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googlenet-gpu

This commit is contained in:
panfengfeng 2020-07-29 20:53:47 +08:00
parent d4b5cda934
commit 7d5a67e9f0
5 changed files with 204 additions and 54 deletions
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13
model_zoo/official/cv/googlenet/eval.py
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@ -16,6 +16,8 @@
##############test googlenet example on cifar10#################
python eval.py
"""
import argparse
import mindspore.nn as nn
from mindspore import context
from mindspore.nn.optim.momentum import Momentum
@ -26,18 +28,27 @@ from src.config import cifar_cfg as cfg
from src.dataset import create_dataset
from src.googlenet import GoogleNet
parser = argparse.ArgumentParser(description='googlenet')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
args_opt = parser.parse_args()
if __name__ == '__main__':
device_target = cfg.device_target
context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
if device_target == "Ascend":
context.set_context(device_id=cfg.device_id)
net = GoogleNet(num_classes=cfg.num_classes)
net = GoogleNet(num_classes=cfg.num_classes, platform=device_target)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 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'})
if device_target == "Ascend":
param_dict = load_checkpoint(cfg.checkpoint_path)
else: # GPU
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
dataset = create_dataset(cfg.data_path, 1, False)

43
model_zoo/official/cv/googlenet/scripts/run_eval_gpu.sh Normal file
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@ -0,0 +1,43 @@
#!/bin/bash
# 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.
# ============================================================================
ulimit -u unlimited
if [ $# != 1 ]
then
echo "GPU: sh run_eval_gpu.sh [CHECKPOINT_PATH]"
exit 1
fi
# check checkpoint file
if [ ! -f $1 ]
then
echo "error: CHECKPOINT_PATH=$1 is not a file"
exit 1
fi
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
export DEVICE_ID=0
if [ -d "../eval" ];
then
rm -rf ../eval
fi
mkdir ../eval
cd ../eval || exit
python3 ${BASEPATH}/../eval.py --checkpoint_path=$1 > ./eval.log 2>&1 &

45
model_zoo/official/cv/googlenet/scripts/run_train_gpu.sh Normal file
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@ -0,0 +1,45 @@
#!/bin/bash
# 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.
# ============================================================================
if [ $# -lt 2 ]
then
echo "Usage:\n \
sh run_train.sh [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)]\n \
"
exit 1
fi
if [ $1 -lt 1 ] && [ $1 -gt 8 ]
then
echo "error: DEVICE_NUM=$1 is not in (1-8)"
exit 1
fi
export DEVICE_NUM=$1
export RANK_SIZE=$1
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
if [ -d "../train" ];
then
rm -rf ../train
fi
mkdir ../train
cd ../train || exit
export CUDA_VISIBLE_DEVICES="$2"
mpirun -n $1 --allow-run-as-root \
python3 ${BASEPATH}/../train.py > train.log 2>&1 &

70
model_zoo/official/cv/googlenet/src/googlenet.py
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@ -56,24 +56,35 @@ class Inception(nn.Cell):
Inception Block
"""
def __init__(self, in_channels, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
def __init__(self, in_channels, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes, platform="Ascend"):
super(Inception, self).__init__()
self.platform = platform
self.b1 = Conv2dBlock(in_channels, n1x1, kernel_size=1)
self.b2 = nn.SequentialCell([Conv2dBlock(in_channels, n3x3red, kernel_size=1),
Conv2dBlock(n3x3red, n3x3, kernel_size=3, padding=0)])
self.b3 = nn.SequentialCell([Conv2dBlock(in_channels, n5x5red, kernel_size=1),
Conv2dBlock(n5x5red, n5x5, kernel_size=3, padding=0)])
if self.platform == "Ascend":
self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=1, padding="same")
else: # GPU
self.maxpool = P.MaxPool(ksize=3, strides=1, padding="same")
self.b4 = Conv2dBlock(in_channels, pool_planes, kernel_size=1)
self.concat = P.Concat(axis=1)
def construct(self, x):
'''
construct inception model
'''
branch1 = self.b1(x)
branch2 = self.b2(x)
branch3 = self.b3(x)
if self.platform == "Ascend":
cell, argmax = self.maxpool(x)
branch4 = self.b4(cell)
_ = argmax
else: # GPU
cell = self.maxpool(x)
branch4 = self.b4(cell)
return self.concat((branch1, branch2, branch3, branch4))
@ -82,55 +93,77 @@ class GoogleNet(nn.Cell):
Googlenet architecture
"""
def __init__(self, num_classes):
def __init__(self, num_classes, platform="Ascend"):
super(GoogleNet, self).__init__()
self.conv1 = Conv2dBlock(3, 64, kernel_size=7, stride=2, padding=0)
self.maxpool1 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
self.platform = platform
self.conv2 = Conv2dBlock(64, 64, kernel_size=1)
self.conv3 = Conv2dBlock(64, 192, kernel_size=3, padding=0)
self.block3a = Inception(192, 64, 96, 128, 16, 32, 32, platform=self.platform)
self.block3b = Inception(256, 128, 128, 192, 32, 96, 64, platform=self.platform)
self.block4a = Inception(480, 192, 96, 208, 16, 48, 64, platform=self.platform)
self.block4b = Inception(512, 160, 112, 224, 24, 64, 64, platform=self.platform)
self.block4c = Inception(512, 128, 128, 256, 24, 64, 64, platform=self.platform)
self.block4d = Inception(512, 112, 144, 288, 32, 64, 64, platform=self.platform)
self.block4e = Inception(528, 256, 160, 320, 32, 128, 128, platform=self.platform)
if self.platform == "Ascend":
self.maxpool1 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
self.maxpool2 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
self.block3a = Inception(192, 64, 96, 128, 16, 32, 32)
self.block3b = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
self.block4a = Inception(480, 192, 96, 208, 16, 48, 64)
self.block4b = Inception(512, 160, 112, 224, 24, 64, 64)
self.block4c = Inception(512, 128, 128, 256, 24, 64, 64)
self.block4d = Inception(512, 112, 144, 288, 32, 64, 64)
self.block4e = Inception(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = P.MaxPoolWithArgmax(ksize=2, strides=2, padding="same")
self.block5a = Inception(832, 256, 160, 320, 32, 128, 128)
self.block5b = Inception(832, 384, 192, 384, 48, 128, 128)
else: # GPU
self.maxpool1 = P.MaxPool(ksize=3, strides=2, padding="same")
self.maxpool2 = P.MaxPool(ksize=3, strides=2, padding="same")
self.maxpool3 = P.MaxPool(ksize=3, strides=2, padding="same")
self.maxpool4 = P.MaxPool(ksize=2, strides=2, padding="same")
self.block5a = Inception(832, 256, 160, 320, 32, 128, 128, platform=self.platform)
self.block5b = Inception(832, 384, 192, 384, 48, 128, 128, platform=self.platform)
self.mean = P.ReduceMean(keep_dims=True)
self.dropout = nn.Dropout(keep_prob=0.8)
self.flatten = nn.Flatten()
self.classifier = nn.Dense(1024, num_classes, weight_init=weight_variable(),
bias_init=weight_variable())
def construct(self, x):
'''
construct googlenet model
'''
x = self.conv1(x)
if self.platform == "Ascend":
x, argmax = self.maxpool1(x)
else: # GPU
x = self.maxpool1(x)
x = self.conv2(x)
x = self.conv3(x)
if self.platform == "Ascend":
x, argmax = self.maxpool2(x)
else: # GPU
x = self.maxpool2(x)
x = self.block3a(x)
x = self.block3b(x)
if self.platform == "Ascend":
x, argmax = self.maxpool3(x)
else: # GPU
x = self.maxpool3(x)
x = self.block4a(x)
x = self.block4b(x)
x = self.block4c(x)
x = self.block4d(x)
x = self.block4e(x)
if self.platform == "Ascend":
x, argmax = self.maxpool4(x)
x = self.block5a(x)
x = self.block5b(x)
x = self.mean(x, (2, 3))
x = self.flatten(x)
x = self.classifier(x)
_ = argmax
else: # GPU
x = self.maxpool4(x)
x = self.block5a(x)
x = self.block5b(x)
@ -138,5 +171,4 @@ class GoogleNet(nn.Cell):
x = self.flatten(x)
x = self.classifier(x)
_ = argmax
return x

29
model_zoo/official/cv/googlenet/train.py
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@ -25,7 +25,7 @@ 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.communication.management import init, get_rank
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.model import Model, ParallelMode
@ -38,7 +38,6 @@ from src.googlenet import GoogleNet
random.seed(1)
np.random.seed(1)
def lr_steps(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
"""Set learning rate."""
lr_each_step = []
@ -65,23 +64,36 @@ if __name__ == '__main__':
parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
args_opt = parser.parse_args()
device_target = cfg.device_target
context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
device_num = int(os.environ.get("DEVICE_NUM", 1))
if device_target == "Ascend":
if args_opt.device_id is not None:
context.set_context(device_id=args_opt.device_id)
else:
context.set_context(device_id=cfg.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()
elif device_target == "GPU":
init("nccl")
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)
else:
raise ValueError("Unsupport platform.")
dataset = create_dataset(cfg.data_path, 1)
batch_num = dataset.get_dataset_size()
net = GoogleNet(num_classes=cfg.num_classes)
net = GoogleNet(num_classes=cfg.num_classes, platform=device_target)
# Continue training if set pre_trained to be True
if cfg.pre_trained:
param_dict = load_checkpoint(cfg.checkpoint_path)
@ -90,12 +102,19 @@ if __name__ == '__main__':
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)
if device_target == "Ascend":
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
ckpt_save_dir = "./"
else: # GPU
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=True, loss_scale_manager=None)
ckpt_save_dir = "./ckpt_" + str(get_rank()) + "/"
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_googlenet_cifar10", directory="./", config=config_ck)
ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory=ckpt_save_dir, config=config_ck)
loss_cb = LossMonitor()
model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
print("train success")