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on
Zero
Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig | |
| 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): | |
| # TODO: better detector is needed. | |
| print(f"The checkpoint seems to contain `vision_tower` weights: `unfreeze_mm_vision_tower`: True.") | |
| 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 | |
| # import pdb; pdb.set_trace() | |
| 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): | |
| select_feature_type = self.select_feature | |
| if self.select_feature in ["slicefour_patch", "slicefour_cls_patch"]: | |
| select_every_k_layer = len(image_forward_outs.hidden_states) // 4 | |
| image_features = torch.cat([image_forward_outs.hidden_states[i] for i in range(select_every_k_layer + self.select_layer, len(image_forward_outs.hidden_states), select_every_k_layer)], dim=-1) | |
| select_feature_type = select_feature_type.replace("slicefour_", "") | |
| elif self.select_feature in ["slice_m25811_f6_patch", "slice_m25811_f6_cls_patch"]: | |
| select_layers = [-2, -5, -8, -11, 6] | |
| image_features = torch.cat([image_forward_outs.hidden_states[i] for i in select_layers], dim=-1) | |
| select_feature_type = select_feature_type.replace("slice_m25811_f6_", "") | |
| else: | |
| image_features = image_forward_outs.hidden_states[self.select_layer] | |
| if select_feature_type == "patch": | |
| image_features = image_features[:, 1:] | |
| elif select_feature_type == "cls_patch": | |
| image_features = image_features | |
| else: | |
| raise ValueError(f"Unexpected select feature: {select_feature_type}") | |
| 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 | |
| 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): | |
| _hidden_size = self.config.hidden_size | |
| if "slicefour" in self.select_feature: | |
| _hidden_size *= 4 | |
| if "slice_m25811_f6" in self.select_feature: | |
| _hidden_size *= 5 | |
| return _hidden_size | |
| def num_patches_per_side(self): | |
| return self.config.image_size // self.config.patch_size | |
| def num_patches(self): | |
| _num_patches = (self.config.image_size // self.config.patch_size) ** 2 | |
| if "cls_patch" in self.select_feature: | |
| _num_patches += 1 | |
| return _num_patches | |
| def image_size(self): | |
| return self.config.image_size |