# 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.
import paddle
import paddle.fluid as fluid
import math
import os
from imagenet_reader import train, val
__all__ = [
"SE_ResNeXt", "SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d",
"SE_ResNeXt152_32x4d", "get_model"
]
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [30, 60, 90],
"steps": [0.1, 0.01, 0.001, 0.0001]
}
}
class SE_ResNeXt():
def __init__(self, layers=50, is_train=True):
self.params = train_parameters
self.layers = layers
self.is_train = is_train
def net(self, input, class_dim=1000):
layers = self.layers
supported_layers = [50, 101, 152]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers, layers)
if layers == 50:
cardinality = 32
reduction_ratio = 16
depth = [3, 4, 6, 3]
num_filters = [128, 256, 512, 1024]
conv = self.conv_bn_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act='relu')
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
elif layers == 101:
cardinality = 32
reduction_ratio = 16
depth = [3, 4, 23, 3]
num_filters = [128, 256, 512, 1024]
conv = self.conv_bn_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act='relu')
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
elif layers == 152:
cardinality = 64
reduction_ratio = 16
depth = [3, 8, 36, 3]
num_filters = [128, 256, 512, 1024]
conv = self.conv_bn_layer(
input=input,
num_filters=64,
filter_size=3,
stride=2,
act='relu')
conv = self.conv_bn_layer(
input=conv, num_filters=64, filter_size=3, stride=1, act='relu')
conv = self.conv_bn_layer(
input=conv,
num_filters=128,
filter_size=3,
stride=1,
act='relu')
conv = fluid.layers.pool2d(
input=conv, pool_size=3, pool_stride=2, pool_padding=1, \
pool_type='max')
for block in range(len(depth)):
for i in range(depth[block]):
conv = self.bottleneck_block(
input=conv,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
cardinality=cardinality,
reduction_ratio=reduction_ratio)
pool = fluid.layers.pool2d(
input=conv, pool_size=7, pool_type='avg', global_pooling=True)
drop = fluid.layers.dropout(x=pool, dropout_prob=0.5)
stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
out = fluid.layers.fc(input=drop,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv,
stdv)))
return out
def shortcut(self, input, ch_out, stride):
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1:
filter_size = 1
return self.conv_bn_layer(input, ch_out, filter_size, stride)
else:
return input
def bottleneck_block(self, input, num_filters, stride, cardinality,
reduction_ratio):
conv0 = self.conv_bn_layer(
input=input, num_filters=num_filters, filter_size=1, act='relu')
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=stride,
groups=cardinality,
act='relu')
conv2 = self.conv_bn_layer(
input=conv1, num_filters=num_filters * 2, filter_size=1, act=None)
scale = self.squeeze_excitation(
input=conv2,
num_channels=num_filters * 2,
reduction_ratio=reduction_ratio)
short = self.shortcut(input, num_filters * 2, stride)
return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) / 2,
groups=groups,
act=None,
bias_attr=False)
return fluid.layers.batch_norm(
input=conv, act=act, is_test=not self.is_train)
def squeeze_excitation(self, input, num_channels, reduction_ratio):
pool = fluid.layers.pool2d(
input=input, pool_size=0, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
squeeze = fluid.layers.fc(input=pool,
size=num_channels / reduction_ratio,
act='relu',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(
-stdv, stdv)))
stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
excitation = fluid.layers.fc(input=squeeze,
size=num_channels,
act='sigmoid',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(
-stdv, stdv)))
scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
return scale
def SE_ResNeXt50_32x4d():
model = SE_ResNeXt(layers=50)
return model
def SE_ResNeXt101_32x4d():
model = SE_ResNeXt(layers=101)
return model
def SE_ResNeXt152_32x4d():
model = SE_ResNeXt(layers=152)
return model
def get_model(args, is_train, main_prog, startup_prog):
model = SE_ResNeXt(layers=50)
batched_reader = None
pyreader = None
trainer_count = int(os.getenv("PADDLE_TRAINERS"))
dshape = train_parameters["input_size"]
with fluid.program_guard(main_prog, startup_prog):
with fluid.unique_name.guard():
if args.use_reader_op:
pyreader = fluid.layers.py_reader(
capacity=10,
shapes=([-1] + dshape, (-1, 1)),
dtypes=('float32', 'int64'),
name="train_reader" if is_train else "test_reader",
use_double_buffer=True)
input, label = fluid.layers.read_file(pyreader)
else:
input = fluid.layers.data(
name='data', shape=dshape, dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
out = model.net(input=input)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
optimizer = None
if is_train:
total_images = 1281167 / trainer_count
step = int(total_images / args.batch_size + 1)
epochs = [40, 80, 100]
bd = [step * e for e in epochs]
base_lr = args.learning_rate
lr = []
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
optimizer = fluid.optimizer.Momentum(
# learning_rate=base_lr,
learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
optimizer.minimize(avg_cost)
if args.memory_optimize:
fluid.memory_optimize(main_prog)
# config readers
if is_train:
reader = train()
else:
reader = val()
if not args.use_reader_op:
batched_reader = paddle.batch(
reader, batch_size=args.batch_size * args.gpus, drop_last=True)
else:
pyreader.decorate_paddle_reader(
paddle.batch(
reader, batch_size=args.batch_size))
return avg_cost, optimizer, [acc_top1, acc_top5], batched_reader, pyreader