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(OLD) [Feature] Support LoveDA dataset #1006
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #1006 +/- ##
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+ Coverage 89.73% 89.76% +0.02%
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Files 118 120 +2
Lines 6568 6614 +46
Branches 1021 1030 +9
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+ Hits 5894 5937 +43
- Misses 473 476 +3
Partials 201 201
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Sentry. |
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We need to check the inference and training performance between mmsegmentaion and the original paper implementation.
Hi @Junjue-Wang |
The original paper reports the accuracies on the test set. However, the labels of the test set are private. If you really want to test. Please submit the predictions on this Contest and you will get the scores smoothly. Or you can directly report the accuracies on the providied validation set. |
Thanks for your careful revision. Am I going to re-pull request after modification? Or you can directly help me fix the lint errors? |
For further participation of our open source codebase project, I suggest you follow these links Chinese article and English document, you need to use Feel free to ask us if you have any problems. Best, |
Hi, @Junjue-Wang . Thanks for your nice help! We would test your code and try to train several models of MMSegmentation on LoveDA next week. From your paper, it seems that HRNet is best model, maybe we can try some transformer models such as Swin Transformer and Segformer. Best, |
Yes, I wish you all the best on the test. If you encounter any problems, please feel free to contact me. |
…on. (open-mmlab#1002) * fix single loss type * fix error in ohem & point_head * fix coverage miss * fix uncoverage error of PointHead loss * fix coverage miss * fix uncoverage error of PointHead loss * nn.modules.container.ModuleList to nn.ModuleList * more simple format * merge unittest def
* add TIMMBackbone and unittests * add timm to tests requirements * deprecate pt1.3.1 * reduce the unittests input of timm backbone * fix ci * fix ci * fix ci * fix ci * fix ci * fix ci * fix ci * fix ci * fix ci * remove unittests of large models of timm backbone * generate coverage report for all unittests env * reduce the unittests input of timm backbone * reduce the unittests input of timm backbone
* change version to v0.19.0 * update changelog
open-mmlab#999) * [Fix] Fix the bug that vit cannot load pretrain properly when using init_cfg to specify the pretrain scheme * [Fix] fix the coverage problem * Update mmseg/models/backbones/vit.py Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn> * [Fix] make the predicate more concise and clearer * [Fix] Modified the judgement logic * Update tests/test_models/test_backbones/test_vit.py Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn> * add comments Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
Please fix the lint error. pre-commit run --all-files before commit. |
@Junjue-Wang |
Update: Hi, @Junjue-Wang Please grant authorization on me about your forked MMSegmentation followed here. Thus I could push my modifications on your branch. Here is some results on validation and test set:
FYI, the results of original paper: TO DO:
|
I have already granted the authorization to you. Please help me check the lint errors, Thank you! |
Too many files changed. |
'DarkZurichDataset', | ||
'NightDrivingDataset', | ||
'COCOStuffDataset', | ||
'LoveDADataset', |
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'LoveDADataset', | |
'LoveDADataset' |
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@MengzhangLI What should we do? Create a new PR for what?
Hi, @Junjue-Wang |
* fix `upsample_nearest_nhwc` for large bsz * fix `upsample_nearest_nhwc` for large bsz
* map location to cpu when load checkpoint (open-mmlab#1007) * [Enhancement] Support minus output feature index in mobilenet_v3 (open-mmlab#1005) * fix typo in mobilenet_v3 * fix typo in mobilenet_v3 * use -1 to indicate output tensors from final stage * support negative out_indices * [Enhancement] inference speed and flops tools. (open-mmlab#986) * add the function to test the dummy forward speed of models. * add tools to test the flops and inference speed of multiple models. * [Fix] Update pose tracking demo to be compatible with latest mmtrakcing (open-mmlab#1014) * update mmtracking demo * support both track_bboxes and track_results * add docstring * [Fix] fix skeleton_info of coco wholebody dataset (open-mmlab#1010) * fix wholebody base dataset * fix lint * fix lint Co-authored-by: ly015 <liyining0712@gmail.com> * [Feature] Add ViPNAS models for wholebody keypoint detection (open-mmlab#1009) * add configs * add dark configs * add checkpoint and readme * update webcam demo * fix model path in webcam demo * fix unittest * [Fix] Fix bbox label visualization (open-mmlab#1020) * update model metafiles (open-mmlab#1001) * update hourglass ae .md (open-mmlab#1027) * [Feature] Add ViPNAS mbv3 (open-mmlab#1025) * add vipnas mbv3 * test other variants * submission for mmpose * add unittest * add readme * update .yml * fix lint * rebase * fix pytest Co-authored-by: jin-s13 <jinsheng13@foxmail.com> * [Enhancement] Set a random seed when the user does not set a seed (open-mmlab#1030) * fix randseed * fix lint * fix import * fix isort * update yapf hook * revert yapf version * add cfg file for flops and speed test, change the bulid_posenet to init_pose_model and fix some typo in cfg (open-mmlab#1028) * [Enhancement] Add more functions for speed test tool (open-mmlab#1034) * add batch size and device args in speed test script, and remove MMDataParallel warper * add vipnas_mbv3 model * fix dead link (open-mmlab#1038) * Skip CI when some specific files were changed (open-mmlab#1041) * update sigmas (open-mmlab#1040) * add more configs, ckpts and logs for HRNet on PoseTrack18 (open-mmlab#1035) * [Feature] Add PoseWarper dataset (open-mmlab#1006) * add PoseWarper dataset and base class * modify pipelines related to video * add unittest for PoseWarper dataset * add unittest for evaluation function in posetrack18-realted dataset, and add some annotations json files * fix typo * fix unittest CI failure * fix typo * add PoseWarper dataset and base class * modify pipelines related to video * add unittest for PoseWarper dataset * add unittest for evaluation function in posetrack18-realted dataset, and add some annotations json files * fix typo * fix unittest CI failure * fix typo * modify some methods in the base class to improve code coverage rate * recover some mistakenly-deleted notes * remove test_dataset_info part for the new TopDownPoseTrack18VideoDataset class * cancel uncompleted previous runs (open-mmlab#1053) * [Doc] Add inference speed results (open-mmlab#1044) * add docs related to inference speed results * add corresponding Chinese docs and fix some typos * add Chinese docs in readthedocs * remove the massive table in readme * minor modification to wording Co-authored-by: ly015 <liyining0712@gmail.com> * [Feature] Add PoseWarper detector model (open-mmlab#932) * Add top down video detector module * Add PoseWarper neck * add function _freeze_stages * fix typo * modify PoseWarper detector and PoseWarperNeck * fix typo * modify posewarper detector and neck * Delete top_down_video.py change the base class of `PoseWarper` detector from `TopDownVideo` to `TopDown` * fix spell typo * modify detector and neck * add unittest for detector and neck * modify unittest for posewarper forward * Add top down video detector module * Add PoseWarper neck * add function _freeze_stages * fix typo * modify PoseWarper detector and PoseWarperNeck * fix typo * modify posewarper detector and neck * Delete top_down_video.py change the base class of `PoseWarper` detector from `TopDownVideo` to `TopDown` * fix spell typo * modify detector and neck * add unittest for detector and neck * modify unittest for posewarper forward * modify dependency on mmcv version in posewarper neck * reduce memory cost in test * modify flops tool for more flexible input format * Add top down video detector module * Add PoseWarper neck * add function _freeze_stages * fix typo * modify PoseWarper detector and PoseWarperNeck * fix typo * modify posewarper detector and neck * Delete top_down_video.py change the base class of `PoseWarper` detector from `TopDownVideo` to `TopDown` * fix spell typo * modify detector and neck * add unittest for detector and neck * modify unittest for posewarper forward * Add PoseWarper neck * modify PoseWarper detector and PoseWarperNeck * modify posewarper detector and neck * Delete top_down_video.py change the base class of `PoseWarper` detector from `TopDownVideo` to `TopDown` * fix spell typo * modify detector and neck * add unittest for detector and neck * modify unittest for posewarper forward * modify dependency on mmcv version in posewarper neck * reduce memory cost in test * modify flops tool for more flexible input format * modify the posewarper detector description * modify some arguments and related fields * modify default values for some args * fix readthedoc bulid typo * fix ignore path (open-mmlab#1059) * [Doc] Add related docs for PoseWarper (open-mmlab#1036) * add related docs for PoseWarper * add related readme docs for posewarper * modify related args in posewarper stage2 config * modify posewarper stage2 config path * add description about val_boxes path for data preparation (open-mmlab#1060) * bump version to v0.21.0 (open-mmlab#1061) * [Feature] Add ViPNAS_Mbv3 wholebody model (open-mmlab#1055) * add vipnas mbv3 coco_wholebody * add vipnas mbv3 coco_wholebody md&yml * fix lint Co-authored-by: ly015 <liyining0712@gmail.com> Co-authored-by: Lumin <30328525+luminxu@users.noreply.github.com> Co-authored-by: zengwang430521 <zengwang430521@gmail.com> Co-authored-by: Jas <jinsheng@sensetime.com> Co-authored-by: jin-s13 <jinsheng13@foxmail.com> Co-authored-by: Qikai Li <87690686+liqikai9@users.noreply.github.com> Co-authored-by: QwQ2000 <396707050@qq.com>
Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
Motivation
Invited by MengzhangLI, I would like to add LoveDA dataset into mmsegmentation.
Modification
The corresponding dataset api and dataset_prepare.md has been updated.
BC-breaking (Optional)
None.
Use cases (Optional)
The users can easily use the LoveDA dataset and benchmarked segmentation models with MMSegmentation.
Checklist