[Fix] bug fix for old MMCV format for mobilenetv3 configs file (changed to MMEngine format) #3674
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…lab#2829) ## Motivation If the module does not actually exist, setting locations will report an error. open-mmlab/mmengine#1010 ## Modification mmseg/registry/registry.py
## Motivation As title, lead users to follow our migration document. ## Checklist - [x] open-mmlab#2801
## Motivation fix squeeze error when N=1 and C=1 ## Modification fix squeeze error when N=1 and C=1
## Motivation https://github.com/open-mmlab/mmsegmentation/blob/3a14c3597418d3e6eb64a3c65044fcf91f0c105d/mmseg/datasets/pipelines/transforms.py#L1348 ## Modification Add albu to dev-1.x
## Motivation Update repo information and URLs in README. ## Modification ## BC-breaking (Optional) ## Use cases (Optional) --------- Co-authored-by: CSH <40987381+csatsurnh@users.noreply.github.com>
…lab#2951) Motivation Typo in docs/en/user_guides/visualization_feature_map.md. Modification reature -> feature Checklist - [x] Pre-commit or other linting tools are used to fix the potential lint issues. - [x] The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. - [x] If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. - [x] The documentation has been modified accordingly, like docstring or example tutorials.
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 Support DDRNet Paper: [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://arxiv.org/pdf/2101.06085) official Code: https://github.com/ydhongHIT/DDRNet There is already a PR open-mmlab#1722 , but it has been inactive for a long time. ## Current Result ### Cityscapes #### inference with converted official weights | Method | Backbone | mIoU(official) | mIoU(converted weight) | | ------ | ------------- | -------------- | ---------------------- | | DDRNet | DDRNet23-slim | 77.8 | 77.84 | | DDRNet | DDRNet23 | 79.5 | 79.53 | #### training with converted pretrained backbone | Method | Backbone | Crop Size | Lr schd | Inf time(fps) | Device | mIoU | mIoU(ms+flip) | config | download | | ------ | ------------- | --------- | ------- | ------- | -------- | ----- | ------------- | ------------ | ------------ | | DDRNet | DDRNet23-slim | 1024x1024 | 120000 | 85.85 | RTX 8000 | 77.85 | 79.80 | [config](https://github.com/whu-pzhang/mmsegmentation/blob/ddrnet/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py) | model \| log | | DDRNet | DDRNet23 | 1024x1024 | 120000 | 33.41 | RTX 8000 | 79.53 | 80.98 | [config](https://github.com/whu-pzhang/mmsegmentation/blob/ddrnet/configs/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py) | model \| log | The converted pretrained backbone weights download link: 1. [ddrnet23s_in1k_mmseg.pth](https://drive.google.com/file/d/1Ni4F1PMGGjuld-1S9fzDTmneLfpMuPTG/view?usp=sharing) 2. [ddrnet23_in1k_mmseg.pth](https://drive.google.com/file/d/11rsijC1xOWB6B0LgNQkAG-W6e1OdbCyJ/view?usp=sharing) ## To do - [x] support inference with converted official weights - [x] support training on cityscapes dataset --------- Co-authored-by: xiexinch <xiexinch@outlook.com>
…or NVIDIA Jetson (open-mmlab#3372) Fine tune ONNX Models (MMSegemetation) Inference for NVIDIA Jetson
## Motivation open-mmlab#3383 ## Modification - Add bpe_simple_vocab_16e6.txt.gz to `MANIFEST.in`
## Motivation open-mmlab#3384 ## Modification - mmseg/apis/inference.py
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 Fixes open-mmlab#3412 ## Modification We just need to replace tensor creation using torch.stack() instead of torch.tensor(). ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 4. The documentation has been modified accordingly, like docstring or example tutorials.
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 Current Visualization Hook can only get instances of `SegLocalVisualizer`. This makes impossible to use any other custom implementation. ## Modification This PR just allows to instantiate a different visualizer (following mmdetection implementation): https://github.com/open-mmlab/mmdetection/blob/main/mmdet/engine/hooks/visualization_hook.py#L58
## Motivation
When using `test_cfg` for `data_preprocessor`, `predict_by_feat` resizes
to the original size, not the padded size.
```
data_preprocessor = dict(
type="SegDataPreProcessor",
#type="SegDataPreProcessorWithPad",
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255,
test_cfg=dict(size=(128, 128)))
```
Refar to:
https://github.com/open-mmlab/mmsegmentation/blob/main/mmseg/models/decode_heads/san_head.py#L589-L592
## Checklist
1. Pre-commit or other linting tools are used to fix the potential lint
issues.
2. The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
3. If the modification has potential influence on downstream projects,
this PR should be tested with downstream projects, like MMDet or
MMDet3D.
4. The documentation has been modified accordingly, like docstring or
example tutorials.
…t labels (open-mmlab#3466) 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 It is difficult to visualize without "labels" when using the inferencer. - While using the `MMSegInferencer`, the visualized prediction contains labels on the mask, but it is difficult to pass `withLabels=False` without rewriting the config (which is harder to do when you initialize the inferencer with a model name rather than the config). - I thought it would be easier to just pass `withLabels=False` to `inferencer.__call__()` since you can also pass `opacity` and other parameters anyway. ## Modification Please briefly describe what modification is made in this PR. - Added `with_labels` to `visualize_kwargs` inside `MMSegInferencer`. - Modified to `visualize()` function. ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 4. The documentation has been modified accordingly, like docstring or example tutorials. --------- Co-authored-by: xiexinch <xiexinch@outlook.com>
# Optimized home page format for easier reading ## English version: ### Before:  ### After:   ## Chineses version ### Before:  ### After: 
## Motivation The motivation is to add a hyperspectral dataset [HSI Drive 2.0](https://ipaccess.ehu.eus/HSI-Drive/) to the dataset registry which would be, as far as I know, the first hyperspectral database of mmsegmentation. This database has been presented in [HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving](https://ieeexplore.ieee.org/document/10371793) and the initival v1 was presented in [HSI-Drive: A Dataset for the Research of Hyperspectral Image Processing Applied to Autonomous Driving Systems](https://ieeexplore.ieee.org/document/9575298) ## Modification I have created/modified the following aspects: - READMEs: `README.md` and `README_zh-CN.md` (sorry if translation is not accurate). - Example project: `projects/hsidrive20_dataset` has been created and filled for users to know how to work with this database. - Documentation: `docs/en/user_guides/2_dataset_prepare.md` and `docs/zh_cn/user_guides/2_dataset_prepare.md` (sorry if translation is not accurate) have been updated for users to know how to download and configure the dataset. - Database related files: `mmseg/datasets/__init__.py`, `mmseg/datasets/hsi_drive.py` and `configs/_base_/datasets/hsi_drive.py` where the dataset is described and also prepared for training/validation/test. - Transforms related files: `mmsegmentation/mmseg/datasets/transforms/loading.py` to *include support for loading images from .npy files* such as the hyperspectral images of this dataset. - Training config with well-known neural network: `configs/unet/unet-s5-d16_fcn_4xb4-160k_hsidrive-192x384.py` for people to train a standard neural network with this dataset. - Tests: added necessary files under `tests/data/pseudo_hsidrive20_dataset`. **Important:** I have also modified `.pre-commit-config.yaml` to ignore HSI error in codespell. ## BC-breaking (Optional) No. ## Use cases (Optional) A train example has been added under `projects/hsidrive20_dataset` and documentation has been updated as it is explained in Modification section. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 4. The documentation has been modified accordingly, like docstring or example tutorials. Regarding 1. I don't know how to solve this problem. Could you help me, please? This causes 2 checks not to be successful. --------- Co-authored-by: xiexinch <xiexinch@outlook.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 Please describe the motivation of this PR and the goal you want to achieve through this PR. ## Modification Please briefly describe what modification is made in this PR. ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 4. The documentation has been modified accordingly, like docstring or example tutorials.
…edules, default_runtime new configs (open-mmlab#3542) # [New Configs] Add mmseg/configs folder & Support loveda, potsdam, schedules, default_runtime new configs - As the title , the new configs path is mmseg/configs/ - The configs files for the dataset have been tested. - The purpose of this PR is to enable other community members migrating to the new config to reference the new configs files for schedules and default runtime. Hoping for a quick merge~~~. - Details of this task can be found at: https://github.com/AI-Tianlong/mmseg-new-config 
## Motivation `inputs` and `input_shape` can't be both set to mmengine api `get_model_complexity_info` open-mmlab/mmengine#1056 ## Modification Set `input_shape` to None.
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 Please describe the motivation of this PR and the goal you want to achieve through this PR. ## Modification Please briefly describe what modification is made in this PR. ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 4. The documentation has been modified accordingly, like docstring or example tutorials.
## Motivation Use `MODELS.build` instead of `build_loss` ## Modification Please briefly describe what modification is made in this PR.
## Motivation Fix the bug that data augmentation only takes effect on one image in the change detection task. ## Modification configs/base/datasets/levir_256x256.py configs/swin/swin-tiny-patch4-window7_upernet_1xb8-20k_levir-256x256.py mmseg/datasets/transforms/transforms.py
## Motivation The current `SegVisualizationHook` implements the `_after_iter` method, which is invoked during the validation and testing pipelines. However, when in [test_mode](https://github.com/open-mmlab/mmsegmentation/blob/main/mmseg/engine/hooks/visualization_hook.py#L97), the implementation attempts to access `runner.iter`. This attribute is defined in the [`mmengine` codebase](https://github.com/open-mmlab/mmengine/blob/main/mmengine/runner/runner.py#L538) and is designed to return `train_loop.iter`. Accessing this property during testing can be problematic, particularly in scenarios where the model is being evaluated post-training, without initiating a training loop. This can lead to a crash if the implementation tries to build a training dataset for which the annotation file is unavailable at the time of evaluation. Thus, it is crucial to avoid relying on this property in test mode. ## Modification To resolve this issue, the proposal is to replace the `_after_iter` method with `after_val_iter` and `after_test_iter` methods, modifying their behavior accordingly. Specifically, when in testing mode, the implementation should utilize a `test_index` counter instead of accessing `runner.iter`. This adjustment will circumvent the issue of accessing `train_loop.iter` during test mode, ensuring the process does not attempt to access or build a training dataset, thereby preventing potential crashes due to missing annotation files.
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the configs file for mobilenetv3 still using the "Runner" settting which are old MMCV format, by refering at this link, "Runner" is replace with train_cfg. with using the old "Runner", even though the iteration is set to 320000, the train_cfg will initialize as 160000 iter, which does not fulfill trainng setting in this link. training and inferencing had been tested on modified configs.
Before:
After: (modified configs)
mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py(training)
mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py(training)
mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py(training)
mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py(training)
mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py(inferencing)
mobilenet-v3-d8-scratch_lraspp_4xb4-320k_citys
capes-512x1024.py(inferencing)
mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py(inferencing)
mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py(inferencing)
