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209 lines
11 KiB
209 lines
11 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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"""
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Train CenterNet and get network model files(.ckpt)
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"""
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import os
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import argparse
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import mindspore.communication.management as D
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from mindspore.communication.management import get_rank
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from mindspore import context
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from mindspore.train.model import Model
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from mindspore.context import ParallelMode
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.nn.optim import Adam
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from mindspore import log as logger
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from mindspore.common import set_seed
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from mindspore.profiler import Profiler
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from src.dataset import COCOHP
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from src import CenterNetMultiPoseLossCell, CenterNetWithLossScaleCell
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from src import CenterNetWithoutLossScaleCell
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from src.utils import LossCallBack, CenterNetPolynomialDecayLR, CenterNetMultiEpochsDecayLR
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from src.config import dataset_config, net_config, train_config
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_current_dir = os.path.dirname(os.path.realpath(__file__))
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parser = argparse.ArgumentParser(description='CenterNet training')
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parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'CPU'],
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help='device where the code will be implemented. (Default: Ascend)')
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parser.add_argument("--distribute", type=str, default="false", choices=["true", "false"],
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help="Run distribute, default is false.")
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parser.add_argument("--need_profiler", type=str, default="false", choices=["true", "false"],
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help="Profiling to parsing runtime info, default is false.")
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parser.add_argument("--profiler_path", type=str, default=" ", help="The path to save profiling data")
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parser.add_argument("--epoch_size", type=int, default="1", help="Epoch size, default is 1.")
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parser.add_argument("--train_steps", type=int, default=-1, help="Training Steps, default is -1,"
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"i.e. run all steps according to epoch number.")
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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("--enable_save_ckpt", type=str, default="true", choices=["true", "false"],
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help="Enable save checkpoint, default is true.")
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parser.add_argument("--do_shuffle", type=str, default="true", choices=["true", "false"],
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help="Enable shuffle for dataset, default is true.")
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parser.add_argument("--enable_data_sink", type=str, default="true", choices=["true", "false"],
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help="Enable data sink, default is true.")
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parser.add_argument("--data_sink_steps", type=int, default="1", help="Sink steps for each epoch, default is 1.")
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parser.add_argument("--save_checkpoint_path", type=str, default="", help="Save checkpoint path")
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parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path")
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parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, default is 1000.")
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parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
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parser.add_argument("--mindrecord_dir", type=str, default="", help="Mindrecord dataset files directory")
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parser.add_argument("--mindrecord_prefix", type=str, default="coco_hp.train.mind",
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help="Prefix of MindRecord dataset filename.")
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parser.add_argument("--visual_image", type=str, default="false", help="Visulize the ground truth and predicted image")
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parser.add_argument("--save_result_dir", type=str, default="", help="The path to save the predict results")
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args_opt = parser.parse_args()
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def _set_parallel_all_reduce_split():
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"""set centernet all_reduce fusion split"""
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if net_config.last_level == 5:
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context.set_auto_parallel_context(all_reduce_fusion_config=[16, 56, 96, 136, 175])
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elif net_config.last_level == 6:
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context.set_auto_parallel_context(all_reduce_fusion_config=[18, 59, 100, 141, 182])
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else:
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raise ValueError("The total num of allreduced grads for last level = {} is unknown,"
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"please re-split after known the true value".format(net_config.last_level))
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def _get_params_groups(network, optimizer):
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"""
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Get param groups
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"""
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params = network.trainable_params()
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decay_params = list(filter(lambda x: not optimizer.decay_filter(x), params))
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other_params = list(filter(optimizer.decay_filter, params))
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group_params = [{'params': decay_params, 'weight_decay': optimizer.weight_decay},
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{'params': other_params, 'weight_decay': 0.0},
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{'order_params': params}]
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return group_params
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def _get_optimizer(network, dataset_size):
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"""get optimizer, only support Adam right now."""
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if train_config.optimizer == 'Adam':
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group_params = _get_params_groups(network, train_config.Adam)
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if train_config.lr_schedule == "PolyDecay":
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lr_schedule = CenterNetPolynomialDecayLR(learning_rate=train_config.PolyDecay.learning_rate,
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end_learning_rate=train_config.PolyDecay.end_learning_rate,
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warmup_steps=train_config.PolyDecay.warmup_steps,
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decay_steps=args_opt.train_steps,
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power=train_config.PolyDecay.power)
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optimizer = Adam(group_params, learning_rate=lr_schedule, eps=train_config.PolyDecay.eps, loss_scale=1.0)
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elif train_config.lr_schedule == "MultiDecay":
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multi_epochs = train_config.MultiDecay.multi_epochs
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if not isinstance(multi_epochs, (list, tuple)):
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raise TypeError("multi_epochs must be list or tuple.")
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if not multi_epochs:
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multi_epochs = [args_opt.epoch_size]
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lr_schedule = CenterNetMultiEpochsDecayLR(learning_rate=train_config.MultiDecay.learning_rate,
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warmup_steps=train_config.MultiDecay.warmup_steps,
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multi_epochs=multi_epochs,
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steps_per_epoch=dataset_size,
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factor=train_config.MultiDecay.factor)
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optimizer = Adam(group_params, learning_rate=lr_schedule, eps=train_config.MultiDecay.eps, loss_scale=1.0)
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else:
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raise ValueError("Don't support lr_schedule {}, only support [PolynormialDecay, MultiEpochDecay]".
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format(train_config.optimizer))
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else:
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raise ValueError("Don't support optimizer {}, only support [Lamb, Momentum, Adam]".
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format(train_config.optimizer))
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return optimizer
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def train():
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"""training CenterNet"""
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
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context.set_context(reserve_class_name_in_scope=False)
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context.set_context(save_graphs=False)
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ckpt_save_dir = args_opt.save_checkpoint_path
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rank = 0
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device_num = 1
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num_workers = 8
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if args_opt.device_target == "Ascend":
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context.set_context(enable_auto_mixed_precision=False)
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context.set_context(device_id=args_opt.device_id)
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if args_opt.distribute == "true":
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D.init()
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device_num = args_opt.device_num
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rank = args_opt.device_id % device_num
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ckpt_save_dir = args_opt.save_checkpoint_path + 'ckpt_' + str(get_rank()) + '/'
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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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_set_parallel_all_reduce_split()
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else:
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args_opt.distribute = "false"
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args_opt.need_profiler = "false"
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args_opt.enable_data_sink = "false"
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# Start create dataset!
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# mindrecord files will be generated at args_opt.mindrecord_dir such as centernet.mindrecord0, 1, ... file_num.
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logger.info("Begin creating dataset for CenterNet")
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coco = COCOHP(dataset_config, run_mode="train", net_opt=net_config,
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enable_visual_image=(args_opt.visual_image == "true"), save_path=args_opt.save_result_dir)
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dataset = coco.create_train_dataset(args_opt.mindrecord_dir, args_opt.mindrecord_prefix,
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batch_size=train_config.batch_size, device_num=device_num, rank=rank,
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num_parallel_workers=num_workers, do_shuffle=args_opt.do_shuffle == 'true')
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dataset_size = dataset.get_dataset_size()
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logger.info("Create dataset done!")
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net_with_loss = CenterNetMultiPoseLossCell(net_config)
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new_repeat_count = args_opt.epoch_size * dataset_size // args_opt.data_sink_steps
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if args_opt.train_steps > 0:
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new_repeat_count = min(new_repeat_count, args_opt.train_steps // args_opt.data_sink_steps)
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else:
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args_opt.train_steps = args_opt.epoch_size * dataset_size
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logger.info("train steps: {}".format(args_opt.train_steps))
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optimizer = _get_optimizer(net_with_loss, dataset_size)
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enable_static_time = args_opt.device_target == "CPU"
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callback = [TimeMonitor(args_opt.data_sink_steps), LossCallBack(dataset_size, enable_static_time)]
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if args_opt.enable_save_ckpt == "true" and args_opt.device_id % min(8, device_num) == 0:
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config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_checkpoint_steps,
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keep_checkpoint_max=args_opt.save_checkpoint_num)
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ckpoint_cb = ModelCheckpoint(prefix='checkpoint_centernet',
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directory=None if ckpt_save_dir == "" else ckpt_save_dir, config=config_ck)
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callback.append(ckpoint_cb)
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if args_opt.load_checkpoint_path:
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param_dict = load_checkpoint(args_opt.load_checkpoint_path)
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load_param_into_net(net_with_loss, param_dict)
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if args_opt.device_target == "Ascend":
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net_with_grads = CenterNetWithLossScaleCell(net_with_loss, optimizer=optimizer,
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sens=train_config.loss_scale_value)
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else:
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net_with_grads = CenterNetWithoutLossScaleCell(net_with_loss, optimizer=optimizer)
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model = Model(net_with_grads)
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model.train(new_repeat_count, dataset, callbacks=callback, dataset_sink_mode=(args_opt.enable_data_sink == "true"),
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sink_size=args_opt.data_sink_steps)
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if __name__ == '__main__':
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if args_opt.need_profiler == "true":
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profiler = Profiler(output_path=args_opt.profiler_path)
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set_seed(0)
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train()
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if args_opt.need_profiler == "true":
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profiler.analyse()
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