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120 lines
5.3 KiB
120 lines
5.3 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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"""train CTPN and get checkpoint files."""
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import os
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import time
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import argparse
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import ast
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import mindspore.common.dtype as mstype
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from mindspore import context, Tensor
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from mindspore.communication.management import init
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from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor
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from mindspore.train import Model
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from mindspore.context import ParallelMode
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.nn import Momentum
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from mindspore.common import set_seed
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from src.ctpn import CTPN
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from src.config import config, pretrain_config, finetune_config
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from src.dataset import create_ctpn_dataset
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from src.lr_schedule import dynamic_lr
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from src.network_define import LossCallBack, LossNet, WithLossCell, TrainOneStepCell
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set_seed(1)
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parser = argparse.ArgumentParser(description="CTPN training")
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parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default: false.")
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parser.add_argument("--pre_trained", type=str, default="", help="Pretrained file path.")
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default: 0.")
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parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default: 1.")
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parser.add_argument("--rank_id", type=int, default=0, help="Rank id, default: 0.")
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parser.add_argument("--task_type", type=str, default="Pretraining",\
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choices=['Pretraining', 'Finetune'], help="task type, default:Pretraining")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id, save_graphs=True)
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if __name__ == '__main__':
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if args_opt.run_distribute:
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rank = args_opt.rank_id
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device_num = args_opt.device_num
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context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
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gradients_mean=True)
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init()
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else:
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rank = 0
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device_num = 1
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if args_opt.task_type == "Pretraining":
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print("Start to do pretraining")
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mindrecord_file = config.pretraining_dataset_file
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training_cfg = pretrain_config
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else:
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print("Start to do finetune")
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mindrecord_file = config.finetune_dataset_file
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training_cfg = finetune_config
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print("CHECKING MINDRECORD FILES ...")
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while not os.path.exists(mindrecord_file + ".db"):
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time.sleep(5)
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print("CHECKING MINDRECORD FILES DONE!")
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loss_scale = float(config.loss_scale)
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# When create MindDataset, using the fitst mindrecord file, such as ctpn_pretrain.mindrecord0.
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dataset = create_ctpn_dataset(mindrecord_file, repeat_num=1,\
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batch_size=config.batch_size, device_num=device_num, rank_id=rank)
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dataset_size = dataset.get_dataset_size()
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net = CTPN(config=config, is_training=True)
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net = net.set_train()
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load_path = args_opt.pre_trained
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if args_opt.task_type == "Pretraining":
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print("load backbone vgg16 ckpt {}".format(args_opt.pre_trained))
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param_dict = load_checkpoint(load_path)
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for item in list(param_dict.keys()):
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if not item.startswith('vgg16_feature_extractor'):
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param_dict.pop(item)
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load_param_into_net(net, param_dict)
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else:
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if load_path != "":
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print("load pretrain ckpt {}".format(args_opt.pre_trained))
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param_dict = load_checkpoint(load_path)
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load_param_into_net(net, param_dict)
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loss = LossNet()
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lr = Tensor(dynamic_lr(training_cfg, dataset_size), mstype.float32)
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opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,\
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weight_decay=config.weight_decay, loss_scale=config.loss_scale)
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net_with_loss = WithLossCell(net, loss)
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if args_opt.run_distribute:
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net = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale, reduce_flag=True,
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mean=True, degree=device_num)
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else:
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net = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale)
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time_cb = TimeMonitor(data_size=dataset_size)
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loss_cb = LossCallBack(rank_id=rank)
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cb = [time_cb, loss_cb]
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if config.save_checkpoint:
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ckptconfig = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*dataset_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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save_checkpoint_path = os.path.join(config.save_checkpoint_path, "ckpt_" + str(rank) + "/")
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ckpoint_cb = ModelCheckpoint(prefix='ctpn', directory=save_checkpoint_path, config=ckptconfig)
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cb += [ckpoint_cb]
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model = Model(net)
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model.train(training_cfg.total_epoch, dataset, callbacks=cb, dataset_sink_mode=True)
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