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| import torch.nn as nn |
| from transformers import ViTImageProcessor, ViTModel, AutoImageProcessor, AutoModel, Dinov2Model |
|
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| class DinoWrapper(nn.Module): |
| """ |
| Dino v1 wrapper using huggingface transformer implementation. |
| """ |
| def __init__(self, model_name: str, freeze: bool = True): |
| super().__init__() |
| self.model, self.processor = self._build_dino(model_name) |
| if freeze: |
| self._freeze() |
|
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| def forward(self, image): |
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| inputs = self.processor(images=image.float(), return_tensors="pt", do_rescale=False, do_resize=False).to(self.model.device) |
| |
| outputs = self.model(**inputs) |
| last_hidden_states = outputs.last_hidden_state |
| return last_hidden_states |
|
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| def _freeze(self): |
| print(f"======== Freezing DinoWrapper ========") |
| self.model.eval() |
| for name, param in self.model.named_parameters(): |
| param.requires_grad = False |
|
|
| @staticmethod |
| def _build_dino(model_name: str, proxy_error_retries: int = 3, proxy_error_cooldown: int = 5): |
| import requests |
| try: |
| processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base') |
| processor.do_center_crop = False |
| model = AutoModel.from_pretrained('facebook/dinov2-base') |
| return model, processor |
| except requests.exceptions.ProxyError as err: |
| if proxy_error_retries > 0: |
| print(f"Huggingface ProxyError: Retrying in {proxy_error_cooldown} seconds...") |
| import time |
| time.sleep(proxy_error_cooldown) |
| return DinoWrapper._build_dino(model_name, proxy_error_retries - 1, proxy_error_cooldown) |
| else: |
| raise err |