs/1809.02983) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the selfattention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data. We make the code and trained model publicly available at [this https URL](https://github.com/junfu1115/DANet). + + +
+ +
+ +## Citation + +```bibtex +@article{fu2018dual, + title={Dual Attention Network for Scene Segmentation}, + author={Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu}, + booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2019} +} +``` + +## Results and models + +### Cityscapes + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DANet | R-50-D8 | 512x1024 | 40000 | 7.4 | 2.66 | 78.74 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json) | +| DANet | R-101-D8 | 512x1024 | 40000 | 10.9 | 1.99 | 80.52 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json) | +| DANet | R-50-D8 | 769x769 | 40000 | 8.8 | 1.56 | 78.88 | 80.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json) | +| DANet | R-101-D8 | 769x769 | 40000 | 12.8 | 1.07 | 79.88 | 81.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json) | +| DANet | R-50-D8 | 512x1024 | 80000 | - | - | 79.34 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json) | +| DANet | R-101-D8 | 512x1024 | 80000 | - | - | 80.41 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json) | +| DANet | R-50-D8 | 769x769 | 80000 | - | - | 79.27 | 80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json) | +| DANet | R-101-D8 | 769x769 | 80000 | - | - | 80.47 | 82.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json) | + +### ADE20K + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DANet | R-50-D8 | 512x512 | 80000 | 11.5 | 21.20 | 41.66 | 42.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json) | +| DANet | R-101-D8 | 512x512 | 80000 | 15 | 14.18 | 43.64 | 45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json) | +| DANet | R-50-D8 | 512x512 | 160000 | - | - | 42.45 | 43.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json) | +| DANet | R-101-D8 | 512x512 | 160000 | - | - | 44.17 | 45.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json) | + +### Pascal VOC 2012 + Aug + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DANet | R-50-D8 | 512x512 | 20000 | 6.5 | 20.94 | 74.45 | 75.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json) | +| DANet | R-101-D8 | 512x512 | 20000 | 9.9 | 13.76 | 76.02 | 77.23 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json) | +| DANet | R-50-D8 | 512x512 | 40000 | - | - | 76.37 | 77.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json) | +| DANet | R-101-D8 | 512x512 | 40000 | - | - | 76.51 | 77.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json) | diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet.yml b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet.yml new file mode 100644 index 0000000000000000000000000000000000000000..ca2d6ff982c5185cf820ed87227d282e58617f19 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet.yml @@ -0,0 +1,301 @@ +Collections: +- Name: DANet + Metadata: + Training Data: + - Cityscapes + - ADE20K + - Pascal VOC 2012 + Aug + Paper: + URL: https://arxiv.org/abs/1809.02983 + Title: Dual Attention Network for Scene Segmentation + README: configs/danet/README.md + Code: + URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76 + Version: v0.17.0 + Converted From: + Code: https://github.com/junfu1115/DANet/ +Models: +- Name: danet_r50-d8_512x1024_40k_cityscapes + In Collection: DANet + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 375.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 7.4 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.74 + Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth +- Name: danet_r101-d8_512x1024_40k_cityscapes + In Collection: DANet + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 502.51 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 10.9 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.52 + Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth +- Name: danet_r50-d8_769x769_40k_cityscapes + In Collection: DANet + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 641.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 8.8 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.88 + mIoU(ms+flip): 80.62 + Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth +- Name: danet_r101-d8_769x769_40k_cityscapes + In Collection: DANet + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 934.58 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 12.8 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.88 + mIoU(ms+flip): 81.47 + Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth +- Name: danet_r50-d8_512x1024_80k_cityscapes + In Collection: DANet + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.34 + Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth +- Name: danet_r101-d8_512x1024_80k_cityscapes + In Collection: DANet + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.41 + Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth +- Name: danet_r50-d8_769x769_80k_cityscapes + In Collection: DANet + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.27 + mIoU(ms+flip): 80.96 + Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth +- Name: danet_r101-d8_769x769_80k_cityscapes + In Collection: DANet + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.47 + mIoU(ms+flip): 82.02 + Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth +- Name: danet_r50-d8_512x512_80k_ade20k + In Collection: DANet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 47.17 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 11.5 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 41.66 + mIoU(ms+flip): 42.9 + Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth +- Name: danet_r101-d8_512x512_80k_ade20k + In Collection: DANet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 70.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 15.0 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 43.64 + mIoU(ms+flip): 45.19 + Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth +- Name: danet_r50-d8_512x512_160k_ade20k + In Collection: DANet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.45 + mIoU(ms+flip): 43.25 + Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth +- Name: danet_r101-d8_512x512_160k_ade20k + In Collection: DANet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 44.17 + mIoU(ms+flip): 45.02 + Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth +- Name: danet_r50-d8_512x512_20k_voc12aug + In Collection: DANet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 47.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 6.5 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 74.45 + mIoU(ms+flip): 75.69 + Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth +- Name: danet_r101-d8_512x512_20k_voc12aug + In Collection: DANet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 72.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 9.9 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.02 + mIoU(ms+flip): 77.23 + Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth +- Name: danet_r50-d8_512x512_40k_voc12aug + In Collection: DANet + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.37 + mIoU(ms+flip): 77.29 + Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth +- Name: danet_r101-d8_512x512_40k_voc12aug + In Collection: DANet + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.51 + mIoU(ms+flip): 77.32 + Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..3bfb9bdb3064275c2ac3bf2a057ef8eb79c308df --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_512x1024_40k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..d80b2ec160ae1c41499d45242713a99122d8adf8 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_512x1024_80k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..0f22d0fb6362252ac02f3f152a42997c68b90343 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_160k_ade20k.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_512x512_160k_ade20k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..709f93cba3e3bca6ce0635457ab1823b04123bf8 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_512x512_20k_voc12aug.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..5c623eb56836760694b50f3e4e66aa0f1fc069df --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_512x512_40k_voc12aug.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_80k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..bd31bc8f283fe8c322ee4876deadb89569dc1743 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_512x512_80k_ade20k.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_512x512_80k_ade20k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..597d76de79610780b03cd91dba5f3a4f10147bcd --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_769x769_40k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..70f9b31966128e8d9ec37859f57a7edfd8e6d1b2 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_769x769_80k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..1b70c5b8d49f04661e23604ca4da56a82b1b99c9 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/danet_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..03734310d7338c75d48c914cb325500961c04a79 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/danet_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..22aaf857c3212d0b36b0b04e7990616025a3ef9b --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_160k_ade20k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/danet_r50-d8.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +model = dict( + decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_20k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_20k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..010f86f1aac1b5c827dec29f692d137dc1c399bf --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_20k_voc12aug.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/danet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +model = dict( + decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..0cef0f09bfa2290d14fc3a783ea500d6c3da2931 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/danet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +model = dict( + decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_80k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_80k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..154e84890ed73fe4813dddc8c321de6cd2854fc1 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_512x512_80k_ade20k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/danet_r50-d8.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +model = dict( + decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_769x769_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_769x769_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..5c5b94e5a27d7f902d4bdea7ef6c4ef0b816bb99 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_769x769_40k_cityscapes.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/danet_r50-d8.py', + '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +model = dict( + decode_head=dict(align_corners=True), + auxiliary_head=dict(align_corners=True), + test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_769x769_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_769x769_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..c7237ae03c601204dc7c03018ca17ed363090569 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/danet/danet_r50-d8_769x769_80k_cityscapes.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/danet_r50-d8.py', + '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_80k.py' +] +model = dict( + decode_head=dict(align_corners=True), + auxiliary_head=dict(align_corners=True), + test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/README.md b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a5d85a5efdaf4b1f0f865ccf35d0dec868c37224 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/README.md @@ -0,0 +1,116 @@ +# DeepLabV3 + +[Rethinking atrous convolution for semantic image segmentation](https://arxiv.org/abs/1706.05587) + +## Introduction + + + +Official Repo + +Code Snippet + +## Abstract + + + +In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark. + + +
+ +
+ +## Citation + +```bibtext +@article{chen2017rethinking, + title={Rethinking atrous convolution for semantic image segmentation}, + author={Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig}, + journal={arXiv preprint arXiv:1706.05587}, + year={2017} +} +``` + +## Results and models + +### Cityscapes + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | --------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DeepLabV3 | R-50-D8 | 512x1024 | 40000 | 6.1 | 2.57 | 79.09 | 80.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449.log.json) | +| DeepLabV3 | R-101-D8 | 512x1024 | 40000 | 9.6 | 1.92 | 77.12 | 79.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241.log.json) | +| DeepLabV3 | R-50-D8 | 769x769 | 40000 | 6.9 | 1.11 | 78.58 | 79.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723.log.json) | +| DeepLabV3 | R-101-D8 | 769x769 | 40000 | 10.9 | 0.83 | 79.27 | 80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809.log.json) | +| DeepLabV3 | R-18-D8 | 512x1024 | 80000 | 1.7 | 13.78 | 76.70 | 78.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes-20201225_021506.log.json) | +| DeepLabV3 | R-50-D8 | 512x1024 | 80000 | - | - | 79.32 | 80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404.log.json) | +| DeepLabV3 | R-101-D8 | 512x1024 | 80000 | - | - | 80.20 | 81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503.log.json) | +| DeepLabV3 (FP16) | R-101-D8 | 512x1024 | 80000 | 5.75 | 3.86 | 80.48 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json) | +| DeepLabV3 | R-18-D8 | 769x769 | 80000 | 1.9 | 5.55 | 76.60 | 78.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes-20201225_021506.log.json) | +| DeepLabV3 | R-50-D8 | 769x769 | 80000 | - | - | 79.89 | 81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338.log.json) | +| DeepLabV3 | R-101-D8 | 769x769 | 80000 | - | - | 79.67 | 80.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353.log.json) | +| DeepLabV3 | R-101-D16-MG124 | 512x1024 | 40000 | 4.7 | - 6.96 | 76.71 | 78.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) | +| DeepLabV3 | R-101-D16-MG124 | 512x1024 | 80000 | - | - | 78.36 | 79.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) | +| DeepLabV3 | R-18b-D8 | 512x1024 | 80000 | 1.6 | 13.93 | 76.26 | 77.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes-20201225_094144.log.json) | +| DeepLabV3 | R-50b-D8 | 512x1024 | 80000 | 6.0 | 2.74 | 79.63 | 80.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes-20201225_155148.log.json) | +| DeepLabV3 | R-101b-D8 | 512x1024 | 80000 | 9.5 | 1.81 | 80.01 | 81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes-20201226_171821.log.json) | +| DeepLabV3 | R-18b-D8 | 769x769 | 80000 | 1.8 | 5.79 | 76.63 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes-20201225_094144.log.json) | +| DeepLabV3 | R-50b-D8 | 769x769 | 80000 | 6.8 | 1.16 | 78.80 | 80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes-20201225_155404.log.json) | +| DeepLabV3 | R-101b-D8 | 769x769 | 80000 | 10.7 | 0.82 | 79.41 | 80.73 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes-20201226_190843.log.json) | + +### ADE20K + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DeepLabV3 | R-50-D8 | 512x512 | 80000 | 8.9 | 14.76 | 42.42 | 43.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 80000 | 12.4 | 10.14 | 44.08 | 45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256.log.json) | +| DeepLabV3 | R-50-D8 | 512x512 | 160000 | - | - | 42.66 | 44.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 160000 | - | - | 45.00 | 46.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) | + +### Pascal VOC 2012 + Aug + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DeepLabV3 | R-50-D8 | 512x512 | 20000 | 6.1 | 13.88 | 76.17 | 77.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 20000 | 9.6 | 9.81 | 78.70 | 79.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932.log.json) | +| DeepLabV3 | R-50-D8 | 512x512 | 40000 | - | - | 77.68 | 78.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 40000 | - | - | 77.92 | 79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432.log.json) | + +### Pascal Context + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DeepLabV3 | R-101-D8 | 480x480 | 40000 | 9.2 | 7.09 | 46.55 | 47.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context-20200911_204118.log.json) | +| DeepLabV3 | R-101-D8 | 480x480 | 80000 | - | - | 46.42 | 47.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context-20200911_170155.log.json) | + +### Pascal Context 59 + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DeepLabV3 | R-101-D8 | 480x480 | 40000 | - | - | 52.61 | 54.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59-20210416_110332.log.json) | +| DeepLabV3 | R-101-D8 | 480x480 | 80000 | - | - | 52.46 | 54.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59-20210416_113002.log.json) | + +### COCO-Stuff 10k + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DeepLabV3 | R-50-D8 | 512x512 | 20000 | 9.6 | 10.8 | 34.66 | 36.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 20000 | 13.2 | 8.7 | 37.30 | 38.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json) | +| DeepLabV3 | R-50-D8 | 512x512 | 40000 | - | - | 35.73 | 37.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 40000 | - | - | 37.81 | 38.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json) | + +### COCO-Stuff 164k + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DeepLabV3 | R-50-D8 | 512x512 | 80000 | 9.6 | 10.8 | 39.38 | 40.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 80000 | 13.2 | 8.7 | 40.87 | 41.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252.log.json) | +| DeepLabV3 | R-50-D8 | 512x512 | 160000 | - | - | 41.09 | 41.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 160000 | - | - | 41.82 | 42.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402.log.json) | +| DeepLabV3 | R-50-D8 | 512x512 | 320000 | - | - | 41.37 | 42.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 320000 | - | - | 42.61 | 43.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402.log.json) | + +Note: + +- `D-8` here corresponding to the output stride 8 setting for DeepLab series. +- `FP16` means Mixed Precision (FP16) is adopted in training. diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3.yml b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3.yml new file mode 100644 index 0000000000000000000000000000000000000000..559af4f69ced5b2a8c3dcc4c68d8b5532f8fb879 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3.yml @@ -0,0 +1,756 @@ +Collections: +- Name: DeepLabV3 + Metadata: + Training Data: + - Cityscapes + - ADE20K + - Pascal VOC 2012 + Aug + - Pascal Context + - Pascal Context 59 + - COCO-Stuff 10k + - COCO-Stuff 164k + Paper: + URL: https://arxiv.org/abs/1706.05587 + Title: Rethinking atrous convolution for semantic image segmentation + README: configs/deeplabv3/README.md + Code: + URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 + Version: v0.17.0 + Converted From: + Code: https://github.com/tensorflow/models/tree/master/research/deeplab +Models: +- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 389.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 6.1 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.09 + mIoU(ms+flip): 80.45 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth +- Name: deeplabv3_r101-d8_512x1024_40k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 40000 + inference time (ms/im): + - value: 520.83 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 9.6 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 77.12 + mIoU(ms+flip): 79.61 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth +- Name: deeplabv3_r50-d8_769x769_40k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 900.9 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 6.9 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.58 + mIoU(ms+flip): 79.89 + Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth +- Name: deeplabv3_r101-d8_769x769_40k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 40000 + inference time (ms/im): + - value: 1204.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 10.9 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.27 + mIoU(ms+flip): 80.11 + Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth +- Name: deeplabv3_r18-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-18-D8 + crop size: (512,1024) + lr schd: 80000 + inference time (ms/im): + - value: 72.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 1.7 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.7 + mIoU(ms+flip): 78.27 + Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth +- Name: deeplabv3_r50-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.32 + mIoU(ms+flip): 80.57 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth +- Name: deeplabv3_r101-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.2 + mIoU(ms+flip): 81.21 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth +- Name: deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,1024) + lr schd: 80000 + inference time (ms/im): + - value: 259.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (512,1024) + Training Memory (GB): 5.75 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.48 + Config: configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth +- Name: deeplabv3_r18-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-18-D8 + crop size: (769,769) + lr schd: 80000 + inference time (ms/im): + - value: 180.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 1.9 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.6 + mIoU(ms+flip): 78.26 + Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth +- Name: deeplabv3_r50-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.89 + mIoU(ms+flip): 81.06 + Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth +- Name: deeplabv3_r101-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (769,769) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.67 + mIoU(ms+flip): 80.81 + Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth +- Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D16-MG124 + crop size: (512,1024) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.36 + mIoU(ms+flip): 79.84 + Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth +- Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-18b-D8 + crop size: (512,1024) + lr schd: 80000 + inference time (ms/im): + - value: 71.79 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 1.6 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.26 + mIoU(ms+flip): 77.88 + Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth +- Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-50b-D8 + crop size: (512,1024) + lr schd: 80000 + inference time (ms/im): + - value: 364.96 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 6.0 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.63 + mIoU(ms+flip): 80.98 + Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth +- Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-101b-D8 + crop size: (512,1024) + lr schd: 80000 + inference time (ms/im): + - value: 552.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,1024) + Training Memory (GB): 9.5 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 80.01 + mIoU(ms+flip): 81.21 + Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth +- Name: deeplabv3_r18b-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-18b-D8 + crop size: (769,769) + lr schd: 80000 + inference time (ms/im): + - value: 172.71 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 1.8 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 76.63 + mIoU(ms+flip): 77.51 + Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth +- Name: deeplabv3_r50b-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-50b-D8 + crop size: (769,769) + lr schd: 80000 + inference time (ms/im): + - value: 862.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 6.8 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 78.8 + mIoU(ms+flip): 80.27 + Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth +- Name: deeplabv3_r101b-d8_769x769_80k_cityscapes + In Collection: DeepLabV3 + Metadata: + backbone: R-101b-D8 + crop size: (769,769) + lr schd: 80000 + inference time (ms/im): + - value: 1219.51 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (769,769) + Training Memory (GB): 10.7 + Results: + - Task: Semantic Segmentation + Dataset: Cityscapes + Metrics: + mIoU: 79.41 + mIoU(ms+flip): 80.73 + Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth +- Name: deeplabv3_r50-d8_512x512_80k_ade20k + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 67.75 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 8.9 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.42 + mIoU(ms+flip): 43.28 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth +- Name: deeplabv3_r101-d8_512x512_80k_ade20k + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 98.62 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 12.4 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 44.08 + mIoU(ms+flip): 45.19 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth +- Name: deeplabv3_r50-d8_512x512_160k_ade20k + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 42.66 + mIoU(ms+flip): 44.09 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth +- Name: deeplabv3_r101-d8_512x512_160k_ade20k + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: ADE20K + Metrics: + mIoU: 45.0 + mIoU(ms+flip): 46.66 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth +- Name: deeplabv3_r50-d8_512x512_20k_voc12aug + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 72.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 6.1 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 76.17 + mIoU(ms+flip): 77.42 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth +- Name: deeplabv3_r101-d8_512x512_20k_voc12aug + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 101.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 9.6 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 78.7 + mIoU(ms+flip): 79.95 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth +- Name: deeplabv3_r50-d8_512x512_40k_voc12aug + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.68 + mIoU(ms+flip): 78.78 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth +- Name: deeplabv3_r101-d8_512x512_40k_voc12aug + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal VOC 2012 + Aug + Metrics: + mIoU: 77.92 + mIoU(ms+flip): 79.18 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth +- Name: deeplabv3_r101-d8_480x480_40k_pascal_context + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (480,480) + lr schd: 40000 + inference time (ms/im): + - value: 141.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (480,480) + Training Memory (GB): 9.2 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 46.55 + mIoU(ms+flip): 47.81 + Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth +- Name: deeplabv3_r101-d8_480x480_80k_pascal_context + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (480,480) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context + Metrics: + mIoU: 46.42 + mIoU(ms+flip): 47.53 + Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth +- Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59 + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (480,480) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context 59 + Metrics: + mIoU: 52.61 + mIoU(ms+flip): 54.28 + Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth +- Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59 + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (480,480) + lr schd: 80000 + Results: + - Task: Semantic Segmentation + Dataset: Pascal Context 59 + Metrics: + mIoU: 52.46 + mIoU(ms+flip): 54.09 + Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth +- Name: deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 92.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 9.6 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 10k + Metrics: + mIoU: 34.66 + mIoU(ms+flip): 36.08 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth +- Name: deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 20000 + inference time (ms/im): + - value: 114.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 13.2 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 10k + Metrics: + mIoU: 37.3 + mIoU(ms+flip): 38.42 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth +- Name: deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 10k + Metrics: + mIoU: 35.73 + mIoU(ms+flip): 37.09 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth +- Name: deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 40000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 10k + Metrics: + mIoU: 37.81 + mIoU(ms+flip): 38.8 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth +- Name: deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 92.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 9.6 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 39.38 + mIoU(ms+flip): 40.03 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth +- Name: deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 80000 + inference time (ms/im): + - value: 114.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + Training Memory (GB): 13.2 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 40.87 + mIoU(ms+flip): 41.5 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth +- Name: deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 41.09 + mIoU(ms+flip): 41.69 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth +- Name: deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 41.82 + mIoU(ms+flip): 42.49 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth +- Name: deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k + In Collection: DeepLabV3 + Metadata: + backbone: R-50-D8 + crop size: (512,512) + lr schd: 320000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 41.37 + mIoU(ms+flip): 42.22 + Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth +- Name: deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k + In Collection: DeepLabV3 + Metadata: + backbone: R-101-D8 + crop size: (512,512) + lr schd: 320000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 42.61 + mIoU(ms+flip): 43.42 + Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..f20f260e23a95dfee9dfdceef9badab992246f53 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py @@ -0,0 +1,11 @@ +_base_ = './deeplabv3_r50-d8_512x1024_40k_cityscapes.py' +model = dict( + pretrained='open-mmlab://resnet101_v1c', + backbone=dict( + depth=101, + dilations=(1, 1, 1, 2), + strides=(1, 2, 2, 1), + multi_grid=(1, 2, 4)), + decode_head=dict( + dilations=(1, 6, 12, 18), + sampler=dict(type='OHEMPixelSampler', min_kept=100000))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..de4a8a5e9f030f1e8a8802596885186163f23eed --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py @@ -0,0 +1,11 @@ +_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' +model = dict( + pretrained='open-mmlab://resnet101_v1c', + backbone=dict( + depth=101, + dilations=(1, 1, 1, 2), + strides=(1, 2, 2, 1), + multi_grid=(1, 2, 4)), + decode_head=dict( + dilations=(1, 6, 12, 18), + sampler=dict(type='OHEMPixelSampler', min_kept=100000))) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py new file mode 100644 index 0000000000000000000000000000000000000000..0b5256f7b7b053cbe8d9e4ca2ec6139bb02387f6 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py new file mode 100644 index 0000000000000000000000000000000000000000..4874121fd01e4024bfde445f451b7368c6834511 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py new file mode 100644 index 0000000000000000000000000000000000000000..001b7a69c15299fc1fe5b269a5accf92c5ece032 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_480x480_80k_pascal_context.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py new file mode 100644 index 0000000000000000000000000000000000000000..032dc8b6219421698c3a1bfb4bca5addfeea1ab3 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_480x480_80k_pascal_context_59.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..8c707c79d659bc544d242352bcb29686eb40b004 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_512x1024_40k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..6804a5781369d1031f179d421a3b5a160fd575d3 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py new file mode 100644 index 0000000000000000000000000000000000000000..df6f36ef7c3b71ba7979aa7a1b226b3e3ebd9bb4 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_512x512_160k_ade20k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..40f5f62373e59d1c6c01ca3f57777698461127c9 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_512x512_20k_voc12aug.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py new file mode 100644 index 0000000000000000000000000000000000000000..fb2be22f8bc2e10cdfba4f58b2ad1ced913b4ea4 --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_512x512_40k_voc12aug.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py new file mode 100644 index 0000000000000000000000000000000000000000..76b124248e14c5dccea55ea2ea91c63409e0ffec --- /dev/null +++ b/EgoHOS/raw/script/mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))