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Florence-2
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layerdiffusion
299
extensions-builtin/forge_space_florence_2/forge_app.py
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299
extensions-builtin/forge_space_florence_2/forge_app.py
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import spaces
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import gradio as gr
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from transformers import AutoProcessor, AutoModelForCausalLM
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import os
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import requests
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import copy
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from PIL import Image, ImageDraw, ImageFont
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import io
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import random
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import numpy as np
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# import subprocess
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# subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
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from unittest.mock import patch
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from transformers.dynamic_module_utils import get_imports
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def fixed_get_imports(filename: str | os.PathLike) -> list[str]:
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if not str(filename).endswith("modeling_florence2.py"):
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return get_imports(filename)
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imports = get_imports(filename)
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imports.remove("flash_attn")
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return imports
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with spaces.GPUObject() as gpu_object:
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with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
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models = {
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'microsoft/Florence-2-large-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large-ft', attn_implementation='sdpa', trust_remote_code=True).to("cuda").eval(),
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# 'microsoft/Florence-2-large': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to("cuda").eval(),
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# 'microsoft/Florence-2-base-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True).to("cuda").eval(),
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# 'microsoft/Florence-2-base': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to("cuda").eval(),
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}
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processors = {
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'microsoft/Florence-2-large-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True),
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# 'microsoft/Florence-2-large': AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True),
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# 'microsoft/Florence-2-base-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True),
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# 'microsoft/Florence-2-base': AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True),
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}
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DESCRIPTION = "# [Florence-2 Demo](https://huggingface.co/microsoft/Florence-2-large)"
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colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
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'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
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def fig_to_pil(fig):
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buf = io.BytesIO()
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fig.savefig(buf, format='png')
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buf.seek(0)
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return Image.open(buf)
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@spaces.GPU(gpu_objects=[gpu_object], manual_load=False)
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def run_example(task_prompt, image, text_input=None, model_id='microsoft/Florence-2-large'):
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model = models[model_id]
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processor = processors[model_id]
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if text_input is None:
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prompt = task_prompt
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else:
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prompt = task_prompt + text_input
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inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
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generated_ids = model.generate(
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input_ids=inputs["input_ids"],
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pixel_values=inputs["pixel_values"],
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max_new_tokens=1024,
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early_stopping=False,
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do_sample=False,
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num_beams=3,
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
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parsed_answer = processor.post_process_generation(
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generated_text,
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task=task_prompt,
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image_size=(image.width, image.height)
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)
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return parsed_answer
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def plot_bbox(image, data):
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fig, ax = plt.subplots()
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ax.imshow(image)
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for bbox, label in zip(data['bboxes'], data['labels']):
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x1, y1, x2, y2 = bbox
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rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
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ax.add_patch(rect)
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plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
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ax.axis('off')
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return fig
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def draw_polygons(image, prediction, fill_mask=False):
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draw = ImageDraw.Draw(image)
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scale = 1
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for polygons, label in zip(prediction['polygons'], prediction['labels']):
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color = random.choice(colormap)
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fill_color = random.choice(colormap) if fill_mask else None
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for _polygon in polygons:
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_polygon = np.array(_polygon).reshape(-1, 2)
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if len(_polygon) < 3:
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print('Invalid polygon:', _polygon)
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continue
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_polygon = (_polygon * scale).reshape(-1).tolist()
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if fill_mask:
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draw.polygon(_polygon, outline=color, fill=fill_color)
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else:
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draw.polygon(_polygon, outline=color)
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draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color)
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return image
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def convert_to_od_format(data):
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bboxes = data.get('bboxes', [])
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labels = data.get('bboxes_labels', [])
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od_results = {
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'bboxes': bboxes,
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'labels': labels
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}
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return od_results
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def draw_ocr_bboxes(image, prediction):
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scale = 1
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draw = ImageDraw.Draw(image)
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bboxes, labels = prediction['quad_boxes'], prediction['labels']
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for box, label in zip(bboxes, labels):
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color = random.choice(colormap)
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new_box = (np.array(box) * scale).tolist()
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draw.polygon(new_box, width=3, outline=color)
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draw.text((new_box[0]+8, new_box[1]+2),
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"{}".format(label),
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align="right",
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fill=color)
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return image
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def process_image(image, task_prompt, text_input=None, model_id='microsoft/Florence-2-large'):
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image = Image.fromarray(image) # Convert NumPy array to PIL Image
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if task_prompt == 'Caption':
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task_prompt = '<CAPTION>'
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results = run_example(task_prompt, image, model_id=model_id)
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return results, None
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elif task_prompt == 'Detailed Caption':
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task_prompt = '<DETAILED_CAPTION>'
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results = run_example(task_prompt, image, model_id=model_id)
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return results, None
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elif task_prompt == 'More Detailed Caption':
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task_prompt = '<MORE_DETAILED_CAPTION>'
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results = run_example(task_prompt, image, model_id=model_id)
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return results, None
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elif task_prompt == 'Caption + Grounding':
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task_prompt = '<CAPTION>'
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results = run_example(task_prompt, image, model_id=model_id)
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text_input = results[task_prompt]
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task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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results = run_example(task_prompt, image, text_input, model_id)
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results['<CAPTION>'] = text_input
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fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Detailed Caption + Grounding':
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task_prompt = '<DETAILED_CAPTION>'
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results = run_example(task_prompt, image, model_id=model_id)
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text_input = results[task_prompt]
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task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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results = run_example(task_prompt, image, text_input, model_id)
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results['<DETAILED_CAPTION>'] = text_input
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fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'More Detailed Caption + Grounding':
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task_prompt = '<MORE_DETAILED_CAPTION>'
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results = run_example(task_prompt, image, model_id=model_id)
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text_input = results[task_prompt]
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task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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results = run_example(task_prompt, image, text_input, model_id)
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results['<MORE_DETAILED_CAPTION>'] = text_input
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fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Object Detection':
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task_prompt = '<OD>'
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results = run_example(task_prompt, image, model_id=model_id)
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fig = plot_bbox(image, results['<OD>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Dense Region Caption':
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task_prompt = '<DENSE_REGION_CAPTION>'
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results = run_example(task_prompt, image, model_id=model_id)
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fig = plot_bbox(image, results['<DENSE_REGION_CAPTION>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Region Proposal':
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task_prompt = '<REGION_PROPOSAL>'
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results = run_example(task_prompt, image, model_id=model_id)
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fig = plot_bbox(image, results['<REGION_PROPOSAL>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Caption to Phrase Grounding':
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task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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results = run_example(task_prompt, image, text_input, model_id)
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fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Referring Expression Segmentation':
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task_prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
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results = run_example(task_prompt, image, text_input, model_id)
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output_image = copy.deepcopy(image)
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output_image = draw_polygons(output_image, results['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True)
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return results, output_image
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elif task_prompt == 'Region to Segmentation':
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task_prompt = '<REGION_TO_SEGMENTATION>'
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results = run_example(task_prompt, image, text_input, model_id)
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output_image = copy.deepcopy(image)
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output_image = draw_polygons(output_image, results['<REGION_TO_SEGMENTATION>'], fill_mask=True)
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return results, output_image
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elif task_prompt == 'Open Vocabulary Detection':
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task_prompt = '<OPEN_VOCABULARY_DETECTION>'
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results = run_example(task_prompt, image, text_input, model_id)
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bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>'])
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fig = plot_bbox(image, bbox_results)
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return results, fig_to_pil(fig)
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elif task_prompt == 'Region to Category':
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task_prompt = '<REGION_TO_CATEGORY>'
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results = run_example(task_prompt, image, text_input, model_id)
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return results, None
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elif task_prompt == 'Region to Description':
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task_prompt = '<REGION_TO_DESCRIPTION>'
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results = run_example(task_prompt, image, text_input, model_id)
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return results, None
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elif task_prompt == 'OCR':
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task_prompt = '<OCR>'
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results = run_example(task_prompt, image, model_id=model_id)
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return results, None
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elif task_prompt == 'OCR with Region':
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task_prompt = '<OCR_WITH_REGION>'
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results = run_example(task_prompt, image, model_id=model_id)
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output_image = copy.deepcopy(image)
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output_image = draw_ocr_bboxes(output_image, results['<OCR_WITH_REGION>'])
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return results, output_image
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else:
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return "", None # Return empty string and None for unknown task prompts
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css = """
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#output {
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height: 500px;
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overflow: auto;
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border: 1px solid #ccc;
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}
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"""
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single_task_list =[
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'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection',
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'Dense Region Caption', 'Region Proposal', 'Caption to Phrase Grounding',
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'Referring Expression Segmentation', 'Region to Segmentation',
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'Open Vocabulary Detection', 'Region to Category', 'Region to Description',
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'OCR', 'OCR with Region'
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]
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cascased_task_list =[
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'Caption + Grounding', 'Detailed Caption + Grounding', 'More Detailed Caption + Grounding'
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]
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def update_task_dropdown(choice):
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if choice == 'Cascased task':
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return gr.Dropdown(choices=cascased_task_list, value='Caption + Grounding')
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else:
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return gr.Dropdown(choices=single_task_list, value='Caption')
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with gr.Blocks(css=css) as demo:
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gr.Markdown(DESCRIPTION)
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with gr.Tab(label="Florence-2 Image Captioning"):
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with gr.Row():
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with gr.Column():
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input_img = gr.Image(label="Input Picture")
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model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value=list(models.keys())[0])
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task_type = gr.Radio(choices=['Single task', 'Cascased task'], label='Task type selector', value='Single task')
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task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Detailed Caption")
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task_type.change(fn=update_task_dropdown, inputs=task_type, outputs=task_prompt)
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text_input = gr.Textbox(label="Text Input (optional)")
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submit_btn = gr.Button(value="Submit")
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with gr.Column():
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output_text = gr.Textbox(label="Output Text")
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output_img = gr.Image(label="Output Image")
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gr.Examples(
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examples=[
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[spaces.convert_root_path() + "image1.jpg", "Detailed Caption"],
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[spaces.convert_root_path() + "image2.jpg", 'OCR with Region']
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],
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inputs=[input_img, task_prompt],
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outputs=[output_text, output_img],
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fn=process_image,
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cache_examples=False,
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label='Try examples'
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)
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submit_btn.click(process_image, [input_img, task_prompt, text_input, model_selector], [output_text, output_img])
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if __name__ == "__main__":
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demo.launch(debug=True)
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@@ -0,0 +1,6 @@
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{
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"tag": "Computer Vision: Image Caption, Object Detection, and Image Segmentation",
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"title": "Florence-2: Image Captioning and Object Detection",
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"repo_id": "gokaygokay/Florence-2",
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"revision": "9ff9f99df447d124bf0261f1de6a22898ccdce4e"
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}
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