# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """YoloV3-Darknet53-Quant train.""" import os import time import argparse import datetime from mindspore import ParallelMode from mindspore.nn.optim.momentum import Momentum from mindspore import Tensor from mindspore import context from mindspore.communication.management import init, get_rank, get_group_size from mindspore.train.callback import ModelCheckpoint, RunContext from mindspore.train.callback import _InternalCallbackParam, CheckpointConfig import mindspore as ms from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.train.quant import quant from src.yolo import YOLOV3DarkNet53, YoloWithLossCell, TrainingWrapper from src.logger import get_logger from src.util import AverageMeter, get_param_groups from src.lr_scheduler import warmup_step_lr, warmup_cosine_annealing_lr, \ warmup_cosine_annealing_lr_V2, warmup_cosine_annealing_lr_sample from src.yolo_dataset import create_yolo_dataset from src.initializer import default_recurisive_init from src.config import ConfigYOLOV3DarkNet53 from src.transforms import batch_preprocess_true_box, batch_preprocess_true_box_single from src.util import ShapeRecord devid = int(os.getenv('DEVICE_ID')) context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, device_target="Ascend", save_graphs=True, device_id=devid) def parse_args(): """Parse train arguments.""" parser = argparse.ArgumentParser('mindspore coco training') # dataset related parser.add_argument('--data_dir', type=str, default='', help='Train data dir. Default: ""') parser.add_argument('--per_batch_size', default=16, type=int, help='Batch size for per device. Default: 16') # network related parser.add_argument('--resume_yolov3', default='', type=str,\ help='The ckpt file of yolov3-darknet53, which used to yolov3-darknet53 quant. Default: ""') # optimizer and lr related parser.add_argument('--lr_scheduler', default='exponential', type=str,\ help='Learning rate scheduler, option type: exponential, ' 'cosine_annealing. Default: exponential') parser.add_argument('--lr', default=0.012, type=float, help='Learning rate of the training') parser.add_argument('--lr_epochs', type=str, default='92,105',\ help='Epoch of lr changing. Default: 92,105') parser.add_argument('--lr_gamma', type=float, default=0.1,\ help='Decrease lr by a factor of exponential lr_scheduler. Default: 0.1') parser.add_argument('--eta_min', type=float, default=0.,\ help='Eta_min in cosine_annealing scheduler. Default: 0.') parser.add_argument('--T_max', type=int, default=135,\ help='T-max in cosine_annealing scheduler. Default: 135') parser.add_argument('--max_epoch', type=int, default=135,\ help='Max epoch num to train the model. Default: 135') parser.add_argument('--warmup_epochs', type=float, default=0, help='Warmup epochs. Default: 0') parser.add_argument('--weight_decay', type=float, default=0.0005, help='Weight decay. Default: 0.0005') parser.add_argument('--momentum', type=float, default=0.9, help='Momentum. Default: 0.9') # loss related parser.add_argument('--loss_scale', type=int, default=1024, help='Static loss scale. Default: 1024') parser.add_argument('--label_smooth', type=int, default=0, help='Whether to use label smooth in CE. Default: 0') parser.add_argument('--label_smooth_factor', type=float, default=0.1,\ help='Smooth strength of original one-hot. Default: 0.1') # logging related parser.add_argument('--log_interval', type=int, default=100, help='Logging interval steps. Default: 100') parser.add_argument('--ckpt_path', type=str, default='outputs/',\ help='Checkpoint save location. Default: "outputs/"') parser.add_argument('--ckpt_interval', type=int, default=None, help='Save checkpoint interval. Default: None') parser.add_argument('--is_save_on_master', type=int, default=1,\ help='Save ckpt on master or all rank, 1 for master, 0 for all ranks. Default: 1') # distributed related parser.add_argument('--is_distributed', type=int, default=0,\ help='Distribute train or not, 1 for yes, 0 for no. Default: 0') parser.add_argument('--rank', type=int, default=0, help='Local rank of distributed, Default: 0') parser.add_argument('--group_size', type=int, default=1, help='World size of device, Default: 1') # profiler init parser.add_argument('--need_profiler', type=int, default=0,\ help='Whether use profiler, 1 for yes, 0 for no, Default: 0') # reset default config parser.add_argument('--training_shape', type=str, default="", help='Fix training shape. Default: ""') parser.add_argument('--resize_rate', type=int, default=None,\ help='Resize rate for multi-scale training. Default: None') args, _ = parser.parse_known_args() if args.lr_scheduler == 'cosine_annealing' and args.max_epoch > args.T_max: args.T_max = args.max_epoch args.lr_epochs = list(map(int, args.lr_epochs.split(','))) args.data_root = os.path.join(args.data_dir, 'train2014') args.annFile = os.path.join(args.data_dir, 'annotations/instances_train2014.json') return args def conver_training_shape(args): training_shape = [int(args.training_shape), int(args.training_shape)] return training_shape def train(): """Train function.""" args = parse_args() # init distributed if args.is_distributed: init() 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.resume_yolov3: param_dict = load_checkpoint(args.resume_yolov3) param_dict_new = {} for key, values in param_dict.items(): args.logger.info('ckpt param name = {}'.format(key)) if key.startswith('moments.') or key.startswith('global_') or \ key.startswith('learning_rate') or key.startswith('momentum'): continue elif key.startswith('yolo_network.'): key_new = key[13:] if key_new.endswith('1.beta'): key_new = key_new.replace('1.beta', 'batchnorm.beta') if key_new.endswith('1.gamma'): key_new = key_new.replace('1.gamma', 'batchnorm.gamma') if key_new.endswith('1.moving_mean'): key_new = key_new.replace('1.moving_mean', 'batchnorm.moving_mean') if key_new.endswith('1.moving_variance'): key_new = key_new.replace('1.moving_variance', 'batchnorm.moving_variance') if key_new.endswith('.weight'): if key_new.endswith('0.weight'): key_new = key_new.replace('0.weight', 'conv.weight') else: key_new = key_new.replace('.weight', '.conv.weight') if key_new.endswith('.bias'): key_new = key_new.replace('.bias', '.conv.bias') param_dict_new[key_new] = values args.logger.info('in resume {}'.format(key_new)) else: param_dict_new[key] = values args.logger.info('in resume {}'.format(key)) args.logger.info('resume finished') for _, param in network.parameters_and_names(): args.logger.info('network param name = {}'.format(param.name)) if param.name not in param_dict_new: args.logger.info('not match param name = {}'.format(param.name)) load_param_into_net(network, param_dict_new) args.logger.info('load_model {} success'.format(args.resume_yolov3)) config = ConfigYOLOV3DarkNet53() # convert fusion network to quantization aware network if config.quantization_aware: network = quant.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, False]) network = YoloWithLossCell(network) args.logger.info('finish get network') 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) 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() shape_record = ShapeRecord() 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])) shape_record.set(input_shape) images = Tensor(images) annos = data["annotation"] if args.group_size == 1: batch_y_true_0, batch_y_true_1, batch_y_true_2, batch_gt_box0, batch_gt_box1, batch_gt_box2 = \ batch_preprocess_true_box(annos, config, input_shape) else: batch_y_true_0, batch_y_true_1, batch_y_true_2, batch_gt_box0, batch_gt_box1, batch_gt_box2 = \ batch_preprocess_true_box_single(annos, config, input_shape) batch_y_true_0 = Tensor(batch_y_true_0) batch_y_true_1 = Tensor(batch_y_true_1) batch_y_true_2 = Tensor(batch_y_true_2) batch_gt_box0 = Tensor(batch_gt_box0) batch_gt_box1 = Tensor(batch_gt_box1) batch_gt_box2 = Tensor(batch_gt_box2) 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===============') if __name__ == "__main__": train()