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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """train GENet."""
- import os
- import argparse
- from mindspore import context
- from mindspore import Tensor
- from mindspore.nn.optim import Momentum
- from mindspore.train.model import Model
- from mindspore.context import ParallelMode
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
- from mindspore.train.callback import LossMonitor, TimeMonitor
- from mindspore.train.loss_scale_manager import FixedLossScaleManager
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.communication.management import init
- from mindspore.common import set_seed
- from mindspore.parallel import set_algo_parameters
- import mindspore.nn as nn
- import mindspore.common.initializer as weight_init
- from src.CrossEntropySmooth import CrossEntropySmooth
- from src.GENet import GE_resnet50 as net
- from src.lr_generator import get_lr
- from src.dataset import create_dataset
-
- parser = argparse.ArgumentParser(description='Image classification')
-
- parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
- parser.add_argument('--train_url', type=str, default=None, help='Train output path')
- parser.add_argument('--device_target', type=str, default='Ascend', choices=("Ascend", "GPU", "CPU"),
- help="Device target, support Ascend, GPU and CPU.")
- parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
- parser.add_argument('--extra', type=str, default="True",
- help='whether to use Depth-wise conv to down sample')
- parser.add_argument('--mlp', type=str, default="True", help='bottleneck . whether to use 1*1 conv')
- parser.add_argument('--is_modelarts', type=str, default="False", help='is train on modelarts')
- args_opt = parser.parse_args()
-
- if args_opt.extra.lower() == "false":
- from src.config import config3 as config
- else:
- if args_opt.mlp.lower() == "false":
- from src.config import config2 as config
- else:
- from src.config import config1 as config
-
- if args_opt.is_modelarts == "True":
- import moxing as mox
-
- set_seed(1)
-
- def filter_checkpoint_parameter_by_list(origin_dict, param_filter):
- """remove useless parameters according to filter_list"""
- for key in list(origin_dict.keys()):
- for name in param_filter:
- if name in key:
- print("Delete parameter from checkpoint: ", key)
- del origin_dict[key]
- break
-
- def trans_char_to_bool(str_):
- """
- Args:
- str_: string
-
- Returns:
- bool
- """
- result = False
- if str_.lower() == "true":
- result = True
- return result
-
- if __name__ == '__main__':
-
- device_id = int(os.getenv('DEVICE_ID'))
- device_num = int(os.getenv("RANK_SIZE"))
-
- ckpt_save_dir = config.save_checkpoint_path
- local_train_data_url = args_opt.data_url
-
- if args_opt.is_modelarts == "True":
- local_summary_dir = "/cache/summary"
- local_data_url = "/cache/data"
- local_train_url = "/cache/ckpt"
- local_zipfolder_url = "/cache/tarzip"
- ckpt_save_dir = local_train_url
- mox.file.make_dirs(local_train_url)
- mox.file.make_dirs(local_summary_dir)
- filename = "imagenet_original.tar.gz"
- # transfer dataset
- local_data_url = os.path.join(local_data_url, str(device_id))
- mox.file.make_dirs(local_data_url)
- local_zip_path = os.path.join(local_zipfolder_url, str(device_id), filename)
- obs_zip_path = os.path.join(args_opt.data_url, filename)
- mox.file.copy(obs_zip_path, local_zip_path)
- unzip_command = "tar -xvf %s -C %s" % (local_zip_path, local_data_url)
- os.system(unzip_command)
- local_train_data_url = os.path.join(local_data_url, "imagenet_original", "train")
-
- target = args_opt.device_target
- if target != 'Ascend':
- raise ValueError("Unsupported device target.")
-
- run_distribute = False
-
- if device_num > 1:
- run_distribute = True
-
- # init context
- context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
-
- if run_distribute:
-
- context.set_context(device_id=device_id)
- context.set_auto_parallel_context(device_num=device_num,
- parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
- set_algo_parameters(elementwise_op_strategy_follow=True)
- context.set_auto_parallel_context(all_reduce_fusion_config=[85, 160])
- init()
-
- # create dataset
- dataset = create_dataset(dataset_path=local_train_data_url, do_train=True, repeat_num=1,
- batch_size=config.batch_size, target=target, distribute=run_distribute)
- step_size = dataset.get_dataset_size()
-
- # define net
- mlp = trans_char_to_bool(args_opt.mlp)
- extra = trans_char_to_bool(args_opt.extra)
-
- net = net(class_num=config.class_num, extra=extra, mlp=mlp)
-
- # init weight
- if args_opt.pre_trained:
- param_dict = load_checkpoint(args_opt.pre_trained)
-
- load_param_into_net(net, param_dict)
- else:
- for _, cell in net.cells_and_names():
- if isinstance(cell, nn.Conv2d):
- cell.weight.set_data(weight_init.initializer(weight_init.HeUniform(),
- cell.weight.shape,
- cell.weight.dtype))
- if isinstance(cell, nn.Dense):
- cell.weight.set_data(weight_init.initializer(weight_init.TruncatedNormal(),
- cell.weight.shape,
- cell.weight.dtype))
-
- lr = get_lr(config.lr_init, config.lr_end, config.epoch_size, step_size, config.decay_mode)
-
- lr = Tensor(lr)
-
- # define opt
- decayed_params = []
- no_decayed_params = []
- for param in net.trainable_params():
- if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
- decayed_params.append(param)
- else:
- no_decayed_params.append(param)
-
- group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
- {'params': no_decayed_params},
- {'order_params': net.trainable_params()}]
-
- opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
- # define loss, model
- if target == "Ascend":
- if not config.use_label_smooth:
- config.label_smooth_factor = 0.0
-
- loss = CrossEntropySmooth(sparse=True, reduction="mean",
- smooth_factor=config.label_smooth_factor,
- num_classes=config.class_num)
-
- loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
- model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale,
- metrics={'acc'}, amp_level="O2", keep_batchnorm_fp32=False)
- else:
- raise ValueError("Unsupported device target.")
-
- # define callbacks
- time_cb = TimeMonitor(data_size=step_size)
- loss_cb = LossMonitor()
- rank_id = int(os.getenv("RANK_ID"))
-
- cb = [time_cb, loss_cb]
-
- if rank_id == 0:
- config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*step_size,
- keep_checkpoint_max=config.keep_checkpoint_max)
- ckpt_cb = ModelCheckpoint(prefix="GENet", directory=ckpt_save_dir, config=config_ck)
- cb += [ckpt_cb]
-
- dataset_sink_mode = target != "CPU"
- model.train(config.epoch_size, dataset, callbacks=cb,
- sink_size=dataset.get_dataset_size(), dataset_sink_mode=dataset_sink_mode)
-
- if device_id == 0 and args_opt.is_modelarts == "True":
- mox.file.copy_parallel(ckpt_save_dir, args_opt.train_url)
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