dzhwinter
8 years ago
commit
4118782b31
@ -0,0 +1,51 @@
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set -e
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function train() {
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unset OMP_NUM_THREADS MKL_NUM_THREADS
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export OMP_DYNAMIC="FALSE"
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export KMP_AFFINITY="granularity=fine,compact,0,0"
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topology=$1
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bs=$2
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use_mkldnn=$3
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if [ $3 == "True" ]; then
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thread=1
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log="logs/${topology}-mkldnn-${bs}.log"
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elif [ $3 == "False" ]; then
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thread=`nproc`
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# each trainer_count use only 1 core to avoid conflict
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export OMP_NUM_THREADS=1
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export MKL_NUM_THREADS=1
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log="logs/${topology}-${thread}mklml-${bs}.log"
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else
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echo "Wrong input $3, use True or False."
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exit 0
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fi
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args="batch_size=${bs}"
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config="${topology}.py"
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paddle train --job=time \
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--config=$config \
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--use_mkldnn=$use_mkldnn \
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--use_gpu=False \
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--trainer_count=$thread \
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--log_period=10 \
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--test_period=100 \
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--config_args=$args \
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2>&1 | tee ${log}
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}
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if [ ! -d "train.list" ]; then
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echo " " > train.list
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fi
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if [ ! -d "logs" ]; then
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mkdir logs
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fi
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#========== mkldnn ==========#
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train vgg 64 True
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train vgg 128 True
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train vgg 256 True
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#========== mklml ===========#
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train vgg 64 False
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train vgg 128 False
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train vgg 256 False
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@ -0,0 +1,103 @@
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#!/usr/bin/env python
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from paddle.trainer_config_helpers import *
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height = 224
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width = 224
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num_class = 1000
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batch_size = get_config_arg('batch_size', int, 64)
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layer_num = get_config_arg('layer_num', int, 19)
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args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
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define_py_data_sources2(
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"train.list", None, module="provider", obj="process", args=args)
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settings(
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batch_size=batch_size,
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learning_rate=0.01 / batch_size,
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learning_method=MomentumOptimizer(0.9),
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regularization=L2Regularization(0.0005 * batch_size))
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img = data_layer(name='image', size=height * width * 3)
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def vgg_network(vgg_num=3):
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tmp = img_conv_group(
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input=img,
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num_channels=3,
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conv_padding=1,
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conv_num_filter=[64, 64],
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_size=2,
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pool_stride=2,
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pool_type=MaxPooling())
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tmp = img_conv_group(
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input=tmp,
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conv_num_filter=[128, 128],
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conv_padding=1,
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_stride=2,
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pool_type=MaxPooling(),
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pool_size=2)
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channels = []
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for i in range(vgg_num):
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channels.append(256)
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tmp = img_conv_group(
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input=tmp,
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conv_num_filter=channels,
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conv_padding=1,
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_stride=2,
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pool_type=MaxPooling(),
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pool_size=2)
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channels = []
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for i in range(vgg_num):
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channels.append(512)
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tmp = img_conv_group(
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input=tmp,
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conv_num_filter=channels,
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conv_padding=1,
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_stride=2,
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pool_type=MaxPooling(),
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pool_size=2)
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tmp = img_conv_group(
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input=tmp,
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conv_num_filter=channels,
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conv_padding=1,
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_stride=2,
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pool_type=MaxPooling(),
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pool_size=2)
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tmp = fc_layer(
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input=tmp,
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size=4096,
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act=ReluActivation(),
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layer_attr=ExtraAttr(drop_rate=0.5))
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tmp = fc_layer(
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input=tmp,
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size=4096,
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act=ReluActivation(),
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layer_attr=ExtraAttr(drop_rate=0.5))
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return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
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if layer_num == 16:
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vgg = vgg_network(3)
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elif layer_num == 19:
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vgg = vgg_network(4)
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else:
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print("Wrong layer number.")
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lab = data_layer('label', num_class)
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loss = cross_entropy(input=vgg, label=lab)
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outputs(loss)
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