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| import torch | |
| import torch.nn as nn | |
| from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig, AutoProcessor, AutoModelForCausalLM | |
| class CLIPVisionTower(nn.Module): | |
| def __init__(self, vision_tower, args, delay_load=False): | |
| super().__init__() | |
| self.is_loaded = False | |
| self.vision_tower_name = vision_tower | |
| self.select_layer = args.mm_vision_select_layer | |
| self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch') | |
| if not delay_load: | |
| self.load_model() | |
| elif getattr(args, 'unfreeze_mm_vision_tower', False): | |
| self.load_model() | |
| else: | |
| self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name) | |
| def load_model(self, device_map=None): | |
| if self.is_loaded: | |
| print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name)) | |
| return | |
| self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name) | |
| self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map) | |
| self.vision_tower.requires_grad_(False) | |
| self.is_loaded = True | |
| def feature_select(self, image_forward_outs): | |
| image_features = image_forward_outs.hidden_states[self.select_layer] | |
| if self.select_feature == 'patch': | |
| image_features = image_features[:, 1:] | |
| elif self.select_feature == 'cls_patch': | |
| image_features = image_features | |
| else: | |
| raise ValueError(f'Unexpected select feature: {self.select_feature}') | |
| return image_features | |
| def forward(self, images): | |
| if type(images) is list: | |
| image_features = [] | |
| for image in images: | |
| image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True) | |
| image_feature = self.feature_select(image_forward_out).to(image.dtype) | |
| image_features.append(image_feature) | |
| else: | |
| image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True) | |
| image_features = self.feature_select(image_forward_outs).to(images.dtype) | |
| return image_features, image_features | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.vision_tower.dtype | |
| def device(self): | |
| return self.vision_tower.device | |
| def config(self): | |
| if self.is_loaded: | |
| return self.vision_tower.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| return self.config.hidden_size | |
| def num_patches_per_side(self): | |
| return self.config.image_size // self.config.patch_size | |
| def num_patches(self): | |
| return (self.config.image_size // self.config.patch_size) ** 2 | |
| class FlorenceVisionTower(nn.Module): | |
| def __init__(self, vision_tower, args, delay_load=False): | |
| super().__init__() | |
| self.is_loaded = False | |
| self.vision_tower_name = vision_tower | |
| if not delay_load: | |
| self.load_model() | |
| elif getattr(args, 'unfreeze_mm_vision_tower', False): | |
| self.load_model() | |
| else: | |
| self.load_model() | |
| def load_model(self, device_map=None): | |
| if self.is_loaded: | |
| print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name)) | |
| return | |
| self.image_processor = AutoProcessor.from_pretrained(self.vision_tower_name, trust_remote_code=True) | |
| self.vision_tower = AutoModelForCausalLM.from_pretrained(self.vision_tower_name, trust_remote_code=True).to(torch.bfloat16) | |
| self.vision_tower.requires_grad_(False) | |
| self.is_loaded = True | |
| def forward(self, images): | |
| ## hard code for the task prompt | |
| # task = [ | |
| # 'Describe in detail what is shown in the image.', | |
| # 'What is the text in the image?', | |
| # 'Locate the objects in the image, with their descriptions.', | |
| # 'Locate the region proposals in the image.' | |
| # ] | |
| task_ids = torch.tensor([ | |
| [0, 47066, 21700, 11, 4617, 99, 16, 2343, 11, 5, 2274, 4, 2, 1], | |
| [0, 2264, 16, 5, 2788, 11, 5, 2274, 116, 2, 1, 1, 1, 1], | |
| [0, 574, 22486, 5, 8720, 11, 5, 2274, 6, 19, 49, 24173, 4, 2] | |
| ]).to(device=self.device) | |
| # task = [ | |
| # 'What is the text in the image?', | |
| # 'What is the text in the image, with regions?', | |
| # 'What does the image describe?', | |
| # 'Describe in detail what is shown in the image.', | |
| # 'Describe with a paragraph what is shown in the image.', | |
| # 'Locate the objects with category name in the image.', | |
| # 'Locate the objects in the image, with their descriptions.', | |
| # 'Locate the region proposals in the image.' | |
| # ] | |
| # task_ids = torch.tensor([ | |
| # [0, 2264, 16, 5, 2788, 11, 5, 2274, 116, 2, 1, 1, 1, 1], | |
| # [0, 2264, 16, 5, 2788, 11, 5, 2274, 6, 19, 3806, 116, 2, 1], | |
| # [0, 2264, 473, 5, 2274, 6190, 116, 2, 1, 1, 1, 1, 1, 1], | |
| # [0, 47066, 21700, 11, 4617, 99, 16, 2343, 11, 5, 2274, 4, 2, 1], | |
| # [0, 47066, 21700, 19, 10, 17818, 99, 16, 2343, 11, 5, 2274, 4, 2], | |
| # [0, 574, 22486, 5, 8720, 19, 4120, 766, 11, 5, 2274, 4, 2, 1], | |
| # [0, 574, 22486, 5, 8720, 11, 5, 2274, 6, 19, 49, 24173, 4, 2], | |
| # [0, 574, 22486, 5, 976, 5327, 11, 5, 2274, 4, 2, 1, 1, 1] | |
| # ]).to(device=self.device) | |
| with torch.no_grad(): | |
| generated_ids, image_feature, encoder_last_hidden_state = self.vision_tower.generate( | |
| input_ids=task_ids, | |
| pixel_values=images, | |
| max_new_tokens=1, | |
| do_sample=False, | |
| num_beams=1, | |
| ) | |
| return image_feature, encoder_last_hidden_state | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.vision_tower.dtype | |
| def device(self): | |
| return self.vision_tower.device | |
| def config(self): | |
| if self.is_loaded: | |
| return self.vision_tower.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| return self.config.hidden_size | |
| def num_patches_per_side(self): | |
| return self.config.image_size // self.config.patch_size | |
| def num_patches(self): | |
| return (self.config.image_size // self.config.patch_size) ** 2 | |