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import torch |
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import torch.nn as nn |
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from .processor import Blip2ImageTrainProcessor |
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from .eva_vit import create_eva_vit_g |
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class EvaClipVisionTower(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_feature = getattr(args, 'mm_vision_select_feature', 'patch') |
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self.args = args |
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if not delay_load: |
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self.load_model() |
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def load_model(self, device_map=None): |
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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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if not hasattr(self.args, 'dynamic_resolution'): |
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dynamic_resolution = None |
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else: |
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dynamic_resolution = self.args.dynamic_resolution |
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if (not hasattr(self.args, 'freeze_vision_encoder')) or self.args.freeze_vision_encoder: |
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use_checkpoint = False |
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else: |
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use_checkpoint = True |
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assert self.args.vit_precision == 'fp32', 'if the vision encoder is training, the type needs to be fp32' |
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self.image_processor = Blip2ImageTrainProcessor( |
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image_size=self.args.img_size, |
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dynamic_resolution= dynamic_resolution |
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) |
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self.vision_tower = create_eva_vit_g( |
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img_size=self.args.img_size, |
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drop_path_rate=self.args.drop_path_rate, |
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precision=self.args.vit_precision, |
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vit_model_path=self.args.vit_model_path, |
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use_checkpoint=use_checkpoint |
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) |
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self.is_loaded = True |
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def feature_select(self, image_features): |
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if self.select_feature == 'patch': |
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image_features = image_features[:, 1:] |
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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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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_tower(image.unsqueeze(0)) |
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image_features.append(self.feature_select(image_forward_out).to(image.dtype)) |
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else: |
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image_features = self.vision_tower(images.to(dtype=self.dtype)) |
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image_features = self.feature_select(image_features).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, dtype=torch.float) |
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@property |
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def hidden_size(self): |
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return self.vision_tower.hidden_size |
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@property |
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def num_patches(self): |
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return (self.vision_tower.image_size // self.vision_tower.patch_size) ** 2 |
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@property |
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def num_patches_per_side(self): |
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return (self.vision_tower.image_size // self.vision_tower.patch_size) |
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@property |
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def dtype(self): |
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return self.vision_tower.pos_embed.dtype |
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