API update

models.py

@@ -0,0 +1,84 @@
+import torch
+from torch import nn
+from transformers import CLIPVisionModel, CLIPImageProcessor
+
+class VisualToGPTMapping(nn.Module):
+    def __init__(self, visual_emb_dim, gpt_emb_dim, num_gpt_embs, num_heads):
+        super(VisualToGPTMapping, self).__init__()
+        self.transformer_layer = TransformerEncoderLayer(d_model=visual_emb_dim, nhead=num_heads, batch_first=True, norm_first=False)
+        self.linear = Linear(visual_emb_dim, gpt_emb_dim)
+        self.n_embeddings = num_gpt_embs
+        self.embedding_dim = gpt_emb_dim
+    def forward(self, visual_embs):
+        out = self.transformer_layer(visual_embs)
+        out = self.linear(out).view(-1, self.n_embeddings, self.embedding_dim)
+        return out
+
+class CLIPVisionTower(nn.Module):
+    def __init__(self, vision_tower, delay_load=False):
+        super().__init__()
+
+        self.is_loaded = False
+
+        self.vision_tower_name = vision_tower
+        self.select_layer = -2
+        self.select_feature = 'patch'
+
+        if not delay_load:
+            self.load_model()
+        else:
+            self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
+
+    def load_model(self):
+        self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
+        self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
+        self.vision_tower.requires_grad_(False)
+
+        self.is_loaded = True
+
+    def feature_select(self, image_forward_outs):
+        image_features = image_forward_outs.hidden_states[self.select_layer]
+        if self.select_feature == 'patch':
+            image_features = image_features[:, 1:]
+        elif self.select_feature == 'cls_patch':
+            image_features = image_features
+        else:
+            raise ValueError(f'Unexpected select feature: {self.select_feature}')
+        return image_features
+
+    @torch.no_grad()
+    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
+
+    @property
+    def dummy_feature(self):
+        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
+
+    @property
+    def dtype(self):
+        return self.vision_tower.dtype
+
+    @property
+    def device(self):
+        return self.vision_tower.device
+
+    @property
+    def config(self):
+        if self.is_loaded:
+            return self.vision_tower.config
+        else:
+            return self.cfg_only
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size