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...@@ -7,3 +7,6 @@ ...@@ -7,3 +7,6 @@
[submodule "PaddleCV/PaddleSeg"] [submodule "PaddleCV/PaddleSeg"]
path = PaddleCV/PaddleSeg path = PaddleCV/PaddleSeg
url = https://github.com/PaddlePaddle/PaddleSeg url = https://github.com/PaddlePaddle/PaddleSeg
[submodule "AutoDL"]
path = AutoDL
url = https://github.com/PaddlePaddle/AutoDL.git
AutoDL @ 5447bcf7
Subproject commit 5447bcf72d92b58e9efe38e4aa0d47bab68bec31
AutoDL/HiNAS_models/README.md 已删除 100755 → 0
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# Image Classification Models
This directory contains six image classification models, which are models automatically discovered by Baidu Big Data Lab (BDL) Hierarchical Neural Architecture Search project (HiNAS), achieving 96.1% accuracy on CIFAR-10 dataset. These models are divided into two categories. The first three have no skip link, named HiNAS 0-2, and the last three networks contain skip links, which are similar to the shortcut connections in Resnet, named HiNAS 3-5.
We also recommend users to take a look at the  [IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/122279)
---
## Table of Contents
- [Installation](#installation)
- [Data preparation](#data-preparation)
- [Training a model](#training-a-model)
- [Model performances](#model-performances)
## Installation
Running the trainer in current directory requires:
- PadddlePaddle Fluid >= v0.15.0
- CuDNN >=6.0
If PaddlePaddle and CuDNN in your runtime environment do not meet the requirements, please follow the instructions in [installation document](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html) and make an update.
## Data preparation
When you run the sample code for the first time, the trainer will automatically download the cifar-10 dataset. Please make sure your environment has an internet connection.
The dataset will be downloaded to `dataset/cifar/cifar-10-python.tar.gz` in the same directory as the Trainer. If automatic download fails, you can go to https://www.cs.toronto.edu/~kriz/cifar.html and download cifar-10-python.tar.gz to the location mentioned above.
## Training a model
After the environment is ready, you can train the model. There are two entrances: `train_hinas.py` and `train_hinas_res.py`. The former is used to train Model 0-2 (without skip link), and the latter is used to train Model 3-5 (contains skip link).
Train Model 0~2 (without skip link)：
```
python train_hinas.py --model=m_id # m_id can be 0, 1 or 2.
```
Train Model 3~5 (with skip link)：
```
python train_hinas_res.py --model=m_id # m_id can be 0, 1 or 2.
```
In addition, both `train_hinas.py` and `train_hinas_res.py` support the following parameters:
- **random_flip_left_right**: Random flip image horizontally. (Default: True)
- **random_flip_up_down**: Randomly flip image vertically. (Default: False)
- **cutout**: Add cutout action to image. (Default: True)
- **standardize_image**: Image standardize. (Default: True)
- **pad_and_cut_image**: Random padding image and then crop back to the original size. (Default: True)
- **shuffle_image**: Shuffle the order of the input images during training. (Default: True)
- **lr_max**: Learning rate at the begin of training. (Default: 0.1)
- **lr_min**: Learning rate at the end of training. (Default: 0.0001)
- **batch_size**: Training batch size (Default: 128)
- **num_epochs**: Total training epoch (Default: 200)
- **weight_decay**: L2 Regularization value (Default: 0.0004)
- **momentum**: The momentum parameter in momentum optimizer (Default: 0.9)
- **dropout_rate**: Dropout rate of the dropout layer (Default: 0.5)
- **bn_decay**: The decay/momentum parameter (or called moving average decay) in batch norm layer (Default: 0.9)
## Model performances
Train all six models using same hyperparameters:
- learning rate: 0.1 -> 0.0001 with cosine annealing
- total epoch: 200
- batch size: 128
- L2 decay: 0.000400
- optimizer: momentum optimizer with m=0.9 and use nesterov
- preprocess: random horizontal flip + image standardization + cutout
And below is the accuracy on CIFAR-10 dataset：
| model | round 1 | round 2 | round 3 | max | avg |
|----------|---------|---------|---------|--------|--------|
| HiNAS-0 | 0.9548 | 0.9520 | 0.9513 | 0.9548 | 0.9527 |
| HiNAS-1 | 0.9452 | 0.9462 | 0.9420 | 0.9462 | 0.9445 |
| HiNAS-2 | 0.9508 | 0.9506 | 0.9483 | 0.9508 | 0.9499 |
| HiNAS-3 | 0.9607 | 0.9623 | 0.9601 | 0.9623 | 0.9611 |
| HiNAS-4 | 0.9611 | 0.9584 | 0.9586 | 0.9611 | 0.9594 |
| HiNAS-5 | 0.9578 | 0.9588 | 0.9594 | 0.9594 | 0.9586 |
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# Image Classification Models
本目录下包含6个图像分类模型，都是百度大数据实验室 Hierarchical Neural Architecture Search (HiNAS) 项目通过机器自动发现的模型，在CIFAR-10数据集上达到96.1%的准确率。这6个模型分为两类，前3个没有skip link，分别命名为 HiNAS 0-2号，后三个网络带有skip link，功能类似于Resnet中的shortcut connection，分别命名 HiNAS 3-5号。
同时推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/122279)
---
## Table of Contents
- [Installation](#installation)
- [Data preparation](#data-preparation)
- [Training a model](#training-a-model)
- [Model performances](#model-performances)
## Installation
最低环境要求:
- PadddlePaddle Fluid >= v0.15.0
- Cudnn >=6.0
如果您的运行环境无法满足要求，可以参考此文档升级PaddlePaddle：[installation document](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html)
## Data preparation
第一次训练模型的时候，Trainer会自动下载CIFAR-10数据集，请确保您的环境有互联网连接。
数据集会被下载到Trainer同目录下的`dataset/cifar/cifar-10-python.tar.gz`，如果自动下载失败，您可以自行从 https://www.cs.toronto.edu/~kriz/cifar.html 下载cifar-10-python.tar.gz，然后放到上述位置。
## Training a model
准备好环境后，可以训练模型，训练有2个入口，`train_hinas.py`和`train_hinas_res.py`，前者用来训练0-2号不含skip link的模型，后者用来训练3-5号包含skip link的模型。
训练0~2号不含skip link的模型：
```
python train_hinas.py --model=m_id # m_id can be 0, 1 or 2.
```
训练3~5号包含skip link的模型：
```
python train_hinas_res.py --model=m_id # m_id can be 0, 1 or 2.
```
此外，`train_hinas.py`和`train_hinas_res.py` 都支持以下参数：
初始化部分：
- random_flip_left_right：图片随机水平翻转（Default：True）
- random_flip_up_down：图片随机垂直翻转（Default：False）
- cutout：图片随机遮挡（Default：True）
- standardize_image：对图片每个像素做 standardize（Default：True）
- pad_and_cut_image：图片随机padding，并裁剪回原大小（Default：True）
- shuffle_image：训练时对输入图片的顺序做shuffle（Default：True）
- lr_max：训练开始时的learning rate（Default：0.1）
- lr_min：训练结束时的learning rate（Default：0.0001）
- batch_size：训练的batch size（Default：128）
- num_epochs：训练总的epoch（Default：200）
- weight_decay：训练时L2 Regularization大小（Default：0.0004）
- momentum：momentum优化器中的momentum系数（Default：0.9）
- dropout_rate：dropout层的dropout_rate（Default：0.5）
- bn_decay：batch norm层的decay/momentum系数（即moving average decay）大小（Default：0.9）
## Model performances
6个模型使用相同的参数训练：
- learning rate: 0.1 -> 0.0001 with cosine annealing
- total epoch: 200
- batch size: 128
- L2 decay: 0.000400
- optimizer: momentum optimizer with m=0.9 and use nesterov
- preprocess: random horizontal flip + image standardization + cutout
以下是6个模型在CIFAR-10数据集上的准确率：
| model | round 1 | round 2 | round 3 | max | avg |
|----------|---------|---------|---------|--------|--------|
| HiNAS-0 | 0.9548 | 0.9520 | 0.9513 | 0.9548 | 0.9527 |
| HiNAS-1 | 0.9452 | 0.9462 | 0.9420 | 0.9462 | 0.9445 |
| HiNAS-2 | 0.9508 | 0.9506 | 0.9483 | 0.9508 | 0.9499 |
| HiNAS-3 | 0.9607 | 0.9623 | 0.9601 | 0.9623 | 0.9611 |
| HiNAS-4 | 0.9611 | 0.9584 | 0.9586 | 0.9611 | 0.9594 |
| HiNAS-5 | 0.9578 | 0.9588 | 0.9594 | 0.9594 | 0.9586 |
AutoDL/HiNAS_models/build/layers.py 已删除 100755 → 0
浏览文件 @ 7af02eb6
# 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 operator
import numpy as np
import paddle.fluid as fluid
from absl import flags
FLAGS = flags.FLAGS
flags.DEFINE_float("bn_decay", 0.9, "batch norm decay")
flags.DEFINE_float("dropout_rate", 0.5, "dropout rate")
def calc_padding(img_width, stride, dilation, filter_width):
""" calculate pixels to padding in order to keep input/output size same. """
filter_width = dilation * (filter_width - 1) + 1
if img_width % stride == 0:
pad_along_width = max(filter_width - stride, 0)
else:
pad_along_width = max(filter_width - (img_width % stride), 0)
return pad_along_width // 2, pad_along_width - pad_along_width // 2
def conv(inputs,
filters,
kernel,
strides=(1, 1),
dilation=(1, 1),
num_groups=1,
conv_param=None):
""" normal conv layer """
if isinstance(kernel, (tuple, list)):
n = operator.mul(*kernel) * inputs.shape[1]
else:
n = kernel * kernel * inputs.shape[1]
# pad input
padding = (0, 0, 0, 0) \
+ calc_padding(inputs.shape[2], strides[0], dilation[0], kernel[0]) \
+ calc_padding(inputs.shape[3], strides[1], dilation[1], kernel[1])
if sum(padding) > 0:
inputs = fluid.layers.pad(inputs, padding, 0)
param_attr = fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
0.0, scale=np.sqrt(2.0 / n)),
regularizer=fluid.regularizer.L2Decay(FLAGS.weight_decay))
bias_attr = fluid.param_attr.ParamAttr(
regularizer=fluid.regularizer.L2Decay(0.))
return fluid.layers.conv2d(
inputs,
filters,
kernel,
stride=strides,
padding=0,
dilation=dilation,
groups=num_groups,
param_attr=param_attr if conv_param is None else conv_param,
use_cudnn=False if num_groups == inputs.shape[1] == filters else True,
bias_attr=bias_attr,
act=None)
def sep(inputs, filters, kernel, strides=(1, 1), dilation=(1, 1)):
""" Separable convolution layer """
if isinstance(kernel, (tuple, list)):
n_depth = operator.mul(*kernel)
else:
n_depth = kernel * kernel
n_point = inputs.shape[1]
if isinstance(strides, (tuple, list)):
multiplier = strides[0]
else:
multiplier = strides
depthwise_param = fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
0.0, scale=np.sqrt(2.0 / n_depth)),
regularizer=fluid.regularizer.L2Decay(FLAGS.weight_decay))
pointwise_param = fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
0.0, scale=np.sqrt(2.0 / n_point)),
regularizer=fluid.regularizer.L2Decay(FLAGS.weight_decay))
depthwise_conv = conv(
inputs=inputs,
kernel=kernel,
filters=int(filters * multiplier),
strides=strides,
dilation=dilation,
num_groups=int(filters * multiplier),
conv_param=depthwise_param)
return conv(
inputs=depthwise_conv,
kernel=(1, 1),
filters=int(filters * multiplier),
strides=(1, 1),
dilation=dilation,
conv_param=pointwise_param)
def maxpool(inputs, kernel, strides=(1, 1)):
padding = (0, 0, 0, 0) \
+ calc_padding(inputs.shape[2], strides[0], 1, kernel[0]) \
+ calc_padding(inputs.shape[3], strides[1], 1, kernel[1])
if sum(padding) > 0:
inputs = fluid.layers.pad(inputs, padding, 0)
return fluid.layers.pool2d(
inputs, kernel, 'max', strides, pool_padding=0, ceil_mode=False)
def avgpool(inputs, kernel, strides=(1, 1)):
padding_pixel = (0, 0, 0, 0)
padding_pixel += calc_padding(inputs.shape[2], strides[0], 1, kernel[0])
padding_pixel += calc_padding(inputs.shape[3], strides[1], 1, kernel[1])
if padding_pixel[4] == padding_pixel[5] and padding_pixel[
6] == padding_pixel[7]:
# same padding pixel num on all sides.
return fluid.layers.pool2d(
inputs,
kernel,
'avg',
strides,
pool_padding=(padding_pixel[4], padding_pixel[6]),
ceil_mode=False)
elif padding_pixel[4] + 1 == padding_pixel[5] and padding_pixel[6] + 1 == padding_pixel[7] \
and strides == (1, 1):
# different padding size: first pad then crop.
x = fluid.layers.pool2d(
inputs,
kernel,
'avg',
strides,
pool_padding=(padding_pixel[5], padding_pixel[7]),
ceil_mode=False)
x_shape = x.shape
return fluid.layers.crop(
x,
shape=(-1, x_shape[1], x_shape[2] - 1, x_shape[3] - 1),
offsets=(0, 0, 1, 1))
else:
# not support. use padding-zero and pool2d.
print("Warning: use zero-padding in avgpool")
outputs = fluid.layers.pad(inputs, padding_pixel, 0)
return fluid.layers.pool2d(
outputs, kernel, 'avg', strides, pool_padding=0, ceil_mode=False)
def global_avgpool(inputs):
return fluid.layers.pool2d(
inputs,
1,
'avg',
1,
pool_padding=0,
global_pooling=True,
ceil_mode=True)
def fully_connected(inputs, units):
n = inputs.shape[1]
param_attr = fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
0.0, scale=np.sqrt(2.0 / n)),
regularizer=fluid.regularizer.L2Decay(FLAGS.weight_decay))
bias_attr = fluid.param_attr.ParamAttr(
regularizer=fluid.regularizer.L2Decay(0.))
return fluid.layers.fc(inputs,
units,
param_attr=param_attr,
bias_attr=bias_attr)
def bn_relu(inputs):
""" batch norm + rely layer """
output = fluid.layers.batch_norm(
inputs, momentum=FLAGS.bn_decay, epsilon=0.001, data_layout="NCHW")
return fluid.layers.relu(output)
def dropout(inputs):
""" dropout layer """
return fluid.layers.dropout(inputs, dropout_prob=FLAGS.dropout_rate)
AutoDL/HiNAS_models/build/ops.py 已删除 100755 → 0
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# 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 build.layers as layers
def conv_1x1(inputs, downsample=False):
return conv_base(inputs, (1, 1), downsample=downsample)
def conv_2x2(inputs, downsample=False):
return conv_base(inputs, (2, 2), downsample=downsample)
def conv_3x3(inputs, downsample=False):
return conv_base(inputs, (3, 3), downsample=downsample)
def dilated_2x2(inputs, downsample=False):
return conv_base(inputs, (2, 2), (2, 2), downsample)
def conv_1x2_2x1(inputs, downsample=False):
return pair_base(inputs, 2, downsample)
def conv_1x3_3x1(inputs, downsample=False):
return pair_base(inputs, 3, downsample)
def sep_2x2(inputs, downsample=False):
return sep_base(inputs, (2, 2), downsample=downsample)
def sep_3x3(inputs, downsample=False):
return sep_base(inputs, (3, 3), downsample=downsample)
def maxpool_2x2(inputs, downsample=False):
return maxpool_base(inputs, (2, 2), downsample)
def maxpool_3x3(inputs, downsample=False):
return maxpool_base(inputs, (3, 3), downsample)
def avgpool_2x2(inputs, downsample=False):
return avgpool_base(inputs, (2, 2), downsample)
def avgpool_3x3(inputs, downsample=False):
return avgpool_base(inputs, (3, 3), downsample)
def conv_base(inputs, kernel, dilation=(1, 1), downsample=False):
filters = inputs.shape[1]
if downsample:
output = layers.conv(inputs, filters * 2, kernel, (2, 2))
else:
output = layers.conv(inputs, filters, kernel, dilation=dilation)
return output
def pair_base(inputs, kernel, downsample=False):
filters = inputs.shape[1]
if downsample:
output = layers.conv(inputs, filters, (1, kernel), (1, 2))
output = layers.conv(output, filters, (kernel, 1), (2, 1))
output = layers.conv(output, filters * 2, (1, 1))
else:
output = layers.conv(inputs, filters, (1, kernel))
output = layers.conv(output, filters, (kernel, 1))
return output
def sep_base(inputs, kernel, dilation=(1, 1), downsample=False):
filters = inputs.shape[1]
if downsample:
output = layers.sep(inputs, filters * 2, kernel, (2, 2))
else:
output = layers.sep(inputs, filters, kernel, dilation=dilation)
return output
def maxpool_base(inputs, kernel, downsample=False):
if downsample:
filters = inputs.shape[1]
output = layers.maxpool(inputs, kernel, (2, 2))
output = layers.conv(output, filters * 2, (1, 1))
else:
output = layers.maxpool(inputs, kernel)
return output
def avgpool_base(inputs, kernel, downsample=False):
if downsample:
filters = inputs.shape[1]
output = layers.avgpool(inputs, kernel, (2, 2))
output = layers.conv(output, filters * 2, (1, 1))
else:
output = layers.avgpool(inputs, kernel)
return output
AutoDL/HiNAS_models/build/resnet_base.py 已删除 100755 → 0
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# 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 paddle.fluid as fluid
from absl import flags
import build.layers as layers
import build.ops as _ops
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_stages", 3, "number of stages")
flags.DEFINE_integer("num_blocks", 5, "number of blocks per stage")
flags.DEFINE_integer("num_ops", 2, "number of operations per block")
flags.DEFINE_integer("width", 64, "network width")
flags.DEFINE_string("downsample", "pool", "conv or pool")
num_classes = 10
ops = [
_ops.conv_1x1,
_ops.conv_2x2,
_ops.conv_3x3,
_ops.dilated_2x2,
_ops.conv_1x2_2x1,
_ops.conv_1x3_3x1,
_ops.sep_2x2,
_ops.sep_3x3,
_ops.maxpool_2x2,
_ops.maxpool_3x3,
_ops.avgpool_2x2,
_ops.avgpool_3x3,
]
def net(inputs, tokens):
""" build network with skip links """
x = layers.conv(inputs, FLAGS.width, (3, 3))
num_ops = FLAGS.num_blocks * FLAGS.num_ops
x = stage(x, tokens[:num_ops], pre_activation=True)
for i in range(1, FLAGS.num_stages):
x = stage(x, tokens[i * num_ops:(i + 1) * num_ops], downsample=True)
x = layers.bn_relu(x)
x = layers.global_avgpool(x)
x = layers.dropout(x)
logits = layers.fully_connected(x, num_classes)
return fluid.layers.softmax(logits)
def stage(x, tokens, pre_activation=False, downsample=False):
""" build network's stage. Stage consists of blocks """
x = block(x, tokens[:FLAGS.num_ops], pre_activation, downsample)
for i in range(1, FLAGS.num_blocks):
print("-" * 12)
x = block(x, tokens[i * FLAGS.num_ops:(i + 1) * FLAGS.num_ops])
print("=" * 12)
return x
def block(x, tokens, pre_activation=False, downsample=False):
""" build block. """
if pre_activation:
x = layers.bn_relu(x)
res = x
else:
res = x
x = layers.bn_relu(x)
x = ops[tokens[0]](x, downsample)
print("%s \t-> shape %s" % (ops[0].__name__, x.shape))
for token in tokens[1:]:
x = layers.bn_relu(x)
x = ops[token](x)
print("%s \t-> shape %s" % (ops[token].__name__, x.shape))
if downsample:
filters = res.shape[1]
if FLAGS.downsample == "conv":
res = layers.conv(res, filters * 2, (1, 1), (2, 2))
elif FLAGS.downsample == "pool":
res = layers.avgpool(res, (2, 2), (2, 2))
res = fluid.layers.pad(res, (0, 0, filters // 2, filters // 2, 0, 0,
0, 0))
else:
raise NotImplementedError
return x + res
AutoDL/HiNAS_models/build/vgg_base.py 已删除 100755 → 0
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# 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 paddle.fluid as fluid
from absl import flags
import build.layers as layers
import build.ops as _ops
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_stages", 5, "number of stages")
flags.DEFINE_integer("width", 64, "network width")
num_classes = 10
ops = [
_ops.conv_1x1, #0
_ops.conv_2x2, #1
_ops.conv_3x3, #2
_ops.dilated_2x2, #3
_ops.conv_1x2_2x1, #4
_ops.conv_1x3_3x1, #5
_ops.sep_2x2, #6
_ops.sep_3x3, #7
_ops.maxpool_2x2, #8
_ops.maxpool_3x3,
_ops.avgpool_2x2, #10
_ops.avgpool_3x3,
]
def net(inputs, tokens):
depth = len(tokens)
q, r = divmod(depth + 1, FLAGS.num_stages)
downsample_steps = [
i * q + max(0, i + r - FLAGS.num_stages + 1) - 2
for i in range(1, FLAGS.num_stages)
]
x = layers.conv(inputs, FLAGS.width, (3, 3))
x = layers.bn_relu(x)
for i, token in enumerate(tokens):
downsample = i in downsample_steps
x = ops[token](x, downsample)
print("%s \t-> shape %s" % (ops[token].__name__, x.shape))
if downsample:
print("=" * 12)
x = layers.bn_relu(x)
x = layers.global_avgpool(x)
x = layers.dropout(x)
logits = layers.fully_connected(x, num_classes)
return fluid.layers.softmax(logits)
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# 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 math
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.contrib.trainer import *
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
import reader
from absl import flags
# import preprocess
FLAGS = flags.FLAGS
flags.DEFINE_float("lr_max", 0.1, "initial learning rate")
flags.DEFINE_float("lr_min", 0.0001, "limiting learning rate")
flags.DEFINE_integer("batch_size", 128, "batch size")
flags.DEFINE_integer("num_epochs", 200, "total epochs to train")
flags.DEFINE_float("weight_decay", 0.0004, "weight decay")
flags.DEFINE_float("momentum", 0.9, "momentum")
flags.DEFINE_boolean("shuffle_image", True, "shuffle input images on training")
dataset_train_size = 50000
class Model(object):
def __init__(self, build_fn, tokens):
print("learning rate: %f -> %f, cosine annealing" %
(FLAGS.lr_max, FLAGS.lr_min))
print("epoch: %d" % FLAGS.num_epochs)
print("batch size: %d" % FLAGS.batch_size)
print("L2 decay: %f" % FLAGS.weight_decay)
self.max_step = dataset_train_size * FLAGS.num_epochs // FLAGS.batch_size
self.build_fn = build_fn
self.tokens = tokens
print("Token is %s" % ",".join(map(str, tokens)))
def cosine_annealing(self):
step = _decay_step_counter()
lr = FLAGS.lr_min + (FLAGS.lr_max - FLAGS.lr_min) / 2 \
* (1.0 + fluid.layers.ops.cos(step / self.max_step * math.pi))
return lr
def optimizer_program(self):
return fluid.optimizer.Momentum(
learning_rate=self.cosine_annealing(),
momentum=FLAGS.momentum,
use_nesterov=True,
regularization=fluid.regularizer.L2DecayRegularizer(
FLAGS.weight_decay))
def inference_network(self):
images = fluid.layers.data(
name='pixel', shape=[3, 32, 32], dtype='float32')
return self.build_fn(images, self.tokens)
def train_network(self):
predict = self.inference_network()
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label)
# self.parameters = fluid.parameters.create(avg_cost)
return [avg_cost, accuracy]
def run(self):
train_files = reader.train10()
test_files = reader.test10()
if FLAGS.shuffle_image:
train_reader = paddle.batch(
paddle.reader.shuffle(train_files, dataset_train_size),
batch_size=FLAGS.batch_size)
else:
train_reader = paddle.batch(
train_files, batch_size=FLAGS.batch_size)
test_reader = paddle.batch(test_files, batch_size=FLAGS.batch_size)
costs = []
accs = []
def event_handler(event):
if isinstance(event, EndStepEvent):
costs.append(event.metrics[0])
accs.append(event.metrics[1])
if event.step % 20 == 0:
print("Epoch %d, Step %d, Loss %f, Acc %f" % (
event.epoch, event.step, np.mean(costs), np.mean(accs)))
del costs[:]
del accs[:]
if isinstance(event, EndEpochEvent):
if event.epoch % 3 == 0 or event.epoch == FLAGS.num_epochs - 1:
avg_cost, accuracy = trainer.test(
reader=test_reader, feed_order=['pixel', 'label'])
event_handler.best_acc = max(event_handler.best_acc,
accuracy)
print("Test with epoch %d, Loss %f, Acc %f" %
(event.epoch, avg_cost, accuracy))
print("Best acc %f" % event_handler.best_acc)
event_handler.best_acc = 0.0
place = fluid.CUDAPlace(0)
trainer = Trainer(
train_func=self.train_network,
optimizer_func=self.optimizer_program,
place=place)
trainer.train(
reader=train_reader,
num_epochs=FLAGS.num_epochs,
event_handler=event_handler,
feed_order=['pixel', 'label'])
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# 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.
"""
CIFAR-10 dataset.
This module will download dataset from
https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into
paddle reader creators.
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test images.
"""
from PIL import Image
from PIL import ImageOps
import numpy as np
import cPickle
import itertools
import paddle.dataset.common
import tarfile
from absl import flags
FLAGS = flags.FLAGS
flags.DEFINE_boolean("random_flip_left_right", True,
"random flip left and right")
flags.DEFINE_boolean("random_flip_up_down", False, "random flip up and down")
flags.DEFINE_boolean("cutout", True, "cutout")
flags.DEFINE_boolean("standardize_image", True, "standardize input images")
flags.DEFINE_boolean("pad_and_cut_image", True, "pad and cut input images")
__all__ = ['train10', 'test10', 'convert']
URL_PREFIX = 'https://www.cs.toronto.edu/~kriz/'
CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
paddle.dataset.common.DATA_HOME = "dataset/"
image_size = 32
image_depth = 3
half_length = 8
def preprocess(sample, is_training):
image_array = sample.reshape(3, image_size, image_size)
rgb_array = np.transpose(image_array, (1, 2, 0))
img = Image.fromarray(rgb_array, 'RGB')
if is_training:
if FLAGS.pad_and_cut_image:
# pad and ramdom crop
img = ImageOps.expand(
img, (2, 2, 2, 2), fill=0) # pad to 36 * 36 * 3
left_top = np.random.randint(5, size=2) # rand 0 - 4
img = img.crop((left_top[0], left_top[1], left_top[0] + image_size,
left_top[1] + image_size))
if FLAGS.random_flip_left_right and np.random.randint(2):
img = img.transpose(Image.FLIP_LEFT_RIGHT)
if FLAGS.random_flip_up_down and np.random.randint(2):
img = img.transpose(Image.FLIP_TOP_BOTTOM)
img = np.array(img).astype(np.float32)
if FLAGS.standardize_image:
# per_image_standardization
img_float = img / 255.0
mean = np.mean(img_float)
std = max(np.std(img_float), 1.0 / np.sqrt(3 * image_size * image_size))
img = (img_float - mean) / std
if is_training and FLAGS.cutout:
center = np.random.randint(image_size, size=2)
offset_width = max(0, center[0] - half_length)
offset_height = max(0, center[1] - half_length)
target_width = min(center[0] + half_length, image_size)
target_height = min(center[1] + half_length, image_size)
for i in range(offset_height, target_height):
for j in range(offset_width, target_width):
img[i][j][:] = 0.0
img = np.transpose(img, (2, 0, 1))
return img.reshape(3 * image_size * image_size)
def reader_creator(filename, sub_name, is_training):
def read_batch(batch):
data = batch['data']
labels = batch.get('labels', batch.get('fine_labels', None))
assert labels is not None
for sample, label in itertools.izip(data, labels):
yield preprocess(sample, is_training), int(label)
def reader():
with tarfile.open(filename, mode='r') as f:
names = [
each_item.name for each_item in f if sub_name in each_item.name
]
names.sort()
for name in names:
print("Reading file " + name)
batch = cPickle.load(f.extractfile(name))
for item in read_batch(batch):
yield item
return reader
def train10():
"""
CIFAR-10 training set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Training reader creator
:rtype: callable
"""
return reader_creator(
paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
'data_batch', True)
def test10():
"""
CIFAR-10 test set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Test reader creator.
:rtype: callable
"""
return reader_creator(
paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
'test_batch', False)
def fetch():
paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5)
def convert(path):
"""
Converts dataset to recordio format
"""
paddle.dataset.common.convert(path, train10(), 1000, "cifar_train10")
paddle.dataset.common.convert(path, test10(), 1000, "cifar_test10")
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p11
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S'\x05\x00\x00\x00\x07\x00\x00\x00\x02\x00\x00\x00\x05\x00\x00\x00\x05\x00\x00\x00\x02\x00\x00\x00\x08\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x01\x00\x00\x00\n\x00\x00\x00\t\x00\x00\x00\x03\x00\x00\x00\x08\x00\x00\x00\x0b\x00\x00\x00\x03\x00\x00\x00\t\x00\x00\x00\x02\x00\x00\x00\x06\x00\x00\x00\x01\x00\x00\x00\x06\x00\x00\x00'
p13
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S'\x07\x00\x00\x00\x07\x00\x00\x00\x02\x00\x00\x00\x05\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x08\x00\x00\x00\x08\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x02\x00\x00\x00\n\x00\x00\x00\x08\x00\x00\x00\x02\x00\x00\x00\t\x00\x00\x00\x0b\x00\x00\x00\t\x00\x00\x00\x06\x00\x00\x00\x04\x00\x00\x00\x04\x00\x00\x00\n\x00\x00\x00'
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# 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 os
import pickle
from absl import app
from absl import flags
import nn_paddle as nn
from build import vgg_base
FLAGS = flags.FLAGS
flags.DEFINE_string("tokdir", "tokens/", "token directory")
flags.DEFINE_integer("model", 0, "model")
mid = [17925, 18089, 15383]
def main(_):
f = os.path.join(FLAGS.tokdir, str(mid[FLAGS.model]) + ".pkl")
tokens = pickle.load(open(f, "rb"))
model = nn.Model(vgg_base.net, tokens)
model.run()
if __name__ == "__main__":
app.run(main)
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# 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 os
import pickle
from absl import app
from absl import flags
import nn_paddle as nn
from build import resnet_base
FLAGS = flags.FLAGS
flags.DEFINE_string("tokdir", "tokens/", "token directory")
flags.DEFINE_integer("model", 0, "model")
mid = [17754, 15113, 15613]
def main(_):
f = os.path.join(FLAGS.tokdir, str(mid[FLAGS.model]) + ".pkl")
tokens = pickle.load(open(f, "rb"))
model = nn.Model(resnet_base.net, tokens)
model.run()
if __name__ == "__main__":
app.run(main)
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# LRC Local Rademachar Complexity Regularization
Regularization of Deep Neural Networks(DNNs) for the sake of improving their generalization capability is important and chllenging. This directory contains image classification model based on a novel regularizer rooted in Local Rademacher Complexity (LRC). We appreciate the contribution by [DARTS](https://arxiv.org/abs/1806.09055) for our research. The regularization by LRC and DARTS are combined in this model on CIFAR-10 dataset. Code accompanying the paper
> [An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity](https://arxiv.org/abs/1902.00873)\
> Yingzhen Yang, Xingjian Li, Jun Huan.\
> _arXiv:1902.00873_.
---
# Table of Contents
- [Installation](#installation)
- [Data preparation](#data-preparation)
- [Training](#training)
## Installation
Running sample code in this directory requires PaddelPaddle Fluid v.1.2.0 and later. If the PaddlePaddle on your device is lower than this version, please follow the instructions in [installation document](http://www.paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html#paddlepaddle) and make an update.
## Data preparation
When you want to use the cifar-10 dataset for the first time, you can download the dataset as:
sh ./dataset/download.sh
Please make sure your environment has an internet connection.
The dataset will be downloaded to `dataset/cifar/cifar-10-batches-py` in the same directory as the `train.py`. If automatic download fails, you can download cifar-10-python.tar.gz from https://www.cs.toronto.edu/~kriz/cifar.html and decompress it to the location mentioned above.
## Training
After data preparation, one can start the training step by:
python -u train_mixup.py \
--batch_size=80 \
--auxiliary \
--weight_decay=0.0003 \
--learning_rate=0.025 \
--lrc_loss_lambda=0.7 \
--cutout
- Set ```export CUDA_VISIBLE_DEVICES=0``` to specifiy one GPU to train.
- For more help on arguments:
python train_mixup.py --help
**data reader introduction:**
* Data reader is defined in `reader.py`.
* Reshape the images to 32 * 32.
* In training stage, images are padding to 40 * 40 and cropped randomly to the original size.
* In training stage, images are horizontally random flipped.
* Images are standardized to (0, 1).
* In training stage, cutout images randomly.
* Shuffle the order of the input images during training.
**model configuration:**
* Use auxiliary loss and auxiliary\_weight=0.4.
* Use dropout and drop\_path\_prob=0.2.
* Set lrc\_loss\_lambda=0.7.
**training strategy:**
* Use momentum optimizer with momentum=0.9.
* Weight decay is 0.0003.
* Use cosine decay with init\_lr=0.025.
* Total epoch is 600.
* Use Xaiver initalizer to weight in conv2d, Constant initalizer to weight in batch norm and Normal initalizer to weight in fc.
* Initalize bias in batch norm and fc to zero constant and do not add bias to conv2d.
## Reference
- DARTS: Differentiable Architecture Search [`paper`](https://arxiv.org/abs/1806.09055)
- Differentiable architecture search in PyTorch [`code`](https://github.com/quark0/darts)
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# LRC 局部Rademachar复杂度正则化
为了在深度神经网络中提升泛化能力，正则化的选择十分重要也具有挑战性。本目录包括了一种基于局部rademacher复杂度的新型正则（LRC）的图像分类模型。十分感谢[DARTS](https://arxiv.org/abs/1806.09055)模型对本研究提供的帮助。该模型将LRC正则和DARTS网络相结合，在CIFAR-10数据集中得到了很出色的效果。代码和文章一同发布
> [An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity](https://arxiv.org/abs/1902.00873)\
> Yingzhen Yang, Xingjian Li, Jun Huan.\
> _arXiv:1902.00873_.
---
# 内容
- [安装](#安装)
- [数据准备](#数据准备)
- [模型训练](#模型训练)
## 安装
在当前目录下运行样例代码需要PadddlePaddle Fluid的v.1.2.0或以上的版本。如果你的运行环境中的PaddlePaddle低于此版本，请根据[安装文档](http://www.paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html#paddlepaddle)中的说明来更新PaddlePaddle。
## 数据准备
第一次使用CIFAR-10数据集时，您可以通过如果命令下载：
sh ./dataset/download.sh
请确保您的环境有互联网连接。数据会下载到`train.py`同目录下的`dataset/cifar/cifar-10-batches-py`。如果下载失败，您可以自行从https://www.cs.toronto.edu/~kriz/cifar.html上下载cifar-10-python.tar.gz并解压到上述位置。
## 模型训练
数据准备好后，可以通过如下命令开始训练：
python -u train_mixup.py \
--batch_size=80 \
--auxiliary \
--weight_decay=0.0003 \
--learning_rate=0.025 \
--lrc_loss_lambda=0.7 \
--cutout
- 通过设置 ```export CUDA_VISIBLE_DEVICES=0```指定单张GPU训练。
- 可选参数见：
python train_mixup.py --help
**数据读取器说明：**
* 数据读取器定义在`reader.py`中
* 输入图像尺寸统一变换为32 * 32
* 训练时将图像填充为40 * 40然后随机剪裁为原输入图像大小
* 训练时图像随机水平翻转
* 对图像每个像素做归一化处理
* 训练时对图像做随机遮挡
* 训练时对输入图像做随机洗牌
**模型配置：**
* 使用辅助损失，辅助损失权重为0.4
* 使用dropout，随机丢弃率为0.2
* 设置lrc\_loss\_lambda为0.7
**训练策略：**
* 采用momentum优化算法训练，momentum=0.9
* 权重衰减系数为0.0001
* 采用正弦学习率衰减，初始学习率为0.025
* 总共训练600轮
* 对卷积权重采用Xaiver初始化，对batch norm权重采用固定初始化，对全连接层权重采用高斯初始化
* 对batch norm和全连接层偏差采用固定初始化，不对卷积设置偏差
## 引用
- DARTS: Differentiable Architecture Search [`论文`](https://arxiv.org/abs/1806.09055)
- Differentiable Architecture Search in PyTorch [`代码`](https://github.com/quark0/darts)
AutoDL/LRC/dataset/download.sh 已删除 100644 → 0
浏览文件 @ 7af02eb6
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"
mkdir cifar
cd cifar
# Download the data.
echo "Downloading..."
wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
# Extract the data.
echo "Extracting..."
tar zvxf cifar-10-python.tar.gz
AutoDL/LRC/genotypes.py 已删除 100644 → 0
浏览文件 @ 7af02eb6
# Copyright (c) 2019 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.
#
# Based on:
# --------------------------------------------------------
# DARTS
# Copyright (c) 2018, Hanxiao Liu.
# Licensed under the Apache License, Version 2.0;
# --------------------------------------------------------
from collections import namedtuple
Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
PRIMITIVES = [
'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect', 'sep_conv_3x3',
'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5'
]
NASNet = Genotype(
normal=[
('sep_conv_5x5', 1),
('sep_conv_3x3', 0),
('sep_conv_5x5', 0),
('sep_conv_3x3', 0),
('avg_pool_3x3', 1),
('skip_connect', 0),
('avg_pool_3x3', 0),
('avg_pool_3x3', 0),
('sep_conv_3x3', 1),
('skip_connect', 1),
],
normal_concat=[2, 3, 4, 5, 6],
reduce=[
('sep_conv_5x5', 1),
('sep_conv_7x7', 0),
('max_pool_3x3', 1),
('sep_conv_7x7', 0),
('avg_pool_3x3', 1),
('sep_conv_5x5', 0),
('skip_connect', 3),
('avg_pool_3x3', 2),
('sep_conv_3x3', 2),
('max_pool_3x3', 1),
],
reduce_concat=[4, 5, 6], )
AmoebaNet = Genotype(
normal=[
('avg_pool_3x3', 0),
('max_pool_3x3', 1),
('sep_conv_3x3', 0),
('sep_conv_5x5', 2),
('sep_conv_3x3', 0),
('avg_pool_3x3', 3),
('sep_conv_3x3', 1),
('skip_connect', 1),
('skip_connect', 0),
('avg_pool_3x3', 1),
],
normal_concat=[4, 5, 6],
reduce=[
('avg_pool_3x3', 0),
('sep_conv_3x3', 1),
('max_pool_3x3', 0),
('sep_conv_7x7', 2),
('sep_conv_7x7', 0),
('avg_pool_3x3', 1),
('max_pool_3x3', 0),
('max_pool_3x3', 1),
('conv_7x1_1x7', 0),
('sep_conv_3x3', 5),
],
reduce_concat=[3, 4, 6])
DARTS_V1 = Genotype(
normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 0),
('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 1),
('sep_conv_3x3', 0), ('skip_connect', 2)],
normal_concat=[2, 3, 4, 5],
reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2),
('max_pool_3x3', 0), ('max_pool_3x3', 0), ('skip_connect', 2),
('skip_connect', 2), ('avg_pool_3x3', 0)],
reduce_concat=[2, 3, 4, 5])
DARTS_V2 = Genotype(
normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0),
('sep_conv_3x3', 1), ('sep_conv_3x3', 1), ('skip_connect', 0),
('skip_connect', 0), ('dil_conv_3x3', 2)],
normal_concat=[2, 3, 4, 5],
reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('skip_connect', 2),
('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 2),
('skip_connect', 2), ('max_pool_3x3', 1)],
reduce_concat=[2, 3, 4, 5])
MY_DARTS = Genotype(
normal=[('sep_conv_3x3', 0), ('skip_connect', 1), ('skip_connect', 0),
('dil_conv_5x5', 1), ('skip_connect', 0), ('sep_conv_3x3', 1),
('skip_connect', 0), ('sep_conv_3x3', 1)],
normal_concat=range(2, 6),
reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0),
('skip_connect', 2), ('max_pool_3x3', 0), ('skip_connect', 2),
('skip_connect', 2), ('skip_connect', 3)],
reduce_concat=range(2, 6))
DARTS = MY_DARTS
AutoDL/LRC/learning_rate.py 已删除 100644 → 0
浏览文件 @ 7af02eb6
# Copyright (c) 2019 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.
#
# Based on:
# --------------------------------------------------------
# DARTS
# Copyright (c) 2018, Hanxiao Liu.
# Licensed under the Apache License, Version 2.0;
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers.ops as ops
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
import math
from paddle.fluid.initializer import init_on_cpu
def cosine_decay(learning_rate, num_epoch, steps_one_epoch):
"""Applies cosine decay to the learning rate.
lr = 0.5 * (math.cos(epoch * (math.pi / 120)) + 1)
"""
global_step = _decay_step_counter()
with init_on_cpu():
decayed_lr = learning_rate * \
(ops.cos((global_step / steps_one_epoch) \
* math.pi / num_epoch) + 1)/2
return decayed_lr
AutoDL/LRC/model.py 已删除 100644 → 0
浏览文件 @ 7af02eb6
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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.
#
# Based on:
# --------------------------------------------------------
# DARTS
# Copyright (c) 2018, Hanxiao Liu.
# Licensed under the Apache License, Version 2.0;
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import numpy as np
import time
import functools
import paddle
import paddle.fluid as fluid
from operations import *
class Cell():
def __init__(self, genotype, C_prev_prev, C_prev, C, reduction,
reduction_prev):
print(C_prev_prev, C_prev, C)
if reduction_prev:
self.preprocess0 = functools.partial(FactorizedReduce, C_out=C)
else:
self.preprocess0 = functools.partial(
ReLUConvBN, C_out=C, kernel_size=1, stride=1, padding=0)
self.preprocess1 = functools.partial(
ReLUConvBN, C_out=C, kernel_size=1, stride=1, padding=0)
if reduction:
op_names, indices = zip(*genotype.reduce)
concat = genotype.reduce_concat
else:
op_names, indices = zip(*genotype.normal)
concat = genotype.normal_concat
print(op_names, indices, concat, reduction)
self._compile(C, op_names, indices, concat, reduction)
def _compile(self, C, op_names, indices, concat, reduction):
assert len(op_names) == len(indices)
self._steps = len(op_names) // 2
self._concat = concat
self.multiplier = len(concat)
self._ops = []
for name, index in zip(op_names, indices):
stride = 2 if reduction and index < 2 else 1
op = functools.partial(OPS[name], C=C, stride=stride, affine=True)
self._ops += [op]
self._indices = indices
def forward(self, s0, s1, drop_prob, is_train, name):
self.training = is_train
preprocess0_name = name + 'preprocess0.'
preprocess1_name = name + 'preprocess1.'
s0 = self.preprocess0(s0, name=preprocess0_name)
s1 = self.preprocess1(s1, name=preprocess1_name)
out = [s0, s1]
for i in range(self._steps):
h1 = out[self._indices[2 * i]]
h2 = out[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h3 = op1(h1, name=name + '_ops.' + str(2 * i) + '.')
h4 = op2(h2, name=name + '_ops.' + str(2 * i + 1) + '.')
if self.training and drop_prob > 0.:
if h3 != h1:
h3 = fluid.layers.dropout(
h3,
drop_prob,
dropout_implementation='upscale_in_train')
if h4 != h2:
h4 = fluid.layers.dropout(
h4,
drop_prob,
dropout_implementation='upscale_in_train')
s = h3 + h4
out += [s]
return fluid.layers.concat([out[i] for i in self._concat], axis=1)
def AuxiliaryHeadCIFAR(input, num_classes, aux_name='auxiliary_head'):
relu_a = fluid.layers.relu(input)
pool_a = fluid.layers.pool2d(relu_a, 5, 'avg', 3)
conv2d_a = fluid.layers.conv2d(
pool_a,
128,
1,
name=aux_name + '.features.2',
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=aux_name + '.features.2.weight'),
bias_attr=False)
bn_a_name = aux_name + '.features.3'
bn_a = fluid.layers.batch_norm(
conv2d_a,
act='relu',
name=bn_a_name,
param_attr=ParamAttr(
initializer=Constant(1.), name=bn_a_name + '.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name=bn_a_name + '.bias'),
moving_mean_name=bn_a_name + '.running_mean',
moving_variance_name=bn_a_name + '.running_var')
conv2d_b = fluid.layers.conv2d(
bn_a,
768,
2,
name=aux_name + '.features.5',
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=aux_name + '.features.5.weight'),
bias_attr=False)
bn_b_name = aux_name + '.features.6'
bn_b = fluid.layers.batch_norm(
conv2d_b,
act='relu',
name=bn_b_name,
param_attr=ParamAttr(
initializer=Constant(1.), name=bn_b_name + '.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name=bn_b_name + '.bias'),
moving_mean_name=bn_b_name + '.running_mean',
moving_variance_name=bn_b_name + '.running_var')
fc_name = aux_name + '.classifier'
fc = fluid.layers.fc(bn_b,
num_classes,
name=fc_name,
param_attr=ParamAttr(
initializer=Normal(scale=1e-3),
name=fc_name + '.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name=fc_name + '.bias'))
return fc
def StemConv(input, C_out, kernel_size, padding):
conv_a = fluid.layers.conv2d(
input,
C_out,
kernel_size,
padding=padding,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0), name='stem.0.weight'),
bias_attr=False)
bn_a = fluid.layers.batch_norm(
conv_a,
param_attr=ParamAttr(
initializer=Constant(1.), name='stem.1.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name='stem.1.bias'),
moving_mean_name='stem.1.running_mean',
moving_variance_name='stem.1.running_var')
return bn_a
class NetworkCIFAR(object):
def __init__(self, C, class_num, layers, auxiliary, genotype):
self.class_num = class_num
self._layers = layers
self._auxiliary = auxiliary
stem_multiplier = 3
self.drop_path_prob = 0
C_curr = stem_multiplier * C
C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
self.cells = []
reduction_prev = False
for i in range(layers):
if i in [layers // 3, 2 * layers // 3]:
C_curr *= 2
reduction = True
else:
reduction = False
cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction,
reduction_prev)
reduction_prev = reduction
self.cells += [cell]
C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr
if i == 2 * layers // 3:
C_to_auxiliary = C_prev
def forward(self, init_channel, is_train):
self.training = is_train
self.logits_aux = None
num_channel = init_channel * 3
s0 = StemConv(self.image, num_channel, kernel_size=3, padding=1)
s1 = s0
for i, cell in enumerate(self.cells):
name = 'cells.' + str(i) + '.'
s0, s1 = s1, cell.forward(s0, s1, self.drop_path_prob, is_train,
name)
if i == int(2 * self._layers // 3):
if self._auxiliary and self.training:
self.logits_aux = AuxiliaryHeadCIFAR(s1, self.class_num)
out = fluid.layers.adaptive_pool2d(s1, (1, 1), "avg")
self.logits = fluid.layers.fc(out,
size=self.class_num,
param_attr=ParamAttr(
initializer=Normal(scale=1e-3),
name='classifier.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.),
name='classifier.bias'))
return self.logits, self.logits_aux
def build_input(self, image_shape, batch_size, is_train):
if is_train:
py_reader = fluid.layers.py_reader(
capacity=64,
shapes=[[-1] + image_shape, [-1, 1], [-1, 1], [-1, 1], [-1, 1],
[-1, 1], [-1, batch_size, self.class_num - 1]],
lod_levels=[0, 0, 0, 0, 0, 0, 0],
dtypes=[
"float32", "int64", "int64", "float32", "int32", "int32",
"float32"
],
use_double_buffer=True,
name='train_reader')
else:
py_reader = fluid.layers.py_reader(
capacity=64,
shapes=[[-1] + image_shape, [-1, 1]],
lod_levels=[0, 0],
dtypes=["float32", "int64"],
use_double_buffer=True,
name='test_reader')
return py_reader
def train_model(self, py_reader, init_channels, aux, aux_w, batch_size,
loss_lambda):
self.image, self.ya, self.yb, self.lam, self.label_reshape,\
self.non_label_reshape, self.rad_var = fluid.layers.read_file(py_reader)
self.logits, self.logits_aux = self.forward(init_channels, True)
self.mixup_loss = self.mixup_loss(aux, aux_w)
self.lrc_loss = self.lrc_loss(batch_size)
return self.mixup_loss + loss_lambda * self.lrc_loss
def test_model(self, py_reader, init_channels):
self.image, self.ya = fluid.layers.read_file(py_reader)
self.logits, _ = self.forward(init_channels, False)
prob = fluid.layers.softmax(self.logits, use_cudnn=False)
loss = fluid.layers.cross_entropy(prob, self.ya)
acc_1 = fluid.layers.accuracy(self.logits, self.ya, k=1)
acc_5 = fluid.layers.accuracy(self.logits, self.ya, k=5)
return loss, acc_1, acc_5
def mixup_loss(self, auxiliary, auxiliary_weight):
prob = fluid.layers.softmax(self.logits, use_cudnn=False)
loss_a = fluid.layers.cross_entropy(prob, self.ya)
loss_b = fluid.layers.cross_entropy(prob, self.yb)
loss_a_mean = fluid.layers.reduce_mean(loss_a)
loss_b_mean = fluid.layers.reduce_mean(loss_b)
loss = self.lam * loss_a_mean + (1 - self.lam) * loss_b_mean
if auxiliary:
prob_aux = fluid.layers.softmax(self.logits_aux, use_cudnn=False)
loss_a_aux = fluid.layers.cross_entropy(prob_aux, self.ya)
loss_b_aux = fluid.layers.cross_entropy(prob_aux, self.yb)
loss_a_aux_mean = fluid.layers.reduce_mean(loss_a_aux)
loss_b_aux_mean = fluid.layers.reduce_mean(loss_b_aux)
loss_aux = self.lam * loss_a_aux_mean + (1 - self.lam
) * loss_b_aux_mean
return loss + auxiliary_weight * loss_aux
def lrc_loss(self, batch_size):
y_diff_reshape = fluid.layers.reshape(self.logits, shape=(-1, 1))
label_reshape = fluid.layers.squeeze(self.label_reshape, axes=[1])
non_label_reshape = fluid.layers.squeeze(
self.non_label_reshape, axes=[1])
label_reshape.stop_gradient = True
non_label_reshape.stop_graident = True
y_diff_label_reshape = fluid.layers.gather(y_diff_reshape,
label_reshape)
y_diff_non_label_reshape = fluid.layers.gather(y_diff_reshape,
non_label_reshape)
y_diff_label = fluid.layers.reshape(
y_diff_label_reshape, shape=(-1, batch_size, 1))
y_diff_non_label = fluid.layers.reshape(
y_diff_non_label_reshape,
shape=(-1, batch_size, self.class_num - 1))
y_diff_ = y_diff_non_label - y_diff_label
y_diff_ = fluid.layers.transpose(y_diff_, perm=[1, 2, 0])
rad_var_trans = fluid.layers.transpose(self.rad_var, perm=[1, 2, 0])
rad_y_diff_trans = rad_var_trans * y_diff_
lrc_loss_sum = fluid.layers.reduce_sum(rad_y_diff_trans, dim=[0, 1])
lrc_loss_ = fluid.layers.abs(lrc_loss_sum) / (batch_size *
(self.class_num - 1))
lrc_loss_mean = fluid.layers.reduce_mean(lrc_loss_)
return lrc_loss_mean
AutoDL/LRC/operations.py 已删除 100644 → 0
浏览文件 @ 7af02eb6
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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.
#
# Based on:
# --------------------------------------------------------
# DARTS
# Copyright (c) 2018, Hanxiao Liu.
# Licensed under the Apache License, Version 2.0;
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import numpy as np
import time
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.initializer import Xavier
from paddle.fluid.initializer import Normal
from paddle.fluid.initializer import Constant
OPS = {
'none' : lambda input, C, stride, name, affine: Zero(input, stride, name),
'avg_pool_3x3' : lambda input, C, stride, name, affine: fluid.layers.pool2d(input, 3, 'avg', pool_stride=stride, pool_padding=1, name=name),
'max_pool_3x3' : lambda input, C, stride, name, affine: fluid.layers.pool2d(input, 3, 'max', pool_stride=stride, pool_padding=1, name=name),
'skip_connect' : lambda input,C, stride, name, affine: Identity(input, name) if stride == 1 else FactorizedReduce(input, C, name=name, affine=affine),
'sep_conv_3x3' : lambda input,C, stride, name, affine: SepConv(input, C, C, 3, stride, 1, name=name, affine=affine),
'sep_conv_5x5' : lambda input,C, stride, name, affine: SepConv(input, C, C, 5, stride, 2, name=name, affine=affine),
'sep_conv_7x7' : lambda input,C, stride, name, affine: SepConv(input, C, C, 7, stride, 3, name=name, affine=affine),
'dil_conv_3x3' : lambda input,C, stride, name, affine: DilConv(input, C, C, 3, stride, 2, 2, name=name, affine=affine),
'dil_conv_5x5' : lambda input,C, stride, name, affine: DilConv(input, C, C, 5, stride, 4, 2, name=name, affine=affine),
'conv_7x1_1x7' : lambda input,C, stride, name, affine: SevenConv(input, C, name=name, affine=affine)
}
def ReLUConvBN(input, C_out, kernel_size, stride, padding, name='',
affine=True):
relu_a = fluid.layers.relu(input)
conv2d_a = fluid.layers.conv2d(
relu_a,
C_out,
kernel_size,
stride,
padding,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'op.1.weight'),
bias_attr=False)
if affine:
reluconvbn_out = fluid.layers.batch_norm(
conv2d_a,
param_attr=ParamAttr(
initializer=Constant(1.), name=name + 'op.2.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name=name + 'op.2.bias'),
moving_mean_name=name + 'op.2.running_mean',
moving_variance_name=name + 'op.2.running_var')
else:
reluconvbn_out = fluid.layers.batch_norm(
conv2d_a,
param_attr=ParamAttr(
initializer=Constant(1.),
learning_rate=0.,
name=name + 'op.2.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.),
learning_rate=0.,
name=name + 'op.2.bias'),
moving_mean_name=name + 'op.2.running_mean',
moving_variance_name=name + 'op.2.running_var')
return reluconvbn_out
def DilConv(input,
C_in,
C_out,
kernel_size,
stride,
padding,
dilation,
name='',
affine=True):
relu_a = fluid.layers.relu(input)
conv2d_a = fluid.layers.conv2d(
relu_a,
C_in,
kernel_size,
stride,
padding,
dilation,
groups=C_in,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'op.1.weight'),
bias_attr=False,
use_cudnn=False)
conv2d_b = fluid.layers.conv2d(
conv2d_a,
C_out,
1,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'op.2.weight'),
bias_attr=False)
if affine:
dilconv_out = fluid.layers.batch_norm(
conv2d_b,
param_attr=ParamAttr(
initializer=Constant(1.), name=name + 'op.3.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name=name + 'op.3.bias'),
moving_mean_name=name + 'op.3.running_mean',
moving_variance_name=name + 'op.3.running_var')
else:
dilconv_out = fluid.layers.batch_norm(
conv2d_b,
param_attr=ParamAttr(
initializer=Constant(1.),
learning_rate=0.,
name=name + 'op.3.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.),
learning_rate=0.,
name=name + 'op.3.bias'),
moving_mean_name=name + 'op.3.running_mean',
moving_variance_name=name + 'op.3.running_var')
return dilconv_out
def SepConv(input,
C_in,
C_out,
kernel_size,
stride,
padding,
name='',
affine=True):
relu_a = fluid.layers.relu(input)
conv2d_a = fluid.layers.conv2d(
relu_a,
C_in,
kernel_size,
stride,
padding,
groups=C_in,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'op.1.weight'),
bias_attr=False,
use_cudnn=False)
conv2d_b = fluid.layers.conv2d(
conv2d_a,
C_in,
1,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'op.2.weight'),
bias_attr=False)
if affine:
bn_a = fluid.layers.batch_norm(
conv2d_b,
param_attr=ParamAttr(
initializer=Constant(1.), name=name + 'op.3.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name=name + 'op.3.bias'),
moving_mean_name=name + 'op.3.running_mean',
moving_variance_name=name + 'op.3.running_var')
else:
bn_a = fluid.layers.batch_norm(
conv2d_b,
param_attr=ParamAttr(
initializer=Constant(1.),
learning_rate=0.,
name=name + 'op.3.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.),
learning_rate=0.,
name=name + 'op.3.bias'),
moving_mean_name=name + 'op.3.running_mean',
moving_variance_name=name + 'op.3.running_var')
relu_b = fluid.layers.relu(bn_a)
conv2d_d = fluid.layers.conv2d(
relu_b,
C_in,
kernel_size,
1,
padding,
groups=C_in,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'op.5.weight'),
bias_attr=False,
use_cudnn=False)
conv2d_e = fluid.layers.conv2d(
conv2d_d,
C_out,
1,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'op.6.weight'),
bias_attr=False)
if affine:
sepconv_out = fluid.layers.batch_norm(
conv2d_e,
param_attr=ParamAttr(
initializer=Constant(1.), name=name + 'op.7.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name=name + 'op.7.bias'),
moving_mean_name=name + 'op.7.running_mean',
moving_variance_name=name + 'op.7.running_var')
else:
sepconv_out = fluid.layers.batch_norm(
conv2d_e,
param_attr=ParamAttr(
initializer=Constant(1.),
learning_rate=0.,
name=name + 'op.7.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.),
learning_rate=0.,
name=name + 'op.7.bias'),
moving_mean_name=name + 'op.7.running_mean',
moving_variance_name=name + 'op.7.running_var')
return sepconv_out
def SevenConv(input, C_out, stride, name='', affine=True):
relu_a = fluid.layers.relu(input)
conv2d_a = fluid.layers.conv2d(
relu_a,
C_out, (1, 7), (1, stride), (0, 3),
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'op.1.weight'),
bias_attr=False)
conv2d_b = fluid.layers.conv2d(
conv2d_a,
C_out, (7, 1), (stride, 1), (3, 0),
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'op.2.weight'),
bias_attr=False)
if affine:
out = fluid.layers.batch_norm(
conv2d_b,
param_attr=ParamAttr(
initializer=Constant(1.), name=name + 'op.3.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name=name + 'op.3.bias'),
moving_mean_name=name + 'op.3.running_mean',
moving_variance_name=name + 'op.3.running_var')
else:
out = fluid.layers.batch_norm(
conv2d_b,
param_attr=ParamAttr(
initializer=Constant(1.),
learning_rate=0.,
name=name + 'op.3.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.),
learning_rate=0.,
name=name + 'op.3.bias'),
moving_mean_name=name + 'op.3.running_mean',
moving_variance_name=name + 'op.3.running_var')
def Identity(input, name=''):
return input
def Zero(input, stride, name=''):
ones = np.ones(input.shape[-2:])
ones[::stride, ::stride] = 0
ones = fluid.layers.assign(ones)
return input * ones
def FactorizedReduce(input, C_out, name='', affine=True):
relu_a = fluid.layers.relu(input)
conv2d_a = fluid.layers.conv2d(
relu_a,
C_out // 2,
1,
2,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'conv_1.weight'),
bias_attr=False)
h_end = relu_a.shape[2]
w_end = relu_a.shape[3]
slice_a = fluid.layers.slice(relu_a, [2, 3], [1, 1], [h_end, w_end])
conv2d_b = fluid.layers.conv2d(
slice_a,
C_out // 2,
1,
2,
param_attr=ParamAttr(
initializer=Xavier(
uniform=False, fan_in=0),
name=name + 'conv_2.weight'),
bias_attr=False)
out = fluid.layers.concat([conv2d_a, conv2d_b], axis=1)
if affine:
out = fluid.layers.batch_norm(
out,
param_attr=ParamAttr(
initializer=Constant(1.), name=name + 'bn.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.), name=name + 'bn.bias'),
moving_mean_name=name + 'bn.running_mean',
moving_variance_name=name + 'bn.running_var')
else:
out = fluid.layers.batch_norm(
out,
param_attr=ParamAttr(
initializer=Constant(1.),
learning_rate=0.,
name=name + 'bn.weight'),
bias_attr=ParamAttr(
initializer=Constant(0.),
learning_rate=0.,
name=name + 'bn.bias'),
moving_mean_name=name + 'bn.running_mean',
moving_variance_name=name + 'bn.running_var')
return out
AutoDL/LRC/reader.py 已删除 100644 → 0
浏览文件 @ 7af02eb6
# Copyright (c) 2019 PaddlePaddle Authors. All Rig hts 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.
#
# Based on:
# --------------------------------------------------------
# DARTS
# Copyright (c) 2018, Hanxiao Liu.
# Licensed under the Apache License, Version 2.0;
# --------------------------------------------------------
"""
CIFAR-10 dataset.
This module will download dataset from
https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into
paddle reader creators.
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test images.
"""
from PIL import Image
from PIL import ImageOps
import numpy as np
import cPickle
import random
import utils
import paddle.fluid as fluid
import time
import os
import functools
import paddle.reader
__all__ = ['train10', 'test10']
image_size = 32
image_depth = 3
half_length = 8
CIFAR_MEAN = [0.4914, 0.4822, 0.4465]
CIFAR_STD = [0.24703233, 0.24348505, 0.26158768]
def generate_reshape_label(label, batch_size, CIFAR_CLASSES=10):
reshape_label = np.zeros((batch_size, 1), dtype='int32')
reshape_non_label = np.zeros(
(batch_size * (CIFAR_CLASSES - 1), 1), dtype='int32')
num = 0
for i in range(batch_size):
label_i = label[i]
reshape_label[i] = label_i + i * CIFAR_CLASSES
for j in range(CIFAR_CLASSES):
if label_i != j:
reshape_non_label[num] = \
j + i * CIFAR_CLASSES
num += 1
return reshape_label, reshape_non_label
def generate_bernoulli_number(batch_size, CIFAR_CLASSES=10):
rcc_iters = 50
rad_var = np.zeros((rcc_iters, batch_size, CIFAR_CLASSES - 1))
for i in range(rcc_iters):
bernoulli_num = np.random.binomial(size=batch_size, n=1, p=0.5)
bernoulli_map = np.array([])
ones = np.ones((CIFAR_CLASSES - 1, 1))
for batch_id in range(batch_size):
num = bernoulli_num[batch_id]
var_id = 2 * ones * num - 1
bernoulli_map = np.append(bernoulli_map, var_id)
rad_var[i] = bernoulli_map.reshape((batch_size, CIFAR_CLASSES - 1))
return rad_var.astype('float32')
def preprocess(sample, is_training, args):
image_array = sample.reshape(3, image_size, image_size)
rgb_array = np.transpose(image_array, (1, 2, 0))
img = Image.fromarray(rgb_array, 'RGB')
if is_training:
# pad and ramdom crop
img = ImageOps.expand(img, (4, 4, 4, 4), fill=0) # pad to 40 * 40 * 3
left_top = np.random.randint(9, size=2) # rand 0 - 8
img = img.crop((left_top[0], left_top[1], left_top[0] + image_size,
left_top[1] + image_size))
if np.random.randint(2):
img = img.transpose(Image.FLIP_LEFT_RIGHT)
img = np.array(img).astype(np.float32)
# per_image_standardization
img_float = img / 255.0
img = (img_float - CIFAR_MEAN) / CIFAR_STD
if is_training and args.cutout:
center = np.random.randint(image_size, size=2)
offset_width = max(0, center[0] - half_length)
offset_height = max(0, center[1] - half_length)
target_width = min(center[0] + half_length, image_size)
target_height = min(center[1] + half_length, image_size)
for i in range(offset_height, target_height):
for j in range(offset_width, target_width):
img[i][j][:] = 0.0
img = np.transpose(img, (2, 0, 1))
return img
def reader_creator_filepath(filename, sub_name, is_training, args):
files = os.listdir(filename)
names = [each_item for each_item in files if sub_name in each_item]
names.sort()
datasets = []
for name in names:
print("Reading file " + name)
batch = cPickle.load(open(filename + name, 'rb'))
data = batch['data']
labels = batch.get('labels', batch.get('fine_labels', None))
assert labels is not None
dataset = zip(data, labels)
datasets.extend(dataset)
random.shuffle(datasets)
def read_batch(datasets, args):
for sample, label in datasets:
im = preprocess(sample, is_training, args)
yield im, [int(label)]
def reader():
batch_data = []
batch_label = []
for data, label in read_batch(datasets, args):
batch_data.append(data)
batch_label.append(label)
if len(batch_data) == args.batch_size:
batch_data = np.array(batch_data, dtype='float32')
batch_label = np.array(batch_label, dtype='int64')
if is_training:
flatten_label, flatten_non_label = \
generate_reshape_label(batch_label, args.batch_size)
rad_var = generate_bernoulli_number(args.batch_size)
mixed_x, y_a, y_b, lam = utils.mixup_data(
batch_data, batch_label, args.batch_size,
args.mix_alpha)
batch_out = [[mixed_x, y_a, y_b, lam, flatten_label, \
flatten_non_label, rad_var]]
yield batch_out
else:
batch_out = [[batch_data, batch_label]]
yield batch_out
batch_data = []
batch_label = []
return reader
def train10(args):
"""
CIFAR-10 training set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Training reader creator
:rtype: callable
"""
return reader_creator_filepath(args.data, 'data_batch', True, args)
def test10(args):
"""
CIFAR-10 test set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Test reader creator.
:rtype: callable
"""
return reader_creator_filepath(args.data, 'test_batch', False, args)
AutoDL/LRC/run.sh 已删除 100644 → 0
浏览文件 @ 7af02eb6
CUDA_VISIBLE_DEVICES=0 python -u train_mixup.py \
--batch_size=80 \
--auxiliary \
--weight_decay=0.0003 \
--learning_rate=0.025 \
--lrc_loss_lambda=0.7 \
--cutout
AutoDL/LRC/train_mixup.py 已删除 100644 → 0
浏览文件 @ 7af02eb6
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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.
#
# Based on:
# --------------------------------------------------------
# DARTS
# Copyright (c) 2018, Hanxiao Liu.
# Licensed under the Apache License, Version 2.0;
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from learning_rate import cosine_decay
import numpy as np
import argparse
from model import NetworkCIFAR as Network
import reader
import sys
import os
import time
import logging
import genotypes
import paddle.fluid as fluid
import shutil
import utils
import cPickle as cp
parser = argparse.ArgumentParser("cifar")
parser.add_argument(
'--data',
type=str,
default='./dataset/cifar/cifar-10-batches-py/',
help='location of the data corpus')
parser.add_argument('--batch_size', type=int, default=96, help='batch size')
parser.add_argument(
'--learning_rate', type=float, default=0.025, help='init learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument(
'--weight_decay', type=float, default=3e-4, help='weight decay')
parser.add_argument(
'--report_freq', type=float, default=50, help='report frequency')
parser.add_argument(
'--epochs', type=int, default=600, help='num of training epochs')
parser.add_argument(
'--init_channels', type=int, default=36, help='num of init channels')
parser.add_argument(
'--layers', type=int, default=20, help='total number of layers')
parser.add_argument(
'--model_path',
type=str,
default='saved_models',
help='path to save the model')
parser.add_argument(
'--auxiliary',
action='store_true',
default=False,
help='use auxiliary tower')
parser.add_argument(
'--auxiliary_weight',
type=float,
default=0.4,
help='weight for auxiliary loss')
parser.add_argument(
'--cutout', action='store_true', default=False, help='use cutout')
parser.add_argument(
'--cutout_length', type=int, default=16, help='cutout length')
parser.add_argument(
'--drop_path_prob', type=float, default=0.2, help='drop path probability')
parser.add_argument('--save', type=str, default='EXP', help='experiment name')
parser.add_argument(
'--arch', type=str, default='DARTS', help='which architecture to use')
parser.add_argument(
'--grad_clip', type=float, default=5, help='gradient clipping')
parser.add_argument(
'--lr_exp_decay',
action='store_true',
default=False,
help='use exponential_decay learning_rate')
parser.add_argument('--mix_alpha', type=float, default=0.5, help='mixup alpha')
parser.add_argument(
'--lrc_loss_lambda', default=0, type=float, help='lrc_loss_lambda')
parser.add_argument(
'--loss_type',
default=1,
type=float,
help='loss_type 0: cross entropy 1: multi margin loss 2: max margin loss')
args = parser.parse_args()
CIFAR_CLASSES = 10
dataset_train_size = 50000
image_size = 32
def main():
image_shape = [3, image_size, image_size]
devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
devices_num = len(devices.split(","))
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
steps_one_epoch = dataset_train_size / (devices_num * args.batch_size)
train(model, args, image_shape, steps_one_epoch)
def build_program(main_prog, startup_prog, args, is_train, model, im_shape,
steps_one_epoch):
out = []
with fluid.program_guard(main_prog, startup_prog):
py_reader = model.build_input(im_shape, args.batch_size, is_train)
if is_train:
with fluid.unique_name.guard():
loss = model.train_model(py_reader, args.init_channels,
args.auxiliary, args.auxiliary_weight,
args.batch_size, args.lrc_loss_lambda)
optimizer = fluid.optimizer.Momentum(
learning_rate=cosine_decay(args.learning_rate, \
args.epochs, steps_one_epoch),
regularization=fluid.regularizer.L2Decay(\
args.weight_decay),
momentum=args.momentum)
optimizer.minimize(loss)
out = [py_reader, loss]
else:
with fluid.unique_name.guard():
loss, acc_1, acc_5 = model.test_model(py_reader,
args.init_channels)
out = [py_reader, loss, acc_1, acc_5]
return out
def train(model, args, im_shape, steps_one_epoch):
train_startup_prog = fluid.Program()
test_startup_prog = fluid.Program()
train_prog = fluid.Program()
test_prog = fluid.Program()
train_py_reader, loss_train = build_program(train_prog, train_startup_prog,
args, True, model, im_shape,
steps_one_epoch)
test_py_reader, loss_test, acc_1, acc_5 = build_program(
test_prog, test_startup_prog, args, False, model, im_shape,
steps_one_epoch)
test_prog = test_prog.clone(for_test=True)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(train_startup_prog)
exe.run(test_startup_prog)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = 1
train_exe = fluid.ParallelExecutor(
main_program=train_prog,
use_cuda=True,
loss_name=loss_train.name,
exec_strategy=exec_strategy)
train_reader = reader.train10(args)
test_reader = reader.test10(args)
train_py_reader.decorate_paddle_reader(train_reader)
test_py_reader.decorate_paddle_reader(test_reader)
fluid.clip.set_gradient_clip(fluid.clip.GradientClipByNorm(args.grad_clip))
def save_model(postfix, main_prog):
model_path = os.path.join(args.model_path, postfix)
if os.path.isdir(model_path):
shutil.rmtree(model_path)
fluid.io.save_persistables(exe, model_path, main_program=main_prog)
def test(epoch_id):
test_fetch_list = [loss_test, acc_1, acc_5]
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
test_py_reader.start()
test_start_time = time.time()
step_id = 0
try:
while True:
prev_test_start_time = test_start_time
test_start_time = time.time()
loss_test_v, acc_1_v, acc_5_v = exe.run(
test_prog, fetch_list=test_fetch_list)
objs.update(np.array(loss_test_v), args.batch_size)
top1.update(np.array(acc_1_v), args.batch_size)
top5.update(np.array(acc_5_v), args.batch_size)
if step_id % args.report_freq == 0:
print("Epoch {}, Step {}, acc_1 {}, acc_5 {}, time {}".
format(epoch_id, step_id,
np.array(acc_1_v),
np.array(acc_5_v), test_start_time -
prev_test_start_time))
step_id += 1
except fluid.core.EOFException:
test_py_reader.reset()
print("Epoch {0}, top1 {1}, top5 {2}".format(epoch_id, top1.avg,
top5.avg))
train_fetch_list = [loss_train]
epoch_start_time = time.time()
for epoch_id in range(args.epochs):
model.drop_path_prob = args.drop_path_prob * epoch_id / args.epochs
train_py_reader.start()
epoch_end_time = time.time()
if epoch_id > 0:
print("Epoch {}, total time {}".format(epoch_id - 1, epoch_end_time
- epoch_start_time))
epoch_start_time = epoch_end_time
epoch_end_time
start_time = time.time()
step_id = 0
try:
while True:
prev_start_time = start_time
start_time = time.time()
loss_v, = train_exe.run(
fetch_list=[v.name for v in train_fetch_list])
print("Epoch {}, Step {}, loss {}, time {}".format(epoch_id, step_id, \
np.array(loss_v).mean(), start_time-prev_start_time))
step_id += 1
sys.stdout.flush()
except fluid.core.EOFException:
train_py_reader.reset()
if epoch_id % 50 == 0 or epoch_id == args.epochs - 1:
save_model(str(epoch_id), train_prog)
test(epoch_id)
if __name__ == '__main__':
main()
AutoDL/LRC/utils.py 已删除 100644 → 0
浏览文件 @ 7af02eb6
# Copyright (c) 2019 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.
#
# Based on:
# --------------------------------------------------------
# DARTS
# Copyright (c) 2018, Hanxiao Liu.
# Licensed under the Apache License, Version 2.0;
# --------------------------------------------------------
import os
import sys
import time
import math
import numpy as np
def mixup_data(x, y, batch_size, alpha=1.0):
'''Compute the mixup data. Return mixed inputs, pairs of targets, and lambda'''
if alpha > 0.:
lam = np.random.beta(alpha, alpha)
else:
lam = 1.
index = np.random.permutation(batch_size)
mixed_x = lam * x + (1 - lam) * x[index, :]
y_a, y_b = y, y[index]
return mixed_x.astype('float32'), y_a.astype('int64'),\
y_b.astype('int64'), np.array(lam, dtype='float32')
class AvgrageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.avg = 0
self.sum = 0
self.cnt = 0
def update(self, val, n=1):
self.sum += val * n
self.cnt += n
self.avg = self.sum / self.cnt
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