Upload clip_encoder.py with huggingface_hub

clip_encoder.py

@@ -0,0 +1,135 @@
+
+import torch
+import torch.nn as nn
+
+from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
+
+
+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')
+
+        # ##########################################################################
+        # print(delay_load)
+        # print(getattr(args, 'unfreeze_mm_vision_tower', False))
+        # ##########################################################################
+
+        if not delay_load:
+            self.load_model()
+        elif getattr(args, 'unfreeze_mm_vision_tower', False):
+            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:
+            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 = CLIPVisionModel.from_pretrained('laion/CLIP-ViT-bigG-14-laion2B-39B-b160k', device_map=device_map)
+
+        
+        SEEC = False
+        if SEEC == True:
+            print('++++++++++++++++++++++++++ SeeClick Used ++++++++++++++++++++++++++++++')
+            print()
+            new_state_dict = torch.load('vision_encoder.pth')
+            self.vision_tower.load_state_dict(new_state_dict, strict=False)
+            print('++++++++++++++++++++++++++ SeeClick Used ++++++++++++++++++++++++++++++')
+        else:
+            print('++++++++++++++++++++++++++ BigG Used ++++++++++++++++++++++++++++++')
+            print()
+            print('++++++++++++++++++++++++++ BigG Used ++++++++++++++++++++++++++++++')
+
+
+
+
+
+
+        # from transformers import AutoModelForCausalLM, AutoTokenizer
+        # model = AutoModelForCausalLM.from_pretrained("/home/kyr/BiaoWu/SeeClick/SeeClick", device_map="cuda", trust_remote_code=True, bf16=True).eval()
+        # model.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设置 device
+        # model.transformer.visual.hidden_size = 1664
+        # self.vision_tower = model.transformer.visual
+        # # self.vision_tower.hidden_size = 1664
+        # print(dir(self.vision_tower))
+
+        #import pdb; pdb.set_trace()
+        #print(self.vision_tower)
+        #print(self.vision_tower.hidden_size)
+        print('==========================================================')
+        ##########################################################################
+
+
+
+        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
+
+    @property
+    def num_patches_per_side(self):
+        return self.config.image_size // self.config.patch_size
+
+    @property
+    def num_patches(self):
+        return (self.config.image_size // self.config.patch_size) ** 2
+