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import torch |
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import torch.nn as nn |
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from transformers import AutoImageProcessor, AutoModel, AutoConfig |
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class DINOVisionTower(nn.Module): |
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def __init__(self, vision_tower, args, delay_load=False): |
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super().__init__() |
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self.is_loaded = False |
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self.vision_tower_name = vision_tower |
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self.select_layer = args.mm_vision_select_layer |
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self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch') |
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if not delay_load: |
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self.load_model() |
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elif getattr(args, 'unfreeze_mm_vision_tower', False): |
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self.load_model() |
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else: |
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self.cfg_only = AutoConfig.from_pretrained(self.vision_tower_name) |
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def load_model(self): |
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if self.is_loaded: |
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print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name)) |
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return |
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self.image_processor = AutoImageProcessor.from_pretrained(self.vision_tower_name) |
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self.vision_tower = AutoModel.from_pretrained(self.vision_tower_name) |
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self.vision_tower.requires_grad_(False) |
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self.is_loaded = True |
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def get_features(self, images): |
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outputs = self.vision_tower(images, output_hidden_states=True) |
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hidden_states = outputs.hidden_states |
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if self.select_layer == "all": |
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if self.select_feature == "patch": |
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all_layers_features = [hidden_state[:, 1:, :].contiguous() for hidden_state in hidden_states[1:]] |
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elif self.select_feature == "cls_patch": |
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all_layers_features = [hidden_state.contiguous() for hidden_state in hidden_states[1:]] |
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else: |
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raise ValueError(f"Unexpected select feature: {self.select_feature}") |
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return torch.stack(all_layers_features) |
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else: |
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selected_layer_features = hidden_states[int(self.select_layer)] |
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if self.select_feature == "patch": |
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selected_layer_features = selected_layer_features[:, 1:] |
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elif self.select_feature == "cls_patch": |
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selected_layer_features = selected_layer_features |
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else: |
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raise ValueError(f"Unexpected select feature: {self.select_feature}") |
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return torch.stack([selected_layer_features]) |
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@torch.no_grad() |
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def forward(self, images): |
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if images.shape[0] != 2: |
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raise ValueError( |
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f"Expected images.shape[0] == 2, but got {images.shape}. " |
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"Ensure the input includes both current and previous images." |
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) |
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cur_images = images[0] |
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prev_images = images[1] |
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cur_features = self.get_features(cur_images) |
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prev_features = self.get_features(prev_images) |
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cur_features = cur_features.permute(1, 0, 2, 3) |
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prev_features = prev_features.permute(1, 0, 2, 3) |
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images_features = torch.stack([cur_features, prev_features]) |
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return images_features |
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@property |
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def dummy_feature(self): |
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return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) |
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@property |
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def dtype(self): |
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return self.vision_tower.dtype |
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@property |
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def device(self): |
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return self.vision_tower.device |
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@property |
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def config(self): |
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if self.is_loaded: |
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return self.vision_tower.config |
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else: |
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return self.cfg_only |
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@property |
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def hidden_size(self): |
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return self.config.hidden_size |
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@property |
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def num_patches(self): |
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return (self.config.image_size // self.config.patch_size) ** 2 |
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@property |
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def num_layers(self): |
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return self.config.num_hidden_layers |
