Create florencegpu1.py

utils/florencegpu1.py

@@ -0,0 +1,58 @@
+import os
+from typing import Union, Any, Tuple, Dict
+from unittest.mock import patch
+
+import torch
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoProcessor
+from transformers.dynamic_module_utils import get_imports
+
+FLORENCE_CHECKPOINT = "microsoft/Florence-2-large"
+#FLORENCE_CHECKPOINT = "microsoft/Florence-2-large-ft"
+FLORENCE_OBJECT_DETECTION_TASK = '<OD>'
+FLORENCE_DETAILED_CAPTION_TASK = '<MORE_DETAILED_CAPTION>'
+FLORENCE_CAPTION_TO_PHRASE_GROUNDING_TASK = '<CAPTION_TO_PHRASE_GROUNDING>'
+FLORENCE_OPEN_VOCABULARY_DETECTION_TASK = '<OPEN_VOCABULARY_DETECTION>'
+FLORENCE_DENSE_REGION_CAPTION_TASK = '<DENSE_REGION_CAPTION>'
+
+
+def fixed_get_imports(filename: Union[str, os.PathLike]) -> list[str]:
+    """Work around for https://huggingface.co/microsoft/phi-1_5/discussions/72."""
+    if not str(filename).endswith("/modeling_florence2.py"):
+        return get_imports(filename)
+    imports = get_imports(filename)
+    imports.remove("flash_attn")
+    return imports
+
+
+def load_florence_model(
+    device: torch.device, checkpoint: str = FLORENCE_CHECKPOINT
+) -> Tuple[Any, Any]:
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+    model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
+    processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)
+    return model, processor
+
+    
+def run_florence_inference(
+    model: Any,
+    processor: Any,
+    device: torch.device,
+    image: Image,
+    task: str,
+    text: str = ""
+) -> Tuple[str, Dict]:
+    prompt = task + text
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        num_beams=3
+    )
+    generated_text = processor.batch_decode(
+        generated_ids, skip_special_tokens=False)[0]
+    response = processor.post_process_generation(
+        generated_text, task=task, image_size=image.size)
+    return generated_text, response