liaogang
8 years ago
parent
f7ecd312c5
commit
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# 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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def event_handler(event):
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if isinstance(event, paddle.event.EndIteration):
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if event.batch_id % 100 == 0:
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print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id,
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event.cost)
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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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print("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 = ipt.num_filters
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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 bottleneck(ipt, ch_out, stride):
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ch_in = ipt.num_filter
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tmp = conv_bn_layer(ipt, ch_out, 1, stride, 0)
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tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1)
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tmp = conv_bn_layer(tmp, ch_out * 4, 1, 1, 0, paddle.activation.Linear())
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short = shortcut(ipt, ch_in, ch_out * 4, 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_imagenet(ipt, depth=50):
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cfg = {
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18: ([2, 2, 2, 1], basicblock),
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34: ([3, 4, 6, 3], basicblock),
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50: ([3, 4, 6, 3], bottleneck),
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101: ([3, 4, 23, 3], bottleneck),
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152: ([3, 8, 36, 3], bottleneck)
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}
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stages, block_func = cfg[depth]
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tmp = conv_bn_layer(
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ipt, ch_in=3, ch_out=64, filter_size=7, stride=2, padding=3)
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tmp = paddle.layer.img_pool(input=tmp, pool_size=3, stride=2)
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tmp = layer_warp(block_func, tmp, 64, stages[0], 1)
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tmp = layer_warp(block_func, tmp, 128, stages[1], 2)
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tmp = layer_warp(block_func, tmp, 256, stages[2], 2)
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tmp = layer_warp(block_func, tmp, 512, stages[3], 2)
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tmp = paddle.layer.img_pool(
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input=tmp, pool_size=7, stride=1, pool_type=paddle.pooling.Avg())
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tmp = paddle.layer.fc(input=tmp, size=1000, act=paddle.activation.Softmax())
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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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def main():
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datadim = 3 * 32 * 32
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classdim = 10
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paddle.init(use_gpu=False, trainer_count=1)
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image = paddle.layer.data(
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name="image", type=paddle.data_type.dense_vector(datadim))
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net = resnet_cifar10(image, depth=32)
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out = paddle.layer.fc(input=net,
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size=classdim,
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act=paddle.activation.Softmax())
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lbl = paddle.layer.data(
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name="label", type=paddle.data_type.integer_value(classdim))
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cost = paddle.layer.classification_cost(input=out, label=lbl)
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parameters = paddle.parameters.create(cost)
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momentum_optimizer = paddle.optimizer.Momentum(
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momentum=0.9,
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regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
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learning_rate=0.1 / 128.0,
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learning_rate_decay_a=0.1,
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learning_rate_decay_b=50000 * 100,
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learning_rate_schedule='discexp',
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batch_size=128)
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trainer = paddle.trainer.SGD(update_equation=momentum_optimizer)
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trainer.train(
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reader=paddle.reader.batched(
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paddle.reader.shuffle(
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paddle.dataset.cifar.train10(), buf_size=3072),
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batch_size=128),
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cost=cost,
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num_passes=1,
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parameters=parameters,
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event_handler=event_handler,
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reader_dict={'image': 0,
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'label': 1}, )
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if __name__ == '__main__':
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main()
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