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@woctezuma
Last active Oct 2, 2021
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Compare Facebook's DINO implementations: HugggingFace vs. official one
%pip install transformers

Reference: https://huggingface.co/facebook/dino-vits16

from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests
import torch

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vits16')
model = ViTModel.from_pretrained('facebook/dino-vits16', add_pooling_layer=False)

# Remove the pooler
# NB: Actually, not needed if `add_pooling_layer=False`
# model.pooler = torch.nn.Identity()

inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state

last_hidden_states.size()
torch.Size([1, 197, 384])

Reference: https://github.com/facebookresearch/dino#pretrained-models-on-pytorch-hub

import torch
vits16 = torch.hub.load('facebookresearch/dino:main', 'dino_vits16')

# Apply to the same image as with HuggingFace
out = vits16(inputs['pixel_values'])

out.size()
torch.Size([1, 384])

Compare:

import numpy as np

r = []
for i in range(outputs.last_hidden_state.size()[1]):
  d = abs(out - outputs.last_hidden_state[:, i, :]).max()
  r.append(d)
  
for i in np.array(r).argsort()[:3]:
  print(f'{i} -> {r[i]}')

Output: the [CLS] token (indexed 0) is clearly the output of the official model.

0 -> 0.02411365509033203
122 -> 18.719844818115234
130 -> 18.75490951538086
import numpy as np

cls = outputs.last_hidden_state[:, 0, :]
avg = outputs.last_hidden_state[:, 1:, :].mean(1)

d_cls = abs(out - cls).max()
d_avg = abs(out - avg).max()

print(d_cls)
print(d_avg)
tensor(0.0241, grad_fn=<MaxBackward1>)
tensor(22.0325, grad_fn=<MaxBackward1>)
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