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r_is_involved)
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if r_is_involved:
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del r_activation, r_weights, r_heatmap_tmp
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del t_activation, t_weights, score_contrib, t_heatmap_tmp
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del query_contrib, t_contrib, main_coeffs
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def save_heatmaps(args, example_number, s_name, s_value, t_heatmap, img_trg,
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r_heatmap=None, img_src=None, r_is_involved=False):
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"""
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Save provided heatmaps.
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Files are stored in {args.ranking_dir}/{prefix}/{example_number}/, with the
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names {s_name}_on_trg_heatmap.jpg and {s_name}_on_src_heatmap.jpg.
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Additional information is stored as metadata.
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Input:
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args: parsed arguments
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example_number: index of the current data example in a dataloader.
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s_name: score name (or alternatively: what is observed)
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s_value: score value (or alternatively: metric about what is observed)
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t_heatmap: heatmap to apply on the target image.
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img_trg: target image, as it is processed by the model (cropped & resized...)
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r_heatmap: heatmap to apply on the reference image (optional)
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img_src: reference image, as it is processed by the model (cropped & resized...)
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r_is_involved: whether an heatmap and an image is provided for the reference image.
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"""
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# normalize the heatmaps
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if r_is_involved:
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r_heatmap = normalize_heatmap(r_heatmap)
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t_heatmap = normalize_heatmap(t_heatmap)
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# store interpretations
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directory = os.path.join(args.heatmap_dir, args.exp_name, str(example_number))
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if not os.path.isdir(directory):
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os.makedirs(directory)
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filename = os.path.join(directory, '{}_heatmap.jpg')
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if r_is_involved:
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merge_heatmap_on_image(r_heatmap, img_src, filename.format(f"{s_name}_on_src"))
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merge_heatmap_on_image(t_heatmap, img_trg, filename.format(f"{s_name}_on_trg"))
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# store captions as well for later visualization
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with open(os.path.join(directory, "metadata.txt"), "a") as f:
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f.write(f"{example_number}*{s_name}*{s_value}\n")
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def get_weights(model, output, conv_activation):
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"""
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For a given input, the model produces a corresponding activation map
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(`conv_activation`) and value (`output`).
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This method returns the gradients of the value with regard to the activation
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map, pooled over the activation weights.
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"""
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# reset the gradient for a fresh backward pass
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model.zero_grad()
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# backward pass : get the gradient of the output with respect to the last convolutional layer
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gradients = grad(outputs=output, inputs=conv_activation, retain_graph=True)[0] # size (batch_size, 2048 or 512, 7, 7) : batch size, channels, 2D feature map (activation weights)
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# pool the gradients across the channels
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weights = gradients.mean(dim=[2,3]) # size (batch_size, 2048 or 512)
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return weights
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def normalize_heatmap(heatmap):
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heatmap = torch.clamp(heatmap, 0) # relu on top of the heatmap
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heatmap = normalize_image(heatmap)
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return heatmap
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def normalize_image(img):
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if isinstance(img, torch.Tensor):
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img -= torch.min(img)
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maxi_value = torch.max(img)
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if isinstance(img, np.ndarray):
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img -= np.min(img)
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maxi_value = np.max(img)
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img /= maxi_value if maxi_value > 0 else 0.00001
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return img
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def merge_heatmap_on_image(heatmap, initial_img, produced_img_path):
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"""
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Superimpose the heatmap on the initial image.
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The initial image must be the processed version (correctly resized &
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centered/crop) of the original image (since the model made the computation
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on the processed image)
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Input :
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heatmap: torch tensor of size (1, 7, 7), with values between 0 and 1
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initial_img: (cuda) torch tensor of size (1, 3, 224, 224) -
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unknown range of values
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produced_img_path: where to save the new image, consisting in the
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heatmap superimposed on the initial image
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"""
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# consider the first and unique element of the batch
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# the initial image must be of size (heigth, width, color channels) to fit
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# the cv2 processing (hence the permutation)
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heatmap = heatmap[0].data.numpy()
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