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238 lines
8.2 KiB
238 lines
8.2 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 absolute_import
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from __future__ import division
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from __future__ import print_function
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import functools
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import numpy as np
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import time
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import os
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import math
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import cProfile, pstats, StringIO
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import paddle.fluid.profiler as profiler
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from imagenet_reader import train, val
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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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"batch_size": 256,
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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 ResNet():
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def __init__(self, layers=50, is_train=True):
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self.params = train_parameters
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self.layers = layers
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self.is_train = is_train
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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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depth = [3, 4, 6, 3]
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elif layers == 101:
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depth = [3, 4, 23, 3]
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elif layers == 152:
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depth = [3, 8, 36, 3]
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num_filters = [64, 128, 256, 512]
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conv = self.conv_bn_layer(
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input=input, num_filters=64, filter_size=7, stride=2, 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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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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pool = fluid.layers.pool2d(
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input=conv, pool_size=7, pool_type='avg', global_pooling=True)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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out = fluid.layers.fc(input=pool,
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size=class_dim,
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act='softmax',
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv,
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stdv)))
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return out
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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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bias_attr=False)
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return fluid.layers.batch_norm(
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input=conv, act=act, is_test=not self.is_train)
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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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return self.conv_bn_layer(input, ch_out, 1, stride)
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else:
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return input
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def bottleneck_block(self, input, num_filters, stride):
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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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act='relu')
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conv2 = self.conv_bn_layer(
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input=conv1, num_filters=num_filters * 4, filter_size=1, act=None)
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short = self.shortcut(input, num_filters * 4, stride)
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return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
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def _model_reader_dshape_classdim(args, is_train):
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model = None
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reader = None
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if args.data_set == "flowers":
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class_dim = 102
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if args.data_format == 'NCHW':
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dshape = [3, 224, 224]
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else:
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dshape = [224, 224, 3]
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if is_train:
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reader = paddle.dataset.flowers.train()
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else:
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reader = paddle.dataset.flowers.test()
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elif args.data_set == "imagenet":
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class_dim = 1000
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if args.data_format == 'NCHW':
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dshape = [3, 224, 224]
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else:
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dshape = [224, 224, 3]
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if not args.data_path:
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raise Exception(
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"Must specify --data_path when training with imagenet")
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if not args.use_reader_op:
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if is_train:
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reader = train()
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else:
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reader = val()
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else:
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if is_train:
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reader = train(xmap=False)
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else:
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reader = val(xmap=False)
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return reader, dshape, class_dim
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def get_model(args, is_train, main_prog, startup_prog):
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reader, dshape, class_dim = _model_reader_dshape_classdim(args, is_train)
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pyreader = None
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trainer_count = int(os.getenv("PADDLE_TRAINERS"))
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with fluid.program_guard(main_prog, startup_prog):
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with fluid.unique_name.guard():
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if args.use_reader_op:
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pyreader = fluid.layers.py_reader(
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capacity=args.batch_size * args.gpus,
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shapes=([-1] + dshape, (-1, 1)),
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dtypes=('float32', 'int64'),
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name="train_reader" if is_train else "test_reader",
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use_double_buffer=True)
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input, label = fluid.layers.read_file(pyreader)
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else:
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input = fluid.layers.data(
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name='data', shape=dshape, dtype='float32')
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label = fluid.layers.data(
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name='label', shape=[1], dtype='int64')
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model = ResNet(is_train=is_train)
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predict = model.net(input, class_dim=class_dim)
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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batch_acc1 = fluid.layers.accuracy(input=predict, label=label, k=1)
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batch_acc5 = fluid.layers.accuracy(input=predict, label=label, k=5)
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# configure optimize
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optimizer = None
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if is_train:
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total_images = 1281167 / trainer_count
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step = int(total_images / (args.batch_size * args.gpus) + 1)
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epochs = [30, 60, 90]
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bd = [step * e for e in epochs]
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base_lr = args.learning_rate
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lr = []
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lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
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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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optimizer.minimize(avg_cost)
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if args.memory_optimize:
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fluid.memory_optimize(main_prog)
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# config readers
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if not args.use_reader_op:
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batched_reader = paddle.batch(
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reader if args.no_random else paddle.reader.shuffle(
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reader, buf_size=5120),
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batch_size=args.batch_size * args.gpus,
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drop_last=True)
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else:
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batched_reader = None
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pyreader.decorate_paddle_reader(
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paddle.batch(
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reader if args.no_random else paddle.reader.shuffle(
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reader, buf_size=5120),
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batch_size=args.batch_size))
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return avg_cost, optimizer, [batch_acc1,
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batch_acc5], batched_reader, pyreader
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