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## Motivation
Use the pytorch-grad-cam tool to visualize Class Activation Maps (CAM).
## Modification
Use the pytorch-grad-cam tool to visualize Class Activation Maps (CAM).
requirement: pip install grad-cam
run commad: python tools/analysis_tools/visualization_cam.py
## BC-breaking (Optional)
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backward-compatibility of the downstream repos?
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## Use cases (Optional)
If this PR introduces a new feature, it is better to list some use cases
here, and update the documentation.
## Checklist
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## Motivation
Support inference and visualization of VPD
## Modification
1. add a new VPD model that does not generate black border in
predictions
2. update `SegLocalVisualizer` to support depth visualization
3. update `MMSegInferencer` to support save predictions of depth
estimation in method `postprocess`
## 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
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this PR.
## Use cases (Optional)
Run inference with VPD using the this command
```sh
python demo/image_demo_with_inferencer.py demo/classroom__rgb_00283.jpg vpd_depth --out-dir vis_results
```
The following image will be saved under `vis_results/vis`
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## Motivation
Support depth estimation algorithm [VPD](https://github.com/wl-zhao/VPD)
## Modification
1. add VPD backbone
2. add VPD decoder head for depth estimation
3. add a new segmentor `DepthEstimator` based on `EncoderDecoder` for
depth estimation
4. add an integrated metric that calculate common metrics in depth
estimation
5. add SiLog loss for depth estimation
6. add config for VPD
## BC-breaking (Optional)
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## Use cases (Optional)
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here, and update the documentation.
## Checklist
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8. If the modification has potential influence on downstream projects,
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9. The documentation has been modified accordingly, like docstring or
example tutorials.
## Motivation
## Modification
The confusion_matrix.py is not compatible with the current version of
mmseg.
---------
Co-authored-by: xiexinch <xiexinch@outlook.com>
## Motivation
Supports inference for ultra-large-scale remote sensing images.
## Modification
Add RSImageInference.py in demo.
## Use cases
Taking the inference of Vaihingen dataset images using PSPNet as an
example, the following settings are required:
**img**: Specify the path of the image.
**model**: Provide the configuration file for the model.
**checkpoint**: Specify the weight file for the model.
**out**: Set the output path for the results.
**batch_size**: Determine the batch size used during inference.
**win_size**: Specify the width and height(512x512) of the sliding
window.
**stride**: Set the stride(400x400) for sliding the window.
**thread(default: 1)**: Specify the number of threads to be used for
inference.
**Inference device (default: cuda:0)**: Specify the device for inference
(e.g., cuda:0 for CPU).
```shell
python demo/rs_image_inference.py demo/demo.png projects/pp_mobileseg/configs/pp_mobileseg/pp_mobileseg_mobilenetv3_2x16_80k_ade20k_512x512_tiny.py pp_mobileseg_mobilenetv3_2xb16_3rdparty-tiny_512x512-ade20k-a351ebf5.pth --batch-size 8 --device cpu --thread 2
```
---------
Co-authored-by: xiexinch <xiexinch@outlook.com>
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## Motivation
Please describe the motivation of this PR and the goal you want to
achieve through this PR.
Support metrics for the depth estimation task, including RMSE, ABSRel,
and etc.
## 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)
Using the following configuration to compute depth metrics on NYU
```python
dataset_type = 'NYUDataset'
data_root = 'data/nyu'
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(dict(type='LoadDepthAnnotation', depth_rescale_factor=1e-3)),
dict(
type='PackSegInputs',
meta_keys=('img_path', 'depth_map_path', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'flip', 'flip_direction',
'category_id'))
]
val_dataloader = dict(
batch_size=1,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
test_mode=True,
data_prefix=dict(
img_path='images/test', depth_map_path='annotations/test'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='DepthMetric', max_depth_eval=10.0, crop_type='nyu')
test_evaluator = val_evaluator
```
Example log:
## Checklist
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issues.
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## Motivation
It's OpenMMLab Codecamp task.
## Modification
Implementd Kullback-Leibler divergence loss and also added tests for it.
## Checklist
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issues.
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example tutorials.
---------
Co-authored-by: xiexinch <xiexinch@outlook.com>
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## Motivation
translate doc for docs/zh_cn/user_guides/5_deployment.md
## Modification
update `docs/en/user_guides/5_deployment.md`
fix `docs/zh_cn/user_guides/5_deployment.md`
## BC-breaking (Optional)
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## Use cases (Optional)
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## Checklist
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example tutorials.
---------
Co-authored-by: 谢昕辰 <xiexinch@outlook.com>
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## 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.
1. add `NYUDataset`class
2. add script to process NYU dataset
3. add transforms for loading depth map
4. add docs & unittest
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here, and update the documentation.
## Checklist
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7. The documentation has been modified accordingly, like docstring or
example tutorials.
## Motivation
Add a model deployment example.
## Modification
Add an inference script and update the README.
## BC-breaking (Optional)
None
## Use cases (Optional)
In README.
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## Motivation
there is a code mistake in `docs\zh_cn\get_started.md` and
`docs\en\get_started.md`,it use the 0.x api,which is changed in 1.x
## Modification
`docs\zh_cn\get_started.md` ,`docs\en\get_started.md`
fix inference_segmentor --> inference_model
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## Motivation
Support CAT-Seg open-vocabulary semantic segmentation (CVPR2023).
## Modification
Support CAT-Seg open-vocabulary semantic segmentation (CVPR2023).
- [x] Support CAT-Seg model training.
- [x] CLIP model based `backbone` (R101 & Swin-B), aggregation layers
based `neck`, and `decoder` head.
- [x] Provide customized coco-stuff164k_384x384 training configs.
- [x] Language model supports for `open vocabulary` (OV) tasks.
- [x] Support CLIP-based pretrained language model (LM) inference.
- [x] Add commonly used prompts templates.
- [x] Add README tutorials.
- [x] Add zero-shot testing scripts.
**Working on the following tasks.**
- [x] Add unit test.
## 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)
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here, and update the documentation.
## Checklist
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example tutorials.
---------
Co-authored-by: xiexinch <xiexinch@outlook.com>
Added ignore_index param to forward(),
also implemented one hot encoding to ensure the dims of target matches
pred.
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## Motivation
Please describe the motivation of this PR and the goal you want to
achieve through this PR.
Attempted to solve the problems mentioned by #3172
## Modification
Please briefly describe what modification is made in this PR.
Added ignore_index into forward function (although the dice loss itself
does not actually take account for it for some reason).
Added _expand_onehot_labels_dice, which takes the target with shape [N,
H, W] into [N, num_classes, H, W].
## BC-breaking (Optional)
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4. The documentation has been modified accordingly, like docstring or
example tutorials.
This is my first time contributing to open-source code, so I might have
made some stupid mistakes. Please don't hesitate to point it out.
## Motivation
- 'PascalContextDataset' object has no attribute 'file_client', it will
cause an error.
- The attribute ‘ann_file’ is not allowed to be empty, otherwise, an
error will be reported.
## Modification
- Replace file_client with fileio
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## Motivation
https://github.com/open-mmlab/mmsegmentation/issues/3181https://github.com/open-mmlab/mmsegmentation/issues/2965https://github.com/open-mmlab/mmsegmentation/issues/2644https://github.com/open-mmlab/mmsegmentation/issues/1645https://github.com/open-mmlab/mmsegmentation/issues/1444https://github.com/open-mmlab/mmsegmentation/issues/1370https://github.com/open-mmlab/mmsegmentation/issues/125
## Modification
Remove the assertion at data_preprocessor
## BC-breaking (Optional)
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## Checklist
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## Motivation
Some links for datasets in README_zh-CN don't point to the correct
addresses.
## Modification
Update some links in README_zh-CN.
Co-authored-by: xiexinch <xiexinch@outlook.com>
## Motivation
When using the - tta command for multi-scale prediction, and the test
set is not annotated, although format_only has been set true in
test_evaluator, but SegTTAModel class still threw error 'AttributeError:
'SegDataSample' object has no attribute '_gt_sem_seg''.
## Modification
The reason is SegTTAModel didn't determine if there were annotations in
the dataset, so I added the code to make the judgment and let the
program run normally on my computer.
## Motivation
Please describe the motivation of this PR and the goal you want to
achieve through this PR.
## Modification
- add dataset-index.yml
## Dependencies
- [ ] https://github.com/open-mmlab/mim/pull/212
## Motivation
Change the dependency `mmcls` to `mmpretrain`
## Modification
- modify `mmcls` to `mmpretrain`
- modify CI requirements
## BC-breaking (Optional)
If users have installed mmcls but not install mmpretrain, it might raise some error.
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