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Configuration error
Configuration error
| import os | |
| import torch | |
| from torch import nn | |
| from transformers.models.siglip.image_processing_siglip import SiglipImageProcessor | |
| from blip3o.utils import rank0_print | |
| from tok.ta_tok import TextAlignedTokenizer | |
| from tok.utils import ScalingLayer | |
| class TATokVisionTower(nn.Module): | |
| def __init__(self, vision_tower, vision_tower_cfg, delay_load=False): | |
| super().__init__() | |
| self.is_loaded = False | |
| self.config = None | |
| self.image_processor = SiglipImageProcessor() | |
| self.vision_tower_name = vision_tower | |
| if not delay_load: | |
| rank0_print(f"Loading vision tower: {vision_tower}") | |
| self.load_model() | |
| elif getattr(vision_tower_cfg, "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(vision_tower_cfg, "mm_tunable_parts") and "mm_vision_tower" in vision_tower_cfg.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 = self.config | |
| 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.vision_tower = TextAlignedTokenizer.from_checkpoint(self.vision_tower_name, load_teacher=False).to(device_map) | |
| self.vision_tower.bottleneck.regularizer.set_eval_deterministic(deterministic=True) | |
| self.vision_tower.input_type = 'rec' | |
| self.vision_tower.scale_layer = ScalingLayer(mean=[0., 0., 0.], std=[1., 1., 1.]) | |
| self.vision_tower.requires_grad_(False) | |
| self.vision_tower.eval() | |
| self.pool_scales = [1, 1, 2, 3] | |
| input_size = self.vision_tower.input_size | |
| self.image_processor.size = (input_size, input_size) | |
| self.image_processor.crop_size = {'height': input_size, 'width': input_size} | |
| self.image_tokens = self.vision_tower.bottleneck_token_num | |
| self.bottleneck_dim = self.vision_tower.bottleneck_dim | |
| self.num_patches = self.image_tokens | |
| self.num_patches_per_side = int(self.num_patches ** 0.5) | |
| self.hidden_size = self.vision_tower.encoder_hidden_dim | |
| self.image_size = input_size | |
| self.is_loaded = True | |
| def get_embedding(self): | |
| return self.vision_tower.bottleneck.regularizer.get_emb() | |
| def forward(self, images, pool_scale=1): | |
| # load from ENV | |
| # pool_scale from ENV has the highest priority | |
| pool_scale = int(os.environ.get('POOL_SCALE', pool_scale)) | |
| if pool_scale is None: pool_scale = 1 | |
| if type(images) is list: | |
| image_features, tokens = [], [] | |
| for image in images: | |
| image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), pool_scale=pool_scale) | |
| image_feature, token = image_forward_out['vq_feats'].to(image.dtype), image_forward_out['bottleneck_rep'] | |
| image_features.append(image_feature) | |
| tokens.append(token) | |
| else: | |
| image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), pool_scale=pool_scale) | |
| image_features, tokens = image_forward_outs['vq_feats'].to(images.dtype), image_forward_outs['bottleneck_rep'] | |
| return {"image_features": image_features, "tokens": tokens, 'pool_scale': pool_scale} | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| for p in self.vision_tower.parameters(): | |
| return p.dtype | |
| def device(self): | |
| for p in self.vision_tower.parameters(): | |
| return p.device |