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update reset script for benchmark
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‎benchmark/paddle/image/resnet.py

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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, 50)
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is_test = get_config_arg("is_test", bool, False)
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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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#######################Network Configuration #############
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def conv_bn_layer(name,
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input,
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filter_size,
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num_filters,
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stride,
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padding,
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channels=None,
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active_type=ReluActivation()):
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"""
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A wrapper for conv layer with batch normalization layers.
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Note:
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conv layer has no activation.
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"""
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tmp = img_conv_layer(
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name=name + "_conv",
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input=input,
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filter_size=filter_size,
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num_channels=channels,
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num_filters=num_filters,
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stride=stride,
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padding=padding,
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act=LinearActivation(),
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bias_attr=False)
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return batch_norm_layer(
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name=name + "_bn", input=tmp, act=active_type, use_global_stats=is_test)
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def bottleneck_block(name, input, num_filters1, num_filters2):
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"""
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A wrapper for bottlenect building block in ResNet.
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Last conv_bn_layer has no activation.
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Addto layer has activation of relu.
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"""
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last_name = conv_bn_layer(
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name=name + '_branch2a',
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input=input,
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filter_size=1,
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num_filters=num_filters1,
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stride=1,
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padding=0)
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last_name = conv_bn_layer(
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name=name + '_branch2b',
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input=last_name,
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filter_size=3,
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num_filters=num_filters1,
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stride=1,
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padding=1)
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last_name = conv_bn_layer(
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name=name + '_branch2c',
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input=last_name,
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filter_size=1,
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num_filters=num_filters2,
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stride=1,
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padding=0,
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active_type=LinearActivation())
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return addto_layer(
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name=name + "_addto", input=[input, last_name], act=ReluActivation())
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def mid_projection(name, input, num_filters1, num_filters2, stride=2):
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"""
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A wrapper for middile projection in ResNet.
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projection shortcuts are used for increasing dimensions,
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and other shortcuts are identity
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branch1: projection shortcuts are used for increasing
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dimensions, has no activation.
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branch2x: bottleneck building block, shortcuts are identity.
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"""
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# stride = 2
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branch1 = conv_bn_layer(
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name=name + '_branch1',
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input=input,
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filter_size=1,
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num_filters=num_filters2,
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stride=stride,
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padding=0,
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active_type=LinearActivation())
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last_name = conv_bn_layer(
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name=name + '_branch2a',
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input=input,
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filter_size=1,
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num_filters=num_filters1,
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stride=stride,
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padding=0)
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last_name = conv_bn_layer(
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name=name + '_branch2b',
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input=last_name,
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filter_size=3,
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num_filters=num_filters1,
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stride=1,
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padding=1)
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last_name = conv_bn_layer(
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name=name + '_branch2c',
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input=last_name,
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filter_size=1,
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num_filters=num_filters2,
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stride=1,
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padding=0,
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active_type=LinearActivation())
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return addto_layer(
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name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
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img = data_layer(name='image', size=height * width * 3)
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def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
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"""
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A wrapper for 50,101,152 layers of ResNet.
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res2_num: number of blocks stacked in conv2_x
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res3_num: number of blocks stacked in conv3_x
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res4_num: number of blocks stacked in conv4_x
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res5_num: number of blocks stacked in conv5_x
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"""
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# For ImageNet
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# conv1: 112x112
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tmp = conv_bn_layer(
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"conv1",
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input=img,
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filter_size=7,
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channels=3,
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num_filters=64,
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stride=2,
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padding=3)
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tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
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# conv2_x: 56x56
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tmp = mid_projection(
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name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
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for i in xrange(2, res2_num + 1, 1):
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tmp = bottleneck_block(
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name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
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# conv3_x: 28x28
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tmp = mid_projection(
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name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
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for i in xrange(2, res3_num + 1, 1):
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tmp = bottleneck_block(
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name="res3_" + str(i),
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input=tmp,
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num_filters1=128,
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num_filters2=512)
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# conv4_x: 14x14
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tmp = mid_projection(
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name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
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for i in xrange(2, res4_num + 1, 1):
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tmp = bottleneck_block(
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name="res4_" + str(i),
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input=tmp,
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num_filters1=256,
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num_filters2=1024)
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# conv5_x: 7x7
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tmp = mid_projection(
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name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
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for i in xrange(2, res5_num + 1, 1):
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tmp = bottleneck_block(
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name="res5_" + str(i),
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input=tmp,
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num_filters1=512,
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num_filters2=2048)
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tmp = img_pool_layer(
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name='avgpool',
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input=tmp,
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pool_size=7,
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stride=1,
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pool_type=AvgPooling())
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return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
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if layer_num == 50:
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resnet = deep_res_net(3, 4, 6, 3)
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elif layer_num == 101:
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resnet = deep_res_net(3, 4, 23, 3)
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elif layer_num == 152:
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resnet = deep_res_net(3, 8, 36, 3)
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else:
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print("Wrong layer number.")
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lbl = data_layer(name="label", size=num_class)
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loss = cross_entropy(name='loss', input=resnet, label=lbl)
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inputs(img, lbl)
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outputs(loss)

‎benchmark/paddle/image/run_mkldnn.sh

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@@ -3,24 +3,26 @@ 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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# TODO(TJ): auto 1.0 or 0,0 for HT on or off
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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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layer_num=$2
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bs=$3
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use_mkldnn=$4
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if [ $4 == "True" ]; then
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thread=1
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log="logs/${topology}-mkldnn-${bs}.log"
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elif [ $3 == "False" ]; then
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log="logs/${topology}-${layer_num}-mkldnn-${bs}.log"
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elif [ $4 == "False" ]; then
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thread=`nproc`
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# each trainer_count use only 1 core to avoid conflict
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export OMP_NUM_THREADS=1
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export MKL_NUM_THREADS=1
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log="logs/${topology}-${thread}mklml-${bs}.log"
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log="logs/${topology}-${layer_num}-${thread}mklml-${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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args="batch_size=${bs},layer_num=${layer_num}"
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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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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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for use_mkldnn in True False; do
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for batchsize in 64 128 256; do
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# vgg-19 and vgg-16
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train vgg 19 $batchsize $use_mkldnn
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train vgg 16 $batchsize $use_mkldnn
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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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# resnet-50, 101 and 152
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train resnet 50 $batchsize $use_mkldnn
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train resnet 101 $batchsize $use_mkldnn
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train resnet 152 $batchsize $use_mkldnn
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done
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done

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