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import torch |
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from torch import nn |
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from transformers import CLIPVisionModel, CLIPImageProcessor |
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class VisualToGPTMapping(nn.Module): |
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def __init__(self, visual_emb_dim, gpt_emb_dim, num_gpt_embs, num_heads): |
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super(VisualToGPTMapping, self).__init__() |
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self.transformer_layer = TransformerEncoderLayer(d_model=visual_emb_dim, nhead=num_heads, batch_first=True, norm_first=False) |
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self.linear = Linear(visual_emb_dim, gpt_emb_dim) |
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self.n_embeddings = num_gpt_embs |
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self.embedding_dim = gpt_emb_dim |
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def forward(self, visual_embs): |
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out = self.transformer_layer(visual_embs) |
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out = self.linear(out).view(-1, self.n_embeddings, self.embedding_dim) |
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return out |
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class CLIPVisionTower(nn.Module): |
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def __init__(self, vision_tower, 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_layer = -2 |
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self.select_feature = 'patch' |
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if not delay_load: |
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self.load_model() |
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else: |
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self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name) |
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def load_model(self): |
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self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name) |
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self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name) |
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self.vision_tower.requires_grad_(False) |
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self.is_loaded = True |
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def feature_select(self, image_forward_outs): |
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image_features = image_forward_outs.hidden_states[self.select_layer] |
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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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@torch.no_grad() |
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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.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True) |
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image_feature = self.feature_select(image_forward_out).to(image.dtype) |
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image_features.append(image_feature) |
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else: |
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image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True) |
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image_features = self.feature_select(image_forward_outs).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, device=self.device, dtype=self.dtype) |
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@property |
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def dtype(self): |
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return self.vision_tower.dtype |
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@property |
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def device(self): |
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return self.vision_tower.device |
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@property |
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def config(self): |
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if self.is_loaded: |
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return self.vision_tower.config |
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else: |
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return self.cfg_only |
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@property |
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def hidden_size(self): |
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return self.config.hidden_size |
