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266 lines
9.0 KiB
266 lines
9.0 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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from __future__ import print_function
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import numpy as np
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import argparse
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import time
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import math
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.profiler as profiler
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from paddle.fluid import core
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import unittest
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from multiprocessing import Process
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import os
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import sys
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import signal
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from test_dist_base import TestDistRunnerBase, runtime_main
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# Fix seed for test
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fluid.default_startup_program().random_seed = 1
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fluid.default_main_program().random_seed = 1
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train_parameters = {
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"input_size": [3, 224, 224],
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"input_mean": [0.485, 0.456, 0.406],
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"input_std": [0.229, 0.224, 0.225],
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"learning_strategy": {
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"name": "piecewise_decay",
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"epochs": [30, 60, 90],
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"steps": [0.1, 0.01, 0.001, 0.0001]
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}
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}
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class SE_ResNeXt():
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def __init__(self, layers=50):
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self.params = train_parameters
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self.layers = layers
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def net(self, input, class_dim=1000):
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layers = self.layers
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supported_layers = [50, 101, 152]
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assert layers in supported_layers, \
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"supported layers are {} but input layer is {}".format(supported_layers, layers)
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if layers == 50:
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cardinality = 32
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reduction_ratio = 16
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depth = [3, 4, 6, 3]
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num_filters = [128, 256, 512, 1024]
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conv = self.conv_bn_layer(
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input=input,
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num_filters=64,
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filter_size=7,
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stride=2,
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act='relu')
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conv = fluid.layers.pool2d(
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input=conv,
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pool_size=3,
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pool_stride=2,
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pool_padding=1,
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pool_type='max')
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elif layers == 101:
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cardinality = 32
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reduction_ratio = 16
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depth = [3, 4, 23, 3]
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num_filters = [128, 256, 512, 1024]
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conv = self.conv_bn_layer(
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input=input,
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num_filters=64,
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filter_size=7,
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stride=2,
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act='relu')
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conv = fluid.layers.pool2d(
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input=conv,
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pool_size=3,
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pool_stride=2,
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pool_padding=1,
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pool_type='max')
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elif layers == 152:
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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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conv = self.conv_bn_layer(
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input=input,
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num_filters=64,
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filter_size=3,
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stride=2,
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act='relu')
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conv = self.conv_bn_layer(
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input=conv, num_filters=64, filter_size=3, stride=1, act='relu')
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conv = self.conv_bn_layer(
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input=conv,
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num_filters=128,
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filter_size=3,
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stride=1,
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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, \
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pool_type='max')
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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 = self.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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pool = fluid.layers.pool2d(
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input=conv, pool_size=7, pool_type='avg', global_pooling=True)
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drop = fluid.layers.dropout(x=pool, dropout_prob=0.2)
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stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
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out = fluid.layers.fc(
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input=drop,
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size=class_dim,
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act='softmax',
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.05)))
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return out
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def shortcut(self, input, ch_out, stride):
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ch_in = input.shape[1]
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if ch_in != ch_out or stride != 1:
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filter_size = 1
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return self.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(self, input, num_filters, stride, cardinality,
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reduction_ratio):
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conv0 = self.conv_bn_layer(
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input=input, num_filters=num_filters, filter_size=1, act='relu')
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conv1 = self.conv_bn_layer(
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input=conv0,
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num_filters=num_filters,
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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 = self.conv_bn_layer(
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input=conv1, num_filters=num_filters * 2, filter_size=1, act=None)
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scale = self.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 = self.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 conv_bn_layer(self,
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input,
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num_filters,
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filter_size,
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stride=1,
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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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# avoid pserver CPU init differs from GPU
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.05)),
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bias_attr=False)
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return fluid.layers.batch_norm(input=conv, act=act)
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def squeeze_excitation(self, 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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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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squeeze = fluid.layers.fc(
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input=pool,
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size=num_channels // reduction_ratio,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.05)),
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act='relu')
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stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
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excitation = fluid.layers.fc(
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input=squeeze,
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size=num_channels,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.05)),
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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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class DistSeResneXt2x2(TestDistRunnerBase):
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def get_model(self, batch_size=2, use_dgc=False):
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# Input data
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image = fluid.layers.data(
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name="data", shape=[3, 224, 224], dtype='float32')
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label = fluid.layers.data(name="int64", shape=[1], dtype='int64')
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# Train program
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model = SE_ResNeXt(layers=50)
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out = model.net(input=image, class_dim=102)
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cost = fluid.layers.cross_entropy(input=out, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
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acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
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# Evaluator
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test_program = fluid.default_main_program().clone(for_test=True)
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# Optimization
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total_images = 6149 # flowers
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epochs = [30, 60, 90]
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step = int(total_images / batch_size + 1)
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bd = [step * e for e in epochs]
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base_lr = 0.1
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lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
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if not use_dgc:
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optimizer = fluid.optimizer.Momentum(
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learning_rate=fluid.layers.piecewise_decay(
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boundaries=bd, values=lr),
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momentum=0.9,
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regularization=fluid.regularizer.L2Decay(1e-4))
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else:
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optimizer = fluid.optimizer.DGCMomentumOptimizer(
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learning_rate=fluid.layers.piecewise_decay(
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boundaries=bd, values=lr),
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momentum=0.9,
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rampup_begin_step=0,
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regularization=fluid.regularizer.L2Decay(1e-4))
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optimizer.minimize(avg_cost)
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# Reader
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train_reader = paddle.batch(
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paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size)
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test_reader = paddle.batch(
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paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size)
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return test_program, avg_cost, train_reader, test_reader, acc_top1, out
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if __name__ == "__main__":
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runtime_main(DistSeResneXt2x2)
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