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137 lines
6.3 KiB
137 lines
6.3 KiB
# Copyright 2020 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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# less 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 FasterRcnn and get checkpoint files."""
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import os
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import argparse
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import random
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import numpy as np
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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, ParallelMode
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.nn import SGD
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import mindspore.dataset.engine as de
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from src.FasterRcnn.faster_rcnn_r50 import Faster_Rcnn_Resnet50
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from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
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from src.config import config
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from src.dataset import data_to_mindrecord_byte_image, create_fasterrcnn_dataset
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from src.lr_schedule import dynamic_lr
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random.seed(1)
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np.random.seed(1)
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de.config.set_seed(1)
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parser = argparse.ArgumentParser(description="FasterRcnn training")
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parser.add_argument("--only_create_dataset", type=bool, default=False, help="If set it true, only create "
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"Mindrecord, default is false.")
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parser.add_argument("--run_distribute", type=bool, default=False, help="Run distribute, default is false.")
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parser.add_argument("--do_train", type=bool, default=True, help="Do train or not, default is true.")
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parser.add_argument("--do_eval", type=bool, default=False, help="Do eval or not, default is false.")
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parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
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parser.add_argument("--pre_trained", type=str, default="", help="Pretrain file path.")
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
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parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
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parser.add_argument("--rank_id", type=int, default=0, help="Rank id, default is 0.")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True, device_id=args_opt.device_id)
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if __name__ == '__main__':
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if not args_opt.do_eval and 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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mirror_mean=True, parameter_broadcast=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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print("Start create dataset!")
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# It will generate mindrecord file in args_opt.mindrecord_dir,
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# and the file name is FasterRcnn.mindrecord0, 1, ... file_num.
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prefix = "FasterRcnn.mindrecord"
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mindrecord_dir = config.mindrecord_dir
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mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
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if not os.path.exists(mindrecord_file):
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if not os.path.isdir(mindrecord_dir):
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os.makedirs(mindrecord_dir)
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if args_opt.dataset == "coco":
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if os.path.isdir(config.coco_root):
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print("Create Mindrecord.")
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data_to_mindrecord_byte_image("coco", True, prefix)
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print("Create Mindrecord Done, at {}".format(mindrecord_dir))
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else:
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print("coco_root not exits.")
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else:
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if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
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print("Create Mindrecord.")
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data_to_mindrecord_byte_image("other", True, prefix)
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print("Create Mindrecord Done, at {}".format(mindrecord_dir))
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else:
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print("IMAGE_DIR or ANNO_PATH not exits.")
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if not args_opt.only_create_dataset:
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loss_scale = float(config.loss_scale)
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# When create MindDataset, using the fitst mindrecord file, such as FasterRcnn.mindrecord0.
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dataset = create_fasterrcnn_dataset(mindrecord_file, repeat_num=config.epoch_size,
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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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print("Create dataset done!")
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net = Faster_Rcnn_Resnet50(config=config)
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net = net.set_train()
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load_path = args_opt.pre_trained
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if load_path != "":
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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('backbone'):
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param_dict.pop(item)
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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(config, rank_size=device_num), mstype.float32)
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opt = SGD(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, net, 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, net, opt, sens=config.loss_scale)
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time_cb = TimeMonitor(data_size=dataset_size)
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loss_cb = LossCallBack()
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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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ckpoint_cb = ModelCheckpoint(prefix='faster_rcnn', directory=config.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(config.epoch_size, dataset, callbacks=cb)
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