Create florence_encoder.py

llava/model/multimodal_encoder/florence_encoder.py

@@ -0,0 +1,93 @@
+import torch
+import torch.nn as nn
+
+from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig, AutoProcessor, AutoModelForCausalLM 
+
+
+
+
+class FlorenceVisionTower(nn.Module):
+    def __init__(self, vision_tower, args, delay_load=False):
+        super().__init__()
+
+        self.is_loaded = False
+        self.vision_tower_name = vision_tower
+
+        if not delay_load:
+            self.load_model()
+        elif getattr(args, 'unfreeze_mm_vision_tower', False):
+            self.load_model()
+        else:
+            self.load_model()
+
+
+    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 = AutoProcessor.from_pretrained(self.vision_tower_name, trust_remote_code=True)
+        self.vision_tower = AutoModelForCausalLM.from_pretrained(self.vision_tower_name, trust_remote_code=True).to(torch.bfloat16)
+        self.vision_tower.requires_grad_(False)
+
+        self.is_loaded = True
+
+
+    @torch.no_grad()
+    def forward(self, images):
+        
+        ## hard code for the task prompt 
+        # task = [
+        #     'Describe in detail what is shown in the image.',
+        #     'What is the text in the image?',
+        #     'Locate the objects in the image, with their descriptions.',
+        # ]
+
+        task_ids = torch.tensor([
+            [0, 47066, 21700, 11, 4617, 99, 16, 2343, 11, 5, 2274, 4, 2, 1],
+            [0, 2264, 16, 5, 2788, 11, 5, 2274, 116, 2, 1, 1, 1, 1],
+            [0, 574, 22486, 5, 8720, 11, 5, 2274, 6, 19, 49, 24173, 4, 2]
+        ]).to(device=self.device)
+
+
+        with torch.no_grad():
+            generated_ids, image_feature, encoder_last_hidden_state = self.vision_tower.generate(
+                input_ids=task_ids,
+                pixel_values=images,
+                max_new_tokens=1,
+                do_sample=False,
+                num_beams=1,
+            )
+            return image_feature, encoder_last_hidden_state
+
+
+    @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