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 feature_extractor = ViTFeatureExtractor.from_pretrained("facebook/dino-vits8", do_resize=False) model = ViTModel.from_pretrained("facebook/dino-vits8", add_pooling_layer=False) 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 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 = "

Emerging Properties in Self-Supervised Vision Transformers | Github Repo

" 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()