Create visualization

visualization

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+import os
+
+from PIL import Image
+import torchvision.transforms as transforms
+import matplotlib.pyplot as plt
+import pylab
+import torch
+import numpy as np
+import cv2
+import sys
+sys.path.append('ViT_DeiT')
+from samples.CLS2IDX import CLS2IDX
+from baselines.ViT.ViT_LRP import vit_base_patch16_224 as vit_LRP
+from baselines.ViT.ViT_explanation_generator import LRP
+
+normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    normalize,
+])
+use_thresholding =  False
+def show_cam_on_image(img, mask):
+    heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)
+    heatmap = np.float32(heatmap) / 255
+    cam = heatmap + np.float32(img)
+    cam = cam / np.max(cam)
+    return cam
+
+# initialize ViT pretrained
+model = vit_LRP(pretrained=True).cuda()
+model.eval()
+attribution_generator = LRP(model)
+
+def generate_visualization(original_image, class_index=None):
+    transformer_attribution = attribution_generator.generate_LRP(original_image.unsqueeze(0).cuda(), method="transformer_attribution", index=class_index).detach()
+    transformer_attribution = transformer_attribution.reshape(1, 1, 14, 14)
+    transformer_attribution = torch.nn.functional.interpolate(transformer_attribution, scale_factor=16, mode='bilinear')
+    transformer_attribution = transformer_attribution.reshape(224, 224).data.cpu().numpy()
+    transformer_attribution = (transformer_attribution - transformer_attribution.min()) / (transformer_attribution.max() - transformer_attribution.min())
+
+    if use_thresholding:
+      transformer_attribution = transformer_attribution * 255
+      transformer_attribution = transformer_attribution.astype(np.uint8)
+      ret, transformer_attribution = cv2.threshold(transformer_attribution, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+      transformer_attribution[transformer_attribution == 255] = 1
+
+    image_transformer_attribution = original_image.permute(1, 2, 0).data.cpu().numpy()
+    image_transformer_attribution = (image_transformer_attribution - image_transformer_attribution.min()) / (image_transformer_attribution.max() - image_transformer_attribution.min())
+    vis = show_cam_on_image(image_transformer_attribution, transformer_attribution)
+    vis =  np.uint8(255 * vis)
+    vis = cv2.cvtColor(np.array(vis), cv2.COLOR_RGB2BGR)
+    return vis
+
+
+def print_top_classes(predictions, **kwargs):
+    # Print Top-5 predictions
+    prob = torch.softmax(predictions, dim=1)
+    class_indices = predictions.data.topk(5, dim=1)[1][0].tolist()
+    max_str_len = 0
+    class_names = []
+    for cls_idx in class_indices:
+        class_names.append(CLS2IDX[cls_idx])
+        if len(CLS2IDX[cls_idx]) > max_str_len:
+            max_str_len = len(CLS2IDX[cls_idx])
+
+    print('Top 5 classes:')
+    for cls_idx in class_indices:
+        output_string = '\t{} : {}'.format(cls_idx, CLS2IDX[cls_idx])
+        output_string += ' ' * (max_str_len - len(CLS2IDX[cls_idx])) + '\t\t'
+        output_string += 'value = {:.3f}\t prob = {:.1f}%'.format(predictions[0, cls_idx], 100 * prob[0, cls_idx])
+        print(output_string)