Use the pytorch-grad-cam tool to visualize Class Activation Maps (CAM) (open-mmlab#3324)

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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)

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.

Author: zhen6618

tools/analysis_tools/visualization_cam.py

@@ -0,0 +1,134 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Use the pytorch-grad-cam tool to visualize Class Activation Maps (CAM).
+
+requirement: pip install grad-cam
+"""
+
+from argparse import ArgumentParser
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from mmengine.model import revert_sync_batchnorm
+from PIL import Image
+from pytorch_grad_cam import GradCAM, LayerCAM, XGradCAM, GradCAMPlusPlus, EigenCAM, EigenGradCAM
+from pytorch_grad_cam.utils.image import preprocess_image, show_cam_on_image
+
+from mmengine import Config
+from mmseg.apis import inference_model, init_model, show_result_pyplot
+from mmseg.utils import register_all_modules
+
+
+class SemanticSegmentationTarget:
+    """wrap the model.
+
+    requirement: pip install grad-cam
+
+    Args:
+        category (int): Visualization class.
+        mask (ndarray): Mask of class.
+        size (tuple): Image size.
+    """
+
+    def __init__(self, category, mask, size):
+        self.category = category
+        self.mask = torch.from_numpy(mask)
+        self.size = size
+        if torch.cuda.is_available():
+            self.mask = self.mask.cuda()
+
+    def __call__(self, model_output):
+        model_output = torch.unsqueeze(model_output, dim=0)
+        model_output = F.interpolate(
+            model_output, size=self.size, mode='bilinear')
+        model_output = torch.squeeze(model_output, dim=0)
+
+        return (model_output[self.category, :, :] * self.mask).sum()
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('img', help='Image file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--out-file',
+        default='prediction.png',
+        help='Path to output prediction file')
+    parser.add_argument(
+        '--cam-file',
+        default='vis_cam.png',
+        help='Path to output cam file')
+    parser.add_argument(
+        '--target-layers',
+        default='backbone.layer4[2]',
+        help='Target layers to visualize CAM')
+    parser.add_argument(
+        '--category-index',
+        default='7',
+        help='Category to visualize CAM')
+    parser.add_argument(
+        '--device',
+        default='cuda:0',
+        help='Device used for inference')
+    args = parser.parse_args()
+
+    # build the model from a config file and a checkpoint file
+    register_all_modules()
+    model = init_model(args.config, args.checkpoint, device=args.device)
+    if args.device == 'cpu':
+        model = revert_sync_batchnorm(model)
+
+    # test a single image
+    result = inference_model(model, args.img)
+
+    # show the results
+    show_result_pyplot(
+        model,
+        args.img,
+        result,
+        draw_gt=False,
+        show=False if args.out_file is not None else True,
+        out_file=args.out_file)
+
+    # result data conversion
+    prediction_data = result.pred_sem_seg.data
+    pre_np_data = prediction_data.cpu().numpy().squeeze(0)
+
+    target_layers = args.target_layers
+    target_layers = [eval(f'model.{target_layers}')]
+
+    category = int(args.category_index)
+    mask_float = np.float32(pre_np_data == category)
+
+    # data processing
+    image = np.array(Image.open(args.img).convert('RGB'))
+    height, width = image.shape[0], image.shape[1]
+    rgb_img = np.float32(image) / 255
+    config = Config.fromfile(args.config)
+    image_mean = config.data_preprocessor['mean']
+    image_std = config.data_preprocessor['std']
+    input_tensor = preprocess_image(
+        rgb_img,
+        mean=[x / 255 for x in image_mean],
+        std=[x / 255 for x in image_std])
+
+    # Grad CAM(Class Activation Maps)
+    # Can also be LayerCAM, XGradCAM, GradCAMPlusPlus, EigenCAM, EigenGradCAM
+    targets = [
+        SemanticSegmentationTarget(category, mask_float,
+                                   (height, width))
+    ]
+    with GradCAM(
+            model=model,
+            target_layers=target_layers,
+            use_cuda=torch.cuda.is_available()) as cam:
+        grayscale_cam = cam(input_tensor=input_tensor, targets=targets)[0, :]
+        cam_image = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True)
+
+        # save cam file
+        Image.fromarray(cam_image).save(args.cam_file)
+
+
+if __name__ == '__main__':
+    main()