Merge remote-tracking branch 'upstream/develop' into xception

README.cn.md

@@ -98,13 +98,16 @@ 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、ResNet和Xception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、Inception-v4、Inception-Resnet-V2和Xception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)
-- 11.5 [Xception](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-v4](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 11.7 [Inception-Resnet-V2](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 11.8 [Xception](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
 
 ## 12. 目标检测
 

README.md

@@ -72,12 +72,15 @@ 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, ResNet and Xception 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, Inception-v4, Inception-Resnet-V2 and Xception 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)
-- 9.5 [Xception](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-v4](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 9.7 [Inception-Resnet-V2](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 9.8 [Xception](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).

dssm/README.cn.md

@@ -278,7 +278,7 @@ Pairwise Rank复用上面的DNN结构，同一个source对两个target求相似
 - `dnn_dims` 模型各层的维度设置，默认为 `256,128,64,32`，即模型有4层，各层维度如上设置
 
 ## 使用训练好的模型预测
-详细命令行参数请执行 `python train.py --help`进行查阅。重要参数解释如下：
+详细命令行参数请执行 `python infer.py --help`进行查阅。重要参数解释如下：
 
 - `data_path` 需要预测的数据路径
 - `prediction_output_path` 预测的输出路径

image_classification/README.md

 图像分类
 =======================
 
-这里将介绍如何在PaddlePaddle下使用AlexNet、VGG、GoogLeNet、ResNet和Xception模型进行图像分类。图像分类问题的描述和这些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、Inception-v4、Inception-ResNet-v2和Xception模型进行图像分类。图像分类问题的描述和这些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 +20,9 @@ import vgg
 import resnet
 import alexnet
 import googlenet
+import inception_v4
+import inception_resnet_v2
+import xception
 
 
 # PaddlePaddle init
@@ -29,6 +34,7 @@ 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 +47,7 @@ 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、Inception-v4、Inception-ResNet-v2和Xception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
 
@@ -86,7 +92,27 @@ 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Xception模型
+5. 使用Inception-v4模型
+
+Inception-v4模型可以通过下面的代码获取, 本例中使用的模型输入大小为`3 * 224 * 224` (原文献中使用的输入大小为`3 * 299 * 299`)：
+
+```python
+out = inception_v4.inception_v4(image, class_dim=CLASS_DIM)
+```
+
+
+6. 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。
+
+7. 使用Xception模型
 
 Xception模型可以通过下面的代码获取：
 
@@ -181,7 +207,7 @@ 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、Inception-v4、Inception-ResNet-v2和Xception，可以按下面的代码定义训练方法：
 
 ```python
 # Create trainer

image_classification/inception_resnet_v2.py

+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/inception_v4.py

+import paddle.v2 as paddle
+
+__all__ = ['inception_v4']
+
+
+def img_conv(name,
+             input,
+             num_filters,
+             filter_size,
+             stride,
+             padding,
+             num_channels=None):
+    conv = paddle.layer.img_conv(
+        name=name,
+        input=input,
+        num_channels=num_channels,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        stride=stride,
+        padding=padding,
+        act=paddle.activation.Linear())
+    norm = paddle.layer.batch_norm(
+        name=name + '_norm', input=conv, act=paddle.activation.Relu())
+    return norm
+
+
+def stem(input):
+    conv0 = img_conv(
+        name='stem_conv0',
+        input=input,
+        num_channels=3,
+        num_filters=32,
+        filter_size=3,
+        stride=2,
+        padding=1)
+    conv1 = img_conv(
+        name='stem_conv1',
+        input=conv0,
+        num_channels=32,
+        num_filters=32,
+        filter_size=3,
+        stride=1,
+        padding=1)
+    conv2 = img_conv(
+        name='stem_conv2',
+        input=conv1,
+        num_channels=32,
+        num_filters=64,
+        filter_size=3,
+        stride=1,
+        padding=1)
+
+    def block0(input):
+        pool0 = paddle.layer.img_pool(
+            name='stem_branch0_pool0',
+            input=input,
+            num_channels=64,
+            pool_size=3,
+            stride=2,
+            pool_type=paddle.pooling.Max())
+        conv0 = img_conv(
+            name='stem_branch0_conv0',
+            input=input,
+            num_channels=64,
+            num_filters=96,
+            filter_size=3,
+            stride=2,
+            padding=1)
+        return paddle.layer.concat(input=[pool0, conv0])
+
+    def block1(input):
+        l_conv0 = img_conv(
+            name='stem_branch1_l_conv0',
+            input=input,
+            num_channels=160,
+            num_filters=64,
+            filter_size=1,
+            stride=1,
+            padding=0)
+        l_conv1 = img_conv(
+            name='stem_branch1_l_conv1',
+            input=l_conv0,
+            num_channels=64,
+            num_filters=96,
+            filter_size=3,
+            stride=1,
+            padding=1)
+        r_conv0 = img_conv(
+            name='stem_branch1_r_conv0',
+            input=input,
+            num_channels=160,
+            num_filters=64,
+            filter_size=1,
+            stride=1,
+            padding=0)
+        r_conv1 = img_conv(
+            name='stem_branch1_r_conv1',
+            input=r_conv0,
+            num_channels=64,
+            num_filters=64,
+            filter_size=(7, 1),
+            stride=1,
+            padding=(3, 0))
+        r_conv2 = img_conv(
+            name='stem_branch1_r_conv2',
+            input=r_conv1,
+            num_channels=64,
+            num_filters=64,
+            filter_size=(1, 7),
+            stride=1,
+            padding=(0, 3))
+        r_conv3 = img_conv(
+            name='stem_branch1_r_conv3',
+            input=r_conv2,
+            num_channels=64,
+            num_filters=96,
+            filter_size=3,
+            stride=1,
+            padding=1)
+        return paddle.layer.concat(input=[l_conv1, r_conv3])
+
+    def block2(input):
+        conv0 = img_conv(
+            name='stem_branch2_conv0',
+            input=input,
+            num_channels=192,
+            num_filters=192,
+            filter_size=3,
+            stride=2,
+            padding=1)
+        pool0 = paddle.layer.img_pool(
+            name='stem_branch2_pool0',
+            input=input,
+            num_channels=192,
+            pool_size=3,
+            stride=2,
+            pool_type=paddle.pooling.Max())
+        return paddle.layer.concat(input=[conv0, pool0])
+
+    conv3 = block0(conv2)
+    conv4 = block1(conv3)
+    conv5 = block2(conv4)
+    return conv5
+
+
+def Inception_A(input, depth):
+    b0_pool0 = paddle.layer.img_pool(
+        name='inceptA{0}_branch0_pool0'.format(depth),
+        input=input,
+        num_channels=384,
+        pool_size=3,
+        stride=1,
+        padding=1,
+        pool_type=paddle.pooling.Avg())
+    b0_conv0 = img_conv(
+        name='inceptA{0}_branch0_conv0'.format(depth),
+        input=b0_pool0,
+        num_channels=384,
+        num_filters=96,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b1_conv0 = img_conv(
+        name='inceptA{0}_branch1_conv0'.format(depth),
+        input=input,
+        num_channels=384,
+        num_filters=96,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv0 = img_conv(
+        name='inceptA{0}_branch2_conv0'.format(depth),
+        input=input,
+        num_channels=384,
+        num_filters=64,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = img_conv(
+        name='inceptA{0}_branch2_conv1'.format(depth),
+        input=b2_conv0,
+        num_channels=64,
+        num_filters=96,
+        filter_size=3,
+        stride=1,
+        padding=1)
+    b3_conv0 = img_conv(
+        name='inceptA{0}_branch3_conv0'.format(depth),
+        input=input,
+        num_channels=384,
+        num_filters=64,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b3_conv1 = img_conv(
+        name='inceptA{0}_branch3_conv1'.format(depth),
+        input=b3_conv0,
+        num_channels=64,
+        num_filters=96,
+        filter_size=3,
+        stride=1,
+        padding=1)
+    b3_conv2 = img_conv(
+        name='inceptA{0}_branch3_conv2'.format(depth),
+        input=b3_conv1,
+        num_channels=96,
+        num_filters=96,
+        filter_size=3,
+        stride=1,
+        padding=1)
+    return paddle.layer.concat(input=[b0_conv0, b1_conv0, b2_conv1, b3_conv2])
+
+
+def Inception_B(input, depth):
+    b0_pool0 = paddle.layer.img_pool(
+        name='inceptB{0}_branch0_pool0'.format(depth),
+        input=input,
+        num_channels=1024,
+        pool_size=3,
+        stride=1,
+        padding=1,
+        pool_type=paddle.pooling.Avg())
+    b0_conv0 = img_conv(
+        name='inceptB{0}_branch0_conv0'.format(depth),
+        input=b0_pool0,
+        num_channels=1024,
+        num_filters=128,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b1_conv0 = img_conv(
+        name='inceptB{0}_branch1_conv0'.format(depth),
+        input=input,
+        num_channels=1024,
+        num_filters=384,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv0 = img_conv(
+        name='inceptB{0}_branch2_conv0'.format(depth),
+        input=input,
+        num_channels=1024,
+        num_filters=192,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = img_conv(
+        name='inceptB{0}_branch2_conv1'.format(depth),
+        input=b2_conv0,
+        num_channels=192,
+        num_filters=224,
+        filter_size=(1, 7),
+        stride=1,
+        padding=(0, 3))
+    b2_conv2 = img_conv(
+        name='inceptB{0}_branch2_conv2'.format(depth),
+        input=b2_conv1,
+        num_channels=224,
+        num_filters=256,
+        filter_size=(7, 1),
+        stride=1,
+        padding=(3, 0))
+    b3_conv0 = img_conv(
+        name='inceptB{0}_branch3_conv0'.format(depth),
+        input=input,
+        num_channels=1024,
+        num_filters=192,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b3_conv1 = img_conv(
+        name='inceptB{0}_branch3_conv1'.format(depth),
+        input=b3_conv0,
+        num_channels=192,
+        num_filters=192,
+        filter_size=(1, 7),
+        stride=1,
+        padding=(0, 3))
+    b3_conv2 = img_conv(
+        name='inceptB{0}_branch3_conv2'.format(depth),
+        input=b3_conv1,
+        num_channels=192,
+        num_filters=224,
+        filter_size=(7, 1),
+        stride=1,
+        padding=(3, 0))
+    b3_conv3 = img_conv(
+        name='inceptB{0}_branch3_conv3'.format(depth),
+        input=b3_conv2,
+        num_channels=224,
+        num_filters=224,
+        filter_size=(1, 7),
+        stride=1,
+        padding=(0, 3))
+    b3_conv4 = img_conv(
+        name='inceptB{0}_branch3_conv4'.format(depth),
+        input=b3_conv3,
+        num_channels=224,
+        num_filters=256,
+        filter_size=(7, 1),
+        stride=1,
+        padding=(3, 0))
+    return paddle.layer.concat(input=[b0_conv0, b1_conv0, b2_conv2, b3_conv4])
+
+
+def Inception_C(input, depth):
+    b0_pool0 = paddle.layer.img_pool(
+        name='inceptC{0}_branch0_pool0'.format(depth),
+        input=input,
+        num_channels=1536,
+        pool_size=3,
+        stride=1,
+        padding=1,
+        pool_type=paddle.pooling.Avg())
+    b0_conv0 = img_conv(
+        name='inceptC{0}_branch0_conv0'.format(depth),
+        input=b0_pool0,
+        num_channels=1536,
+        num_filters=256,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b1_conv0 = img_conv(
+        name='inceptC{0}_branch1_conv0'.format(depth),
+        input=input,
+        num_channels=1536,
+        num_filters=256,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv0 = img_conv(
+        name='inceptC{0}_branch2_conv0'.format(depth),
+        input=input,
+        num_channels=1536,
+        num_filters=384,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = img_conv(
+        name='inceptC{0}_branch2_conv1'.format(depth),
+        input=b2_conv0,
+        num_channels=384,
+        num_filters=256,
+        filter_size=(1, 3),
+        stride=1,
+        padding=(0, 1))
+    b2_conv2 = img_conv(
+        name='inceptC{0}_branch2_conv2'.format(depth),
+        input=b2_conv0,
+        num_channels=384,
+        num_filters=256,
+        filter_size=(3, 1),
+        stride=1,
+        padding=(1, 0))
+    b3_conv0 = img_conv(
+        name='inceptC{0}_branch3_conv0'.format(depth),
+        input=input,
+        num_channels=1536,
+        num_filters=384,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b3_conv1 = img_conv(
+        name='inceptC{0}_branch3_conv1'.format(depth),
+        input=b3_conv0,
+        num_channels=384,
+        num_filters=448,
+        filter_size=(1, 3),
+        stride=1,
+        padding=(0, 1))
+    b3_conv2 = img_conv(
+        name='inceptC{0}_branch3_conv2'.format(depth),
+        input=b3_conv1,
+        num_channels=448,
+        num_filters=512,
+        filter_size=(3, 1),
+        stride=1,
+        padding=(1, 0))
+    b3_conv3 = img_conv(
+        name='inceptC{0}_branch3_conv3'.format(depth),
+        input=b3_conv2,
+        num_channels=512,
+        num_filters=256,
+        filter_size=(3, 1),
+        stride=1,
+        padding=(1, 0))
+    b3_conv4 = img_conv(
+        name='inceptC{0}_branch3_conv4'.format(depth),
+        input=b3_conv2,
+        num_channels=512,
+        num_filters=256,
+        filter_size=(1, 3),
+        stride=1,
+        padding=(0, 1))
+    return paddle.layer.concat(
+        input=[b0_conv0, b1_conv0, b2_conv1, b2_conv2, b3_conv3, b3_conv4])
+
+
+def Reduction_A(input):
+    b0_pool0 = paddle.layer.img_pool(
+        name='ReductA_branch0_pool0',
+        input=input,
+        num_channels=384,
+        pool_size=3,
+        stride=2,
+        pool_type=paddle.pooling.Max())
+    b1_conv0 = img_conv(
+        name='ReductA_branch1_conv0',
+        input=input,
+        num_channels=384,
+        num_filters=384,
+        filter_size=3,
+        stride=2,
+        padding=1)
+    b2_conv0 = img_conv(
+        name='ReductA_branch2_conv0',
+        input=input,
+        num_channels=384,
+        num_filters=192,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = img_conv(
+        name='ReductA_branch2_conv1',
+        input=b2_conv0,
+        num_channels=192,
+        num_filters=224,
+        filter_size=3,
+        stride=1,
+        padding=1)
+    b2_conv2 = img_conv(
+        name='ReductA_branch2_conv2',
+        input=b2_conv1,
+        num_channels=224,
+        num_filters=256,
+        filter_size=3,
+        stride=2,
+        padding=1)
+    return paddle.layer.concat(input=[b0_pool0, b1_conv0, b2_conv2])
+
+
+def Reduction_B(input):
+    b0_pool0 = paddle.layer.img_pool(
+        name='ReductB_branch0_pool0',
+        input=input,
+        num_channels=1024,
+        pool_size=3,
+        stride=2,
+        pool_type=paddle.pooling.Max())
+    b1_conv0 = img_conv(
+        name='ReductB_branch1_conv0',
+        input=input,
+        num_channels=1024,
+        num_filters=192,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b1_conv1 = img_conv(
+        name='ReductB_branch1_conv1',
+        input=b1_conv0,
+        num_channels=192,
+        num_filters=192,
+        filter_size=3,
+        stride=2,
+        padding=1)
+    b2_conv0 = img_conv(
+        name='ReductB_branch2_conv0',
+        input=input,
+        num_channels=1024,
+        num_filters=256,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = img_conv(
+        name='ReductB_branch2_conv1',
+        input=b2_conv0,
+        num_channels=256,
+        num_filters=256,
+        filter_size=(1, 7),
+        stride=1,
+        padding=(0, 3))
+    b2_conv2 = img_conv(
+        name='ReductB_branch2_conv2',
+        input=b2_conv1,
+        num_channels=256,
+        num_filters=320,
+        filter_size=(7, 1),
+        stride=1,
+        padding=(3, 0))
+    b2_conv3 = img_conv(
+        name='ReductB_branch2_conv3',
+        input=b2_conv2,
+        num_channels=320,
+        num_filters=320,
+        filter_size=3,
+        stride=2,
+        padding=1)
+    return paddle.layer.concat(input=[b0_pool0, b1_conv1, b2_conv3])
+
+
+def inception_v4(input, class_dim):
+    conv = stem(input)
+
+    for i in range(4):
+        conv = Inception_A(conv, i)
+    conv = Reduction_A(conv)
+    for i in range(7):
+        conv = Inception_B(conv, i)
+    conv = Reduction_B(conv)
+    for i in range(3):
+        conv = Inception_C(conv, i)
+
+    pool = paddle.layer.img_pool(
+        name='incept_avg_pool',
+        input=conv,
+        num_channels=1536,
+        pool_size=7,
+        stride=1,
+        pool_type=paddle.pooling.Avg())
+    drop = paddle.layer.dropout(input=pool, dropout_rate=0.2)
+    out = paddle.layer.fc(
+        name='incept_fc',
+        input=drop,
+        size=class_dim,
+        act=paddle.activation.Softmax())
+    return out

image_classification/infer.py

+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 inception_v4
+import inception_resnet_v2
 import xception
-import argparse
-import os
-from PIL import Image
-import numpy as np
 
-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
 
 
@@ -29,7 +30,7 @@ def main():
         help='The model for image classification',
         choices=[
             'alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet',
-            'xception'
+            'inception-resnet-v2', 'inception_v4', 'xception'
         ])
     parser.add_argument(
         'params_path', help='The file which stores the parameters')
@@ -53,6 +54,12 @@ 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)
+    elif args.model == 'inception_v4':
+        out = inception_v4.inception_v4(image, class_dim=CLASS_DIM)
     elif args.model == 'xception':
         out = xception.xception(image, class_dim=CLASS_DIM)
 

image_classification/train.py

 import gzip
+import argparse
+
 import paddle.v2.dataset.flowers as flowers
 import paddle.v2 as paddle
 import reader
@@ -6,10 +8,11 @@ import vgg
 import resnet
 import alexnet
 import googlenet
+import inception_v4
+import inception_resnet_v2
 import xception
-import argparse
 
-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
 
@@ -22,7 +25,7 @@ def main():
         help='The model for image classification',
         choices=[
             'alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet',
-            'xception'
+            'inception-resnet-v2', 'inception_v4', 'xception'
         ])
     args = parser.parse_args()
 
@@ -56,6 +59,12 @@ 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)
+    elif args.model == 'inception_v4':
+        out = inception_v4.inception_v4(image, class_dim=CLASS_DIM)
     elif args.model == 'xception':
         out = xception.xception(image, class_dim=CLASS_DIM)