forked from mindspore-Ecosystem/mindspore
140 lines
5.9 KiB
Python
140 lines
5.9 KiB
Python
# 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 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.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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from src.model_utils.config import config
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from src.model_utils.moxing_adapter import moxing_wrapper
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@moxing_wrapper()
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def train_net():
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"""train net"""
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Ascend", device_id=config.device_id)
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if config.run_distribute:
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device_num = config.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 = config.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 config.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 = os.path.join(config.data_path, "MindRecord_COCO")
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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 config.dataset == "coco":
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coco_root = os.path.join(config.data_path, "coco_ori")
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if os.path.isdir(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 config.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 config.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 ssd.mindrecord0.
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dataset = create_ssd_dataset(mindrecord_file, repeat_num=1,
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batch_size=config.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)
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net = SSDWithLossCell(ssd)
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init_net_param(net)
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# checkpoint
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ckpt_save_dir = os.path.join(config.output_path, config.checkpoint_path)
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ckpt_config = CheckpointConfig(
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save_checkpoint_steps=dataset_size * config.save_checkpoint_epochs, keep_checkpoint_max=60)
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ckpoint_cb = ModelCheckpoint(
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prefix="ssd", directory=ckpt_save_dir, config=ckpt_config)
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if config.pre_trained:
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if config.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(config.pre_trained)
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if config.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 * config.lr, lr_max=config.lr,
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warmup_epochs=config.warmup_epochs,
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total_epochs=config.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 config.sink_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(config.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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train_net()
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