def run(args): # Export if we know how, otherwise return if args.file: args.copy = False args.build = False export.run(args) else: if not os.path.exists(os.path.join(args.directory, args.project)): logging.debug("The project: %s does not exist." % os.path.join(args.directory, args.project)) return if args.file: # known project from records workspace = Workspace(args.file, os.getcwd()) if args.project: workspace.build_project(args.project, args.tool) else: workspace.build_projects(args.tool) else: # not project known by pgen project_settings = ProjectSettings() project_files = [os.path.join(args.directory, args.project)] builder = ToolsSupported().get_value(args.tool, 'builder') build(builder, args.project, project_files, args.tool, project_settings)
def test( weights=ROOT / 'yolov5s.pt', # weights path imgsz=640, # inference size (pixels) batch_size=1, # batch size data=ROOT / 'data/coco128.yaml', # dataset.yaml path device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu half=False, # use FP16 half-precision inference test=False, # test exports only pt_only=False, # test PyTorch only ): y, t = [], time.time() device = select_device(device) for i, (name, f, suffix, gpu) in export.export_formats().iterrows( ): # index, (name, file, suffix, gpu-capable) try: w = weights if f == '-' else \ export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights assert suffix in str(w), 'export failed' y.append([name, True]) except Exception: y.append([name, False]) # mAP, t_inference # Print results LOGGER.info('\n') parse_opt() notebook_init() # print system info py = pd.DataFrame(y, columns=['Format', 'Export']) LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)') LOGGER.info(str(py)) return py
def run( weights=ROOT / 'yolov5s.pt', # weights path imgsz=640, # inference size (pixels) batch_size=1, # batch size data=ROOT / 'data/coco128.yaml', # dataset.yaml path device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu half=False, # use FP16 half-precision inference ): y, t = [], time.time() formats = export.export_formats() device = select_device(device) for i, (name, f, suffix, gpu) in formats.iterrows( ): # index, (name, file, suffix, gpu-capable) try: if device.type != 'cpu': assert gpu, f'{name} inference not supported on GPU' if f == '-': w = weights # PyTorch format else: w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # all others assert suffix in str(w), 'export failed' result = val.run(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half) metrics = result[ 0] # metrics (mp, mr, map50, map, *losses(box, obj, cls)) speeds = result[2] # times (preprocess, inference, postprocess) y.append([name, metrics[3], speeds[1]]) # mAP, t_inference except Exception as e: LOGGER.warning(f'WARNING: Benchmark failure for {name}: {e}') y.append([name, None, None]) # mAP, t_inference # Print results LOGGER.info('\n') parse_opt() notebook_init() # print system info py = pd.DataFrame( y, columns=['Format', '[email protected]:0.95', 'Inference time (ms)']) LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)') LOGGER.info(str(py)) return py
def run(args): # Export if we know how, otherwise return if args.file: args.copy = False args.build = False export.run(args) else: if not os.path.exists(os.path.join(args.directory, args.project)): logging.debug("The project: %s does not exist." % os.path.join(args.directory, args.project)) return if args.file: # known project from records workspace = Workspace(args.file, os.getcwd()) if args.project: workspace.build_project(args.project, args.tool) else: workspace.build_projects(args.tool) else: # not project known by pgen project_settings = ProjectSettings() project_files = [os.path.join(args.directory, args.project)] builder = ToolsSupported().get_value(args.tool, 'builder') build(builder, args.project, project_files, args.tool, project_settings)
def run( weights=ROOT / 'yolov5s.pt', # weights path imgsz=640, # inference size (pixels) batch_size=1, # batch size data=ROOT / 'data/coco128.yaml', # dataset.yaml path ): y, t = [], time.time() formats = export.export_formats() for i, (name, f, suffix) in formats.iterrows(): # index, (name, file, suffix) try: w = weights if f == '-' else export.run( weights=weights, imgsz=[imgsz], include=[f], device='cpu')[-1] assert suffix in str(w), 'export failed' result = val.run(data, w, batch_size, imgsz=imgsz, plots=False, device='cpu', task='benchmark') metrics = result[ 0] # metrics (mp, mr, map50, map, *losses(box, obj, cls)) speeds = result[2] # times (preprocess, inference, postprocess) y.append([name, metrics[3], speeds[1]]) # mAP, t_inference except Exception as e: LOGGER.warning(f'WARNING: Benchmark failure for {name}: {e}') y.append([name, None, None]) # mAP, t_inference # Print results LOGGER.info('\n') parse_opt() notebook_init() # print system info py = pd.DataFrame( y, columns=['Format', '[email protected]:0.95', 'Inference time (ms)']) LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)') LOGGER.info(str(py)) return py
def run( weights=ROOT / 'yolov5s.pt', # weights path imgsz=640, # inference size (pixels) batch_size=1, # batch size data=ROOT / 'data/coco128.yaml', # dataset.yaml path device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu half=False, # use FP16 half-precision inference test=False, # test exports only pt_only=False, # test PyTorch only ): y, t = [], time.time() device = select_device(device) for i, (name, f, suffix, gpu) in export.export_formats().iterrows( ): # index, (name, file, suffix, gpu-capable) try: assert i != 9, 'Edge TPU not supported' assert i != 10, 'TF.js not supported' if device.type != 'cpu': assert gpu, f'{name} inference not supported on GPU' # Export if f == '-': w = weights # PyTorch format else: w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # all others assert suffix in str(w), 'export failed' # Validate result = val.run(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half) metrics = result[ 0] # metrics (mp, mr, map50, map, *losses(box, obj, cls)) speeds = result[2] # times (preprocess, inference, postprocess) y.append([ name, round(file_size(w), 1), round(metrics[3], 4), round(speeds[1], 2) ]) # MB, mAP, t_inference except Exception as e: LOGGER.warning(f'WARNING: Benchmark failure for {name}: {e}') y.append([name, None, None, None]) # mAP, t_inference if pt_only and i == 0: break # break after PyTorch # Print results LOGGER.info('\n') parse_opt() notebook_init() # print system info c = ['Format', 'Size (MB)', '[email protected]:0.95', 'Inference time (ms)' ] if map else ['Format', 'Export', '', ''] py = pd.DataFrame(y, columns=c) LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)') LOGGER.info(str(py if map else py.iloc[:, :2])) return py