def main(): args = parser.parse_args() print_arguments(args) check_cuda(args.use_gpu) data_dir = args.data_dir dataset = args.dataset assert dataset in ['pascalvoc', 'coco2014', 'coco2017'] # for pascalvoc label_file = 'label_list' train_file_list = 'trainval.txt' val_file_list = 'test.txt' if dataset == 'coco2014': train_file_list = 'annotations/instances_train2014.json' val_file_list = 'annotations/instances_val2014.json' elif dataset == 'coco2017': train_file_list = 'annotations/instances_train2017.json' val_file_list = 'annotations/instances_val2017.json' mean_BGR = [float(m) for m in args.mean_BGR.split(",")] image_shape = [int(m) for m in args.image_shape.split(",")] train_parameters[dataset]['image_shape'] = image_shape train_parameters[dataset]['batch_size'] = args.batch_size train_parameters[dataset]['lr'] = args.learning_rate train_parameters[dataset]['epoc_num'] = args.epoc_num train_parameters[dataset]['ap_version'] = args.ap_version data_args = reader.Settings(dataset=args.dataset, data_dir=data_dir, label_file=label_file, resize_h=image_shape[1], resize_w=image_shape[2], mean_value=mean_BGR, apply_distort=True, apply_expand=True, ap_version=args.ap_version) train(args, data_args, train_parameters[dataset], train_file_list=train_file_list, val_file_list=val_file_list)
def main(): args = parser.parse_args() print_arguments(args) check_cuda(args.use_gpu) train_async(args)
def main(args): devices = os.getenv("CUDA_VISIBLE_DEVICES") or "" devices_num = len(devices.split(",")) startup_prog = fluid.Program() infer_prog = fluid.Program() infer_fetch_list = build_program( main_prog=infer_prog, startup_prog=startup_prog, args=args) infer_prog = infer_prog.clone(for_test=True) place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) valid_reader = reader.train_valid( batch_size=args.batch_size, is_train=False, is_shuffle=False, args=args) fluid.io.load_persistables(exe, args.model_dir, main_program=infer_prog) infer_prog = fluid.CompiledProgram(infer_prog) top1 = infer(infer_prog, exe, valid_reader, infer_fetch_list, args) logger.info("test_acc {:.6f}".format(top1)) if __name__ == '__main__': args = parser.parse_args() utility.print_arguments(args) utility.check_cuda(args.use_gpu) main(args)
def main(): paddle.enable_static() args = parser.parse_args() print_arguments(args) check_cuda(args.use_gpu) eval(args)
def main(): args = parser.parse_args() print_arguments(args) check_cuda(args.use_gpu) infer(args)
batch_id = 0 while True: test_map, = exe.run(test_prog, fetch_list=[accum_map]) if batch_id % 10 == 0: print("Batch {0}, map {1}".format(batch_id, test_map)) batch_id += 1 except (fluid.core.EOFException, StopIteration): test_py_reader.reset() print("Test model {0}, map {1}".format(model_dir, test_map)) if __name__ == '__main__': args = parser.parse_args() print_arguments(args) check_cuda(args.use_gpu) data_dir = 'data/pascalvoc' test_list = 'test.txt' label_file = 'label_list' if not os.path.exists(args.model_dir): raise ValueError("The model path [%s] does not exist." % (args.model_dir)) if 'coco' in args.dataset: data_dir = 'data/coco' if '2014' in args.dataset: test_list = 'annotations/instances_val2014.json' elif '2017' in args.dataset: test_list = 'annotations/instances_val2017.json'
def main(): paddle.enable_static() args = parser.parse_args() print_arguments(args) check_cuda(args.use_gpu) save_inference_model(args)