83 lines
No EOL
2.9 KiB
Python
83 lines
No EOL
2.9 KiB
Python
import torch
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import torch.nn as nn
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from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
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class CLIPVisionEncoder(nn.Module):
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def __init__(self, encoder_name="openai/clip-vit-large-patch14", delay_load=False):
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super().__init__()
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self.is_loaded = False
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self.vision_encoder_name = encoder_name
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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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self.select_layer = -1
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self.select_feature = "patch"
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if not delay_load:
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self.load_model()
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else:
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self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_encoder_name)
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def load_model(self):
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self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_encoder_name)
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self.vision_encoder = CLIPVisionModel.from_pretrained(self.vision_encoder_name)
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self.vision_encoder.requires_grad_(False)
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self.is_loaded = True
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def feature_select(self, image_forward_outs):
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image_features = image_forward_outs.hidden_states[self.select_layer]
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if self.select_feature == 'patch':
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image_features = image_features[:, :]
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elif self.select_feature == 'cls_patch':
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image_features = image_features
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else:
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raise ValueError(f'Unexpected select feature: {self.select_feature}')
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return image_features
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@torch.no_grad()
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def forward(self, images):
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if type(images) is list:
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image_features = []
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for image in images:
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image_forward_out = self.vision_encoder(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
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image_feature = self.feature_select(image_forward_out).to(image.dtype)
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image_features.append(image_feature)
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else:
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image_forward_outs = self.vision_encoder(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
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image_features = self.feature_select(image_forward_outs).to(images.dtype)
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# print("image feature shape", image_features.shape)
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# print(type(image_forward_outs))
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# print(type(image_forward_outs.shape))
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# image_features = image_forward_outs.to(images.dtype)
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return image_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_encoder.dtype
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@property
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def device(self):
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return self.vision_encoder.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_encoder.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 |