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125 lines
5.4 KiB
125 lines
5.4 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import os
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from mindspore import context
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from mindspore.context import ParallelMode
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from mindspore.communication.management import init, get_rank, get_group_size
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from mindspore.train import Model
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.nn.optim import Adam
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from src.dataset import create_dataset
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from src.openposenet import OpenPoseNet
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from src.loss import openpose_loss, BuildTrainNetwork
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from src.config import params
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from src.utils import parse_args, get_lr, load_model, MyLossMonitor
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
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def train():
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"""Train function."""
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args = parse_args()
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args.outputs_dir = params['save_model_path']
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if args.group_size > 1:
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init()
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context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
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gradients_mean=True)
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args.outputs_dir = os.path.join(args.outputs_dir, "ckpt_{}/".format(str(get_rank())))
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args.rank = get_rank()
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else:
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args.outputs_dir = os.path.join(args.outputs_dir, "ckpt_0/")
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args.rank = 0
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# with out loss_scale
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if args.group_size > 1:
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args.loss_scale = params['loss_scale'] / 2
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args.lr_steps = list(map(int, params["lr_steps_NP"].split(',')))
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else:
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args.loss_scale = params['loss_scale']
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args.lr_steps = list(map(int, params["lr_steps"].split(',')))
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# create network
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print('start create network')
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criterion = openpose_loss()
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criterion.add_flags_recursive(fp32=True)
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network = OpenPoseNet(vggpath=params['vgg_path'])
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# network.add_flags_recursive(fp32=True)
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if params["load_pretrain"]:
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print("load pretrain model:", params["pretrained_model_path"])
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load_model(network, params["pretrained_model_path"])
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train_net = BuildTrainNetwork(network, criterion)
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# create dataset
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if os.path.exists(args.jsonpath_train) and os.path.exists(args.imgpath_train) \
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and os.path.exists(args.maskpath_train):
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print('start create dataset')
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else:
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print('Error: wrong data path')
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num_worker = 20 if args.group_size > 1 else 48
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de_dataset_train = create_dataset(args.jsonpath_train, args.imgpath_train, args.maskpath_train,
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batch_size=params['batch_size'],
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rank=args.rank,
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group_size=args.group_size,
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num_worker=num_worker,
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multiprocessing=True,
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shuffle=True,
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repeat_num=1)
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steps_per_epoch = de_dataset_train.get_dataset_size()
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print("steps_per_epoch: ", steps_per_epoch)
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# lr scheduler
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lr_stage, lr_base, lr_vgg = get_lr(params['lr'] * args.group_size,
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params['lr_gamma'],
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steps_per_epoch,
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params["max_epoch_train"],
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args.lr_steps,
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args.group_size)
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vgg19_base_params = list(filter(lambda x: 'base.vgg_base' in x.name, train_net.trainable_params()))
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base_params = list(filter(lambda x: 'base.conv' in x.name, train_net.trainable_params()))
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stages_params = list(filter(lambda x: 'base' not in x.name, train_net.trainable_params()))
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group_params = [{'params': vgg19_base_params, 'lr': lr_vgg},
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{'params': base_params, 'lr': lr_base},
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{'params': stages_params, 'lr': lr_stage}]
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opt = Adam(group_params, loss_scale=args.loss_scale)
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train_net.set_train(True)
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loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
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model = Model(train_net, optimizer=opt, loss_scale_manager=loss_scale_manager)
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params['ckpt_interval'] = max(steps_per_epoch, params['ckpt_interval'])
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config_ck = CheckpointConfig(save_checkpoint_steps=params['ckpt_interval'],
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keep_checkpoint_max=params["keep_checkpoint_max"])
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ckpoint_cb = ModelCheckpoint(prefix='{}'.format(args.rank), directory=args.outputs_dir, config=config_ck)
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time_cb = TimeMonitor(data_size=de_dataset_train.get_dataset_size())
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callback_list = [MyLossMonitor(), time_cb, ckpoint_cb]
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print("============== Starting Training ==============")
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model.train(params["max_epoch_train"], de_dataset_train, callbacks=callback_list,
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dataset_sink_mode=False)
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if __name__ == "__main__":
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# mindspore.common.seed.set_seed(1)
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train()
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