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import os
from typing import Union, Any, Tuple, Dict, List
from unittest.mock import patch
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor
from transformers.dynamic_module_utils import get_imports
import importlib
FLORENCE_CHECKPOINT = "microsoft/Florence-2-base"
# FLORENCE_CHECKPOINT = "microsoft/Florence-2-large"
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>'
# Removing the unnecessary flash_attn import which causes issues on CPU or MPS backends
def fixed_get_imports(filename) -> list[str]:
if not str(filename).endswith("modeling_florence2.py"):
return get_imports(filename)
imports = get_imports(filename)
imports.remove("flash_attn")
return imports
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports): #workaround for unnecessary flash_attn requirement
florence_model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base",trust_remote_code=True).to(device)
florence_processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)
def load_florence_model(
device: torch.device, checkpoint: str = FLORENCE_CHECKPOINT
) -> Tuple[Any, Any]:
with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
model = AutoModelForCausalLM.from_pretrained(
checkpoint, trust_remote_code=True).to(device).eval()
processor = AutoProcessor.from_pretrained(
checkpoint, 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
load_florence_model(device='cuda') |