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261 lines
9.0 KiB
261 lines
9.0 KiB
9 years ago
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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.trainer_config_helpers import *
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"""
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paper: https://arxiv.org/abs/1512.03385
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"""
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is_test = get_config_arg("is_test", bool, False)
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is_predict = get_config_arg("is_predict", bool, False)
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data_provider = get_config_arg("data_provider", bool, True)
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layer_num = get_config_arg("layer_num", int, 50)
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if not is_predict and data_provider:
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train_list = 'train.list' if not is_test else None
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# mean.meta is mean file of ImageNet dataset.
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# mean.meta size : 3 x 224 x 224.
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# If you use three mean value, set like:
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# "mean_value:103.939,116.779,123.68;"
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args={
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'mean_meta': "model/mean_meta_224/mean.meta",
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'image_size': 224, 'crop_size': 224,
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'color': True,'swap_channel:': [2, 1, 0]}
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define_py_data_sources2(train_list,
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'example/test.list',
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module="example.image_list_provider",
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obj="processData",
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args=args)
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batch_size = 1
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learning_rate = 0.1 / batch_size
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momentum = 0.9
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weight_decay = 0.0001 * batch_size
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default_momentum(momentum)
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default_decay_rate(weight_decay)
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Settings(
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algorithm='sgd',
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batch_size=batch_size,
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learning_rate=learning_rate,
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# set the appropriate parameters according your schedule
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learning_method='momentum',
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learning_rate_decay_a=0.5,
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learning_rate_decay_b=1200000 * 10,
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learning_rate_schedule="discexp",
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)
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def conv_bn_layer(name, input, filter_size, num_filters,
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stride, padding, 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(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(name=name + "_bn",
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input=tmp,
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act=active_type,
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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(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(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(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(name=name + "_addto",
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input=[input, last_name],
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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(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(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(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(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(name=name + "_addto",
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input=[branch1, last_name],
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act=ReluActivation())
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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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img = data_layer(name='input', size=224 * 224 * 3)
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tmp = conv_bn_layer("conv1", 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(name="res2_1",
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input=tmp,
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num_filters1=64,
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num_filters2=256,
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stride=1)
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for i in xrange(2, res2_num + 1, 1):
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tmp = bottleneck_block(name="res2_" + str(i),
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input=tmp,
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num_filters1=64,
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num_filters2=256)
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# conv3_x: 28x28
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tmp = mid_projection(name="res3_1",
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input=tmp,
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num_filters1=128,
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num_filters2=512)
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for i in xrange(2, res3_num + 1, 1):
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tmp = bottleneck_block(name="res3_" + str(i),
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input=tmp, num_filters1=128,
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num_filters2=512)
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# conv4_x: 14x14
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tmp = mid_projection(name="res4_1", input=tmp,
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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(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(name="res5_1", input=tmp,
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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(name="res5_" + str(i),
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input=tmp, num_filters1=512,
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num_filters2=2048)
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tmp = img_pool_layer(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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output = fc_layer(name='output',
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input=tmp,
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size=1000,
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act=SoftmaxActivation())
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if not is_predict:
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classification_cost(input=output, label=data_layer(name='label',
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size=1))
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def res_net_50():
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deep_res_net(3, 4, 6, 3)
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def res_net_101():
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deep_res_net(3, 4, 23, 3)
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def res_net_152():
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deep_res_net(3, 8, 36, 3)
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if not is_predict:
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Inputs("input", "label")
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else:
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Inputs("input")
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# Outputs("cost-softmax" if not is_predict else "output")
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Outputs("res5_3_branch2c_conv", "res5_3_branch2c_bn")
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if layer_num == 50:
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res_net_50()
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elif layer_num == 101:
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res_net_101()
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elif layer_num == 152:
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res_net_152()
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else:
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print("Wrong layer number.")
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