Initial commit

llava/model/multimodal_encoder/hf_vision.py

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+import torch
+import torch.nn as nn
+
+from transformers import AutoModel, AutoImageProcessor, AutoConfig, CLIPImageProcessor
+from llava.utils import rank0_print
+
+
+class HFVisionTower(nn.Module):
+    def __init__(self, vision_tower, args, delay_load=False):
+        super().__init__()
+
+        self.is_loaded = False
+
+        self.vision_tower_name = vision_tower.replace("hf:", "", 1)
+        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 = AutoConfig.from_pretrained(self.vision_tower_name)
+
+    def load_model(self):
+        try:
+            self.image_processor = AutoImageProcessor.from_pretrained(self.vision_tower_name)
+        except Exception as e:
+            if "448" in self.vision_tower_name:
+                image_size = 448
+                # use image processor with conig
+                self.image_processor = CLIPImageProcessor(size={"shortest_edge": image_size}, do_center_crop=True, crop_size=image_size)
+            else:
+                self.image_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
+        rank0_print(f"Loaded image processor: {self.image_processor}")
+        self.vision_tower = AutoModel.from_pretrained(self.vision_tower_name, torch_dtype=torch.bfloat16, trust_remote_code=True).to("cuda")
+        self.device = self.vision_tower.device
+        self.dtype = self.vision_tower.dtype
+        self.config = self.vision_tower.config
+
+        if hasattr(self.vision_tower, "vision_model"):
+            self.vision_tower = self.vision_tower.vision_model
+        self.vision_tower.requires_grad_(False)
+        # self.vision_tower.eval()
+        self.is_loaded = True
+
+    def feature_select(self, image_forward_outs):
+        select_feature_type = self.select_feature
+
+        if self.select_feature in ["slicefour_patch", "slicefour_cls_patch"]:
+            select_every_k_layer = len(image_forward_outs.hidden_states) // 4
+            image_features = torch.cat([image_forward_outs.hidden_states[i] for i in range(select_every_k_layer + self.select_layer, len(image_forward_outs.hidden_states), select_every_k_layer)], dim=-1)
+            select_feature_type = select_feature_type.replace("slicefour_", "")
+        else:
+            image_features = image_forward_outs.hidden_states[self.select_layer]
+
+        if select_feature_type == "patch":
+            image_features = image_features[:, 1:]
+        elif select_feature_type == "cls_patch":
+            image_features = image_features
+        else:
+            raise ValueError(f"Unexpected select feature: {select_feature_type}")
+        return image_features
+
+    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 hidden_size(self):
+        try:
+            _hidden_size = self.config.hidden_size
+        except:
+            _hidden_size = self.config.vision_config.hidden_size
+        if "slicefour" in self.select_feature:
+            _hidden_size *= 4
+        return _hidden_size
+
+    @property
+    def num_patches(self):
+        _num_patches = (self.config.image_size // self.config.patch_size) ** 2
+        if "cls_patch" in self.select_feature:
+            _num_patches += 1
+        return _num_patches
+
+    @property
+    def num_patches_per_side(self):
+        return self.config.image_size // self.config.patch_size
+
+    @property
+    def image_size(self):
+        return self.config.image_size