Delete ViT_DeiT/VIT-EXPL.py

ViT_DeiT/VIT-EXPL.py

@@ -1,96 +0,0 @@
-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
-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)
-
-
-image = Image.open('samples/dogcat2.png')
-dog_cat_image = transform(image)
-
-fig, axs = plt.subplots(1, 3)
-axs[0].imshow(image);
-axs[0].axis('off');
-
-output = model(dog_cat_image.unsqueeze(0).cuda())
-print_top_classes(output)
-
-# cat - the predicted class
-cat = generate_visualization(dog_cat_image)
-
-# dog
-# generate visualization for class 243: 'bull mastiff'
-dog = generate_visualization(dog_cat_image, class_index=243)
-
-
-axs[1].imshow(cat);
-axs[1].axis('off');
-axs[2].imshow(dog);
-axs[2].axis('off');
-pylab.show()