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264 lines
9.5 KiB
264 lines
9.5 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 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 recordio_converter import imagenet_train, imagenet_test
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from imagenet_reader import train_raw, val
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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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act='relu',
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is_train=True):
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conv1 = fluid.layers.conv2d(
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input=input,
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filter_size=filter_size,
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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=None,
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bias_attr=False)
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return fluid.layers.batch_norm(input=conv1, act=act, is_test=not is_train)
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def shortcut(input, ch_out, stride, is_train=True):
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ch_in = input.shape[1] # if args.data_format == 'NCHW' else input.shape[-1]
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if ch_in != ch_out:
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return conv_bn_layer(
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input, ch_out, 1, stride, 0, None, is_train=is_train)
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else:
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return input
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def basicblock(input, ch_out, stride, is_train=True):
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short = shortcut(input, ch_out, stride, is_train=is_train)
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conv1 = conv_bn_layer(input, ch_out, 3, stride, 1, is_train=is_train)
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conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None, is_train=is_train)
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return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
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def bottleneck(input, ch_out, stride, is_train=True):
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short = shortcut(input, ch_out * 4, stride, is_train=is_train)
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conv1 = conv_bn_layer(input, ch_out, 1, stride, 0, is_train=is_train)
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conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, is_train=is_train)
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conv3 = conv_bn_layer(
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conv2, ch_out * 4, 1, 1, 0, act=None, is_train=is_train)
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return fluid.layers.elementwise_add(x=short, y=conv3, act='relu')
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def layer_warp(block_func, input, ch_out, count, stride):
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res_out = block_func(input, ch_out, stride)
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for i in range(1, count):
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res_out = block_func(res_out, ch_out, 1)
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return res_out
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def resnet_imagenet(input,
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class_dim,
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depth=50,
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data_format='NCHW',
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is_train=True):
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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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conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3)
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pool1 = fluid.layers.pool2d(
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input=conv1, pool_type='avg', pool_size=3, pool_stride=2)
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res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
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res2 = layer_warp(block_func, res1, 128, stages[1], 2)
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res3 = layer_warp(block_func, res2, 256, stages[2], 2)
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res4 = layer_warp(block_func, res3, 512, stages[3], 2)
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pool2 = fluid.layers.pool2d(
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input=res4,
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pool_size=7,
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pool_type='avg',
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pool_stride=1,
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global_pooling=True)
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out = fluid.layers.fc(input=pool2, size=class_dim, act='softmax')
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return out
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def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'):
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assert (depth - 2) % 6 == 0
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n = (depth - 2) // 6
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conv1 = conv_bn_layer(
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input=input, 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 = fluid.layers.pool2d(
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input=res3, pool_size=8, pool_type='avg', pool_stride=1)
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out = fluid.layers.fc(input=pool, size=class_dim, act='softmax')
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return out
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def _model_reader_dshape_classdim(args, is_train):
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model = resnet_cifar10
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reader = None
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if args.data_set == "cifar10":
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class_dim = 10
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if args.data_format == 'NCHW':
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dshape = [3, 32, 32]
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else:
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dshape = [32, 32, 3]
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model = resnet_cifar10
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if is_train:
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reader = paddle.dataset.cifar.train10()
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else:
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reader = paddle.dataset.cifar.test10()
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elif 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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model = resnet_imagenet
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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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model = resnet_imagenet
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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_raw()
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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_raw()
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else:
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reader = val(xmap=False)
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return model, reader, dshape, class_dim
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def get_model(args, is_train, main_prog, startup_prog):
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model, reader, dshape, class_dim = _model_reader_dshape_classdim(args,
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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=('uint8', '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='uint8')
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label = fluid.layers.data(
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name='label', shape=[1], dtype='int64')
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# add imagenet preprocessors
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random_crop = fluid.layers.random_crop(input, dshape)
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casted = fluid.layers.cast(random_crop, 'float32')
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# input is HWC
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trans = fluid.layers.transpose(casted, [0, 3, 1, 2]) / 255.0
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img_mean = fluid.layers.tensor.assign(
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np.array([0.485, 0.456, 0.406]).astype('float32').reshape((3, 1,
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1)))
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img_std = fluid.layers.tensor.assign(
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np.array([0.229, 0.224, 0.225]).astype('float32').reshape((3, 1,
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1)))
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h1 = fluid.layers.elementwise_sub(trans, img_mean, axis=1)
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h2 = fluid.layers.elementwise_div(h1, img_std, axis=1)
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# pre_out = (trans - img_mean) / img_std
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predict = model(h2, class_dim, is_train=is_train)
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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 + 1)
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epochs = [30, 60, 80, 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=base_lr,
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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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reader,
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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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