122 lines
4.2 KiB
122 lines
4.2 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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"""VGG16 benchmark in Fluid"""
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from __future__ import print_function
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import sys
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import time
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import numpy as np
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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 argparse
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import functools
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import os
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def vgg16_bn_drop(input):
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def conv_block(input, num_filter, groups, dropouts):
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return fluid.nets.img_conv_group(
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input=input,
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pool_size=2,
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pool_stride=2,
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conv_num_filter=[num_filter] * groups,
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conv_filter_size=3,
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conv_act='relu',
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conv_with_batchnorm=True,
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conv_batchnorm_drop_rate=dropouts,
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pool_type='max')
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conv1 = conv_block(input, 64, 2, [0.3, 0])
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conv2 = conv_block(conv1, 128, 2, [0.4, 0])
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conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
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conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
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conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
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drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
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fc1 = fluid.layers.fc(input=drop, size=512, act=None)
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bn = fluid.layers.batch_norm(input=fc1, act='relu')
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drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
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fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
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return fc2
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def get_model(args):
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if args.data_set == "cifar10":
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classdim = 10
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if args.data_format == 'NCHW':
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data_shape = [3, 32, 32]
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else:
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data_shape = [32, 32, 3]
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else:
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classdim = 102
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if args.data_format == 'NCHW':
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data_shape = [3, 224, 224]
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else:
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data_shape = [224, 224, 3]
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if args.use_reader_op:
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filelist = [
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os.path.join(args.data_path, f) for f in os.listdir(args.data_path)
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]
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data_file = fluid.layers.open_files(
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filenames=filelist,
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shapes=[[-1] + data_shape, (-1, 1)],
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lod_levels=[0, 0],
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dtypes=["float32", "int64"],
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thread_num=args.gpus,
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pass_num=args.pass_num)
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data_file = fluid.layers.double_buffer(
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fluid.layers.batch(
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data_file, batch_size=args.batch_size))
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images, label = fluid.layers.read_file(data_file)
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else:
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images = fluid.layers.data(
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name='data', shape=data_shape, dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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# Train program
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net = vgg16_bn_drop(images)
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predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
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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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# Evaluator
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batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
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batch_acc = fluid.layers.accuracy(
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input=predict, label=label, total=batch_size_tensor)
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# inference program
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inference_program = fluid.default_main_program().clone()
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with fluid.program_guard(inference_program):
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inference_program = fluid.io.get_inference_program(
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target_vars=[batch_acc, batch_size_tensor])
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# Optimization
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optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
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# data reader
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.cifar.train10()
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if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
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buf_size=5120),
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batch_size=args.batch_size * args.gpus)
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test_reader = paddle.batch(
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paddle.dataset.cifar.test10()
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if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
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batch_size=args.batch_size)
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return avg_cost, inference_program, optimizer, train_reader, test_reader, batch_acc
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