Do not use LLAVA_CLIP_PATH

llava/model/multimodal_encoder/clip_encoder.py

@@ -1,84 +1,83 @@
-import torch
-import torch.nn as nn
-
-from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
-from CKPT_PTH import LLAVA_CLIP_PATH
-
-
-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
-        print(f'Loading vision tower: {self.vision_tower_name}')
-        self.select_layer = args.mm_vision_select_layer
-        self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
-
-        if not delay_load:
-            self.load_model()
-        else:
-            # self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
-            self.cfg_only = CLIPVisionConfig.from_pretrained(
-                self.vision_tower_name if LLAVA_CLIP_PATH is None else LLAVA_CLIP_PATH)
-
-    def load_model(self):
-        self.image_processor = CLIPImageProcessor.from_pretrained(
-            self.vision_tower_name if LLAVA_CLIP_PATH is None else LLAVA_CLIP_PATH)
-        self.vision_tower = CLIPVisionModel.from_pretrained(
-            self.vision_tower_name if LLAVA_CLIP_PATH is None else LLAVA_CLIP_PATH)
-        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(self):
-        return (self.config.image_size // self.config.patch_size) ** 2
+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
+        print(f'Loading vision tower: {self.vision_tower_name}')
+        self.select_layer = args.mm_vision_select_layer
+        self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
+
+        if not delay_load:
+            self.load_model()
+        else:
+            # self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
+            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
+
+    @property
+    def num_patches(self):
+        return (self.config.image_size // self.config.patch_size) ** 2