Upload extensions_built_in/dataset_tools/tools/fuyu_utils.py with huggingface_hub

extensions_built_in/dataset_tools/tools/fuyu_utils.py

@@ -0,0 +1,66 @@
+from transformers import  CLIPImageProcessor, BitsAndBytesConfig, AutoTokenizer
+
+from .caption import default_long_prompt, default_short_prompt, default_replacements, clean_caption
+import torch
+from PIL import Image
+
+
+class FuyuImageProcessor:
+    def __init__(self, device='cuda'):
+        from transformers import FuyuProcessor, FuyuForCausalLM
+        self.device = device
+        self.model: FuyuForCausalLM = None
+        self.processor: FuyuProcessor = None
+        self.dtype = torch.bfloat16
+        self.tokenizer: AutoTokenizer
+        self.is_loaded = False
+
+    def load_model(self):
+        from transformers import FuyuProcessor, FuyuForCausalLM
+        model_path = "adept/fuyu-8b"
+        kwargs = {"device_map": self.device}
+        kwargs['load_in_4bit'] = True
+        kwargs['quantization_config'] = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=self.dtype,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type='nf4'
+        )
+        self.processor = FuyuProcessor.from_pretrained(model_path)
+        self.model = FuyuForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+        self.is_loaded = True
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = FuyuForCausalLM.from_pretrained(model_path, torch_dtype=self.dtype, **kwargs)
+        self.processor = FuyuProcessor(image_processor=FuyuImageProcessor(), tokenizer=self.tokenizer)
+
+    def generate_caption(
+            self, image: Image,
+            prompt: str = default_long_prompt,
+            replacements=default_replacements,
+            max_new_tokens=512
+    ):
+        # prepare inputs for the model
+        # text_prompt = f"{prompt}\n"
+
+        # image = image.convert('RGB')
+        model_inputs = self.processor(text=prompt, images=[image])
+        model_inputs = {k: v.to(dtype=self.dtype if torch.is_floating_point(v) else v.dtype, device=self.device) for k, v in
+                        model_inputs.items()}
+
+        generation_output = self.model.generate(**model_inputs, max_new_tokens=max_new_tokens)
+        prompt_len = model_inputs["input_ids"].shape[-1]
+        output = self.tokenizer.decode(generation_output[0][prompt_len:], skip_special_tokens=True)
+        output = clean_caption(output, replacements=replacements)
+        return output
+
+        # inputs = self.processor(text=text_prompt, images=image, return_tensors="pt")
+        # for k, v in inputs.items():
+        #     inputs[k] = v.to(self.device)
+
+        # # autoregressively generate text
+        # generation_output = self.model.generate(**inputs, max_new_tokens=max_new_tokens)
+        # generation_text = self.processor.batch_decode(generation_output[:, -max_new_tokens:], skip_special_tokens=True)
+        # output = generation_text[0]
+        #
+        # return clean_caption(output, replacements=replacements)