forked from huawei/mindspore2022
209 lines
9.1 KiB
Python
Executable File
209 lines
9.1 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 time
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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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from src.model_utils.config import config
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from src.model_utils.moxing_adapter import moxing_wrapper
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from src.model_utils.device_adapter import get_device_id, get_device_num
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set_seed(1)
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if config.dataset == 'cifar10':
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from src.dataset import create_dataset1 as create_dataset
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else:
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from src.dataset import create_dataset2 as create_dataset
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def modelarts_pre_process():
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def unzip(zip_file, save_dir):
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import zipfile
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s_time = time.time()
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if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
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zip_isexist = zipfile.is_zipfile(zip_file)
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if zip_isexist:
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fz = zipfile.ZipFile(zip_file, 'r')
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data_num = len(fz.namelist())
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print("Extract Start...")
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print("unzip file num: {}".format(data_num))
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data_print = int(data_num / 100) if data_num > 100 else 1
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i = 0
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for file in fz.namelist():
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if i % data_print == 0:
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print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
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i += 1
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fz.extract(file, save_dir)
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print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),\
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int(int(time.time() - s_time) % 60)))
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print("Extract Done")
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else:
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print("This is not zip.")
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else:
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print("Zip has been extracted.")
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if config.need_modelarts_dataset_unzip:
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zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
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save_dir_1 = os.path.join(config.data_path)
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sync_lock = "/tmp/unzip_sync.lock"
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# Each server contains 8 devices as most
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if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
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print("Zip file path: ", zip_file_1)
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print("Unzip file save dir: ", save_dir_1)
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unzip(zip_file_1, save_dir_1)
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print("===Finish extract data synchronization===")
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try:
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os.mknod(sync_lock)
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except IOError:
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pass
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while True:
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if os.path.exists(sync_lock):
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break
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time.sleep(1)
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print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
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print("#" * 200, os.listdir(save_dir_1))
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print("#" * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
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config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
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config.save_checkpoint_path = config.output_path
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@moxing_wrapper(pre_process=modelarts_pre_process)
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def train_mobilenetv1():
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""" train_mobilenetv1 """
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if config.dataset == 'imagenet2012':
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config.dataset_path = os.path.join(config.dataset_path, 'train')
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target = config.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 config.parameter_server:
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context.set_ps_context(enable_ps=True)
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device_id = int(os.getenv('DEVICE_ID', '0'))
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if config.run_distribute:
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if target == "Ascend":
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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=get_device_num(), parallel_mode=ParallelMode.DATA_PARALLEL,
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gradients_mean=True)
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init()
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context.set_auto_parallel_context(all_reduce_fusion_config=[75])
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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=config.dataset_path, do_train=True, device_num=config.device_num,
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repeat_num=1, 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 config.parameter_server:
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net.set_param_ps()
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# init weight
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if config.pre_trained:
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param_dict = load_checkpoint(config.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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if target == "Ascend":
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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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else:
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
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config.weight_decay)
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# define loss, model
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if config.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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if target == "Ascend":
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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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model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
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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 and device_id % min(8, get_device_num()) == 0:
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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 config.parameter_server))
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if __name__ == '__main__':
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train_mobilenetv1()
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