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75 lines
2.4 KiB
75 lines
2.4 KiB
# Copyright (c) 2016 PaddlePaddle Authors. 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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import paddle.v2 as paddle
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__all__ = ['resnet_cifar10']
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def conv_bn_layer(input,
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ch_out,
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filter_size,
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stride,
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padding,
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active_type=paddle.activation.Relu(),
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ch_in=None):
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tmp = paddle.layer.img_conv(
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input=input,
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filter_size=filter_size,
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num_channels=ch_in,
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num_filters=ch_out,
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stride=stride,
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padding=padding,
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act=paddle.activation.Linear(),
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bias_attr=False)
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return paddle.layer.batch_norm(input=tmp, act=active_type)
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def shortcut(ipt, n_in, n_out, stride):
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if n_in != n_out:
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return conv_bn_layer(ipt, n_out, 1, stride, 0,
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paddle.activation.Linear())
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else:
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return ipt
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def basicblock(ipt, ch_out, stride):
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ch_in = ch_out * 2
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tmp = conv_bn_layer(ipt, ch_out, 3, stride, 1)
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tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, paddle.activation.Linear())
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short = shortcut(ipt, ch_in, ch_out, stride)
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return paddle.layer.addto(input=[tmp, short], act=paddle.activation.Relu())
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def layer_warp(block_func, ipt, features, count, stride):
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tmp = block_func(ipt, features, stride)
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for i in range(1, count):
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tmp = block_func(tmp, features, 1)
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return tmp
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def resnet_cifar10(ipt, depth=32):
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# depth should be one of 20, 32, 44, 56, 110, 1202
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assert (depth - 2) % 6 == 0
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n = (depth - 2) / 6
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nStages = {16, 64, 128}
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conv1 = conv_bn_layer(
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ipt, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1)
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res1 = layer_warp(basicblock, conv1, 16, n, 1)
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res2 = layer_warp(basicblock, res1, 32, n, 2)
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res3 = layer_warp(basicblock, res2, 64, n, 2)
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pool = paddle.layer.img_pool(
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input=res3, pool_size=8, stride=1, pool_type=paddle.pooling.Avg())
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return pool
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