You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
209 lines
7.3 KiB
209 lines
7.3 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import functools
|
|
import numpy as np
|
|
import time
|
|
import os
|
|
|
|
import cProfile, pstats, StringIO
|
|
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
import paddle.fluid.core as core
|
|
import paddle.fluid.profiler as profiler
|
|
from recordio_converter import imagenet_train, imagenet_test
|
|
|
|
|
|
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
|
|
conv1 = fluid.layers.conv2d(
|
|
input=input,
|
|
filter_size=filter_size,
|
|
num_filters=ch_out,
|
|
stride=stride,
|
|
padding=padding,
|
|
act=None,
|
|
bias_attr=False)
|
|
return fluid.layers.batch_norm(input=conv1, act=act)
|
|
|
|
|
|
def shortcut(input, ch_out, stride):
|
|
ch_in = input.shape[1] # if args.data_format == 'NCHW' else input.shape[-1]
|
|
if ch_in != ch_out:
|
|
return conv_bn_layer(input, ch_out, 1, stride, 0, None)
|
|
else:
|
|
return input
|
|
|
|
|
|
def basicblock(input, ch_out, stride):
|
|
short = shortcut(input, ch_out, stride)
|
|
conv1 = conv_bn_layer(input, ch_out, 3, stride, 1)
|
|
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None)
|
|
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
|
|
|
|
|
|
def bottleneck(input, ch_out, stride):
|
|
short = shortcut(input, ch_out * 4, stride)
|
|
conv1 = conv_bn_layer(input, ch_out, 1, stride, 0)
|
|
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1)
|
|
conv3 = conv_bn_layer(conv2, ch_out * 4, 1, 1, 0, act=None)
|
|
return fluid.layers.elementwise_add(x=short, y=conv3, act='relu')
|
|
|
|
|
|
def layer_warp(block_func, input, ch_out, count, stride):
|
|
res_out = block_func(input, ch_out, stride)
|
|
for i in range(1, count):
|
|
res_out = block_func(res_out, ch_out, 1)
|
|
return res_out
|
|
|
|
|
|
def resnet_imagenet(input, class_dim, depth=50, data_format='NCHW'):
|
|
|
|
cfg = {
|
|
18: ([2, 2, 2, 1], basicblock),
|
|
34: ([3, 4, 6, 3], basicblock),
|
|
50: ([3, 4, 6, 3], bottleneck),
|
|
101: ([3, 4, 23, 3], bottleneck),
|
|
152: ([3, 8, 36, 3], bottleneck)
|
|
}
|
|
stages, block_func = cfg[depth]
|
|
conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3)
|
|
pool1 = fluid.layers.pool2d(
|
|
input=conv1, pool_type='avg', pool_size=3, pool_stride=2)
|
|
res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
|
|
res2 = layer_warp(block_func, res1, 128, stages[1], 2)
|
|
res3 = layer_warp(block_func, res2, 256, stages[2], 2)
|
|
res4 = layer_warp(block_func, res3, 512, stages[3], 2)
|
|
pool2 = fluid.layers.pool2d(
|
|
input=res4,
|
|
pool_size=7,
|
|
pool_type='avg',
|
|
pool_stride=1,
|
|
global_pooling=True)
|
|
out = fluid.layers.fc(input=pool2, size=class_dim, act='softmax')
|
|
return out
|
|
|
|
|
|
def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'):
|
|
assert (depth - 2) % 6 == 0
|
|
|
|
n = (depth - 2) // 6
|
|
|
|
conv1 = conv_bn_layer(
|
|
input=input, ch_out=16, filter_size=3, stride=1, padding=1)
|
|
res1 = layer_warp(basicblock, conv1, 16, n, 1)
|
|
res2 = layer_warp(basicblock, res1, 32, n, 2)
|
|
res3 = layer_warp(basicblock, res2, 64, n, 2)
|
|
pool = fluid.layers.pool2d(
|
|
input=res3, pool_size=8, pool_type='avg', pool_stride=1)
|
|
out = fluid.layers.fc(input=pool, size=class_dim, act='softmax')
|
|
return out
|
|
|
|
|
|
def get_model(args):
|
|
model = resnet_cifar10
|
|
if args.data_set == "cifar10":
|
|
class_dim = 10
|
|
if args.data_format == 'NCHW':
|
|
dshape = [3, 32, 32]
|
|
else:
|
|
dshape = [32, 32, 3]
|
|
model = resnet_cifar10
|
|
train_reader = paddle.dataset.cifar.train10()
|
|
test_reader = paddle.dataset.cifar.test10()
|
|
elif args.data_set == "flowers":
|
|
class_dim = 102
|
|
if args.data_format == 'NCHW':
|
|
dshape = [3, 224, 224]
|
|
else:
|
|
dshape = [224, 224, 3]
|
|
model = resnet_imagenet
|
|
train_reader = paddle.dataset.flowers.train()
|
|
test_reader = paddle.dataset.flowers.test()
|
|
elif args.data_set == "imagenet":
|
|
class_dim = 1000
|
|
if args.data_format == 'NCHW':
|
|
dshape = [3, 224, 224]
|
|
else:
|
|
dshape = [224, 224, 3]
|
|
model = resnet_imagenet
|
|
if not args.data_path:
|
|
raise Exception(
|
|
"Must specify --data_path when training with imagenet")
|
|
train_reader = imagenet_train(args.data_path)
|
|
test_reader = imagenet_test(args.data_path)
|
|
|
|
if args.use_reader_op:
|
|
filelist = [
|
|
os.path.join(args.data_path, f) for f in os.listdir(args.data_path)
|
|
]
|
|
data_file = fluid.layers.open_files(
|
|
filenames=filelist,
|
|
shapes=[[-1] + dshape, (-1, 1)],
|
|
lod_levels=[0, 0],
|
|
dtypes=["float32", "int64"],
|
|
thread_num=args.gpus,
|
|
pass_num=args.pass_num)
|
|
data_file = fluid.layers.double_buffer(
|
|
fluid.layers.batch(
|
|
data_file, batch_size=args.batch_size))
|
|
input, label = fluid.layers.read_file(data_file)
|
|
else:
|
|
input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
|
|
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
|
|
|
if args.device == 'CPU' and args.cpus > 1:
|
|
places = fluid.layers.get_places(args.cpus)
|
|
pd = fluid.layers.ParallelDo(places)
|
|
with pd.do():
|
|
predict = model(pd.read_input(input), class_dim)
|
|
label = pd.read_input(label)
|
|
cost = fluid.layers.cross_entropy(input=predict, label=label)
|
|
avg_cost = fluid.layers.mean(x=cost)
|
|
batch_acc = fluid.layers.accuracy(input=predict, label=label)
|
|
|
|
pd.write_output(avg_cost)
|
|
pd.write_output(batch_acc)
|
|
|
|
avg_cost, batch_acc = pd()
|
|
avg_cost = fluid.layers.mean(avg_cost)
|
|
batch_acc = fluid.layers.mean(batch_acc)
|
|
else:
|
|
predict = model(input, class_dim)
|
|
cost = fluid.layers.cross_entropy(input=predict, label=label)
|
|
avg_cost = fluid.layers.mean(x=cost)
|
|
batch_acc = fluid.layers.accuracy(input=predict, label=label)
|
|
|
|
inference_program = fluid.default_main_program().clone()
|
|
with fluid.program_guard(inference_program):
|
|
inference_program = fluid.io.get_inference_program(
|
|
target_vars=[batch_acc])
|
|
|
|
optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
|
|
|
|
batched_train_reader = paddle.batch(
|
|
train_reader if args.no_random else paddle.reader.shuffle(
|
|
train_reader, buf_size=5120),
|
|
batch_size=args.batch_size * args.gpus,
|
|
drop_last=True)
|
|
batched_test_reader = paddle.batch(
|
|
test_reader, batch_size=args.batch_size, drop_last=True)
|
|
|
|
return avg_cost, inference_program, optimizer, batched_train_reader,\
|
|
batched_test_reader, batch_acc
|