opt = Momentum(params=get_param_groups(network), learning_rate=Tensor(lr), momentum=args.momentum, weight_decay=args.weight_decay, loss_scale=args.loss_scale) is_gpu = context.get_context("device_target") == "GPU" if is_gpu: loss_scale_value = 1.0 loss_scale = FixedLossScaleManager(loss_scale_value, drop_overflow_update=False) network = amp.build_train_network(network, optimizer=opt, loss_scale_manager=loss_scale, level="O2", keep_batchnorm_fp32=False) keep_loss_fp32(network) else: network = TrainingWrapper(network, opt) network.set_train() if args.rank_save_ckpt_flag: # checkpoint save ckpt_max_num = 10 ckpt_config = CheckpointConfig( save_checkpoint_steps=args.ckpt_interval, keep_checkpoint_max=ckpt_max_num) save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/') ckpt_cb = ModelCheckpoint(config=ckpt_config, directory=save_ckpt_path, prefix='{}'.format(args.rank))
def train(): """Train function.""" args = parse_args() devid = int(os.getenv('DEVICE_ID', '0')) context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, device_target=args.device_target, save_graphs=True, device_id=devid) if args.need_profiler: from mindspore.profiler.profiling import Profiler profiler = Profiler(output_path=args.outputs_dir, is_detail=True, is_show_op_path=True) loss_meter = AverageMeter('loss') context.reset_auto_parallel_context() parallel_mode = ParallelMode.STAND_ALONE degree = 1 if args.is_distributed: parallel_mode = ParallelMode.DATA_PARALLEL degree = get_group_size() context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=degree) network = YOLOV3DarkNet53(is_training=True) # default is kaiming-normal default_recurisive_init(network) load_yolov3_params(args, network) network = YoloWithLossCell(network) args.logger.info('finish get network') config = ConfigYOLOV3DarkNet53() config.label_smooth = args.label_smooth config.label_smooth_factor = args.label_smooth_factor if args.training_shape: config.multi_scale = [conver_training_shape(args)] if args.resize_rate: config.resize_rate = args.resize_rate ds, data_size = create_yolo_dataset(image_dir=args.data_root, anno_path=args.annFile, is_training=True, batch_size=args.per_batch_size, max_epoch=args.max_epoch, device_num=args.group_size, rank=args.rank, config=config) args.logger.info('Finish loading dataset') args.steps_per_epoch = int(data_size / args.per_batch_size / args.group_size) if not args.ckpt_interval: args.ckpt_interval = args.steps_per_epoch lr = get_lr(args) opt = Momentum(params=get_param_groups(network), learning_rate=Tensor(lr), momentum=args.momentum, weight_decay=args.weight_decay, loss_scale=args.loss_scale) is_gpu = context.get_context("device_target") == "GPU" if is_gpu: loss_scale_value = 1.0 loss_scale = FixedLossScaleManager(loss_scale_value, drop_overflow_update=False) network = amp.build_train_network(network, optimizer=opt, loss_scale_manager=loss_scale, level="O2", keep_batchnorm_fp32=True) keep_loss_fp32(network) else: network = TrainingWrapper(network, opt) network.set_train() if args.rank_save_ckpt_flag: # checkpoint save ckpt_max_num = args.max_epoch * args.steps_per_epoch // args.ckpt_interval ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval, keep_checkpoint_max=ckpt_max_num) save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/') ckpt_cb = ModelCheckpoint(config=ckpt_config, directory=save_ckpt_path, prefix='{}'.format(args.rank)) cb_params = _InternalCallbackParam() cb_params.train_network = network cb_params.epoch_num = ckpt_max_num cb_params.cur_epoch_num = 1 run_context = RunContext(cb_params) ckpt_cb.begin(run_context) old_progress = -1 t_end = time.time() data_loader = ds.create_dict_iterator(output_numpy=True) for i, data in enumerate(data_loader): images = data["image"] input_shape = images.shape[2:4] args.logger.info('iter[{}], shape{}'.format(i, input_shape[0])) images = Tensor.from_numpy(images) batch_y_true_0 = Tensor.from_numpy(data['bbox1']) batch_y_true_1 = Tensor.from_numpy(data['bbox2']) batch_y_true_2 = Tensor.from_numpy(data['bbox3']) batch_gt_box0 = Tensor.from_numpy(data['gt_box1']) batch_gt_box1 = Tensor.from_numpy(data['gt_box2']) batch_gt_box2 = Tensor.from_numpy(data['gt_box3']) input_shape = Tensor(tuple(input_shape[::-1]), ms.float32) loss = network(images, batch_y_true_0, batch_y_true_1, batch_y_true_2, batch_gt_box0, batch_gt_box1, batch_gt_box2, input_shape) loss_meter.update(loss.asnumpy()) if args.rank_save_ckpt_flag: # ckpt progress cb_params.cur_step_num = i + 1 # current step number cb_params.batch_num = i + 2 ckpt_cb.step_end(run_context) if i % args.log_interval == 0: time_used = time.time() - t_end epoch = int(i / args.steps_per_epoch) fps = args.per_batch_size * (i - old_progress) * args.group_size / time_used if args.rank == 0: args.logger.info( 'epoch[{}], iter[{}], {}, {:.2f} imgs/sec, lr:{}'.format(epoch, i, loss_meter, fps, lr[i])) t_end = time.time() loss_meter.reset() old_progress = i if (i + 1) % args.steps_per_epoch == 0 and args.rank_save_ckpt_flag: cb_params.cur_epoch_num += 1 if args.need_profiler: if i == 10: profiler.analyse() break args.logger.info('==========end training===============')
def train(): """Train function.""" args = parse_args() devid = int(os.getenv('DEVICE_ID', '0')) context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, device_target=args.device_target, save_graphs=False, device_id=devid) loss_meter = AverageMeter('loss') network = YOLOV4CspDarkNet53(is_training=True) # default is kaiming-normal default_recursive_init(network) if args.pretrained_backbone: pretrained_backbone_slice = args.pretrained_backbone.split('/') backbone_ckpt_file = pretrained_backbone_slice[ len(pretrained_backbone_slice) - 1] local_backbone_ckpt_path = '/cache/' + backbone_ckpt_file # download backbone checkpoint mox.file.copy_parallel(src_url=args.pretrained_backbone, dst_url=local_backbone_ckpt_path) args.pretrained_backbone = local_backbone_ckpt_path load_yolov4_params(args, network) network = YoloWithLossCell(network) args.logger.info('finish get network') config = ConfigYOLOV4CspDarkNet53() config.label_smooth = args.label_smooth config.label_smooth_factor = args.label_smooth_factor if args.training_shape: config.multi_scale = [convert_training_shape(args)] if args.resize_rate: config.resize_rate = args.resize_rate # data download local_data_path = '/cache/data' local_ckpt_path = '/cache/ckpt_file' print('Download data.') mox.file.copy_parallel(src_url=args.data_url, dst_url=local_data_path) ds, data_size = create_yolo_dataset( image_dir=os.path.join(local_data_path, 'images'), anno_path=os.path.join(local_data_path, 'annotation.json'), is_training=True, batch_size=args.per_batch_size, max_epoch=args.max_epoch, device_num=args.group_size, rank=args.rank, config=config) args.logger.info('Finish loading dataset') args.steps_per_epoch = int(data_size / args.per_batch_size / args.group_size) if not args.ckpt_interval: args.ckpt_interval = args.steps_per_epoch * 10 lr = get_lr(args) opt = Momentum(params=get_param_groups(network), learning_rate=Tensor(lr), momentum=args.momentum, weight_decay=args.weight_decay, loss_scale=args.loss_scale) is_gpu = context.get_context("device_target") == "GPU" if is_gpu: loss_scale_value = 1.0 loss_scale = FixedLossScaleManager(loss_scale_value, drop_overflow_update=False) network = amp.build_train_network(network, optimizer=opt, loss_scale_manager=loss_scale, level="O2", keep_batchnorm_fp32=False) keep_loss_fp32(network) else: network = TrainingWrapper(network, opt) network.set_train() # checkpoint save ckpt_max_num = 10 ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval, keep_checkpoint_max=ckpt_max_num) ckpt_cb = ModelCheckpoint(config=ckpt_config, directory=local_ckpt_path, prefix='yolov4') cb_params = _InternalCallbackParam() cb_params.train_network = network cb_params.epoch_num = ckpt_max_num cb_params.cur_epoch_num = 1 run_context = RunContext(cb_params) ckpt_cb.begin(run_context) old_progress = -1 t_end = time.time() data_loader = ds.create_dict_iterator(output_numpy=True, num_epochs=1) for i, data in enumerate(data_loader): images = data["image"] input_shape = images.shape[2:4] images = Tensor.from_numpy(images) batch_y_true_0 = Tensor.from_numpy(data['bbox1']) batch_y_true_1 = Tensor.from_numpy(data['bbox2']) batch_y_true_2 = Tensor.from_numpy(data['bbox3']) batch_gt_box0 = Tensor.from_numpy(data['gt_box1']) batch_gt_box1 = Tensor.from_numpy(data['gt_box2']) batch_gt_box2 = Tensor.from_numpy(data['gt_box3']) input_shape = Tensor(tuple(input_shape[::-1]), ms.float32) loss = network(images, batch_y_true_0, batch_y_true_1, batch_y_true_2, batch_gt_box0, batch_gt_box1, batch_gt_box2, input_shape) loss_meter.update(loss.asnumpy()) # ckpt progress cb_params.cur_step_num = i + 1 # current step number cb_params.batch_num = i + 2 ckpt_cb.step_end(run_context) if i % args.log_interval == 0: time_used = time.time() - t_end epoch = int(i / args.steps_per_epoch) fps = args.per_batch_size * ( i - old_progress) * args.group_size / time_used if args.rank == 0: args.logger.info( 'epoch[{}], iter[{}], {}, {:.2f} imgs/sec, lr:{}'.format( epoch, i, loss_meter, fps, lr[i])) t_end = time.time() loss_meter.reset() old_progress = i if (i + 1) % args.steps_per_epoch == 0: cb_params.cur_epoch_num += 1 args.logger.info('==========end training===============') # upload checkpoint files print('Upload checkpoint.') mox.file.copy_parallel(src_url=local_ckpt_path, dst_url=args.train_url)
def train(): """Train function.""" args = parse_args() devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0 context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, device_target=args.device_target, save_graphs=True, device_id=devid) # init distributed if args.is_distributed: if args.device_target == "Ascend": init() else: init("nccl") args.rank = get_rank() args.group_size = get_group_size() # select for master rank save ckpt or all rank save, compatiable for model parallel args.rank_save_ckpt_flag = 0 if args.is_save_on_master: if args.rank == 0: args.rank_save_ckpt_flag = 1 else: args.rank_save_ckpt_flag = 1 # logger args.outputs_dir = os.path.join(args.ckpt_path, datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S')) args.logger = get_logger(args.outputs_dir, args.rank) args.logger.save_args(args) if args.need_profiler: from mindspore.profiler.profiling import Profiler profiler = Profiler(output_path=args.outputs_dir, is_detail=True, is_show_op_path=True) loss_meter = AverageMeter('loss') context.reset_auto_parallel_context() if args.is_distributed: parallel_mode = ParallelMode.DATA_PARALLEL degree = get_group_size() else: parallel_mode = ParallelMode.STAND_ALONE degree = 1 context.set_auto_parallel_context(parallel_mode=parallel_mode, mirror_mean=True, device_num=degree) network = YOLOV3DarkNet53(is_training=True) # default is kaiming-normal default_recurisive_init(network) if args.pretrained_backbone: network = load_backbone(network, args.pretrained_backbone, args) args.logger.info('load pre-trained backbone {} into network'.format(args.pretrained_backbone)) else: args.logger.info('Not load pre-trained backbone, please be careful') if args.resume_yolov3: param_dict = load_checkpoint(args.resume_yolov3) param_dict_new = {} for key, values in param_dict.items(): if key.startswith('moments.'): continue elif key.startswith('yolo_network.'): param_dict_new[key[13:]] = values args.logger.info('in resume {}'.format(key)) else: param_dict_new[key] = values args.logger.info('in resume {}'.format(key)) args.logger.info('resume finished') load_param_into_net(network, param_dict_new) args.logger.info('load_model {} success'.format(args.resume_yolov3)) network = YoloWithLossCell(network) args.logger.info('finish get network') config = ConfigYOLOV3DarkNet53() config.label_smooth = args.label_smooth config.label_smooth_factor = args.label_smooth_factor if args.training_shape: config.multi_scale = [conver_training_shape(args)] if args.resize_rate: config.resize_rate = args.resize_rate ds, data_size = create_yolo_dataset(image_dir=args.data_root, anno_path=args.annFile, is_training=True, batch_size=args.per_batch_size, max_epoch=args.max_epoch, device_num=args.group_size, rank=args.rank, config=config) args.logger.info('Finish loading dataset') args.steps_per_epoch = int(data_size / args.per_batch_size / args.group_size) if not args.ckpt_interval: args.ckpt_interval = args.steps_per_epoch # lr scheduler if args.lr_scheduler == 'exponential': lr = warmup_step_lr(args.lr, args.lr_epochs, args.steps_per_epoch, args.warmup_epochs, args.max_epoch, gamma=args.lr_gamma, ) elif args.lr_scheduler == 'cosine_annealing': lr = warmup_cosine_annealing_lr(args.lr, args.steps_per_epoch, args.warmup_epochs, args.max_epoch, args.T_max, args.eta_min) elif args.lr_scheduler == 'cosine_annealing_V2': lr = warmup_cosine_annealing_lr_V2(args.lr, args.steps_per_epoch, args.warmup_epochs, args.max_epoch, args.T_max, args.eta_min) elif args.lr_scheduler == 'cosine_annealing_sample': lr = warmup_cosine_annealing_lr_sample(args.lr, args.steps_per_epoch, args.warmup_epochs, args.max_epoch, args.T_max, args.eta_min) else: raise NotImplementedError(args.lr_scheduler) opt = Momentum(params=get_param_groups(network), learning_rate=Tensor(lr), momentum=args.momentum, weight_decay=args.weight_decay, loss_scale=args.loss_scale) enable_amp = False is_gpu = context.get_context("device_target") == "GPU" if is_gpu: enable_amp = True if enable_amp: loss_scale_value = 1.0 loss_scale = FixedLossScaleManager(loss_scale_value, drop_overflow_update=False) network = amp.build_train_network(network, optimizer=opt, loss_scale_manager=loss_scale, level="O2", keep_batchnorm_fp32=True) keep_loss_fp32(network) else: network = TrainingWrapper(network, opt) network.set_train() if args.rank_save_ckpt_flag: # checkpoint save ckpt_max_num = args.max_epoch * args.steps_per_epoch // args.ckpt_interval ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval, keep_checkpoint_max=ckpt_max_num) ckpt_cb = ModelCheckpoint(config=ckpt_config, directory=args.outputs_dir, prefix='{}'.format(args.rank)) cb_params = _InternalCallbackParam() cb_params.train_network = network cb_params.epoch_num = ckpt_max_num cb_params.cur_epoch_num = 1 run_context = RunContext(cb_params) ckpt_cb.begin(run_context) old_progress = -1 t_end = time.time() data_loader = ds.create_dict_iterator() for i, data in enumerate(data_loader): images = data["image"] input_shape = images.shape[2:4] args.logger.info('iter[{}], shape{}'.format(i, input_shape[0])) images = Tensor(images) batch_y_true_0 = Tensor(data['bbox1']) batch_y_true_1 = Tensor(data['bbox2']) batch_y_true_2 = Tensor(data['bbox3']) batch_gt_box0 = Tensor(data['gt_box1']) batch_gt_box1 = Tensor(data['gt_box2']) batch_gt_box2 = Tensor(data['gt_box3']) input_shape = Tensor(tuple(input_shape[::-1]), ms.float32) loss = network(images, batch_y_true_0, batch_y_true_1, batch_y_true_2, batch_gt_box0, batch_gt_box1, batch_gt_box2, input_shape) loss_meter.update(loss.asnumpy()) if args.rank_save_ckpt_flag: # ckpt progress cb_params.cur_step_num = i + 1 # current step number cb_params.batch_num = i + 2 ckpt_cb.step_end(run_context) if i % args.log_interval == 0: time_used = time.time() - t_end epoch = int(i / args.steps_per_epoch) fps = args.per_batch_size * (i - old_progress) * args.group_size / time_used if args.rank == 0: args.logger.info( 'epoch[{}], iter[{}], {}, {:.2f} imgs/sec, lr:{}'.format(epoch, i, loss_meter, fps, lr[i])) t_end = time.time() loss_meter.reset() old_progress = i if (i + 1) % args.steps_per_epoch == 0 and args.rank_save_ckpt_flag: cb_params.cur_epoch_num += 1 if args.need_profiler: if i == 10: profiler.analyse() break args.logger.info('==========end training===============')