forked from mindspore-Ecosystem/mindspore
99 lines
5.0 KiB
Python
Executable File
99 lines
5.0 KiB
Python
Executable File
# Copyright 2020 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_imagenet."""
|
|
import os
|
|
import argparse
|
|
import random
|
|
import numpy as np
|
|
from dataset import create_dataset
|
|
from lr_generator import warmup_cosine_annealing_lr
|
|
from config import config
|
|
from mindspore import context
|
|
from mindspore import Tensor
|
|
from mindspore.model_zoo.resnet import resnet101
|
|
from mindspore.parallel._auto_parallel_context import auto_parallel_context
|
|
from mindspore.nn.optim.momentum import Momentum
|
|
from mindspore.train.model import Model, ParallelMode
|
|
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
|
|
from mindspore.train.loss_scale_manager import FixedLossScaleManager
|
|
import mindspore.dataset.engine as de
|
|
from mindspore.communication.management import init
|
|
import mindspore.nn as nn
|
|
import mindspore.common.initializer as weight_init
|
|
from crossentropy import CrossEntropy
|
|
|
|
random.seed(1)
|
|
np.random.seed(1)
|
|
de.config.set_seed(1)
|
|
|
|
parser = argparse.ArgumentParser(description='Image classification')
|
|
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
|
|
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
|
|
parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
|
|
parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
|
|
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
|
args_opt = parser.parse_args()
|
|
|
|
device_id = int(os.getenv('DEVICE_ID'))
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
|
|
context.set_context(enable_task_sink=True)
|
|
context.set_context(enable_loop_sink=True)
|
|
context.set_context(enable_mem_reuse=True)
|
|
|
|
if __name__ == '__main__':
|
|
if not args_opt.do_eval and args_opt.run_distribute:
|
|
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
|
|
mirror_mean=True, parameter_broadcast=True)
|
|
auto_parallel_context().set_all_reduce_fusion_split_indices([180, 313])
|
|
init()
|
|
|
|
epoch_size = config.epoch_size
|
|
net = resnet101(class_num=config.class_num)
|
|
# weight init
|
|
for _, cell in net.cells_and_names():
|
|
if isinstance(cell, nn.Conv2d):
|
|
cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
|
|
cell.weight.default_input.shape(),
|
|
cell.weight.default_input.dtype())
|
|
if isinstance(cell, nn.Dense):
|
|
cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
|
|
cell.weight.default_input.shape(),
|
|
cell.weight.default_input.dtype())
|
|
if not config.label_smooth:
|
|
config.label_smooth_factor = 0.0
|
|
loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
|
|
if args_opt.do_train:
|
|
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
|
|
repeat_num=epoch_size, batch_size=config.batch_size)
|
|
step_size = dataset.get_dataset_size()
|
|
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
|
|
|
|
# learning rate strategy with cosine
|
|
lr = Tensor(warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size))
|
|
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
|
|
config.weight_decay, config.loss_scale)
|
|
model = Model(net, loss_fn=loss, optimizer=opt, amp_level='O2', keep_batchnorm_fp32=False,
|
|
loss_scale_manager=loss_scale, metrics={'acc'})
|
|
time_cb = TimeMonitor(data_size=step_size)
|
|
loss_cb = LossMonitor()
|
|
cb = [time_cb, loss_cb]
|
|
if config.save_checkpoint:
|
|
config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*step_size,
|
|
keep_checkpoint_max=config.keep_checkpoint_max)
|
|
ckpt_cb = ModelCheckpoint(prefix="resnet", directory=config.save_checkpoint_path, config=config_ck)
|
|
cb += [ckpt_cb]
|
|
model.train(epoch_size, dataset, callbacks=cb)
|