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import numpy as np |
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from pytorch_grad_cam.base_cam import BaseCAM |
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from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection |
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class LayerCAM(BaseCAM): |
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def __init__( |
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self, |
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model, |
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target_layers, |
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use_cuda=False, |
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reshape_transform=None): |
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super( |
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LayerCAM, |
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self).__init__( |
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model, |
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target_layers, |
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use_cuda, |
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reshape_transform) |
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def get_cam_image(self, |
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input_tensor, |
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target_layer, |
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target_category, |
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activations, |
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grads, |
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eigen_smooth): |
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spatial_weighted_activations = np.maximum(grads, 0) * activations |
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if eigen_smooth: |
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cam = get_2d_projection(spatial_weighted_activations) |
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else: |
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cam = spatial_weighted_activations.sum(axis=1) |
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return cam |
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