You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
108 lines
4.6 KiB
108 lines
4.6 KiB
# 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 vgg16 example on cifar10########################
|
|
python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
|
|
"""
|
|
import argparse
|
|
import os
|
|
import random
|
|
|
|
import numpy as np
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore import context
|
|
from mindspore.communication.management import init
|
|
from mindspore.nn.optim.momentum import Momentum
|
|
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
|
|
from mindspore.train.model import Model, ParallelMode
|
|
from mindspore.train.serialization import load_param_into_net, load_checkpoint
|
|
from src.config import cifar_cfg as cfg
|
|
from src.dataset import vgg_create_dataset
|
|
from src.vgg import vgg16
|
|
|
|
random.seed(1)
|
|
np.random.seed(1)
|
|
|
|
|
|
def lr_steps(global_step, lr_init, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
|
|
"""Set learning rate."""
|
|
lr_each_step = []
|
|
total_steps = steps_per_epoch * total_epochs
|
|
warmup_steps = steps_per_epoch * warmup_epochs
|
|
if warmup_steps != 0:
|
|
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
|
|
else:
|
|
inc_each_step = 0
|
|
for i in range(total_steps):
|
|
if i < warmup_steps:
|
|
lr_value = float(lr_init) + inc_each_step * float(i)
|
|
else:
|
|
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
|
|
lr_value = float(lr_max) * base * base
|
|
if lr_value < 0.0:
|
|
lr_value = 0.0
|
|
lr_each_step.append(lr_value)
|
|
|
|
current_step = global_step
|
|
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
|
learning_rate = lr_each_step[current_step:]
|
|
|
|
return learning_rate
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Cifar10 classification')
|
|
parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
|
|
help='device where the code will be implemented. (Default: Ascend)')
|
|
parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
|
|
parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
|
|
parser.add_argument('--pre_trained', type=str, default=None, help='the pretrained checkpoint file path.')
|
|
args_opt = parser.parse_args()
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
|
|
context.set_context(device_id=args_opt.device_id)
|
|
|
|
device_num = int(os.environ.get("DEVICE_NUM", 1))
|
|
if device_num > 1:
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
|
|
mirror_mean=True)
|
|
init()
|
|
|
|
dataset = vgg_create_dataset(args_opt.data_path, cfg.epoch_size)
|
|
batch_num = dataset.get_dataset_size()
|
|
|
|
net = vgg16(num_classes=cfg.num_classes)
|
|
# pre_trained
|
|
if args_opt.pre_trained:
|
|
load_param_into_net(net, load_checkpoint(args_opt.pre_trained))
|
|
|
|
lr = lr_steps(0, lr_init=cfg.lr_init, lr_max=cfg.lr_max, warmup_epochs=cfg.warmup_epochs,
|
|
total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
|
|
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum,
|
|
weight_decay=cfg.weight_decay)
|
|
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
|
|
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
|
|
amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
|
|
|
|
config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
|
|
time_cb = TimeMonitor(data_size=batch_num)
|
|
ckpoint_cb = ModelCheckpoint(prefix="train_vgg_cifar10", directory="./", config=config_ck)
|
|
loss_cb = LossMonitor()
|
|
model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
|
|
print("train success")
|