# Copyright (c) Tencent Inc. All rights reserved. import os import argparse import os.path as osp from functools import partial from io import BytesIO from copy import deepcopy import onnx import onnxsim import torch import gradio as gr import numpy as np from PIL import Image from torchvision.ops import nms from mmengine.config import Config, ConfigDict, DictAction from mmengine.runner import Runner from mmengine.runner.amp import autocast from mmengine.dataset import Compose from mmdet.visualization import DetLocalVisualizer from mmdet.datasets import CocoDataset from mmyolo.registry import RUNNERS from yolo_world.easydeploy.model import DeployModel, MMYOLOBackend def parse_args(): parser = argparse.ArgumentParser( description='YOLO-World Demo') parser.add_argument('config', help='test config file path') parser.add_argument('checkpoint', help='checkpoint file') parser.add_argument( '--work-dir', help='the directory to save the file containing evaluation metrics') parser.add_argument( '--cfg-options', nargs='+', action=DictAction, help='override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file. If the value to ' 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' 'Note that the quotation marks are necessary and that no white space ' 'is allowed.') args = parser.parse_args() return args def run_image(runner, image, text, max_num_boxes, score_thr, nms_thr, image_path='./work_dirs/demo.png'): os.makedirs('./work_dirs', exist_ok=True) image.save(image_path) texts = [[t.strip()] for t in text.split(',')] + [[' ']] data_info = dict(img_id=0, img_path=image_path, texts=texts) data_info = runner.pipeline(data_info) data_batch = dict(inputs=data_info['inputs'].unsqueeze(0), data_samples=[data_info['data_samples']]) with autocast(enabled=False), torch.no_grad(): output = runner.model.test_step(data_batch)[0] pred_instances = output.pred_instances keep_idxs = nms(pred_instances.bboxes, pred_instances.scores, iou_threshold=nms_thr) pred_instances = pred_instances[keep_idxs] pred_instances = pred_instances[ pred_instances.scores.float() > score_thr] if len(pred_instances.scores) > max_num_boxes: indices = pred_instances.scores.float().topk(max_num_boxes)[1] pred_instances = pred_instances[indices] output.pred_instances = pred_instances image = np.array(image) visualizer = DetLocalVisualizer() visualizer.dataset_meta['classes'] = [t[0] for t in texts] visualizer.add_datasample('image', np.array(image), output, draw_gt=False, out_file=image_path, pred_score_thr=score_thr) image = Image.open(image_path) return image def export_model(runner, checkpoint, text, max_num_boxes, score_thr, nms_thr): backend = MMYOLOBackend.ONNXRUNTIME postprocess_cfg = ConfigDict( pre_top_k=10 * max_num_boxes, keep_top_k=max_num_boxes, iou_threshold=nms_thr, score_threshold=score_thr) base_model = deepcopy(runner.model) texts = [[t.strip() for t in text.split(',')] + [' ']] base_model.reparameterize(texts) deploy_model = DeployModel( baseModel=base_model, backend=backend, postprocess_cfg=postprocess_cfg) deploy_model.eval() device = (next(iter(base_model.parameters()))).device fake_input = torch.ones([1, 3, 640, 640], device=device) # dry run deploy_model(fake_input) os.makedirs('work_dirs', exist_ok=True) save_onnx_path = os.path.join( 'work_dirs', 'yolow-l.onnx') # export onnx with BytesIO() as f: output_names = ['num_dets', 'boxes', 'scores', 'labels'] torch.onnx.export( deploy_model, fake_input, f, input_names=['images'], output_names=output_names, opset_version=12) f.seek(0) onnx_model = onnx.load(f) onnx.checker.check_model(onnx_model) onnx_model, check = onnxsim.simplify(onnx_model) onnx.save(onnx_model, save_onnx_path) del base_model del deploy_model del onnx_model return gr.update(visible=True), save_onnx_path def demo(runner, args, cfg): with gr.Blocks(title="YOLO-World") as demo: with gr.Row(): gr.Markdown('

YOLO-World: Real-Time Open-Vocabulary ' 'Object Detector

') with gr.Row(): with gr.Column(scale=0.3): with gr.Row(): image = gr.Image(type='pil', label='input image') input_text = gr.Textbox( lines=7, label='Enter the classes to be detected, ' 'separated by comma', value=', '.join(CocoDataset.METAINFO['classes']), elem_id='textbox') with gr.Row(): submit = gr.Button('Submit') clear = gr.Button('Clear') with gr.Row(): export = gr.Button('Deploy and Export ONNX Model') out_download = gr.File(label='Download link', visible=True, height=20) max_num_boxes = gr.Slider( minimum=1, maximum=300, value=100, step=1, interactive=True, label='Maximum Number Boxes') score_thr = gr.Slider( minimum=0, maximum=1, value=0.05, step=0.001, interactive=True, label='Score Threshold') nms_thr = gr.Slider( minimum=0, maximum=1, value=0.7, step=0.001, interactive=True, label='NMS Threshold') with gr.Column(scale=0.7): output_image = gr.Image( type='pil', label='output image') submit.click(partial(run_image, runner), [image, input_text, max_num_boxes, score_thr, nms_thr], [output_image]) clear.click(lambda: [[], '', ''], None, [image, input_text, output_image]) export.click(partial(export_model, runner, cfg.checkpoint), [input_text, max_num_boxes, score_thr, nms_thr], [out_download, out_download]) demo.launch(server_name='0.0.0.0') if __name__ == '__main__': args = parse_args() # load config cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) os.makedirs('./work_dirs', exist_ok=True) if args.work_dir is not None: cfg.work_dir = args.work_dir elif cfg.get('work_dir', None) is None: cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) cfg.load_from = args.checkpoint if 'runner_type' not in cfg: runner = Runner.from_cfg(cfg) else: runner = RUNNERS.build(cfg) runner.call_hook('before_run') runner.load_or_resume() pipeline = cfg.test_dataloader.dataset.pipeline runner.pipeline = Compose(pipeline) runner.model.eval() demo(runner, args, cfg)