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174 lines
7.8 KiB
174 lines
7.8 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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# 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 SSD and get checkpoint files."""
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import os
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import argparse
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import ast
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import mindspore.nn as nn
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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, LossMonitor, 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 src.ssd_ghostnet import SSD300, SSDWithLossCell, TrainingWrapper, ssd_ghostnet
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from src.config_ghostnet_13x import config
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from src.dataset import create_ssd_dataset, data_to_mindrecord_byte_image, voc_data_to_mindrecord
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from src.lr_schedule import get_lr
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from src.init_params import init_net_param, filter_checkpoint_parameter
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def get_args():
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"""
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Parse arguments
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"""
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parser = argparse.ArgumentParser(description="SSD training")
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parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False,
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help="If set it true, only create Mindrecord, default is False.")
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parser.add_argument("--distribute", type=ast.literal_eval, default=False,
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help="Run distribute, default is False.")
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parser.add_argument("--device_id", type=int, default=4,
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help="Device id, default is 0.")
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parser.add_argument("--device_num", type=int, default=1,
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help="Use device nums, default is 1.")
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parser.add_argument("--lr", type=float, default=0.05,
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help="Learning rate, default is 0.05.")
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parser.add_argument("--mode", type=str, default="sink",
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help="Run sink mode or not, default is sink.")
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parser.add_argument("--dataset", type=str, default="coco",
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help="Dataset, default is coco.")
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parser.add_argument("--epoch_size", type=int, default=500,
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help="Epoch size, default is 500.")
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parser.add_argument("--batch_size", type=int, default=32,
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help="Batch size, default is 32.")
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parser.add_argument("--pre_trained", type=str, default=None,
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help="Pretrained Checkpoint file path.")
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parser.add_argument("--pre_trained_epoch_size", type=int,
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default=0, help="Pretrained epoch size.")
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parser.add_argument("--save_checkpoint_epochs", type=int,
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default=10, help="Save checkpoint epochs, default is 10.")
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parser.add_argument("--loss_scale", type=int, default=1024,
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help="Loss scale, default is 1024.")
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parser.add_argument("--filter_weight", type=ast.literal_eval, default=False,
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help="Filter weight parameters, default is False.")
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args_opt = parser.parse_args()
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return args_opt
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def main():
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args_opt = get_args()
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Ascend", device_id=args_opt.device_id)
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if args_opt.distribute:
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device_num = args_opt.device_num
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
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device_num=device_num)
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init()
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rank = args_opt.device_id % device_num
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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 ssd.mindrecord0, 1, ... file_num.
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prefix = "ssd.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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elif args_opt.dataset == "voc":
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if os.path.isdir(config.voc_dir):
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print("Create Mindrecord.")
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voc_data_to_mindrecord(mindrecord_dir, True, prefix)
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print("Create Mindrecord Done, at {}".format(mindrecord_dir))
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else:
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print("voc_dir 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(args_opt.loss_scale)
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# When create MindDataset, using the fitst mindrecord file, such as ssd.mindrecord0.
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dataset = create_ssd_dataset(mindrecord_file, repeat_num=1,
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batch_size=args_opt.batch_size, device_num=device_num, rank=rank)
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dataset_size = dataset.get_dataset_size()
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print("Create dataset done!")
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backbone = ssd_ghostnet()
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ssd = SSD300(backbone=backbone, config=config)
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net = SSDWithLossCell(ssd, config)
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init_net_param(net)
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# checkpoint
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ckpt_config = CheckpointConfig(
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save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs, keep_checkpoint_max=60)
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ckpoint_cb = ModelCheckpoint(
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prefix="ssd", directory=None, config=ckpt_config)
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if args_opt.pre_trained:
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if args_opt.pre_trained_epoch_size <= 0:
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raise KeyError(
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"pre_trained_epoch_size must be greater than 0.")
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param_dict = load_checkpoint(args_opt.pre_trained)
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if args_opt.filter_weight:
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filter_checkpoint_parameter(param_dict)
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load_param_into_net(net, param_dict)
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lr = Tensor(get_lr(global_step=config.global_step,
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lr_init=config.lr_init, lr_end=config.lr_end_rate * args_opt.lr, lr_max=args_opt.lr,
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warmup_epochs=config.warmup_epochs,
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total_epochs=args_opt.epoch_size,
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steps_per_epoch=dataset_size))
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opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr,
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config.momentum, config.weight_decay, loss_scale)
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net = TrainingWrapper(net, opt, loss_scale)
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callback = [TimeMonitor(data_size=dataset_size),
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LossMonitor(), ckpoint_cb]
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model = Model(net)
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dataset_sink_mode = False
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if args_opt.mode == "sink":
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print("In sink mode, one epoch return a loss.")
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dataset_sink_mode = True
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print("Start train SSD, the first epoch will be slower because of the graph compilation.")
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model.train(args_opt.epoch_size, dataset,
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callbacks=callback, dataset_sink_mode=dataset_sink_mode)
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if __name__ == '__main__':
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main()
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