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242 lines
8.3 KiB
242 lines
8.3 KiB
# Copyright (c) 2018 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 unittest
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import paddle.fluid as fluid
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import paddle.v2 as paddle
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import paddle.v2.dataset.mnist as mnist
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import paddle.v2.dataset.flowers as flowers
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import numpy
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def simple_fc_net():
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reader = fluid.layers.open_recordio_file(
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filename='./mnist.recordio',
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shapes=[[-1, 784], [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'])
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img, label = fluid.layers.read_file(reader)
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hidden = img
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for _ in xrange(4):
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hidden = fluid.layers.fc(
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hidden,
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size=200,
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act='tanh',
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=1.0)))
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prediction = fluid.layers.fc(hidden, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def fc_with_batchnorm():
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reader = fluid.layers.open_recordio_file(
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filename='./mnist.recordio',
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shapes=[[-1, 784], [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'])
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img, label = fluid.layers.read_file(reader)
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hidden = img
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for _ in xrange(1):
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hidden = fluid.layers.fc(
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hidden,
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size=200,
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act='tanh',
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=1.0)))
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hidden = fluid.layers.batch_norm(input=hidden)
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prediction = fluid.layers.fc(hidden, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def squeeze_excitation(input, num_channels, reduction_ratio):
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# pool = fluid.layers.pool2d(
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# input=input, pool_size=0, pool_type='avg', global_pooling=True)
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conv = input
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shape = conv.shape
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reshape = fluid.layers.reshape(
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x=conv, shape=[-1, shape[1], shape[2] * shape[3]])
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pool = fluid.layers.reduce_mean(input=reshape, dim=2)
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squeeze = fluid.layers.fc(input=pool,
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size=num_channels / reduction_ratio,
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act='relu')
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excitation = fluid.layers.fc(input=squeeze,
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size=num_channels,
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act='sigmoid')
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scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
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return scale
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def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1,
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act=None):
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) / 2,
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groups=groups,
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act=None,
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bias_attr=False)
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return fluid.layers.batch_norm(input=conv, act=act, momentum=0.1)
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def shortcut(input, ch_out, stride):
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ch_in = input.shape[1]
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if ch_in != ch_out:
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if stride == 1:
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filter_size = 1
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else:
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filter_size = 3
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return conv_bn_layer(input, ch_out, filter_size, stride)
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else:
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return input
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def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio):
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# The number of first 1x1 convolutional channels for each bottleneck build block
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# was halved to reduce the compution cost.
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conv0 = conv_bn_layer(
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input=input, num_filters=num_filters, filter_size=1, act='relu')
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conv1 = conv_bn_layer(
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input=conv0,
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num_filters=num_filters * 2,
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filter_size=3,
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stride=stride,
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groups=cardinality,
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act='relu')
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conv2 = conv_bn_layer(
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input=conv1, num_filters=num_filters * 2, filter_size=1, act=None)
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scale = squeeze_excitation(
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input=conv2,
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num_channels=num_filters * 2,
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reduction_ratio=reduction_ratio)
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short = shortcut(input, num_filters * 2, stride)
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return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
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def SE_ResNeXt152():
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reader = fluid.layers.open_recordio_file(
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filename='./flowers.recordio',
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shapes=[[-1, 3, 224, 224], [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'])
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img, label = fluid.layers.read_file(reader)
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conv = conv_bn_layer(
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input=img, num_filters=64, filter_size=3, stride=2, act='relu')
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conv = conv_bn_layer(
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input=conv, num_filters=64, filter_size=3, stride=1, act='relu')
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conv = conv_bn_layer(
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input=conv, num_filters=128, filter_size=3, stride=1, act='relu')
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conv = fluid.layers.pool2d(
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input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
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cardinality = 64
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reduction_ratio = 16
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depth = [3, 8, 36, 3]
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num_filters = [128, 256, 512, 1024]
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for block in range(len(depth)):
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for i in range(depth[block]):
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conv = bottleneck_block(
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input=conv,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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cardinality=cardinality,
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reduction_ratio=reduction_ratio)
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shape = conv.shape
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reshape = fluid.layers.reshape(
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x=conv, shape=[-1, shape[1], shape[2] * shape[3]])
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pool = fluid.layers.reduce_mean(input=reshape, dim=2)
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dropout = fluid.layers.dropout(x=pool, dropout_prob=0.2)
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# Classifier layer:
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prediction = fluid.layers.fc(input=dropout, size=1000, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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class ParallelExecutor(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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# Convert mnist to recordio file
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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reader = paddle.batch(mnist.train(), batch_size=32)
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feeder = fluid.DataFeeder(
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feed_list=[ # order is image and label
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fluid.layers.data(
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name='image', shape=[784]),
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fluid.layers.data(
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name='label', shape=[1], dtype='int64'),
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],
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place=fluid.CPUPlace())
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fluid.recordio_writer.convert_reader_to_recordio_file(
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'./mnist.recordio', reader, feeder)
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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reader = paddle.batch(flowers.train(), batch_size=4)
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feeder = fluid.DataFeeder(
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feed_list=[
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fluid.layers.data(
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name='image', shape=[3, 224, 224]),
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fluid.layers.data(
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name='label', shape=[1], dtype='int64'),
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],
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place=fluid.CPUPlace())
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fluid.recordio_writer.convert_reader_to_recordio_file(
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"./flowers.recordio", reader, feeder)
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def test_simple_fc(self):
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self.check_network_convergence(simple_fc_net)
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def test_batchnorm_fc(self):
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self.check_network_convergence(fc_with_batchnorm)
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def check_network_convergence(self, method, memory_opt=True, iter=10):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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loss = method()
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adam = fluid.optimizer.Adam()
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adam.minimize(loss)
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if memory_opt:
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fluid.memory_optimize(main)
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exe = fluid.ParallelExecutor(loss_name=loss.name, use_cuda=True)
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first_loss, = exe.run([loss.name])
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first_loss = numpy.array(first_loss)
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for i in xrange(iter):
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exe.run([])
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last_loss, = exe.run([loss.name])
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last_loss = numpy.array(last_loss)
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print first_loss, last_loss
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self.assertGreater(first_loss[0], last_loss[0])
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def test_resnet(self):
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self.check_network_convergence(SE_ResNeXt152, iter=20)
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