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add drn

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\ No newline at end of file
drn/README.md 0 → 100644
浏览文件 @ 56794fe4
The minimum PaddlePaddle version needed for the code sample in this directory is v0.10.0. If you are on a version of PaddlePaddle earlier than v0.10.0, please [update your installation](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html).
-----------------------
# Deep Residual Networks（DRN）
## 简介
在论文[1]中提到了，1202层的ResNet出现了过拟合的问题，有待进一步改进。第二年，何的团队就发表了“Identity Mappings in Deep Residual Networks”这篇文章[2]，分析了ResNet成功的关键因素——residual block背后的算法，并对residual block以及after-addition activation进行改进，通过一系列的ablation experiments验证了，在residual block和after-addition activation上都使用identity mapping（恒等映射）时，能对模型训练产生很好的效果，通过这项改进，也成功的训练出了具有很好效果的ResNet-1001。
## DRN 网络结构
在原始的ResNet中，对于每一个residual building block：
![pic1](./img/pic1.png)
可以表现为以下形式:
$$
y_l = h(x_l) + f(x_l, w_l)
x_{l+1} = f(y_l)
$$
其中$h(x_1)$为一个恒等映射，$f(y_l)$代表ReLU激活函数，在[2]中提出了，如果如果$h(x)$和$f(y)$都是恒等映射，即$h(x_l)=x_l、f(y_l)=y_l$，那么在训练的前向和反向传播阶段，信号可以直接从一个单元传递到另外一个单元，使得训练变得更加简单。
在DNN16中，具有以下优良特性：
（1）对于任意深的单元**L**的特征 $x_L$ 可以表达为浅层单元**l**的特征 $x_l$加上一个形如 $\sum_{i=l}^{L−1}F$的残差函数，这表明了任意单元**L** 和 **l**之间都具有残差特性。
（2）对于任意深的单元 **L**，它的特征 $x_L = x_0 + \sum_{i=0}^{L−1}F(x_i,W_i)$，即为之前所有残差函数输出的总和(加上$x_0$)。而正好相反的是，“plain network”中的特征xL是一系列矩阵向量的乘积，也就是$\prod_{i=0}^{L−1}W_i x_0$，而求和的计算量远远小于求积的计算量。
实验发现，$h(x_l) = x_l$的误差衰减最快，误差也最低(下图a子图所示)：
![pic2](./img/pic2.png)
对于激活函数，验发现，将ReLU和BN都放在预激活中，即full pre-activation(下图子图e所示)在ResNet-110和ResNet-164上的效果都最好。
![pic3](./img/pic3.png)
## 复现文件一览
在复现文件中，包含以下文件：
<table>
<tr>
<th width=60%>文件</th>
<th width=40%>描述</th>
</tr>
<tr>
<td> train.py </td>
<td> DRN模型训练脚本 </td>
</tr>
<tr>
<td> infer.py </td>
<td> 利用训练好的DRN模型做预测 </td>
<tr>
<td> drn.py </td>
<td> 定义DRN的网络结构 </td>
</tr>
</table>
## 基于flower数据集的模型复现
### 数据准备
所使用的的数据集是paddle中自带的flowers数据集进行训练，直接import即可:
```
import paddle.v2.dataset.flowers as flowers
```
### 网络定义
网络的定义在文件```drn.py```中完整实现，其中最主要的是残差网络的部分：
```
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
active_type=paddle.activation.Relu(),
ch_in=None):
tmp = paddle.layer.img_conv(
input=input,
filter_size=filter_size,
num_channels=ch_in,
num_filters=ch_out,
stride=stride,
padding=padding,
act=paddle.activation.Linear(),
bias_attr=False)
return paddle.layer.batch_norm(input=tmp, act=active_type)
```
### 训练
接下来，执行``` python train.py -model drn``` 即可训练过程，在训练过程中，建议使CUDA GPU进行训练，如果使用CPU训练耗时可长达90小时以上，关键代码为：
```
paddle.init(use_gpu=True, trainer_count=1)
image = paddle.layer.data(name="image", type=paddle.data_type.dense_vector(DATA_DIM))
lbl = paddle.layer.data(name="label", type=paddle.data_type.integer_value(CLASS_DIM))
（省略部分代码）
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer,
extra_layers=extra_layers)
（省略部分代码）
trainer.train(
reader=train_reader, num_passes=200, event_handler=event_handler)
```
下面是关于上述代码的解释：
1. 进行``` paddle.init ```以1个GPU的方式初始化
2. 定义```img```图像名 和 ```lbl``` 图像标签
3. 定义```trainer```，包含损失函数、参数、优化器和层数信息
4. 在```train```函数中进行实际训练，共执行200趟
执行过程中，控制台将打印如下所示的信息：
```
Pass 0, Batch 0, Cost 2.2512, ...
Pass 0, Batch 1, Cost 2.1532, ...
```
同时在```train.py```目录下，每趟训练完成时，将生成```params_pass_0.tar,gz```，最后一趟的200.tar.gz文件生成时，训练完成。
### 应用模型
应用训练好的模型，执行``` python infer.py -data_list <文件目录> =model drn```即可：
```
\# load parameters
with gzip.open('params_pass_200.tar.gz', 'r') as f:
parameters = paddle.parameters.Parameters.from_tar(f)
file_list = [line.strip() for line in open(image_list_file)]
test_data = [(paddle.image.load_and_transform(image_file, 256, 224, False)
.flatten().astype('float32'), )
for image_file in file_list]
probs = paddle.infer(
output_layer=out, parameters=parameters, input=test_data)
lab = np.argsort(-probs)
for file_name, result in zip(file_list, lab):
print "Label of %s is: %d" % (file_name, result[0])
```
代码将从图片文件夹中读取对应的图片文件，同时给出预测的标签结果，并进行输出。
drn/drn.py 0 → 100644
浏览文件 @ 56794fe4
import paddle.v2 as paddle
__all__ = ['drn16']
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
active_type=paddle.activation.Relu(),
ch_in=None):
tmp = paddle.layer.img_conv(
input=input,
filter_size=filter_size,
num_channels=ch_in,
num_filters=ch_out,
stride=stride,
padding=padding,
act=paddle.activation.Linear(),
bias_attr=False)
return paddle.layer.batch_norm(input=tmp, act=active_type)
def shortcut(input, ch_out, stride):
if input.num_filters != ch_out:
return conv_bn_layer(input, ch_out, 1, stride, 0,
paddle.activation.Linear())
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, paddle.activation.Linear())
return paddle.layer.addto(
input=[short, conv2], act=paddle.activation.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,
paddle.activation.Linear())
return paddle.layer.addto(
input=[short, conv3], act=paddle.activation.Relu())
def layer_warp(block_func, input, ch_out, count, stride):
conv = block_func(input, ch_out, stride)
for i in range(1, count):
conv = block_func(conv, ch_out, 1)
return conv
def drn16(input, class_dim, depth=32):
assert (depth - 2) % 6 == 0
n = (depth - 2) / 6
conv1 = conv_bn_layer(
input, ch_in=3, 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 = paddle.layer.img_pool(
input=res3, pool_size=8, stride=1, pool_type=paddle.pooling.Avg())
out = paddle.layer.fc(input=pool,
size=class_dim,
act=paddle.activation.Softmax())
return out
\ No newline at end of file
drn/infer.py 0 → 100644
浏览文件 @ 56794fe4
import os
import gzip
import argparse
import numpy as np
from PIL import Image
import paddle.v2 as paddle
import drn
DATA_DIM = 3 * 224 * 224
CLASS_DIM = 102
def main():
# parse the argument
parser = argparse.ArgumentParser()
parser.add_argument(
'data_list',
help='The path of data list file, which consists of one image path per line'
)
parser.add_argument(
'model',
help='The model for image classification',
choices=[
'drn'
])
parser.add_argument(
'params_path', help='The file which stores the parameters')
args = parser.parse_args()
# PaddlePaddle init
paddle.init(use_gpu=True, trainer_count=1)
image = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(DATA_DIM))
if args.model == 'drn':
out = drn.drn16(image, class_dim=CLASS_DIM)
# load parameters
with gzip.open(args.params_path, 'r') as f:
parameters = paddle.parameters.Parameters.from_tar(f)
file_list = [line.strip() for line in open(args.data_list)]
test_data = [(paddle.image.load_and_transform(image_file, 256, 224, False)
.flatten().astype('float32'), ) for image_file in file_list]
probs = paddle.infer(
output_layer=out, parameters=parameters, input=test_data)
lab = np.argsort(-probs)
for file_name, result in zip(file_list, lab):
print "Label of %s is: %d" % (file_name, result[0])
if __name__ == '__main__':
main()
\ No newline at end of file
drn/train.py 0 → 100644
浏览文件 @ 56794fe4
import gzip
import argparse
import paddle.v2.dataset.flowers as flowers
import paddle.v2 as paddle
import drn
DATA_DIM = 3 * 224 * 224
CLASS_DIM = 102
BATCH_SIZE = 128
def main():
# parse the argument
parser = argparse.ArgumentParser()
parser.add_argument(
'model',
help='The model for image classification',
choices=[
'drn'
])
args = parser.parse_args()
# PaddlePaddle init
paddle.init(use_gpu=True, trainer_count=1)
image = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(DATA_DIM))
lbl = paddle.layer.data(
name="label", type=paddle.data_type.integer_value(CLASS_DIM))
extra_layers = None
learning_rate = 0.01
if args.model == 'drn':
out = drn.drn16(image, class_dim=CLASS_DIM)
cost = paddle.layer.classification_cost(input=out, label=lbl)
# Create parameters
parameters = paddle.parameters.create(cost)
# Create optimizer
optimizer = paddle.optimizer.Momentum(
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 *
BATCH_SIZE),
learning_rate=learning_rate / BATCH_SIZE,
learning_rate_decay_a=0.1,
learning_rate_decay_b=128000 * 35,
learning_rate_schedule="discexp", )
train_reader = paddle.batch(
paddle.reader.shuffle(
flowers.train(),
# To use other data, replace the above line with:
# reader.train_reader('train.list'),
buf_size=1000),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
flowers.valid(),
# To use other data, replace the above line with:
# reader.test_reader('val.list'),
batch_size=BATCH_SIZE)
# Create trainer
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer,
extra_layers=extra_layers)
# End batch and end pass event handler
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 1 == 0:
print "\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
if isinstance(event, paddle.event.EndPass):
with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
trainer.save_parameter_to_tar(f)
result = trainer.test(reader=test_reader)
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
trainer.train(
reader=train_reader, num_passes=200, event_handler=event_handler)
if __name__ == '__main__':
main()
\ No newline at end of file
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