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| import os | |
| os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" | |
| import gradio as gr | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| import spaces | |
| import requests | |
| import copy | |
| from PIL import Image, ImageDraw, ImageFont | |
| import io | |
| import matplotlib.pyplot as plt | |
| import matplotlib.patches as patches | |
| import random | |
| import numpy as np | |
| from unittest.mock import patch | |
| from transformers import AutoModelForCausalLM, AutoProcessor | |
| from transformers.dynamic_module_utils import get_imports | |
| def fixed_get_imports(filename: 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 get_device_type(): | |
| import torch | |
| if torch.cuda.is_available(): | |
| return "cuda" | |
| else: | |
| if (torch.backends.mps.is_available() and torch.backends.mps.is_built()): | |
| return "mps" | |
| else: | |
| return "cpu" | |
| model_id = 'microsoft/Florence-2-base-ft' | |
| import subprocess | |
| 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("microsoft/Florence-2-base-ft", trust_remote_code=True) | |
| processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True) | |
| model.to(device) | |
| else: | |
| #https://huggingface.co./microsoft/Florence-2-base-ft/discussions/4 | |
| with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports): | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True) | |
| processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True) | |
| model.to(device) | |
| DESCRIPTION = "# [Florence-2 base-ft Demo with CPU and MPS inference support](https://huggingface.co./microsoft/Florence-2-base-ft)" | |
| 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) | |
| 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=0.5)) | |
| 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 convert_to_od_format(data): | |
| bboxes = data.get('bboxes', []) | |
| labels = data.get('bboxes_labels', []) | |
| od_results = { | |
| 'bboxes': bboxes, | |
| 'labels': labels | |
| } | |
| return od_results | |
| 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 process_image(image, task_prompt, text_input=None): | |
| image = Image.fromarray(image) # Convert NumPy array to PIL Image | |
| if task_prompt == 'Caption': | |
| task_prompt = '<CAPTION>' | |
| results = run_example(task_prompt, image) | |
| return results, None | |
| elif task_prompt == 'Detailed Caption': | |
| task_prompt = '<DETAILED_CAPTION>' | |
| results = run_example(task_prompt, image) | |
| return results, 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 | |
| css = """ | |
| #output { | |
| height: 500px; | |
| overflow: auto; | |
| border: 1px solid #ccc; | |
| } | |
| """ | |
| 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') | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Tab(label="Florence-2 Image Captioning"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_img = gr.Image(label="Input Picture") | |
| task_type = gr.Radio(choices=['Single task', 'Cascaded task'], label='Task type selector', value='Single task') | |
| task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Caption") | |
| task_type.change(fn=update_task_dropdown, inputs=task_type, outputs=task_prompt) | |
| text_input = gr.Textbox(label="Text Input (optional)") | |
| submit_btn = gr.Button(value="Submit") | |
| with gr.Column(): | |
| output_text = gr.Textbox(label="Output Text") | |
| output_img = gr.Image(label="Output Image") | |
| gr.Examples( | |
| examples=[ | |
| ["image1.jpg", 'Object Detection'], | |
| ["image2.jpg", 'OCR with Region'] | |
| ], | |
| inputs=[input_img, task_prompt], | |
| outputs=[output_text, output_img], | |
| fn=process_image, | |
| cache_examples=True, | |
| label='Try examples' | |
| ) | |
| submit_btn.click(process_image, [input_img, task_prompt, text_input], [output_text, output_img]) | |
| demo.launch(debug=True) |