forked from mindspore-Ecosystem/mindspore
164 lines
7.8 KiB
Python
Executable File
164 lines
7.8 KiB
Python
Executable File
# 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 mobilenet_v1."""
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import os
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import argparse
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import ast
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from mindspore import context
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from mindspore import Tensor
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from mindspore.nn.optim.momentum import Momentum
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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, LossMonitor, TimeMonitor
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.communication.management import init, get_rank, get_group_size
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from mindspore.common import set_seed
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import mindspore.nn as nn
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import mindspore.common.initializer as weight_init
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from src.lr_generator import get_lr
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from src.CrossEntropySmooth import CrossEntropySmooth
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from src.mobilenet_v1 import mobilenet_v1 as mobilenet
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012')
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parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute')
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parser.add_argument('--device_num', type=int, default=1, help='Device num.')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
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parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
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parser.add_argument('--parameter_server', type=ast.literal_eval, default=False, help='Run parameter server train')
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args_opt = parser.parse_args()
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set_seed(1)
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if args_opt.dataset == 'cifar10':
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from src.config import config1 as config
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from src.dataset import create_dataset1 as create_dataset
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else:
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from src.config import config2 as config
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from src.dataset import create_dataset2 as create_dataset
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if __name__ == '__main__':
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target = args_opt.device_target
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ckpt_save_dir = config.save_checkpoint_path
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# init context
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context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
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if args_opt.parameter_server:
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context.set_ps_context(enable_ps=True)
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if args_opt.run_distribute:
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if target == "Ascend":
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
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context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
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gradients_mean=True)
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init()
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# GPU target
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else:
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init()
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context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
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gradients_mean=True)
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ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
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# create dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
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batch_size=config.batch_size, target=target)
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step_size = dataset.get_dataset_size()
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# define net
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net = mobilenet(class_num=config.class_num)
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if args_opt.parameter_server:
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net.set_param_ps()
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# init weight
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if args_opt.pre_trained:
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param_dict = load_checkpoint(args_opt.pre_trained)
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load_param_into_net(net, param_dict)
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else:
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for _, cell in net.cells_and_names():
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if isinstance(cell, nn.Conv2d):
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cell.weight.set_data(weight_init.initializer(weight_init.XavierUniform(),
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cell.weight.shape,
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cell.weight.dtype))
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if isinstance(cell, nn.Dense):
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cell.weight.set_data(weight_init.initializer(weight_init.TruncatedNormal(),
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cell.weight.shape,
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cell.weight.dtype))
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# init lr
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lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
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warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size,
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lr_decay_mode=config.lr_decay_mode)
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lr = Tensor(lr)
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# define opt
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decayed_params = []
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no_decayed_params = []
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for param in net.trainable_params():
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if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
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decayed_params.append(param)
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else:
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no_decayed_params.append(param)
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group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
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{'params': no_decayed_params},
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{'order_params': net.trainable_params()}]
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opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
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# define loss, model
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if target == "Ascend":
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if args_opt.dataset == "imagenet2012":
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if not config.use_label_smooth:
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config.label_smooth_factor = 0.0
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loss = CrossEntropySmooth(sparse=True, reduction="mean",
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smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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else:
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
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amp_level="O2", keep_batchnorm_fp32=False)
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else:
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# GPU target
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if args_opt.dataset == "imagenet2012":
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if not config.use_label_smooth:
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config.label_smooth_factor = 0.0
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loss = CrossEntropySmooth(sparse=True, reduction="mean",
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smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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else:
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay,
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config.loss_scale)
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loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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# Mixed precision
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model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
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amp_level="O2", keep_batchnorm_fp32=False)
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# define callbacks
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time_cb = TimeMonitor(data_size=step_size)
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loss_cb = LossMonitor()
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cb = [time_cb, loss_cb]
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if config.save_checkpoint:
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(prefix="mobilenetv1", directory=ckpt_save_dir, config=config_ck)
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cb += [ckpt_cb]
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# train model
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model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb,
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sink_size=dataset.get_dataset_size(), dataset_sink_mode=(not args_opt.parameter_server))
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