Delete modules/florence2.py

modules/florence2.py

@@ -1,91 +0,0 @@
-import os,io,copy,subprocess,spaces,matplotlib.pyplot as plt,matplotlib.patches as patches,random,numpy as np
-from PIL import Image,ImageDraw,ImageFont
-from unittest.mock import patch
-from transformers.dynamic_module_utils import get_imports
-from transformers import AutoProcessor,AutoModelForCausalLM
-import gradio as gr
-
-def fixed_get_imports(filename:str|os.PathLike)->list[str]:
-	if not str(filename).endswith('/modeling_florence2.py'):return get_imports(filename)
-	imports=get_imports(filename)
-	if'flash_attn'in imports:imports.remove('flash_attn')
-	return imports
-@spaces.GPU
-def get_device_type():
-	import torch
-	if torch.cuda.is_available():return'cuda'
-	elif torch.backends.mps.is_available()and torch.backends.mps.is_built():return'mps'
-	else:return'cpu'
-device = get_device_type()
-if (device == "cuda"):
-    subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
-    model = AutoModelForCausalLM.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen-v2.0", trust_remote_code=True)
-    processor = AutoProcessor.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen-v2.0", trust_remote_code=True)
-    model.to(device)
-else:
-    with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
-        model = AutoModelForCausalLM.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen-v2.0", trust_remote_code=True)
-        processor = AutoProcessor.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen-v2.0", trust_remote_code=True)
-        model.to(device)
-
-colormap=['blue','orange','green','purple','brown','pink','gray','olive','cyan','red','lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
-
-def fig_to_pil(fig):buf=io.BytesIO();fig.savefig(buf,format='png');buf.seek(0);return Image.open(buf)
-@spaces.GPU
-def run_example(task_prompt,image,text_input=None):
-	if text_input is None:prompt=task_prompt
-	else:prompt=task_prompt+text_input
-	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,early_stopping=False,do_sample=False,num_beams=3);generated_text=processor.batch_decode(generated_ids,skip_special_tokens=False)[0];parsed_answer=processor.post_process_generation(generated_text,task=task_prompt,image_size=(image.width,image.height));return parsed_answer
-def plot_bbox(image,data):
-	fig,ax=plt.subplots();ax.imshow(image)
-	for(bbox,label)in zip(data['bboxes'],data['labels']):x1,y1,x2,y2=bbox;rect=patches.Rectangle((x1,y1),x2-x1,y2-y1,linewidth=1,edgecolor='r',facecolor='none');ax.add_patch(rect);plt.text(x1,y1,label,color='white',fontsize=8,bbox=dict(facecolor='red',alpha=.5))
-	ax.axis('off');return fig
-def draw_polygons(image,prediction,fill_mask=False):
-	draw=ImageDraw.Draw(image);scale=1
-	for(polygons,label)in zip(prediction['polygons'],prediction['labels']):
-		color=random.choice(colormap);fill_color=random.choice(colormap)if fill_mask else None
-		for _polygon in polygons:
-			_polygon=np.array(_polygon).reshape(-1,2)
-			if len(_polygon)<3:print('Invalid polygon:',_polygon);continue
-			_polygon=(_polygon*scale).reshape(-1).tolist()
-			if fill_mask:draw.polygon(_polygon,outline=color,fill=fill_color)
-			else:draw.polygon(_polygon,outline=color)
-			draw.text((_polygon[0]+8,_polygon[1]+2),label,fill=color)
-	return image
-
-def draw_ocr_bboxes(image,prediction):
-	scale=1;draw=ImageDraw.Draw(image);bboxes,labels=prediction['quad_boxes'],prediction['labels']
-	for(box,label)in zip(bboxes,labels):color=random.choice(colormap);new_box=(np.array(box)*scale).tolist();draw.polygon(new_box,width=3,outline=color);draw.text((new_box[0]+8,new_box[1]+2),'{}'.format(label),align='right',fill=color)
-	return image
-def convert_to_od_format(data):bboxes=data.get('bboxes',[]);labels=data.get('bboxes_labels',[]);od_results={'bboxes':bboxes,'labels':labels};return od_results
-
-def process_image(image,task_prompt,text_input=None):
-	if isinstance(image,str):image=Image.open(image)
-	else:image=Image.fromarray(image)
-	if task_prompt=='Caption':task_prompt='<CAPTION>';results=run_example(task_prompt,image);return results[task_prompt],None
-	elif task_prompt=='Detailed Caption':task_prompt='<DETAILED_CAPTION>';results=run_example(task_prompt,image);return results[task_prompt],None
-	elif task_prompt=='More Detailed Caption':task_prompt='<MORE_DETAILED_CAPTION>';results=run_example(task_prompt,image);return results,None
-	elif task_prompt=='Caption + Grounding':task_prompt='<CAPTION>';results=run_example(task_prompt,image);text_input=results[task_prompt];task_prompt='<CAPTION_TO_PHRASE_GROUNDING>';results=run_example(task_prompt,image,text_input);results['<CAPTION>']=text_input;fig=plot_bbox(image,results['<CAPTION_TO_PHRASE_GROUNDING>']);return results,fig_to_pil(fig)
-	elif task_prompt=='Detailed Caption + Grounding':task_prompt='<DETAILED_CAPTION>';results=run_example(task_prompt,image);text_input=results[task_prompt];task_prompt='<CAPTION_TO_PHRASE_GROUNDING>';results=run_example(task_prompt,image,text_input);results['<DETAILED_CAPTION>']=text_input;fig=plot_bbox(image,results['<CAPTION_TO_PHRASE_GROUNDING>']);return results,fig_to_pil(fig)
-	elif task_prompt=='More Detailed Caption + Grounding':task_prompt='<MORE_DETAILED_CAPTION>';results=run_example(task_prompt,image);text_input=results[task_prompt];task_prompt='<CAPTION_TO_PHRASE_GROUNDING>';results=run_example(task_prompt,image,text_input);results['<MORE_DETAILED_CAPTION>']=text_input;fig=plot_bbox(image,results['<CAPTION_TO_PHRASE_GROUNDING>']);return results,fig_to_pil(fig)
-	elif task_prompt=='Object Detection':task_prompt='<OD>';results=run_example(task_prompt,image);fig=plot_bbox(image,results['<OD>']);return results,fig_to_pil(fig)
-	elif task_prompt=='Dense Region Caption':task_prompt='<DENSE_REGION_CAPTION>';results=run_example(task_prompt,image);fig=plot_bbox(image,results['<DENSE_REGION_CAPTION>']);return results,fig_to_pil(fig)
-	elif task_prompt=='Region Proposal':task_prompt='<REGION_PROPOSAL>';results=run_example(task_prompt,image);fig=plot_bbox(image,results['<REGION_PROPOSAL>']);return results,fig_to_pil(fig)
-	elif task_prompt=='Caption to Phrase Grounding':task_prompt='<CAPTION_TO_PHRASE_GROUNDING>';results=run_example(task_prompt,image,text_input);fig=plot_bbox(image,results['<CAPTION_TO_PHRASE_GROUNDING>']);return results,fig_to_pil(fig)
-	elif task_prompt=='Referring Expression Segmentation':task_prompt='<REFERRING_EXPRESSION_SEGMENTATION>';results=run_example(task_prompt,image,text_input);output_image=copy.deepcopy(image);output_image=draw_polygons(output_image,results['<REFERRING_EXPRESSION_SEGMENTATION>'],fill_mask=True);return results,output_image
-	elif task_prompt=='Region to Segmentation':task_prompt='<REGION_TO_SEGMENTATION>';results=run_example(task_prompt,image,text_input);output_image=copy.deepcopy(image);output_image=draw_polygons(output_image,results['<REGION_TO_SEGMENTATION>'],fill_mask=True);return results,output_image
-	elif task_prompt=='Open Vocabulary Detection':task_prompt='<OPEN_VOCABULARY_DETECTION>';results=run_example(task_prompt,image,text_input);bbox_results=convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>']);fig=plot_bbox(image,bbox_results);return results,fig_to_pil(fig)
-	elif task_prompt=='Region to Category':task_prompt='<REGION_TO_CATEGORY>';results=run_example(task_prompt,image,text_input);return results,None
-	elif task_prompt=='Region to Description':task_prompt='<REGION_TO_DESCRIPTION>';results=run_example(task_prompt,image,text_input);return results,None
-	elif task_prompt=='OCR':task_prompt='<OCR>';results=run_example(task_prompt,image);return results,None
-	elif task_prompt=='OCR with Region':task_prompt='<OCR_WITH_REGION>';results=run_example(task_prompt,image);output_image=copy.deepcopy(image);output_image=draw_ocr_bboxes(output_image,results['<OCR_WITH_REGION>']);return results,output_image
-	else:return'',None # Return empty string and None for unknown task prompts
-
-single_task_list=['Caption','Detailed Caption','More Detailed Caption','Object Detection','Dense Region Caption','Region Proposal','Caption to Phrase Grounding','Referring Expression Segmentation','Region to Segmentation','Open Vocabulary Detection','Region to Category','Region to Description','OCR','OCR with Region']
-cascaded_task_list=['Caption + Grounding','Detailed Caption + Grounding','More Detailed Caption + Grounding']
-
-def update_task_dropdown(choice):
-    if choice == 'Cascaded task':
-        return gr.Dropdown(choices=cascaded_task_list, value='Caption + Grounding')
-    else:
-        return gr.Dropdown(choices=single_task_list, value='Caption')