Initial commit

2025-04-13 09:30:46 +08:00
commit 4f8972e88a
248 changed files with 34689 additions and 0 deletions
@@ -0,0 +1,173 @@
+import torch
+import torch.nn as nn
+from llava.utils import rank0_print
+from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
+
+try:
+    from s2wrapper import forward as multiscale_forward
+except:
+    pass
+
+
+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")
+
+        if not delay_load:
+            rank0_print(f"Loading vision tower: {vision_tower}")
+            self.load_model()
+        elif getattr(args, "unfreeze_mm_vision_tower", False):
+            # TODO: better detector is needed.
+            rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `unfreeze_mm_vision_tower`: True.")
+            self.load_model()
+        elif hasattr(args, "mm_tunable_parts") and "mm_vision_tower" in args.mm_tunable_parts:
+            rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `mm_tunable_parts` contains `mm_vision_tower`.")
+            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:
+            rank0_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.requires_grad_(False)
+
+        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_", "")
+        elif self.select_feature in ["slice_m25811_f6_patch", "slice_m25811_f6_cls_patch"]:
+            select_layers = [-2, -5, -8, -11, 6]
+            image_features = torch.cat([image_forward_outs.hidden_states[i] for i in select_layers], dim=-1)
+            select_feature_type = select_feature_type.replace("slice_m25811_f6_", "")
+        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 config(self):
+        if self.is_loaded:
+            return self.vision_tower.config
+        else:
+            return self.cfg_only
+
+    @property
+    def hidden_size(self):
+        _hidden_size = self.config.hidden_size
+        if "slicefour" in self.select_feature:
+            _hidden_size *= 4
+        if "slice_m25811_f6" in self.select_feature:
+            _hidden_size *= 5
+        return _hidden_size
+
+    @property
+    def num_patches_per_side(self):
+        return self.config.image_size // self.config.patch_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 image_size(self):
+        return self.config.image_size
+
+
+class CLIPVisionTowerS2(CLIPVisionTower):
+    def __init__(self, vision_tower, args, delay_load=False):
+
+        self.s2_scales = getattr(args, "s2_scales", "336,672,1008")
+        self.s2_scales = list(map(int, self.s2_scales.split(",")))
+        self.s2_scales.sort()
+        self.s2_split_size = self.s2_scales[0]
+        self.s2_image_size = self.s2_scales[-1]
+
+        super().__init__(vision_tower, args, delay_load)
+
+        # change resize/crop size in preprocessing to the largest image size in s2_scale
+        if not delay_load or getattr(args, "unfreeze_mm_vision_tower", False):
+            self.image_processor.size["shortest_edge"] = self.s2_image_size
+            self.image_processor.crop_size["height"] = self.image_processor.crop_size["width"] = self.s2_image_size
+
+    def load_model(self, device_map=None):
+        if self.is_loaded:
+            rank0_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.requires_grad_(False)
+
+        self.image_processor.size["shortest_edge"] = self.s2_image_size
+        self.image_processor.crop_size["height"] = self.image_processor.crop_size["width"] = self.s2_image_size
+
+        self.is_loaded = True
+
+    def forward_feature(self, images):
+        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
+
+    def forward(self, images):
+        if type(images) is list:
+            image_features = []
+            for image in images:
+                image_feature = multiscale_forward(self.forward_feature, image.unsqueeze(0), img_sizes=self.s2_scales, max_split_size=self.s2_split_size, split_forward=True)
+                image_features.append(image_feature)
+        else:
+            image_features = multiscale_forward(self.forward_feature, images, img_sizes=self.s2_scales, max_split_size=self.s2_split_size, split_forward=True)
+
+        return image_features
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size * len(self.s2_scales)