Update `feature_visualization()` (#3807)
* Update `feature_visualization()` Only plot for data with height, width > 1 * cleanup * Cleanup
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@ -448,26 +448,28 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
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fig.savefig(Path(save_dir) / 'results.png', dpi=200)
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def feature_visualization(features, module_type, module_idx, n=64):
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def feature_visualization(x, module_type, stage, n=64):
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"""
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features: Features to be visualized
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x: Features to be visualized
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module_type: Module type
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module_idx: Module layer index within model
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stage: Module stage within model
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n: Maximum number of feature maps to plot
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"""
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project, name = 'runs/features', 'exp'
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save_dir = increment_path(Path(project) / name) # increment run
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save_dir.mkdir(parents=True, exist_ok=True) # make dir
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batch, channels, height, width = x.shape # batch, channels, height, width
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if height > 1 and width > 1:
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project, name = 'runs/features', 'exp'
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save_dir = increment_path(Path(project) / name) # increment run
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save_dir.mkdir(parents=True, exist_ok=True) # make dir
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plt.figure(tight_layout=True)
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blocks = torch.chunk(features, features.shape[1], dim=1) # block by channel dimension
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n = min(n, len(blocks))
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for i in range(n):
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feature = transforms.ToPILImage()(blocks[i].squeeze())
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ax = plt.subplot(int(math.sqrt(n)), int(math.sqrt(n)), i + 1)
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ax.axis('off')
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plt.imshow(feature) # cmap='gray'
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plt.figure(tight_layout=True)
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blocks = torch.chunk(x, channels, dim=1) # block by channel dimension
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n = min(n, len(blocks))
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for i in range(n):
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feature = transforms.ToPILImage()(blocks[i].squeeze())
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ax = plt.subplot(int(math.sqrt(n)), int(math.sqrt(n)), i + 1)
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ax.axis('off')
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plt.imshow(feature) # cmap='gray'
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f = f"layer_{module_idx}_{module_type.split('.')[-1]}_features.png"
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print(f'Saving {save_dir / f}...')
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plt.savefig(save_dir / f, dpi=300)
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f = f"stage_{stage}_{module_type.split('.')[-1]}_features.png"
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print(f'Saving {save_dir / f}...')
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plt.savefig(save_dir / f, dpi=300)
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