Use the pytorch-grad-cam tool to visualize Class Activation Maps (CAM) #3324
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xiexinch
reviewed
Sep 14, 2023
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| # select visualization layer, e.g. model.backbone.layer4[2] in | ||
| # deeplabv3_r50 it can be multiple layers | ||
| target_layers = [model.backbone.layer4[2]] |
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Is it possible to make this script a bit more generic? It now only works for resnet.
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Yes, the target layers can now be any layers.
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| input_tensor = preprocess_image( | ||
| rgb_img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
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The mean and std could be obtained from the config file.
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Now the mean and std are obtained from the config file.
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| # data processing | ||
| image = np.array(Image.open(args.img).convert('RGB')) | ||
| Height, Width = image.shape[0], image.shape[1] |
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Suggested change
| Height, Width = image.shape[0], image.shape[1] | |
| height, width = image.shape[0], image.shape[1] |
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| default_cfg = 'configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py' # noqa | ||
| parser.add_argument('--img', default='demo/demo.png', help='Image file') | ||
| parser.add_argument('--config', default=default_cfg, help='Config file') | ||
| parser.add_argument( | ||
| '--checkpoint', | ||
| default='deeplabv3_r50-d8_769x769_40k_cityscapes.pth', | ||
| help='Checkpoint file') |
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It is recommended to force the user to enter img, config and checkpoint.
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Now the user need to enter img, config and checkpoint.
| help='Path to output prediction file') | ||
| parser.add_argument( | ||
| '--cam-file', default='vis_cam.png', help='Path to output cam file') | ||
| parser.add_argument('--device', default='cuda:0', help='cuda:0 or cpu') |
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There are other devices such as mps and npu.
xiexinch
approved these changes
Sep 20, 2023
emily-lin
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Nov 18, 2023
open-mmlab#3324) 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 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) 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. The documentation has been modified accordingly, like docstring or example tutorials.
nahidnazifi87
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Apr 5, 2024
open-mmlab#3324) 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 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) 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. The documentation has been modified accordingly, like docstring or example tutorials.
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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
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)
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