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Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel | |
| from egogpt.utils import rank0_print | |
| try: | |
| from s2wrapper import forward as multiscale_forward | |
| except: | |
| pass | |
| 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: | |
| rank0_print(f"Loading vision tower: {vision_tower}") | |
| self.load_model() | |
| elif getattr(args, "unfreeze_mm_vision_tower", False): | |
| # TODO: better detector is needed. | |
| rank0_print( | |
| f"The checkpoint seems to contain `vision_tower` weights: `unfreeze_mm_vision_tower`: True." | |
| ) | |
| self.load_model() | |
| elif ( | |
| hasattr(args, "mm_tunable_parts") | |
| and "mm_vision_tower" in args.mm_tunable_parts | |
| ): | |
| rank0_print( | |
| f"The checkpoint seems to contain `vision_tower` weights: `mm_tunable_parts` contains `mm_vision_tower`." | |
| ) | |
| 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: | |
| rank0_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): | |
| 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 | |
| class CLIPVisionTowerS2(CLIPVisionTower): | |
| def __init__(self, vision_tower, args, delay_load=False): | |
| self.s2_scales = getattr(args, "s2_scales", "336,672,1008") | |
| self.s2_scales = list(map(int, self.s2_scales.split(","))) | |
| self.s2_scales.sort() | |
| self.s2_split_size = self.s2_scales[0] | |
| self.s2_image_size = self.s2_scales[-1] | |
| super().__init__(vision_tower, args, delay_load) | |
| # change resize/crop size in preprocessing to the largest image size in s2_scale | |
| if not delay_load or getattr(args, "unfreeze_mm_vision_tower", False): | |
| self.image_processor.size["shortest_edge"] = self.s2_image_size | |
| self.image_processor.crop_size["height"] = self.image_processor.crop_size[ | |
| "width" | |
| ] = self.s2_image_size | |
| def load_model(self, device_map=None): | |
| if self.is_loaded: | |
| rank0_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.image_processor.size["shortest_edge"] = self.s2_image_size | |
| self.image_processor.crop_size["height"] = self.image_processor.crop_size[ | |
| "width" | |
| ] = self.s2_image_size | |
| self.is_loaded = True | |
| def forward_feature(self, images): | |
| 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 forward(self, images): | |
| if type(images) is list: | |
| image_features = [] | |
| for image in images: | |
| image_feature = multiscale_forward( | |
| self.forward_feature, | |
| image.unsqueeze(0), | |
| img_sizes=self.s2_scales, | |
| max_split_size=self.s2_split_size, | |
| split_forward=True, | |
| ) | |
| image_features.append(image_feature) | |
| else: | |
| image_features = multiscale_forward( | |
| self.forward_feature, | |
| images, | |
| img_sizes=self.s2_scales, | |
| max_split_size=self.s2_split_size, | |
| split_forward=True, | |
| ) | |
| return image_features | |
| def hidden_size(self): | |
| return self.config.hidden_size * len(self.s2_scales) | |