Add gradio app

app.py

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+import os
+os.system('pip install git+https://github.com/huggingface/transformers.git --upgrade')
+
+import gradio as gr
+from transformers import ViTFeatureExtractor, ViTModel
+import torch
+import torch.nn as nn
+import torchvision
+import matplotlib.pyplot as plt
+
+torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
+
+def get_attention_maps(pixel_values, attentions, nh):
+  threshold = 0.6
+  w_featmap = pixel_values.shape[-2] // model.config.patch_size
+  h_featmap = pixel_values.shape[-1] // model.config.patch_size
+
+  # we keep only a certain percentage of the mass
+  val, idx = torch.sort(attentions)
+  val /= torch.sum(val, dim=1, keepdim=True)
+  cumval = torch.cumsum(val, dim=1)
+  th_attn = cumval > (1 - threshold)
+  idx2 = torch.argsort(idx)
+  for head in range(nh):
+      th_attn[head] = th_attn[head][idx2[head]]
+  th_attn = th_attn.reshape(nh, w_featmap, h_featmap).float()
+
+  # interpolate
+  th_attn = nn.functional.interpolate(th_attn.unsqueeze(0), scale_factor=model.config.patch_size, mode="nearest")[0].cpu().numpy()
+  attentions = attentions.reshape(nh, w_featmap, h_featmap)
+  attentions = nn.functional.interpolate(attentions.unsqueeze(0), scale_factor=model.config.patch_size, mode="nearest")[0].cpu()
+  attentions = attentions.detach().numpy()
+
+  # save attentions heatmaps and return list of filenames
+  output_dir = '.'
+  os.makedirs(output_dir, exist_ok=True)
+  attention_maps = []
+  for j in range(nh):
+      fname = os.path.join(output_dir, "attn-head" + str(j) + ".png")
+
+      # save the attention map
+      plt.imsave(fname=fname, arr=attentions[j], format='png')
+
+      # append file name
+      attention_maps.append(fname)
+
+  return attention_maps
+
+
+def visualize_attention(image):
+  # normalize channels
+  pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values 
+  # forward pass
+  outputs = model(pixel_values, output_attentions=True, interpolate_pos_encoding=True)
+  # get attentions of last layer
+  attentions = outputs.attentions[-1] 
+  nh = attentions.shape[1] # number of heads
+  # we keep only the output patch attention
+  attentions = attentions[0, :, 0, 1:].reshape(nh, -1)
+  attention_maps = get_attention_maps(pixel_values, attentions, nh)
+  
+  return attention_maps
+
+feature_extractor = ViTFeatureExtractor.from_pretrained("facebook/dino-vits8", do_resize=False)
+model = ViTModel.from_pretrained("facebook/dino-vits8", add_pooling_layer=False)
+
+title = "Interactive demo: DINO"
+description = "Demo for Facebook AI's DINO, a new method for self-supervised training of Vision Transformers. Using this method, they are capable of segmenting objects within an image without having ever been trained to do so. This can be observed by displaying the self-attention of the heads from the last layer for the [CLS] token query. This demo uses a ViT-S/8 trained with DINO. To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.14294'>Emerging Properties in Self-Supervised Vision Transformers</a> | <a href='https://github.com/facebookresearch/dino'>Github Repo</a></p>"
+examples =[['cats.jpg']]
+iface = gr.Interface(fn=visualize_attention, 
+                     inputs=gr.inputs.Image(shape=(480, 480), type="pil"), 
+                     outputs=[gr.outputs.Image(type='file', label=f'attention_head_{i}') for i in range(6)],
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples)
+iface.launch()

requirements.txt

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+gradio
+torch
+torchvision
+Pillow
+matplotlib