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Merge pull request #510 from guoshengCS/add-inceptionresnetv2 

Add the Inception-ResNet-v2 model.
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README.cn.md
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......@@ -98,12 +98,14 @@ PaddlePaddle提供了丰富的运算单元，帮助大家以模块化的方式
图像相比文字能够提供更加生动、容易理解及更具艺术感的信息，是人们转递与交换信息的重要来源。图像分类是根据图像的语义信息对不同类别图像进行区分，是计算机视觉中重要的基础问题，也是图像检测、图像分割、物体跟踪、行为分析等其他高层视觉任务的基础，在许多领域都有着广泛的应用。如：安防领域的人脸识别和智能视频分析等，交通领域的交通场景识别，互联网领域基于内容的图像检索和相册自动归类，医学领域的图像识别等。
在图像分类任务中，我们向大家介绍如何训练AlexNet、VGG、GoogLeNet和ResNet模型。同时提供了一个够将Caffe训练好的模型文件转换为PaddlePaddle模型文件的模型转换工具。
在图像分类任务中，我们向大家介绍如何训练AlexNet、VGG、GoogLeNet、ResNet和Inception-Resnet-V2模型。同时提供了能够将Caffe或TensorFlow训练好的模型文件转换为PaddlePaddle模型文件的模型转换工具。
- 11.1 [将Caffe模型文件转换为PaddlePaddle模型文件](https://github.com/PaddlePaddle/models/tree/develop/image_classification/caffe2paddle)
- 11.2 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 11.3 [VGG](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 11.4 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 11.2 [将TensorFlow模型文件转换为PaddlePaddle模型文件](https://github.com/PaddlePaddle/models/tree/develop/image_classification/tf2paddle)
- 11.3 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 11.4 [VGG](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 11.5 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 11.6 [Inception-Resnet-V2](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
## 12. 目标检测
......
README.md
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......@@ -72,11 +72,13 @@ As an example for sequence-to-sequence learning, we take the machine translation
## 9. Image classification
For the example of image classification, we show you how to train AlexNet, VGG, GoogLeNet and ResNet models in PaddlePaddle. It also provides a model conversion tool that converts Caffe trained model files into PaddlePaddle model files.
For the example of image classification, we show you how to train AlexNet, VGG, GoogLeNet, ResNet and Inception-Resnet-V2 models in PaddlePaddle. It also provides model conversion tools that convert Caffe or TensorFlow trained model files into PaddlePaddle model files.
- 9.1 [convert Caffe model file to PaddlePaddle model file](https://github.com/PaddlePaddle/models/tree/develop/image_classification/caffe2paddle)
- 9.2 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 9.3 [VGG](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 9.4 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 9.2 [convert TensorFlow model file to PaddlePaddle model file](https://github.com/PaddlePaddle/models/tree/develop/image_classification/tf2paddle)
- 9.3 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 9.4 [VGG](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 9.5 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
- 9.6 [Inception-Resnet-V2](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
This tutorial is contributed by [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) and licensed under the [Apache-2.0 license](LICENSE).
image_classification/README.md
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图像分类
=======================
这里将介绍如何在PaddlePaddle下使用AlexNet、VGG、GoogLeNet和ResNet模型进行图像分类。图像分类问题的描述和这四种模型的介绍可以参考[PaddlePaddle book](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification)。
这里将介绍如何在PaddlePaddle下使用AlexNet、VGG、GoogLeNet、ResNet和Inception-ResNet-v2模型进行图像分类。图像分类问题的描述和这五种模型的介绍可以参考[PaddlePaddle book](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification)。
## 训练模型
......@@ -11,6 +11,8 @@
```python
import gzip
import argparse
import paddle.v2.dataset.flowers as flowers
import paddle.v2 as paddle
import reader
......@@ -18,6 +20,7 @@ import vgg
import resnet
import alexnet
import googlenet
import inception_resnet_v2
# PaddlePaddle init
......@@ -29,6 +32,7 @@ paddle.init(use_gpu=False, trainer_count=1)
设置算法参数（如数据维度、类别数目和batch size等参数），定义数据输入层`image`和类别标签`lbl`。
```python
# Use 3 * 331 * 331 or 3 * 299 * 299 for DATA_DIM in Inception-ResNet-v2.
DATA_DIM = 3 * 224 * 224
CLASS_DIM = 102
BATCH_SIZE = 128
......@@ -41,7 +45,7 @@ lbl = paddle.layer.data(
### 获得所用模型
这里可以选择使用AlexNet、VGG、GoogLeNet和ResNet模型中的一个模型进行图像分类。通过调用相应的方法可以获得网络最后的Softmax层。
这里可以选择使用AlexNet、VGG、GoogLeNet、ResNet和Inception-ResNet-v2模型中的一个模型进行图像分类。通过调用相应的方法可以获得网络最后的Softmax层。
1. 使用AlexNet模型
......@@ -86,6 +90,17 @@ ResNet模型可以通过下面的代码获取：
out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM)
```
5. 使用Inception-ResNet-v2模型
提供的Inception-ResNet-v2模型支持`3 * 331 * 331`和`3 * 299 * 299`两种大小的输入，同时可以自行设置dropout概率，可以通过如下的代码使用：
```python
out = inception_resnet_v2.inception_resnet_v2(
image, class_dim=CLASS_DIM, dropout_rate=0.5, size=DATA_DIM)
```
注意，由于和其他几种模型输入大小不同，若配合提供的`reader.py`使用Inception-ResNet-v2时请先将`reader.py`中`paddle.image.simple_transform`中的参数为修改为相应大小。
### 定义损失函数
```python
......@@ -173,7 +188,7 @@ def event_handler(event):
### 定义训练方法
对于AlexNet、VGG和ResNet，可以按下面的代码定义训练方法：
对于AlexNet、VGG、ResNet和Inception-ResNet-v2，可以按下面的代码定义训练方法：
```python
# Create trainer
......
image_classification/inception_resnet_v2.py 0 → 100644
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import paddle.v2 as paddle
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding=0,
active_type=paddle.activation.Relu(),
ch_in=None):
"""layer wrapper assembling convolution and batchnorm layer"""
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, epsilon=0.001, act=active_type)
def sequential_block(input, *layers):
"""helper function for sequential layers"""
for layer in layers:
layer_func, layer_conf = layer
input = layer_func(input, **layer_conf)
return input
def mixed_5b_block(input):
branch0 = conv_bn_layer(
input, ch_in=192, ch_out=96, filter_size=1, stride=1)
branch1 = sequential_block(input, (conv_bn_layer, {
"ch_in": 192,
"ch_out": 48,
"filter_size": 1,
"stride": 1
}), (conv_bn_layer, {
"ch_in": 48,
"ch_out": 64,
"filter_size": 5,
"stride": 1,
"padding": 2
}))
branch2 = sequential_block(input, (conv_bn_layer, {
"ch_in": 192,
"ch_out": 64,
"filter_size": 1,
"stride": 1
}), (conv_bn_layer, {
"ch_in": 64,
"ch_out": 96,
"filter_size": 3,
"stride": 1,
"padding": 1
}), (conv_bn_layer, {
"ch_in": 96,
"ch_out": 96,
"filter_size": 3,
"stride": 1,
"padding": 1
}))
branch3 = sequential_block(
input,
(paddle.layer.img_pool, {
"pool_size": 3,
"stride": 1,
"padding": 1,
"pool_type": paddle.pooling.Avg(),
"exclude_mode": False
}),
(conv_bn_layer, {
"ch_in": 192,
"ch_out": 64,
"filter_size": 1,
"stride": 1
}), )
out = paddle.layer.concat(input=[branch0, branch1, branch2, branch3])
return out
def block35(input, scale=1.0):
branch0 = conv_bn_layer(
input, ch_in=320, ch_out=32, filter_size=1, stride=1)
branch1 = sequential_block(input, (conv_bn_layer, {
"ch_in": 320,
"ch_out": 32,
"filter_size": 1,
"stride": 1
}), (conv_bn_layer, {
"ch_in": 32,
"ch_out": 32,
"filter_size": 3,
"stride": 1,
"padding": 1
}))
branch2 = sequential_block(input, (conv_bn_layer, {
"ch_in": 320,
"ch_out": 32,
"filter_size": 1,
"stride": 1
}), (conv_bn_layer, {
"ch_in": 32,
"ch_out": 48,
"filter_size": 3,
"stride": 1,
"padding": 1
}), (conv_bn_layer, {
"ch_in": 48,
"ch_out": 64,
"filter_size": 3,
"stride": 1,
"padding": 1
}))
out = paddle.layer.concat(input=[branch0, branch1, branch2])
out = paddle.layer.img_conv(
input=out,
filter_size=1,
num_channels=128,
num_filters=320,
stride=1,
padding=0,
act=paddle.activation.Linear(),
bias_attr=None)
out = paddle.layer.slope_intercept(out, slope=scale, intercept=0.0)
out = paddle.layer.addto(input=[input, out], act=paddle.activation.Relu())
return out
def mixed_6a_block(input):
branch0 = conv_bn_layer(
input, ch_in=320, ch_out=384, filter_size=3, stride=2)
branch1 = sequential_block(input, (conv_bn_layer, {
"ch_in": 320,
"ch_out": 256,
"filter_size": 1,
"stride": 1
}), (conv_bn_layer, {
"ch_in": 256,
"ch_out": 256,
"filter_size": 3,
"stride": 1,
"padding": 1
}), (conv_bn_layer, {
"ch_in": 256,
"ch_out": 384,
"filter_size": 3,
"stride": 2
}))
branch2 = paddle.layer.img_pool(
input,
num_channels=320,
pool_size=3,
stride=2,
pool_type=paddle.pooling.Max())
out = paddle.layer.concat(input=[branch0, branch1, branch2])
return out
def block17(input, scale=1.0):
branch0 = conv_bn_layer(
input, ch_in=1088, ch_out=192, filter_size=1, stride=1)
branch1 = sequential_block(input, (conv_bn_layer, {
"ch_in": 1088,
"ch_out": 128,
"filter_size": 1,
"stride": 1
}), (conv_bn_layer, {
"ch_in": 128,
"ch_out": 160,
"filter_size": [7, 1],
"stride": 1,
"padding": [3, 0]
}), (conv_bn_layer, {
"ch_in": 160,
"ch_out": 192,
"filter_size": [1, 7],
"stride": 1,
"padding": [0, 3]
}))
out = paddle.layer.concat(input=[branch0, branch1])
out = paddle.layer.img_conv(
input=out,
filter_size=1,
num_channels=384,
num_filters=1088,
stride=1,
padding=0,
act=paddle.activation.Linear(),
bias_attr=None)
out = paddle.layer.slope_intercept(out, slope=scale, intercept=0.0)
out = paddle.layer.addto(input=[input, out], act=paddle.activation.Relu())
return out
def mixed_7a_block(input):
branch0 = sequential_block(
input,
(conv_bn_layer, {
"ch_in": 1088,
"ch_out": 256,
"filter_size": 1,
"stride": 1
}),
(conv_bn_layer, {
"ch_in": 256,
"ch_out": 384,
"filter_size": 3,
"stride": 2
}), )
branch1 = sequential_block(
input,
(conv_bn_layer, {
"ch_in": 1088,
"ch_out": 256,
"filter_size": 1,
"stride": 1
}),
(conv_bn_layer, {
"ch_in": 256,
"ch_out": 288,
"filter_size": 3,
"stride": 2
}), )
branch2 = sequential_block(input, (conv_bn_layer, {
"ch_in": 1088,
"ch_out": 256,
"filter_size": 1,
"stride": 1
}), (conv_bn_layer, {
"ch_in": 256,
"ch_out": 288,
"filter_size": 3,
"stride": 1,
"padding": 1
}), (conv_bn_layer, {
"ch_in": 288,
"ch_out": 320,
"filter_size": 3,
"stride": 2
}))
branch3 = paddle.layer.img_pool(
input,
num_channels=1088,
pool_size=3,
stride=2,
pool_type=paddle.pooling.Max())
out = paddle.layer.concat(input=[branch0, branch1, branch2, branch3])
return out
def block8(input, scale=1.0, no_relu=False):
branch0 = conv_bn_layer(
input, ch_in=2080, ch_out=192, filter_size=1, stride=1)
branch1 = sequential_block(input, (conv_bn_layer, {
"ch_in": 2080,
"ch_out": 192,
"filter_size": 1,
"stride": 1
}), (conv_bn_layer, {
"ch_in": 192,
"ch_out": 224,
"filter_size": [3, 1],
"stride": 1,
"padding": [1, 0]
}), (conv_bn_layer, {
"ch_in": 224,
"ch_out": 256,
"filter_size": [1, 3],
"stride": 1,
"padding": [0, 1]
}))
out = paddle.layer.concat(input=[branch0, branch1])
out = paddle.layer.img_conv(
input=out,
filter_size=1,
num_channels=448,
num_filters=2080,
stride=1,
padding=0,
act=paddle.activation.Linear(),
bias_attr=None)
out = paddle.layer.slope_intercept(out, slope=scale, intercept=0.0)
out = paddle.layer.addto(
input=[input, out],
act=paddle.activation.Linear() if no_relu else paddle.activation.Relu())
return out
def inception_resnet_v2(input,
class_dim,
dropout_rate=0.5,
data_dim=3 * 331 * 331):
conv2d_1a = conv_bn_layer(
input, ch_in=3, ch_out=32, filter_size=3, stride=2)
conv2d_2a = conv_bn_layer(
conv2d_1a, ch_in=32, ch_out=32, filter_size=3, stride=1)
conv2d_2b = conv_bn_layer(
conv2d_2a, ch_in=32, ch_out=64, filter_size=3, stride=1, padding=1)
maxpool_3a = paddle.layer.img_pool(
input=conv2d_2b, pool_size=3, stride=2, pool_type=paddle.pooling.Max())
conv2d_3b = conv_bn_layer(
maxpool_3a, ch_in=64, ch_out=80, filter_size=1, stride=1)
conv2d_4a = conv_bn_layer(
conv2d_3b, ch_in=80, ch_out=192, filter_size=3, stride=1)
maxpool_5a = paddle.layer.img_pool(
input=conv2d_4a, pool_size=3, stride=2, pool_type=paddle.pooling.Max())
mixed_5b = mixed_5b_block(maxpool_5a)
repeat = sequential_block(mixed_5b, *([(block35, {"scale": 0.17})] * 10))
mixed_6a = mixed_6a_block(repeat)
repeat1 = sequential_block(mixed_6a, *([(block17, {"scale": 0.10})] * 20))
mixed_7a = mixed_7a_block(repeat1)
repeat2 = sequential_block(mixed_7a, *([(block8, {"scale": 0.20})] * 9))
block_8 = block8(repeat2, no_relu=True)
conv2d_7b = conv_bn_layer(
block_8, ch_in=2080, ch_out=1536, filter_size=1, stride=1)
avgpool_1a = paddle.layer.img_pool(
input=conv2d_7b,
pool_size=8 if data_dim == 3 * 299 * 299 else 9,
stride=1,
pool_type=paddle.pooling.Avg(),
exclude_mode=False)
drop_out = paddle.layer.dropout(input=avgpool_1a, dropout_rate=dropout_rate)
out = paddle.layer.fc(
input=drop_out, size=class_dim, act=paddle.activation.Softmax())
return out
image_classification/infer.py
浏览文件 @ 4f13cce6
import os
import gzip
import argparse
import numpy as np
from PIL import Image
import paddle.v2 as paddle
import reader
import vgg
import resnet
import alexnet
import googlenet
import argparse
import os
from PIL import Image
import numpy as np
import inception_resnet_v2
WIDTH = 224
HEIGHT = 224
DATA_DIM = 3 * WIDTH * HEIGHT
DATA_DIM = 3 * 224 * 224 # Use 3 * 331 * 331 or 3 * 299 * 299 for Inception-ResNet-v2.
CLASS_DIM = 102
......@@ -26,7 +26,10 @@ def main():
parser.add_argument(
'model',
help='The model for image classification',
choices=['alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet'])
choices=[
'alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet',
'inception-resnet-v2'
])
parser.add_argument(
'params_path', help='The file which stores the parameters')
args = parser.parse_args()
......@@ -49,6 +52,10 @@ def main():
out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM)
elif args.model == 'googlenet':
out, _, _ = googlenet.googlenet(image, class_dim=CLASS_DIM)
elif args.model == 'inception-resnet-v2':
assert DATA_DIM == 3 * 331 * 331 or DATA_DIM == 3 * 299 * 299
out = inception_resnet_v2.inception_resnet_v2(
image, class_dim=CLASS_DIM, dropout_rate=0.5, data_dim=DATA_DIM)
# load parameters
with gzip.open(args.params_path, 'r') as f:
......
image_classification/train.py
浏览文件 @ 4f13cce6
import gzip
import argparse
import paddle.v2.dataset.flowers as flowers
import paddle.v2 as paddle
import reader
......@@ -6,9 +8,9 @@ import vgg
import resnet
import alexnet
import googlenet
import argparse
import inception_resnet_v2
DATA_DIM = 3 * 224 * 224
DATA_DIM = 3 * 224 * 224 # Use 3 * 331 * 331 or 3 * 299 * 299 for Inception-ResNet-v2.
CLASS_DIM = 102
BATCH_SIZE = 128
......@@ -19,7 +21,10 @@ def main():
parser.add_argument(
'model',
help='The model for image classification',
choices=['alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet'])
choices=[
'alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet',
'inception-resnet-v2'
])
args = parser.parse_args()
# PaddlePaddle init
......@@ -52,6 +57,10 @@ def main():
input=out2, label=lbl, coeff=0.3)
paddle.evaluator.classification_error(input=out2, label=lbl)
extra_layers = [loss1, loss2]
elif args.model == 'inception-resnet-v2':
assert DATA_DIM == 3 * 331 * 331 or DATA_DIM == 3 * 299 * 299
out = inception_resnet_v2.inception_resnet_v2(
image, class_dim=CLASS_DIM, dropout_rate=0.5, data_dim=DATA_DIM)
cost = paddle.layer.classification_cost(input=out, label=lbl)
......
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