Tao Luo
8 years ago
committed by
GitHub
commit
330e9929ec
@ -0,0 +1,51 @@
|
||||
set -e
|
||||
|
||||
unset OMP_NUM_THREADS MKL_NUM_THREADS
|
||||
export OMP_DYNAMIC="FALSE"
|
||||
export KMP_AFFINITY="granularity=fine,compact,0,0"
|
||||
|
||||
function train() {
|
||||
topology=$1
|
||||
bs=$2
|
||||
use_mkldnn=$3
|
||||
if [ $3 == "True" ]; then
|
||||
use_mkldnn=$3
|
||||
thread=1
|
||||
log="logs/${topology}-mkldnn-${bs}.log"
|
||||
elif [ $3 == "False" ]; then
|
||||
use_mkldnn=$3
|
||||
thread=`nproc`
|
||||
log="logs/${topology}-${thread}mklml-${bs}.log"
|
||||
else
|
||||
echo "Wrong input $3, use True or False."
|
||||
fi
|
||||
args="batch_size=${bs}"
|
||||
config="${topology}.py"
|
||||
paddle train --job=time \
|
||||
--config=$config \
|
||||
--use_mkldnn=$use_mkldnn \
|
||||
--use_gpu=False \
|
||||
--trainer_count=$thread \
|
||||
--log_period=10 \
|
||||
--test_period=100 \
|
||||
--config_args=$args \
|
||||
2>&1 | tee ${log}
|
||||
}
|
||||
|
||||
if [ ! -d "train.list" ]; then
|
||||
echo " " > train.list
|
||||
fi
|
||||
if [ ! -d "logs" ]; then
|
||||
mkdir logs
|
||||
fi
|
||||
|
||||
#========= mkldnn =========#
|
||||
# vgg
|
||||
train vgg 64 True
|
||||
train vgg 128 True
|
||||
train vgg 256 True
|
||||
|
||||
#========== mklml ===========#
|
||||
train vgg 64 False
|
||||
train vgg 128 False
|
||||
train vgg 256 False
|
||||
@ -0,0 +1,103 @@
|
||||
#!/usr/bin/env python
|
||||
from paddle.trainer_config_helpers import *
|
||||
|
||||
height = 224
|
||||
width = 224
|
||||
num_class = 1000
|
||||
batch_size = get_config_arg('batch_size', int, 64)
|
||||
layer_num = get_config_arg('layer_num', int, 19)
|
||||
|
||||
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
|
||||
define_py_data_sources2(
|
||||
"train.list", None, module="provider", obj="process", args=args)
|
||||
|
||||
settings(
|
||||
batch_size=batch_size,
|
||||
learning_rate=0.01 / batch_size,
|
||||
learning_method=MomentumOptimizer(0.9),
|
||||
regularization=L2Regularization(0.0005 * batch_size))
|
||||
|
||||
img = data_layer(name='image', size=height * width * 3)
|
||||
|
||||
|
||||
def vgg_network(vgg_num=3):
|
||||
tmp = img_conv_group(
|
||||
input=img,
|
||||
num_channels=3,
|
||||
conv_padding=1,
|
||||
conv_num_filter=[64, 64],
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling())
|
||||
|
||||
tmp = img_conv_group(
|
||||
input=tmp,
|
||||
conv_num_filter=[128, 128],
|
||||
conv_padding=1,
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling(),
|
||||
pool_size=2)
|
||||
|
||||
channels = []
|
||||
for i in range(vgg_num):
|
||||
channels.append(256)
|
||||
tmp = img_conv_group(
|
||||
input=tmp,
|
||||
conv_num_filter=channels,
|
||||
conv_padding=1,
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling(),
|
||||
pool_size=2)
|
||||
channels = []
|
||||
for i in range(vgg_num):
|
||||
channels.append(512)
|
||||
tmp = img_conv_group(
|
||||
input=tmp,
|
||||
conv_num_filter=channels,
|
||||
conv_padding=1,
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling(),
|
||||
pool_size=2)
|
||||
tmp = img_conv_group(
|
||||
input=tmp,
|
||||
conv_num_filter=channels,
|
||||
conv_padding=1,
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling(),
|
||||
pool_size=2)
|
||||
|
||||
tmp = fc_layer(
|
||||
input=tmp,
|
||||
size=4096,
|
||||
act=ReluActivation(),
|
||||
layer_attr=ExtraAttr(drop_rate=0.5))
|
||||
|
||||
tmp = fc_layer(
|
||||
input=tmp,
|
||||
size=4096,
|
||||
act=ReluActivation(),
|
||||
layer_attr=ExtraAttr(drop_rate=0.5))
|
||||
|
||||
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
|
||||
|
||||
|
||||
if layer_num == 16:
|
||||
vgg = vgg_network(3)
|
||||
elif layer_num == 19:
|
||||
vgg = vgg_network(4)
|
||||
else:
|
||||
print("Wrong layer number.")
|
||||
|
||||
lab = data_layer('label', num_class)
|
||||
loss = cross_entropy(input=vgg, label=lab)
|
||||
outputs(loss)
|
||||
@ -0,0 +1,63 @@
|
||||
# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved
|
||||
#
|
||||
# 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.
|
||||
|
||||
from paddle.trainer_config_helpers import *
|
||||
|
||||
################################### Data Configuration ###################################
|
||||
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
|
||||
################################### Algorithm Configuration ###################################
|
||||
settings(batch_size = 1000,
|
||||
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
|
||||
################################### Network Configuration ###################################
|
||||
data = data_layer(name ="input", size=784)
|
||||
|
||||
tmp = img_conv_layer(input=data,
|
||||
num_channels=1,
|
||||
filter_size=3,
|
||||
num_filters=32,
|
||||
padding=1,
|
||||
shared_biases=True,
|
||||
act=ReluActivation())
|
||||
|
||||
tmp = img_pool_layer(input=tmp,
|
||||
pool_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
pool_type=AvgPooling())
|
||||
|
||||
tmp = img_conv_layer(input=tmp,
|
||||
filter_size=3,
|
||||
num_filters=64,
|
||||
padding=1,
|
||||
shared_biases=True,
|
||||
act=ReluActivation())
|
||||
|
||||
tmp = img_pool_layer(input=tmp,
|
||||
pool_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
pool_type=MaxPooling())
|
||||
|
||||
tmp = fc_layer(input=tmp, size=64,
|
||||
bias_attr=True,
|
||||
act=ReluActivation())
|
||||
|
||||
output = fc_layer(input=tmp, size=10,
|
||||
bias_attr=True,
|
||||
act=SoftmaxActivation())
|
||||
|
||||
lbl = data_layer(name ="label", size=10)
|
||||
|
||||
cost = classification_cost(input=output, label=lbl)
|
||||
outputs(cost)
|
||||
Loading…
Reference in new issue