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提交 e9cc3282 编写于 作者: Y yangyaming
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix-3736

上级 6bef0796 c2edd2dc
隐藏空白更改
内联 并排
Showing with 7561 addition and 401 deletion
cmake/external/mkldnn.cmake
浏览文件 @ e9cc3282
......@@ -51,7 +51,7 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "v0.9"
GIT_TAG "v0.10"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
......
cmake/external/mklml.cmake
浏览文件 @ e9cc3282
......@@ -28,7 +28,7 @@ INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
SET(MKLML_VER "mklml_lnx_2018.0.20170720")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.9/${MKLML_VER}.tgz")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.10/${MKLML_VER}.tgz")
SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml")
SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "mklml")
......@@ -54,7 +54,8 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${MKLML_SOURCE_DIR}
DOWNLOAD_DIR ${MKLML_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate -qO- ${MKLML_URL} | tar xz -C ${MKLML_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${MKLML_URL} -c -q -O ${MKLML_VER}.tgz
&& tar zxf ${MKLML_VER}.tgz
DOWNLOAD_NO_PROGRESS 1
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLML_INSTALL_ROOT}
......
doc/api/v2/config/layer.rst
浏览文件 @ e9cc3282
......@@ -419,9 +419,14 @@ multi_binary_label_cross_entropy_cost
.. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost
:noindex:
huber_cost
----------
.. autoclass:: paddle.v2.layer.huber_cost
huber_regression_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_regression_cost
:noindex:
huber_classification_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_classification_cost
:noindex:
lambda_cost
......
doc/getstarted/build_and_install/index_cn.rst
浏览文件 @ e9cc3282
......@@ -6,14 +6,12 @@
安装流程
++++++++
PaddlePaddle提供数个预编译的二进制来进行安装，包括Docker镜像，ubuntu的deb安装包等。我们推荐使用Docker镜像来部署环境，同时欢迎贡献更多的安装包。
PaddlePaddle提供Docker镜像来部署环境。
.. toctree::
:maxdepth: 1
docker_install_cn.rst
ubuntu_install_cn.rst
编译流程
......
doc/getstarted/build_and_install/index_en.rst
浏览文件 @ e9cc3282
......@@ -8,14 +8,13 @@ Install PaddlePaddle
:maxdepth: 1
docker_install_en.rst
ubuntu_install_en.rst
Build from Source
-----------------
.. warning::
Please use :code:`deb` package or :code:`docker` image to install paddle. The building guide is used for hacking or contributing PaddlePaddle source code.
Please use :code:`docker` image to install paddle. The building guide is used for hacking or contributing PaddlePaddle source code.
.. toctree::
:maxdepth: 1
......
doc/getstarted/build_and_install/ubuntu_install_cn.rst 已删除 100644 → 0
浏览文件 @ 6bef0796
Ubuntu部署PaddlePaddle
===================================
PaddlePaddle提供了ubuntu 14.04 deb安装包。
安装
------
安装包的下载地址是\: https://github.com/PaddlePaddle/Paddle/releases
它包含四个版本\:
* cpu版本: 支持主流x86处理器平台, 使用了avx指令集。
* cpu-noavx版本：支持主流x86处理器平台，没有使用avx指令集。
* gpu版本：支持主流x86处理器平台，支持nvidia cuda平台，使用了avx指令集。
* gpu-noavx版本：支持主流x86处理器平台，支持nvidia cuda平台，没有使用avx指令集。
下载完相关安装包后，执行:
.. code-block:: shell
sudo apt-get install gdebi
gdebi paddle-*-cpu.deb
或者:
.. code-block:: shell
dpkg -i paddle-*-cpu.deb
apt-get install -f
在 :code:`dpkg -i` 的时候如果报一些依赖未找到的错误是正常的，
在 :code:`apt-get install -f` 里会继续安装 PaddlePaddle。
安装完成后，可以使用命令 :code:`paddle version` 查看安装后的paddle 版本:
.. code-block:: shell
PaddlePaddle 0.8.0b1, compiled with
with_avx: ON
with_gpu: OFF
with_double: OFF
with_python: ON
with_rdma: OFF
with_timer: OFF
with_predict_sdk:
可能遇到的问题
--------------
libcudart.so/libcudnn.so找不到
++++++++++++++++++++++++++++++
安装完成后，运行 :code:`paddle train` 报错\:
.. code-block:: shell
0831 12:36:04.151525 1085 hl_dso_loader.cc:70] Check failed: nullptr != *dso_handle For Gpu version of PaddlePaddle, it couldn't find CUDA library: libcudart.so Please make sure you already specify its path.Note: for training data on Cpu using Gpu version of PaddlePaddle,you must specify libcudart.so via LD_LIBRARY_PATH.
原因是未设置cuda运行时环境变量。 如果使用GPU版本的PaddlePaddle，请安装CUDA 7.5 和CUDNN 5到本地环境中，并设置：
.. code-block:: shell
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda/bin:$PATH
doc/getstarted/build_and_install/ubuntu_install_en.rst 已删除 100644 → 0
浏览文件 @ 6bef0796
Debian Package installation guide
=================================
PaddlePaddle supports :code:`deb` pacakge. The installation of this :code:`deb` package is tested in ubuntu 14.04, but it should be support other debian based linux, too.
There are four versions of debian package, :code:`cpu`, :code:`gpu`, :code:`cpu-noavx`, :code:`gpu-noavx`. And :code:`noavx` version is used to support CPU which does not contain :code:`AVX` instructions. The download url of :code:`deb` package is \: https://github.com/baidu/Paddle/releases/
After downloading PaddlePaddle deb packages, you can use :code:`gdebi` install.
.. code-block:: bash
gdebi paddle-*.deb
If :code:`gdebi` is not installed, you can use :code:`sudo apt-get install gdebi` to install it.
Or you can use following commands to install PaddlePaddle.
.. code-block:: bash
dpkg -i paddle-*.deb
apt-get install -f
And if you use GPU version deb package, you need to install CUDA toolkit and cuDNN, and set related environment variables(such as LD_LIBRARY_PATH) first. It is normal when `dpkg -i` get errors. `apt-get install -f` will continue install paddle, and install dependences.
doc/howto/dev/new_op_cn.md
浏览文件 @ e9cc3282
......@@ -5,12 +5,13 @@
- [定义ProtoMaker类](#定义ProtoMaker类)
- [定义Operator类](#定义Operator类)
- [定义OpKernel类](#定义OpKernel类)
- [注册类](#注册类)
- [注册Operator](#注册Operator)
- [编译](#编译)
- [绑定Python](#绑定Python)
- [实现单元测试](#实现单元测试)
- [前向Operator单测](#前向Operator单测)
- [反向Operator单测](#反向Operator单测)
- [编译和执行](#编译和执行)
## 概念简介
......@@ -22,19 +23,17 @@
- `framework::OperatorWithKernel`：继承自OperatorBase，Op有计算函数，称作有Kernel。
- `class OpProtoAndCheckerMaker`：描述该Op的输入、输出、属性、注释,主要用于Python API接口生成
依据是否包含kernel，将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结如下：
依据是否包含kernel，将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下：
Forward Op需要包含：
- OpProtoMake定义
- Op定义
- Kernel实现
内容 | 定义位置
-------------- | :----------------------
OpProtoMake定义 | `.cc`文件，Backward Op不需要定义OpProtoMake
Op定义 | `.cc`文件
Kernel实现 | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在`.cc`文件，GPU可在`.cu`文件。
注册Op | Op注册在`.cc`文件；Kernel注册CPU在`.cc`文件，GPU在`.cu`文件
与之对应的Backward Op包含：
- Op定义
- Kernel实现
下面以矩阵乘操作，即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。
......@@ -137,8 +136,9 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
```
还需要重写`InferShape`接口。`InferShape`为const函数，不能修改Op的成员变量，参数为`const framework::InferShapeContext &ctx`，通过该参数可获取到输入输出以及属性。它的功能是：
- 1). 做检查， 尽早报错：检查输入数据维度、类型等是否合法
- 2). 设置输出Tensor的形状
- 1). 做检查， 尽早报错：检查输入数据维度、类型等是否合法。
- 2). 设置输出Tensor的形状。
通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和要讲到的注册函数一起放在`.cc`中
......@@ -172,7 +172,7 @@ class MulKernel : public framework::OpKernel {
到此前向Op实现完成，需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似，这里不再重复。但注意，反向Op没有`ProtoMaker`。
### 4. 注册类
### 4. 注册Operator
在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
......@@ -297,4 +297,28 @@ class TestMulOp(unittest.TestCase):
- 调用`create_op("mul")`创建反向Op对应的前向Op。
- 定义输入`inputs`。
- 调用`compare_grad`函数对比CPU、GPU计算结果。
- 调用`check_grad`检查梯度稳定性。
- 调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。
- 第一个参数`op` : 前向op。
- 第二个参数`inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
- 第三个参数`set(["X", "Y"])` : 指定对输入变量`X`、`Y`做梯度检测。
- 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
### 编译和执行
单测完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)里添加编译：
```
py_test(test_mul_op SRCS test_mul_op.py)
```
编译时需要打开`WITH_TESTING`, 即 `cmake paddle_dir -DWITH_TESTING=ON`，编译成功之后执行单测命令为：
```
make test ARGS="-R test_mul_op -V"
```
或者:
```
ctest -R test_mul_op
```
paddle/cuda/include/hl_cnn.h
浏览文件 @ e9cc3282
......@@ -173,6 +173,96 @@ extern void hl_avgpool_backward(const int frameCnt,
real* backGrad,
const int outStride);
extern void hl_maxpool3D_forward(const int frameCnt,
const real* inputData,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int paddingD,
const int paddingH,
const int paddingW,
real* tgtData,
real* maxPoolIdxData,
const int tgtStride);
extern void hl_maxpool3D_backward(const int frameCnt,
const real* outGrad,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int paddingD,
const int paddingH,
const int paddingW,
real scaleA,
real scaleB,
real* targetGrad,
real* maxPoolIdxData,
const int outStride);
extern void hl_avgpool3D_forward(const int frameCnt,
const real* inputData,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int paddingD,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride);
extern void hl_avgpool3D_backward(const int frameCnt,
const real* outGrad,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
int paddingD,
int paddingH,
int paddingW,
real scaleA,
real scaleB,
real* backGrad,
const int outStride);
/**
* @brief Bilinear interpolation forward.
*
......@@ -275,4 +365,4 @@ extern void hl_maxout_backward(real* inGrad,
size_t featLen,
size_t groups);
#endif /* HL_CNN_H_ */
#endif // HL_CNN_H_
paddle/cuda/include/hl_matrix.h
浏览文件 @ e9cc3282
......@@ -224,4 +224,80 @@ extern void hl_matrix_collect_shared_bias(real* B_d,
extern void hl_matrix_rotate(
real* mat, real* matRot, int dimM, int dimN, bool clockWise);
/**
* @brief Matrix vol2Col: Convert 3D volume into col matrix
*
* @param[in] matSrc input matrix.
* @param[in] channel channel of matSrc.
* @param[in] depth depth of matSrc.
* @param[in] height height of matSrc.
* @param[in] width width of matSrc.
* @param[in] filterD depth of filter.
* @param[in] filterH height of filter.
* @param[in] filterW width of filter.
* @param[in] strideD stride in the depth.
* @param[in] strideH stride in the height.
* @param[in] strideW stride in the width.
* @param[in] paddingD padding in the depth.
* @param[in] paddingH padding in the height.
* @param[in] paddingW padding in the width.
* @param[out] dataDst output matrix.
*
*/
extern void hl_matrix_vol2Col(const real* dataSrc,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
real* dataDst);
/**
* @brief Matrix col2Vol: Convert col matrix into 3D volume
*
* @param[out] matDst output matrix.
* @param[in] channel channel of matDst.
* @param[in] depth depth of matDst.
* @param[in] height height of matDst.
* @param[in] width width of matDst.
* @param[in] filterD depth of filter.
* @param[in] filterH height of filter.
* @param[in] filterW width of filter.
* @param[in] strideD stride in the depth.
* @param[in] strideH stride in the height.
* @param[in] strideW stride in the width.
* @param[in] paddingD padding in the depth.
* @param[in] paddingH padding in the height.
* @param[in] paddingW padding in the width.
* @param[in] matSrc input matrix.
* @param[in] beta input
* @param[in] alpha input
*
*/
extern void hl_matrix_col2Vol(real* dataDst,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
const real* dataSrc,
real alpha,
real beta);
#endif /* HL_MATRIX_H_ */
paddle/cuda/include/stub/hl_cnn_stub.h
浏览文件 @ e9cc3282
......@@ -87,6 +87,96 @@ inline void hl_avgpool_backward(const int frameCnt,
real* backGrad,
const int outStride) {}
inline void hl_maxpool3D_forward(const int frameCnt,
const real* inputData,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int paddingD,
const int paddingH,
const int paddingW,
real* tgtData,
real* maxPoolIdxData,
const int tgtStride) {}
inline void hl_maxpool3D_backward(const int frameCnt,
const real* outGrad,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int paddingD,
const int paddingH,
const int paddingW,
real scaleA,
real scaleB,
real* targetGrad,
real* maxPoolIdxData,
const int outStride) {}
inline void hl_avgpool3D_forward(const int frameCnt,
const real* inputData,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int paddingD,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {}
inline void hl_avgpool3D_backward(const int frameCnt,
const real* outGrad,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int paddingD,
const int paddingH,
const int paddingW,
real scaleA,
real scaleB,
real* backGrad,
const int outStride) {}
inline void hl_bilinear_forward(const real* inData,
const size_t inImgH,
const size_t inImgW,
......
paddle/cuda/include/stub/hl_matrix_stub.h
浏览文件 @ e9cc3282
......@@ -99,4 +99,38 @@ inline void hl_matrix_collect_shared_bias(real* B_d,
inline void hl_matrix_rotate(
real* mat, real* matRot, int dimM, int dimN, bool clockWise) {}
inline void hl_matrix_vol2Col(const real* dataSrc,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
real* dataDst) {}
inline void hl_matrix_col2Vol(real* dataDst,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
const real* dataSrc,
real alpha,
real beta) {}
#endif // HL_MATRIX_STUB_H_
paddle/cuda/src/hl_cuda_cnn.cu
浏览文件 @ e9cc3282
......@@ -353,6 +353,433 @@ void hl_avgpool_backward(const int frameCnt,
CHECK_SYNC("hl_avgpool_backward failed");
}
__global__ void KeMaxPool3DForward(const int nthreads,
const real* inputData,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int ksizeD,
const int ksizeH,
const int ksizeW,
const int strideD,
const int strideH,
const int strideW,
const int padD,
const int padH,
const int padW,
real* tgtData,
real* maxPoolIdxData,
const int tgtStride) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads);
index += blockDim.x * gridDim.x) {
int pw = index % pooledW;
int ph = (index / pooledW) % pooledH;
int pd = (index / pooledW / pooledH) % pooledD;
int c = (index / pooledW / pooledH / pooledD) % channels;
int frameNum = index / pooledW / pooledH / pooledD / channels;
int dstart = pd * strideD - padD;
int hstart = ph * strideH - padH;
int wstart = pw * strideW - padW;
int dend = min(dstart + ksizeD, depth);
int hend = min(hstart + ksizeH, height);
int wend = min(wstart + ksizeW, width);
dstart = max(dstart, 0);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
real maxval = -FLT_MAX;
int maxIdx = -1;
inputData += (frameNum * channels + c) * depth * height * width;
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
if (maxval < inputData[(d * height + h) * width + w]) {
maxval = inputData[(d * height + h) * width + w];
maxIdx = (d * height + h) * width + w;
}
}
}
}
int tgtIndex =
index % (pooledW * pooledH * pooledD * channels) + frameNum * tgtStride;
tgtData[tgtIndex] = maxval;
maxPoolIdxData[tgtIndex] = maxIdx;
}
}
void hl_maxpool3D_forward(const int frameCnt,
const real* inputData,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int padD,
const int padH,
const int padW,
real* tgtData,
real* maxPoolIdxData,
const int tgtStride) {
int num_kernels = pooledD * pooledH * pooledW * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
dim3 threads(1024, 1);
dim3 grid(blocks, 1);
KeMaxPool3DForward<<<grid, threads, 0, STREAM_DEFAULT>>>(num_kernels,
inputData,
channels,
depth,
height,
width,
pooledD,
pooledH,
pooledW,
sizeZ,
sizeY,
sizeX,
strideD,
strideH,
strideW,
padD,
padH,
padW,
tgtData,
maxPoolIdxData,
tgtStride);
CHECK_SYNC("hl_maxpool3D_forward failed");
}
__global__ void KeMaxPool3DBackward(const int nthreads,
const real* outGrad,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int padD,
const int padH,
const int padW,
real scaleA,
real scaleB,
real* targetGrad,
real* maxPoolIdxData,
const int outStride) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads);
index += blockDim.x * gridDim.x) {
int offsetW = index % width;
int offsetH = (index / width) % height;
int offsetD = (index / width / height) % depth;
int offsetC = (index / width / height / depth) % channels;
int frameNum = index / width / height / depth / channels;
int pdstart =
(offsetD + padD < sizeZ) ? 0 : (offsetD + padD - sizeZ) / strideD + 1;
int phstart =
(offsetH + padH < sizeY) ? 0 : (offsetH + padH - sizeY) / strideH + 1;
int pwstart =
(offsetW + padW < sizeX) ? 0 : (offsetW + padW - sizeX) / strideW + 1;
int pdend = min((offsetD + padD) / strideD + 1, pooledD);
int phend = min((offsetH + padH) / strideH + 1, pooledH);
int pwend = min((offsetW + padW) / strideW + 1, pooledW);
real gradient = 0;
outGrad += ((frameNum * channels + offsetC) * pooledD * pooledH * pooledW);
maxPoolIdxData +=
((frameNum * channels + offsetC) * pooledD * pooledH * pooledW);
for (int pd = pdstart; pd < pdend; ++pd) {
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
if (((offsetD * height + offsetH) * width + offsetW) ==
maxPoolIdxData[(pd * pooledH + ph) * pooledW + pw])
gradient += outGrad[(pd * pooledH + ph) * pooledW + pw];
}
}
}
targetGrad[index] = scaleA * gradient + scaleB * targetGrad[index];
}
}
void hl_maxpool3D_backward(const int frameCnt,
const real* outGrad,
const int channels,
const int depth,
const int height,
const int width,
const int outputD,
const int outputH,
const int outputW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int paddingD,
const int paddingH,
const int paddingW,
real scaleA,
real scaleB,
real* targetGrad,
real* maxPoolIdxData,
const int outStride) {
int num_kernels = depth * height * width * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
KeMaxPool3DBackward<<<blocks, 1024, 0, STREAM_DEFAULT>>>(num_kernels,
outGrad,
channels,
depth,
height,
width,
outputD,
outputH,
outputW,
sizeZ,
sizeY,
sizeX,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
scaleA,
scaleB,
targetGrad,
maxPoolIdxData,
outStride);
CHECK_SYNC("hl_maxpool3D_backward");
}
__global__ void KeAvgPool3DForward(const int nthreads,
const real* inputData,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int padD,
const int padH,
const int padW,
real* tgtData,
const int tgtStride) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads);
index += blockDim.x * gridDim.x) {
int pw = index % pooledW;
int ph = (index / pooledW) % pooledH;
int pd = (index / pooledW / pooledH) % pooledD;
int c = (index / pooledW / pooledH / pooledD) % channels;
int frameNum = index / pooledW / pooledH / pooledD / channels;
int dstart = pd * strideD - padD;
int hstart = ph * strideH - padH;
int wstart = pw * strideW - padW;
int dend = min(dstart + sizeZ, depth + padD);
int hend = min(hstart + sizeY, height + padH);
int wend = min(wstart + sizeX, width + padW);
int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
dstart = max(dstart, 0);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
dend = min(dend, depth);
hend = min(hend, height);
wend = min(wend, width);
real aveval = 0;
inputData += (frameNum * channels + c) * depth * height * width;
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
aveval += inputData[(d * height + h) * width + w];
}
}
}
int tgtIndex =
index % (pooledW * pooledH * pooledD * channels) + frameNum * tgtStride;
tgtData[tgtIndex] = aveval / pool_size;
}
}
void hl_avgpool3D_forward(const int frameCnt,
const real* inputData,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int paddingD,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {
int num_kernels = pooledD * pooledH * pooledW * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
KeAvgPool3DForward<<<blocks, 1024, 0, STREAM_DEFAULT>>>(num_kernels,
inputData,
channels,
depth,
height,
width,
pooledD,
pooledH,
pooledW,
sizeZ,
sizeY,
sizeX,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
tgtData,
tgtStride);
CHECK_SYNC("hl_avgpool3D_forward failed");
}
__global__ void KeAvgPool3DBackward(const int nthreads,
const real* outGrad,
const int channels,
const int depth,
const int height,
const int width,
const int pooledD,
const int pooledH,
const int pooledW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
const int padD,
const int padH,
const int padW,
real scaleA,
real scaleB,
real* tgtGrad,
const int outStride) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads);
index += blockDim.x * gridDim.x) {
int offsetW = index % width + padW;
int offsetH = (index / width) % height + padH;
int offsetD = (index / width / height) % depth + padD;
int offsetC = (index / width / height / depth) % channels;
int frameNum = index / width / height / depth / channels;
int pdstart = (offsetD < sizeZ) ? 0 : (offsetD - sizeZ) / strideD + 1;
int phstart = (offsetH < sizeY) ? 0 : (offsetH - sizeY) / strideH + 1;
int pwstart = (offsetW < sizeX) ? 0 : (offsetW - sizeX) / strideW + 1;
int pdend = min(offsetD / strideD + 1, pooledD);
int phend = min(offsetH / strideH + 1, pooledH);
int pwend = min(offsetW / strideW + 1, pooledW);
real gradient = 0;
outGrad += (frameNum * channels + offsetC) * pooledD * pooledH * pooledW;
for (int pd = pdstart; pd < pdend; ++pd) {
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
// figure out the pooling size
int dstart = pd * strideD - padD;
int hstart = ph * strideH - padH;
int wstart = pw * strideW - padW;
int dend = min(dstart + sizeZ, depth + padD);
int hend = min(hstart + sizeY, height + padH);
int wend = min(wstart + sizeX, width + padW);
int poolsize = (dend - dstart) * (hend - hstart) * (wend - wstart);
gradient += outGrad[(pd * pooledH + ph) * pooledW + pw] / poolsize;
}
}
}
tgtGrad[index] = scaleA * gradient + scaleB * tgtGrad[index];
}
}
void hl_avgpool3D_backward(const int frameCnt,
const real* outGrad,
const int channels,
const int depth,
const int height,
const int width,
const int outputD,
const int outputH,
const int outputW,
const int sizeZ,
const int sizeY,
const int sizeX,
const int strideD,
const int strideH,
const int strideW,
int paddingD,
int paddingH,
int paddingW,
real scaleA,
real scaleB,
real* backGrad,
const int outStride) {
int num_kernels = depth * height * width * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
KeAvgPool3DBackward<<<blocks, 1024, 0, STREAM_DEFAULT>>>(num_kernels,
outGrad,
channels,
depth,
height,
width,
outputD,
outputH,
outputW,
sizeZ,
sizeY,
sizeX,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
scaleA,
scaleB,
backGrad,
outStride);
CHECK_SYNC("hl_avgpool3D_backward failed");
}
__global__ void KeBilinearInterpFw(const real* in,
const size_t inImgH,
const size_t inImgW,
......
paddle/cuda/src/hl_cuda_matrix.cu
浏览文件 @ e9cc3282
......@@ -592,3 +592,204 @@ void hl_matrix_rotate(
mat, matRot, dimM, dimN, clockWise);
CHECK_SYNC("hl_matrix_rotate failed");
}
__global__ void keMatrixVol2Col(int num_kernels,
const real* dataSrc,
real* dataDst,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
int depth_col,
int height_col,
int width_col) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels;
index += blockDim.x * gridDim.x) {
int w_out = index % width_col;
int h_out = (index / width_col) % height_col;
int d_out = (index / width_col / height_col) % depth_col;
int channel_in = index / width_col / height_col / depth_col;
int channel_out = channel_in * filterD * filterH * filterW;
int w_in = w_out * strideW - paddingW;
int h_in = h_out * strideH - paddingH;
int d_in = d_out * strideD - paddingD;
dataDst +=
((channel_out * depth_col + d_out) * height_col + h_out) * width_col +
w_out;
dataSrc += ((channel_in * depth + d_in) * height + h_in) * width + w_in;
for (int k = 0; k < filterD; ++k) {
for (int i = 0; i < filterH; ++i) {
for (int j = 0; j < filterW; ++j) {
int d = d_in + k;
int h = h_in + i;
int w = w_in + j;
*dataDst = (d >= 0 && d < depth && h >= 0 && h < height && w >= 0 &&
w < width)
? dataSrc[(k * height + i) * width + j]
: 0;
dataDst += depth_col * height_col * width_col;
}
}
}
}
}
void hl_matrix_vol2Col(const real* dataSrc,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
real* dataDst) {
int depth_col = (depth + 2 * paddingD - filterD) / strideD + 1;
int height_col = (height + 2 * paddingH - filterH) / strideH + 1;
int width_col = (width + 2 * paddingW - filterW) / strideW + 1;
int num_kernels = channels * depth_col * height_col * width_col;
const int threads = 512;
const int blocks = DIVUP(num_kernels, threads);
keMatrixVol2Col<<<blocks, threads, 0, STREAM_DEFAULT>>>(num_kernels,
dataSrc,
dataDst,
depth,
height,
width,
filterD,
filterH,
filterW,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
depth_col,
height_col,
width_col);
CHECK_SYNC("hl_matrix_vol2Col failed");
}
__global__ void keMatrixCol2Vol(int num_kernels,
real* dataDst,
const real* dataSrc,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
int depth_col,
int height_col,
int width_col,
real alpha,
real beta) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels;
index += blockDim.x * gridDim.x) {
real srcVal = 0;
real dstVal = dataDst[index];
int w = index % width + paddingW;
int h = (index / width) % height + paddingH;
int d = (index / width / height) % depth + paddingD;
int c = index / width / height / depth;
// compute the start and end of the output
int w_col_start = (w < filterW) ? 0 : (w - filterW) / strideW + 1;
int w_col_end = min(w / strideW + 1, width_col);
int h_col_start = (h < filterH) ? 0 : (h - filterH) / strideH + 1;
int h_col_end = min(h / strideH + 1, height_col);
int d_col_start = (d < filterD) ? 0 : (d - filterD) / strideD + 1;
int d_col_end = min(d / strideD + 1, depth_col);
int offset = (c * filterD * filterW * filterH + d * filterW * filterH +
h * filterW + w) *
depth_col * height_col * width_col;
int coeff_d_col =
(1 - strideD * filterW * filterH * depth_col) * height_col * width_col;
int coeff_h_col =
(1 - strideH * filterW * depth_col * height_col) * width_col;
int coeff_w_col = (1 - strideW * depth_col * height_col * width_col);
for (int d_col = d_col_start; d_col < d_col_end; ++d_col) {
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
srcVal += dataSrc[offset + d_col * coeff_d_col + h_col * coeff_h_col +
w_col * coeff_w_col];
}
}
}
dataDst[index] = alpha * srcVal + beta * dstVal;
}
}
void hl_matrix_col2Vol(real* dataDst,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
const real* dataSrc,
real alpha,
real beta) {
int depth_col = (depth + 2 * paddingD - filterD) / strideD + 1;
int height_col = (height + 2 * paddingH - filterH) / strideH + 1;
int width_col = (width + 2 * paddingW - filterW) / strideW + 1;
int num_kernels = channels * depth * height * width;
const int threads = 512;
const int blocks = DIVUP(num_kernels, threads);
keMatrixCol2Vol<<<blocks, threads, 0, STREAM_DEFAULT>>>(num_kernels,
dataDst,
dataSrc,
depth,
height,
width,
filterD,
filterH,
filterW,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
depth_col,
height_col,
width_col,
alpha,
beta);
CHECK_SYNC("hl_matrix_col2Vol failed");
}
paddle/framework/tensor_impl.h
浏览文件 @ e9cc3282
......@@ -117,6 +117,8 @@ inline void Tensor::CopyFrom(const Tensor& src,
memory::Copy(boost::get<platform::GPUPlace>(dst_place), dst_ptr,
boost::get<platform::GPUPlace>(src_place), src_ptr, size, 0);
}
PADDLE_ENFORCE(cudaStreamSynchronize(0),
"cudaStreamSynchronize failed in Tensor CopyFrom");
#endif
}
......
paddle/function/CMakeLists.txt
浏览文件 @ e9cc3282
......@@ -21,6 +21,8 @@ if(USE_NNPACK)
endif()
endif()
list(APPEND cpp_files neon/NeonDepthwiseConv.cpp)
add_library(paddle_function STATIC ${cpp_files} ${cu_objs})
add_dependencies(paddle_function ${external_project_dependencies})
add_dependencies(paddle_function paddle_proto)
......@@ -42,11 +44,11 @@ if(WITH_GPU)
add_simple_unittest(RowConvOpTest)
add_simple_unittest(BlockExpandOpTest)
add_simple_unittest(CropOpTest)
add_simple_unittest(DepthwiseConvOpTest)
endif()
add_simple_unittest(Im2ColTest)
add_simple_unittest(GemmConvOpTest)
add_simple_unittest(DepthwiseConvOpTest)
endif()
add_style_check_target(paddle_function ${h_files})
......
paddle/function/DepthwiseConvOpTest.cpp
浏览文件 @ e9cc3282
......@@ -34,4 +34,13 @@ TEST(DepthwiseConv, BackwardFilter) {
}
#endif
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
TEST(DepthwiseConv, Forward) {
DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
"GemmConv-CPU", "NeonDepthwiseConv-CPU", forward);
}
#endif
} // namespace paddle
paddle/function/Im2Col.h
浏览文件 @ e9cc3282
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "TensorShape.h"
#include "TensorType.h"
#include "neon/neon_util.h"
namespace paddle {
......@@ -93,4 +94,95 @@ public:
int paddingWidth);
};
template <class T>
struct Padding {
static void run(const T* src,
T* dest,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
}
memcpy(dest, src, inputWidth * sizeof(T));
dest += inputWidth;
src += inputWidth;
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
}
}
}
};
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <>
struct Padding<float> {
static void run(const float* src,
float* dest,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
}
int step = inputWidth >> 2;
int remain = inputWidth & 3;
for (int s = 0; s < step; s++) {
float32x4_t s0 = vld1q_f32(src);
vst1q_f32(dest, s0);
src += 4;
dest += 4;
}
for (int r = 0; r < remain; r++) {
*dest++ = *src++;
}
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
}
}
}
};
#endif
} // namespace paddle
paddle/function/neon/NeonDepthwiseConv.cpp 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 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. */
#include "neon_util.h"
#include "paddle/function/ConvOp.h"
#include "paddle/function/Im2Col.h"
namespace paddle {
namespace neon {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <int filterSize, int stride>
struct DepthwiseConvKernel {};
inline float32_t conv3x3(float32x4_t r0,
float32x4_t r1,
float32x4_t r2,
float32x4_t k0,
float32x4_t k1,
float32x4_t k2) {
float32x4_t tmp;
tmp = vmulq_f32(r0, k0);
tmp = vmlaq_f32(tmp, r1, k1);
tmp = vmlaq_f32(tmp, r2, k2);
return vaddvq_f32(tmp);
}
inline float32_t conv4x4(float32x4_t r0,
float32x4_t r1,
float32x4_t r2,
float32x4_t r3,
float32x4_t k0,
float32x4_t k1,
float32x4_t k2,
float32x4_t k3) {
float32x4_t tmp;
tmp = vmulq_f32(r0, k0);
tmp = vmlaq_f32(tmp, r1, k1);
tmp = vmlaq_f32(tmp, r2, k2);
tmp = vmlaq_f32(tmp, r3, k3);
return vaddvq_f32(tmp);
}
/**
* Each step calculates four elements of the output.
* First step:
* R0[0, 1, 2, 3...] * K[0][0]
* R0[1, 2, 3, 4...] * K[0][1]
* R0[2, 3, 4, 5...] * K[0][2]
* R1[0, 1, 2, 3...] * K[1][0]
* R1[1, 2, 3, 4...] * K[1][1]
* R1[2, 3, 4, 5...] * K[1][2]
* R2[0, 1, 2, 3...] * K[2][0]
* R2[1, 2, 3, 4...] * K[2][1]
* + R2[2, 3, 4, 5...] * K[2][2]
* ------------------------------
* Output[0, 1, 2, 3]
*/
template <>
struct DepthwiseConvKernel<3, 1> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 9) {
// Load the filters
float32x4_t k[3];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 3);
k[2] = vld1q_f32(filterData + 6);
k[0] = vsetq_lane_f32(0.f, k[0], 3);
k[1] = vsetq_lane_f32(0.f, k[1], 3);
k[2] = vsetq_lane_f32(0.f, k[2], 3);
const float* r0 =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
const float* r1 = r0 + inputWidth;
const float* r2 = r0 + inputWidth * 2;
float32x4_t input[3][3];
for (int h = 0; h < outputHeight; h++) {
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4_t tmp;
input[0][0] = vld1q_f32(r0);
tmp = vld1q_f32(r0 + 4);
input[0][1] = vextq_f32(input[0][0], tmp, 1);
input[0][2] = vextq_f32(input[0][0], tmp, 2);
input[1][0] = vld1q_f32(r1);
tmp = vld1q_f32(r1 + 4);
input[1][1] = vextq_f32(input[1][0], tmp, 1);
input[1][2] = vextq_f32(input[1][0], tmp, 2);
input[2][0] = vld1q_f32(r2);
tmp = vld1q_f32(r2 + 4);
input[2][1] = vextq_f32(input[2][0], tmp, 1);
input[2][2] = vextq_f32(input[2][0], tmp, 2);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 4;
r1 += 4;
r2 += 4;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
*outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]);
r0++;
r1++;
r2++;
outputData++;
}
r0 += 2;
r1 += 2;
r2 += 2;
}
}
}
};
/**
* Each step calculates four elements of the output.
* First step:
* R0[0, 2, 4, 6...] * K[0][0]
* R0[1, 3, 5, 7...] * K[0][1]
* R0[2, 4, 6, 8...] * K[0][2]
* R1[0, 2, 4, 6...] * K[1][0]
* R1[1, 3, 5, 7...] * K[1][1]
* R1[2, 4, 6, 8...] * K[1][2]
* R2[0, 2, 4, 6...] * K[2][0]
* R2[1, 3, 5, 7...] * K[2][1]
* R2[2, 4, 6, 8...] * K[2][2]
* ------------------------------
* Output[0, 1, 2, 3]
*/
template <>
struct DepthwiseConvKernel<3, 2> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 9) {
// Load the filters
float32x4_t k[3];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 3);
k[2] = vld1q_f32(filterData + 6);
k[0] = vsetq_lane_f32(0.f, k[0], 3);
k[1] = vsetq_lane_f32(0.f, k[1], 3);
k[2] = vsetq_lane_f32(0.f, k[2], 3);
const float* start =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
float32x4_t input[3][3];
for (int h = 0; h < outputHeight; h++) {
const float* r0 = start + 2 * h * inputWidth;
const float* r1 = start + (2 * h + 1) * inputWidth;
const float* r2 = start + (2 * h + 2) * inputWidth;
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4_t data1;
float32x4x2_t data2;
data2 = vld2q_f32(r0);
input[0][0] = data2.val[0];
input[0][1] = data2.val[1];
data1 = vld1q_f32(r0 + 8);
input[0][2] = vextq_f32(data2.val[0], data1, 1);
data2 = vld2q_f32(r1);
input[1][0] = data2.val[0];
input[1][1] = data2.val[1];
data1 = vld1q_f32(r1 + 8);
input[1][2] = vextq_f32(data2.val[0], data1, 1);
data2 = vld2q_f32(r2);
input[2][0] = data2.val[0];
input[2][1] = data2.val[1];
data1 = vld1q_f32(r2 + 8);
input[2][2] = vextq_f32(data2.val[0], data1, 1);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 8;
r1 += 8;
r2 += 8;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
*outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]);
r0 += 2;
r1 += 2;
r2 += 2;
outputData++;
}
}
}
}
};
/**
* Each step calculates four elements of the output.
*/
template <>
struct DepthwiseConvKernel<4, 1> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 16) {
// Load the filters
float32x4_t k[4];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 4);
k[2] = vld1q_f32(filterData + 8);
k[3] = vld1q_f32(filterData + 12);
const float* r0 =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
const float* r1 = r0 + inputWidth;
const float* r2 = r0 + inputWidth * 2;
const float* r3 = r0 + inputWidth * 3;
float32x4_t input[4][4];
for (int h = 0; h < outputHeight; h++) {
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4_t tmp;
input[0][0] = vld1q_f32(r0);
tmp = vld1q_f32(r0 + 4);
input[0][1] = vextq_f32(input[0][0], tmp, 1);
input[0][2] = vextq_f32(input[0][0], tmp, 2);
input[0][3] = vextq_f32(input[0][0], tmp, 3);
input[1][0] = vld1q_f32(r1);
tmp = vld1q_f32(r1 + 4);
input[1][1] = vextq_f32(input[1][0], tmp, 1);
input[1][2] = vextq_f32(input[1][0], tmp, 2);
input[1][3] = vextq_f32(input[1][0], tmp, 3);
input[2][0] = vld1q_f32(r2);
tmp = vld1q_f32(r2 + 4);
input[2][1] = vextq_f32(input[2][0], tmp, 1);
input[2][2] = vextq_f32(input[2][0], tmp, 2);
input[2][3] = vextq_f32(input[2][0], tmp, 3);
input[3][0] = vld1q_f32(r3);
tmp = vld1q_f32(r3 + 4);
input[3][1] = vextq_f32(input[3][0], tmp, 1);
input[3][2] = vextq_f32(input[3][0], tmp, 2);
input[3][3] = vextq_f32(input[3][0], tmp, 3);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 4;
r1 += 4;
r2 += 4;
r3 += 4;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
float32x4_t i3 = vld1q_f32(r3);
*outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]);
r0++;
r1++;
r2++;
r3++;
outputData++;
}
r0 += 3;
r1 += 3;
r2 += 3;
r3 += 3;
}
}
}
};
/**
* Each step calculates four elements of the output.
*/
template <>
struct DepthwiseConvKernel<4, 2> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 16) {
// Load the filters
float32x4_t k[4];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 4);
k[2] = vld1q_f32(filterData + 8);
k[3] = vld1q_f32(filterData + 12);
const float* start =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
float32x4_t input[4][4];
for (int h = 0; h < outputHeight; h++) {
const float* r0 = start + 2 * h * inputWidth;
const float* r1 = start + (2 * h + 1) * inputWidth;
const float* r2 = start + (2 * h + 2) * inputWidth;
const float* r3 = start + (2 * h + 3) * inputWidth;
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4x2_t data1;
float32x4x2_t data2;
data1 = vld2q_f32(r0);
data2 = vld2q_f32(r0 + 8);
input[0][0] = data1.val[0];
input[0][1] = data1.val[1];
input[0][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[0][3] = vextq_f32(data1.val[1], data2.val[1], 1);
data1 = vld2q_f32(r1);
data2 = vld2q_f32(r1 + 8);
input[1][0] = data1.val[0];
input[1][1] = data1.val[1];
input[1][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[1][3] = vextq_f32(data1.val[1], data2.val[1], 1);
data1 = vld2q_f32(r2);
data2 = vld2q_f32(r2 + 8);
input[2][0] = data1.val[0];
input[2][1] = data1.val[1];
input[2][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[2][3] = vextq_f32(data1.val[1], data2.val[1], 1);
data1 = vld2q_f32(r3);
data2 = vld2q_f32(r3 + 8);
input[3][0] = data1.val[0];
input[3][1] = data1.val[1];
input[3][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[3][3] = vextq_f32(data1.val[1], data2.val[1], 1);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 8;
r1 += 8;
r2 += 8;
r3 += 8;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
float32x4_t i3 = vld1q_f32(r3);
*outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]);
r0 += 2;
r1 += 2;
r2 += 2;
r3 += 2;
outputData++;
}
}
}
}
};
template <DeviceType Device>
class NeonDepthwiseConvFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
checkShape(input, filter, output);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
check(inputs, outputs);
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
size_t batchSize = input[0];
size_t inputChannels = input[1];
size_t inputHeight = input[2];
size_t inputWidth = input[3];
size_t filterHeight = getFilterHeight(filter);
size_t filterWidth = getFilterWidth(filter);
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
size_t filterMultiplier = outputChannels / groups_;
CHECK_EQ(inputChannels, groups_);
// only support strideH() == strideW() and filterHeight == filterWidth.
CHECK_EQ(strideH(), strideW());
CHECK_EQ(filterHeight, filterWidth);
float* inputData = inputs[0].data<float>();
float* filterData = inputs[1].data<float>();
float* outputData = outputs[0].data<float>();
// padding the input
float* inputPadding = inputData;
if (paddingH() > 0 || paddingW() > 0) {
int newSize = batchSize * inputChannels * (inputHeight + 2 * paddingH()) *
(inputWidth + 2 * paddingW());
resizeBuffer<Device>(newSize);
inputPadding = reinterpret_cast<float*>(memory_->getBuf());
Padding<float>::run(inputData,
inputPadding,
batchSize * inputChannels,
inputHeight,
inputWidth,
paddingH(),
paddingW());
// height and width of padding data
inputHeight += 2 * paddingH();
inputWidth += 2 * paddingW();
}
std::function<void(
const float*, const float*, int, int, int, int, int, int, float*)>
DepthWiseConv;
if (filterWidth == 3 && strideW() == 1) {
DepthWiseConv = DepthwiseConvKernel<3, 1>::run;
} else if (filterWidth == 3 && strideW() == 2) {
DepthWiseConv = DepthwiseConvKernel<3, 2>::run;
} else if (filterWidth == 4 && strideW() == 1) {
DepthWiseConv = DepthwiseConvKernel<4, 1>::run;
} else if (filterWidth == 4 && strideW() == 2) {
DepthWiseConv = DepthwiseConvKernel<4, 2>::run;
} else {
LOG(FATAL) << "Not supported";
}
for (size_t i = 0; i < batchSize; i++) {
DepthWiseConv(inputPadding,
filterData,
inputHeight,
inputWidth,
outputChannels,
outputHeight,
outputWidth,
filterMultiplier,
outputData);
inputPadding += inputChannels * inputHeight * inputWidth;
outputData += outputChannels * outputHeight * outputWidth;
}
}
};
REGISTER_TYPED_FUNC(NeonDepthwiseConv, CPU, NeonDepthwiseConvFunction);
#endif
} // namespace neon
} // namespace paddle
paddle/function/neon/neon_util.h 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 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. */
#pragma once
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include <arm_neon.h>
namespace paddle {
namespace neon {
inline float32x4_t vld1q_f32_aligned(const float* p) {
return vld1q_f32(
(const float*)__builtin_assume_aligned(p, sizeof(float32x4_t)));
}
#ifndef __aarch64__
inline float32_t vaddvq_f32(float32x4_t a) {
float32x2_t v = vadd_f32(vget_high_f32(a), vget_low_f32(a));
return vget_lane_f32(vpadd_f32(v, v), 0);
}
inline float32x4_t vmlaq_laneq_f32(float32x4_t a,
float32x4_t b,
float32x4_t v,
const int lane) {
return vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane));
}
#endif
} // namespace neon
} // namespace paddle
#endif
paddle/gserver/layers/Conv3DLayer.cpp 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 Baidu, Inc. 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. */
#include "Conv3DLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(conv3d, Conv3DLayer);
bool Conv3DLayer::init(const LayerMap &layerMap,
const ParameterMap &parameterMap) {
if (!ConvBaseLayer::init(layerMap, parameterMap)) return false;
int index = 0;
for (auto &inputConfig : config_.inputs()) {
const ConvConfig &conf = inputConfig.conv_conf();
M_.push_back(numFilters_ / conf.groups());
K_.push_back(filterPixels_[index] * filterChannels_[index]);
// create a new weight
size_t height, width;
width = filterPixels_[index] * filterChannels_[index];
height = numFilters_;
CHECK_EQ(parameters_[index]->getSize(), width * height);
Weight *w = new Weight(height, width, parameters_[index]);
weights_.emplace_back(w);
++index;
}
if (biasParameter_.get()) {
if (sharedBiases_) {
CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
biases_ =
std::unique_ptr<Weight>(new Weight(1, numFilters_, biasParameter_));
} else {
biases_ =
std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
}
}
return true;
}
size_t Conv3DLayer::getSize() {
CHECK_NE(inputLayers_.size(), 0UL);
outputH_.clear();
outputW_.clear();
outputD_.clear();
N_.clear();
size_t layerSize = 0;
for (size_t i = 0; i < inputLayers_.size(); ++i) {
outputW_.push_back(outputSize(
imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true));
outputH_.push_back(outputSize(
imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
outputD_.push_back(outputSize(
imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize);
layerSize += N_[i] * numFilters_;
}
getOutput().setFrameHeight(outputH_[0]);
getOutput().setFrameWidth(outputW_[0]);
getOutput().setFrameDepth(outputD_[0]);
return layerSize;
}
void Conv3DLayer::forward(PassType passType) {
Layer::forward(passType);
int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
int outWidth = getSize();
resetOutput(batchSize, outWidth);
for (size_t i = 0; i != inputLayers_.size(); ++i) {
REGISTER_TIMER_INFO("FwdConv3D", getName().c_str());
const MatrixPtr &inMat = getInputValue(i);
const MatrixPtr &outMat = getOutputValue();
int M = M_[i];
int N = N_[i];
int K = K_[i];
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
MatrixPtr wMat = weights_[i]->getW();
for (int n = 0; n < batchSize; ++n) {
colBuf_->vol2Col(inMat->getData() + n * inMat->getStride(),
channels_[i],
imgSizeD_[i],
imgSizeH_[i],
imgSizeW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
strideZ_[i],
strideY_[i],
stride_[i],
paddingZ_[i],
paddingY_[i],
padding_[i]);
real *outData = outMat->getData() + n * outMat->getStride();
MatrixPtr outMatSub =
Matrix::create(outData, groups_[i] * M, N, false, useGpu_);
for (int g = 0; g < groups_[i]; g++) {
MatrixPtr wMatSub = wMat->subMatrix(g * M, M);
MatrixPtr in = colBuf_->subMatrix(g * K, K);
MatrixPtr out = outMatSub->subMatrix(g * M, M);
out->mul(*wMatSub, *in, 1.0, 1.0);
}
}
}
if (nullptr != this->biasParameter_) {
REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
this->addBias();
}
forwardActivation();
}
void Conv3DLayer::backward(const UpdateCallback &callback) {
backwardActivation();
if (biases_ && biases_->getWGrad()) {
bpropBiases();
biases_->getParameterPtr()->incUpdate(callback);
}
for (size_t i = 0; i != inputLayers_.size(); ++i) {
REGISTER_TIMER_INFO("BwdConv3D", getName().c_str());
if (weights_[i]->getWGrad()) {
bpropWeights(i);
}
if (getInputGrad(i)) {
bpropData(i);
}
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
weights_[i]->getParameterPtr()->incUpdate(callback);
}
}
void Conv3DLayer::bpropWeights(int i) {
int M = M_[i];
int N = N_[i];
int K = K_[i];
const MatrixPtr &inMat = getInputValue(i);
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
MatrixPtr wGradMat = weights_[i]->getWGrad();
int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
for (int n = 0; n < batchSize; ++n) {
colBuf_->vol2Col(inMat->getData() + n * inMat->getStride(),
channels_[i],
imgSizeD_[i],
imgSizeH_[i],
imgSizeW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
strideZ_[i],
strideY_[i],
stride_[i],
paddingZ_[i],
paddingY_[i],
padding_[i]);
real *outGradData =
getOutputGrad()->getData() + n * getOutputGrad()->getStride();
MatrixPtr outGradSub =
Matrix::create(outGradData, groups_[i] * M, N, false, useGpu_);
for (int g = 0; g < groups_[i]; ++g) {
MatrixPtr inMatSub = colBuf_->subMatrix(g * K, K);
MatrixPtr outG = outGradSub->subMatrix(g * M, M);
MatrixPtr wGradSub = wGradMat->subMatrix(g * M, M);
wGradSub->mul(*outG, *(inMatSub->getTranspose()), 1.0, 1.0);
}
}
}
void Conv3DLayer::bpropData(int i) {
int M = M_[i];
int N = N_[i];
int K = K_[i];
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
MatrixPtr wMat = weights_[i]->getW();
int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
for (int n = 0; n < batchSize; ++n) {
real *outGradData =
getOutputGrad()->getData() + n * getOutputGrad()->getStride();
real *preGradData =
getInputGrad(i)->getData() + n * getInputGrad(i)->getStride();
MatrixPtr outGradSub =
Matrix::create(outGradData, M * groups_[i], N, false, useGpu_);
for (int g = 0; g < groups_[i]; ++g) {
MatrixPtr wMatSub = wMat->subMatrix(g * M, M);
MatrixPtr outG = outGradSub->subMatrix(g * M, M);
MatrixPtr inGradMatSub = colBuf_->subMatrix(g * K, K);
inGradMatSub->mul(*(wMatSub->getTranspose()), *outG, 1.0, 0.0);
}
colBuf_->col2Vol(preGradData,
channels_[i],
imgSizeD_[i],
imgSizeH_[i],
imgSizeW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
strideZ_[i],
strideY_[i],
stride_[i],
paddingZ_[i],
paddingY_[i],
padding_[i],
1.0,
1.0);
}
}
void Conv3DLayer::bpropBiases() {
MatrixPtr outGradMat = getOutputGrad();
if (this->sharedBiases_) {
biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
} else {
biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
}
}
void Conv3DLayer::addBias() {
MatrixPtr outMat = getOutputValue();
if (this->sharedBiases_) {
outMat->addSharedBias(*(biases_->getW()), 1.0f);
} else {
outMat->addBias(*(biases_->getW()), 1.0f);
}
}
} // namespace paddle
paddle/gserver/layers/Conv3DLayer.h 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 Baidu, Inc. 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. */
#pragma once
#include <vector>
#include "ConvBaseLayer.h"
#include "paddle/math/MathUtils.h"
#include "paddle/math/Matrix.h"
namespace paddle {
/**
* @brief A subclass of convolution layer.
* This layer expands input and use matrix multiplication to
* calculate convolution operation.
*/
class Conv3DLayer : public ConvBaseLayer {
public:
explicit Conv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {}
~Conv3DLayer() {}
bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
void forward(PassType passType);
void addBias();
void backward(const UpdateCallback& callback);
void bpropBiases();
void bpropData(int i);
void bpropWeights(int i);
size_t getSize();
protected:
// Figure out the dimensions for individual gemms.
IntV M_; /// numFilters_ / filter_group_;
IntV N_; /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_
IntV K_; /// outputD_ * outputH_ * outputW_
MatrixPtr colBuf_;
};
} // namespace paddle
paddle/gserver/layers/ConvBaseLayer.cpp
浏览文件 @ e9cc3282
......@@ -38,7 +38,6 @@ bool ConvBaseLayer::init(const LayerMap& layerMap,
strideY_.push_back(conf.stride_y());
dilationY_.push_back(conf.dilation_y());
filterSizeY_.push_back(conf.filter_size_y());
filterPixels_.push_back(filterSize_.back() * filterSizeY_.back());
channels_.push_back(conf.channels());
imgSizeH_.push_back(conf.has_img_size_y() ? conf.img_size_y()
: conf.img_size());
......@@ -47,31 +46,20 @@ bool ConvBaseLayer::init(const LayerMap& layerMap,
filterChannels_.push_back(conf.filter_channels());
outputH_.push_back(conf.has_output_y() ? conf.output_y() : conf.output_x());
outputW_.push_back(conf.output_x());
paddingZ_.push_back(conf.padding_z());
strideZ_.push_back(conf.stride_z());
filterSizeZ_.push_back(conf.filter_size_z());
imgSizeD_.push_back(conf.img_size_z());
outputD_.push_back(conf.output_z());
filterPixels_.push_back(filterSize_.back() * filterSizeY_.back() *
filterSizeZ_.back());
}
CHECK(inputLayers_.size() == parameters_.size());
for (size_t i = 0; i < inputLayers_.size(); i++) {
size_t height, width;
height = filterPixels_[i] * filterChannels_[i];
width = (!isDeconv_) ? numFilters_ : channels_[i];
// create a new weight
CHECK_EQ(parameters_[i]->getSize(), width * height);
Weight* w = new Weight(height, width, parameters_[i]);
weights_.emplace_back(w);
}
/* initialize the biases_ */
if (biasParameter_.get()) {
if (sharedBiases_) {
CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
biases_ =
std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
} else {
biases_ =
std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
}
}
// create new weights_ in derived class
// create new biases_ in derived class
// default caffe model
caffeMode_ = true;
......
paddle/gserver/layers/ConvBaseLayer.h
浏览文件 @ e9cc3282
......@@ -62,6 +62,13 @@ protected:
IntV outputH_;
/// The spatial dimensions of output feature map width.
IntV outputW_;
IntV outputD_;
IntV imgSizeD_;
IntV filterSizeZ_;
IntV strideZ_;
IntV paddingZ_;
/// Group size, refer to grouped convolution in
/// Alex Krizhevsky's paper: when group=2, the first half of the
/// filters are only connected to the first half of the input channels,
......
paddle/gserver/layers/CostLayer.cpp
浏览文件 @ e9cc3282
......@@ -572,13 +572,8 @@ void MultiBinaryLabelCrossEntropy::backwardImp(Matrix& output,
}
}
//
// Huber loss for robust 2-classes classification
//
REGISTER_LAYER(huber, HuberTwoClass);
bool HuberTwoClass::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
bool HuberCost::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
CostLayer::init(layerMap, parameterMap);
if (useGpu_) {
tmpCpuInput_.reserve(inputLayers_.size());
......@@ -589,7 +584,7 @@ bool HuberTwoClass::init(const LayerMap& layerMap,
return true;
}
void HuberTwoClass::forwardImp(Matrix& output, Argument& label, Matrix& cost) {
void HuberCost::forwardImp(Matrix& output, Argument& label, Matrix& cost) {
if (useGpu_) {
for (size_t i = 0; i < inputLayers_.size(); i++) {
tmpCpuInput_[i].resizeAndCopyFrom(
......@@ -597,13 +592,87 @@ void HuberTwoClass::forwardImp(Matrix& output, Argument& label, Matrix& cost) {
}
hl_stream_synchronize(HPPL_STREAM_DEFAULT);
}
forwardImpIn(output, label, cost);
}
void HuberTwoClass::forwardImpIn(Matrix& output,
Argument& label,
Matrix& target) {
//
// Huber loss for robust regression.
//
REGISTER_LAYER(huber_regression, HuberRegressionLoss);
bool HuberRegressionLoss::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
HuberCost::init(layerMap, parameterMap);
delta_ = config_.delta();
return true;
}
void HuberRegressionLoss::forwardImp(Matrix& output,
Argument& label,
Matrix& target) {
HuberCost::forwardImp(output, label, target);
size_t numSamples = target.getHeight();
size_t dim = output.getWidth();
CHECK(label.value);
CHECK_EQ((*label.value).getHeight(), numSamples);
CHECK_EQ(output.getHeight(), numSamples);
CHECK_EQ(dim, (*label.value).getWidth());
CHECK_EQ(target.getWidth(), (size_t)1);
real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData();
real* lbl =
useGpu_ ? tmpCpuInput_[1].value->getData() : (*label.value).getData();
std::vector<real> cost(numSamples, 0);
for (size_t i = 0; i < numSamples; ++i) {
for (size_t j = 0; j < dim; ++j) {
int index = i * dim + j;
real a = std::abs(lbl[index] - out[index]);
if (a <= delta_)
cost[i] += a * a / 2;
else
cost[i] += delta_ * (a - delta_ / 2);
}
}
target.copyFrom(cost.data(), numSamples);
}
void HuberRegressionLoss::backwardImp(Matrix& output,
Argument& label,
Matrix& outputG) {
size_t numSamples = output.getHeight();
size_t dim = output.getWidth();
real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData();
real* lbl =
useGpu_ ? tmpCpuInput_[1].value->getData() : (*label.value).getData();
real* grad = useGpu_ ? tmpCpuInput_[0].grad->getData() : outputG.getData();
for (size_t i = 0; i < numSamples; ++i) {
for (size_t j = 0; j < dim; ++j) {
int index = i * dim + j;
real a = lbl[index] - out[index];
if (std::abs(a) <= delta_)
grad[index] += -a;
else
grad[index] += a > 0 ? -delta_ : delta_;
}
}
if (useGpu_) outputG.copyFrom(grad, numSamples * dim);
}
//
// Huber loss for robust 2-classes classification
//
REGISTER_LAYER(huber_classification, HuberTwoClassification);
bool HuberTwoClassification::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
return HuberCost::init(layerMap, parameterMap);
}
void HuberTwoClassification::forwardImp(Matrix& output,
Argument& label,
Matrix& target) {
HuberCost::forwardImp(output, label, target);
size_t numSamples = target.getHeight();
CHECK(label.ids);
CHECK_EQ((*label.ids).getSize(), numSamples);
CHECK_EQ(output.getHeight(), numSamples);
CHECK_EQ(output.getWidth(), (size_t)1);
......@@ -611,47 +680,35 @@ void HuberTwoClass::forwardImpIn(Matrix& output,
real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData();
int* lbl = useGpu_ ? tmpCpuInput_[1].ids->getData() : (*label.ids).getData();
std::vector<real> cost(numSamples);
std::vector<real> cost(numSamples, 0);
for (size_t i = 0; i < numSamples; ++i) {
int y = 2 * lbl[i] - 1;
if (out[i] * y < -1)
cost[i] = -4 * out[i] * y;
else if (out[i] * y < 1)
cost[i] = (1 - out[i] * y) * (1 - out[i] * y);
else
cost[i] = 0;
real a = out[i] * y;
if (a < -1)
cost[i] = -4 * a;
else if (a < 1)
cost[i] = (1 - a) * (1 - a);
}
target.copyFrom(cost.data(), numSamples);
}
void HuberTwoClass::backwardImp(Matrix& outputValue,
Argument& label,
Matrix& outputGrad) {
if (useGpu_) {
backwardImpIn(
*tmpCpuInput_[0].value, tmpCpuInput_[1], *tmpCpuInput_[0].grad);
outputGrad.copyFrom(*tmpCpuInput_[0].grad);
} else {
backwardImpIn(outputValue, label, outputGrad);
}
}
void HuberTwoClass::backwardImpIn(Matrix& output,
Argument& label,
Matrix& outputG) {
void HuberTwoClassification::backwardImp(Matrix& output,
Argument& label,
Matrix& outputG) {
size_t numSamples = output.getHeight();
real* out = output.getData();
real* grad = outputG.getData();
int* lbl = (*label.ids).getData();
real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData();
int* lbl = useGpu_ ? tmpCpuInput_[1].ids->getData() : (*label.ids).getData();
real* grad = useGpu_ ? tmpCpuInput_[0].grad->getData() : outputG.getData();
for (size_t i = 0; i < numSamples; ++i) {
int y = 2 * lbl[i] - 1;
if (y * out[i] < -1)
real a = out[i] * y;
if (a < -1)
grad[i] += -4 * y;
else if (y * out[i] < 1)
grad[i] += -2 * (1 - y * out[i]) * y;
else if (a < 1)
grad[i] += -2 * (1 - a) * y;
}
if (useGpu_) outputG.copyFrom(grad, numSamples);
}
/**
* This cost layer compute the sum of its input as loss.
* \f[
......
paddle/gserver/layers/CostLayer.h
浏览文件 @ e9cc3282
......@@ -304,37 +304,70 @@ public:
Matrix& outputGrad) override;
};
/**
* Huber loss for robust 2-classes classification.
*
* For label={0, 1}, let y=2*label-1. Given output f, the loss is:
* \f[
* Loss =
* \left\{\begin{matrix}
* 4 * y * f & \textit{if} \ \ y* f < -1 \\
* (1 - y * f)^2 & \textit{if} \ \ -1 < y * f < 1 \\
* 0 & \textit{otherwise}
* \end{matrix}\right.
* \f]
/*
* A base layer for HuberRegressionLoss and HuberTwoClassification.
*/
class HuberTwoClass : public CostLayer {
class HuberCost : public CostLayer {
public:
std::vector<Argument> tmpCpuInput_;
public:
explicit HuberTwoClass(const LayerConfig& config) : CostLayer(config) {}
explicit HuberCost(const LayerConfig& config) : CostLayer(config) {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forwardImp(Matrix& output, Argument& label, Matrix& cost) override;
void forwardImpIn(Matrix& output, Argument& label, Matrix& cost);
void backwardImp(Matrix& outputValue,
Argument& label,
Matrix& outputGrad) override {}
};
/**
* Huber loss for robust regression.
*
* Given output f(x), label y and delta, the loss is:
* Loss = 0.5 * (1 - y * f)^2, if abs(y - f) <= delta \\
* Loss = delta * abs(y - f) - 0.5 * delta^2, otherwise
*/
class HuberRegressionLoss : public HuberCost {
public:
explicit HuberRegressionLoss(const LayerConfig& config) : HuberCost(config) {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forwardImp(Matrix& output, Argument& label, Matrix& cost) override;
void backwardImp(Matrix& outputValue,
Argument& label,
Matrix& outputGrad) override;
void backwardImpIn(Matrix& outputValue, Argument& label, Matrix& outputGrad);
protected:
real delta_;
};
/**
* Huber loss for robust 2-classes classification.
*
* For label={0, 1}, let y=2*label-1. Given output f(x), the loss is:
* Loss = 4 * y * f, if y* f < -1 \\
* Loss = (1 - y * f)^2, if -1 < y * f < 1 \\
* Loss = 0, otherwise
*/
class HuberTwoClassification : public HuberCost {
public:
explicit HuberTwoClassification(const LayerConfig& config)
: HuberCost(config) {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forwardImp(Matrix& output, Argument& label, Matrix& cost) override;
void backwardImp(Matrix& outputValue,
Argument& label,
Matrix& outputGrad) override;
};
typedef std::shared_ptr<CostLayer> CostLayerPtr;
......
paddle/gserver/layers/CrossEntropyOverBeam.cpp 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 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. */
#include "CrossEntropyOverBeam.h"
namespace paddle {
void CostForOneSequence::calValidExpandStep() {
validExpansionCount_ = 0;
goldAsExtraPath_ = true;
for (size_t i = 0; i < beams_->expansionCount; ++i) {
real gold = static_cast<real>(beams_->gold[i]);
if (i) {
real* start = beams_->candidateIds[i - 1]->getData();
goldRowIds_[i] = std::count_if(
start,
start + goldRowIds_[i - 1] * beamSize_ + goldColIds_[i - 1],
[](const real& val) { return val != -1.; });
} else {
goldRowIds_[i] = 0;
}
real* start =
beams_->candidateIds[i]->getData() + goldRowIds_[i] * beamSize_;
real* findEnd = std::find(start, start + beamSize_, gold);
validExpansionCount_++;
if (start + beamSize_ == findEnd) return;
goldColIds_[i] = findEnd - start;
}
if (goldColIds_[beams_->expansionCount - 1] != -1) goldAsExtraPath_ = false;
}
size_t CostForOneSequence::initLastExpansion() {
int beamId = validExpansionCount_ - 1;
const MatrixPtr candidates = beams_->candidateIds[beamId];
size_t height = candidates->getHeight();
/* initialization the last expansion. */
size_t pathCount = std::count_if(candidates->getData(),
candidates->getData() + height * beamSize_,
[](const real& val) { return val != -1; });
/*
* if the gold sequence falls off the beam during search, add the gold
* sequence as the last path into the all expanded candidates.
*/
if (goldAsExtraPath_) goldIdsInFinalExpansion_ = pathCount++;
pathRowIdsInEachBeam_.clear();
pathRowIdsInEachBeam_.resize(validExpansionCount_,
std::vector<int>(pathCount, 0));
parentIdsInBeam_.clear();
parentIdsInBeam_.resize(pathCount, 0);
if (goldAsExtraPath_) {
/* add gold sequence into the total expansion. */
pathRowIdsInEachBeam_[beamId].back() =
beams_->gold[beamId] +
getSeqStartPos(beamId, goldRowIds_[validExpansionCount_ - 1]);
parentIdsInBeam_.back() = goldRowIds_[validExpansionCount_ - 1];
} else {
size_t goldOffset = goldRowIds_[beamId] * beamSize_ + goldColIds_[beamId];
goldIdsInFinalExpansion_ =
std::count_if(candidates->getData(),
candidates->getData() + goldOffset,
[](const real& val) { return val != -1.; });
}
/*
* TODO(caoying): fix this, store the indices of selected candidate
* paths into Argument.ids
*/
real* ids = candidates->getData();
size_t curIdx = 0;
for (size_t i = 0; i < height; ++i) {
int basePos = getSeqStartPos(beamId, i);
for (size_t j = 0; j < beamSize_; ++j) {
int id = ids[i * beamSize_ + j];
if (id == -1) continue;
pathRowIdsInEachBeam_[beamId][curIdx] = id + basePos;
parentIdsInBeam_[curIdx++] = i;
}
}
return pathCount;
}
void CostForOneSequence::constructTotalExpansion() {
/*
* construct the entire expanded beam by begining with the last search
* in which gold falls off the beam.
*/
size_t totalPathCount = initLastExpansion();
for (int beamId = validExpansionCount_ - 2; beamId >= 0; --beamId) {
const MatrixPtr candidates = beams_->candidateIds[beamId];
real* ids = candidates->getData();
int lastParentIdInBeam = -1;
int basePos = -1;
for (size_t i = 0;
i < (goldAsExtraPath_ ? totalPathCount - 1 : totalPathCount);
++i) {
int id = ids[parentIdsInBeam_[i]];
int parentRowId = std::div(parentIdsInBeam_[i], beamSize_).quot;
if (parentIdsInBeam_[i] != lastParentIdInBeam)
basePos = getSeqStartPos(beamId, parentRowId);
pathRowIdsInEachBeam_[beamId][i] = id + basePos;
lastParentIdInBeam = parentIdsInBeam_[i];
parentIdsInBeam_[i] = parentRowId;
if (goldAsExtraPath_)
pathRowIdsInEachBeam_[beamId][totalPathCount - 1] =
beams_->gold[beamId] + getSeqStartPos(beamId, goldRowIds_[beamId]);
}
}
}
real CostForOneSequence::globallyNormalizedScore() {
expandedPathScores_.resize(validExpansionCount_);
Matrix::resizeOrCreate(
softmaxOut_, 1, pathRowIdsInEachBeam_[0].size(), false, false);
softmaxOut_->zeroMem();
MatrixPtr tmp = Matrix::create(
softmaxOut_->getData(), softmaxOut_->getWidth(), 1, false, false);
for (size_t i = 0; i < validExpansionCount_; ++i) {
Matrix::resizeOrCreate(expandedPathScores_[i],
pathRowIdsInEachBeam_[i].size(),
1,
false,
false);
expandedPathScores_[i]->zeroMem();
IVectorPtr rowIds = IVector::create(pathRowIdsInEachBeam_[i].data(),
pathRowIdsInEachBeam_[i].size(),
false);
expandedPathScores_[i]->selectRows(*(beams_->scores[i]), *rowIds);
tmp->add(*expandedPathScores_[i]);
}
softmaxOut_->softmax(*softmaxOut_);
return -std::log(softmaxOut_->getData()[goldIdsInFinalExpansion_]);
}
real CostForOneSequence::forward() {
calValidExpandStep();
constructTotalExpansion();
return globallyNormalizedScore();
}
void CostForOneSequence::backward() {
/*
* when softmax layer is the output layer, and it is combined with
* cross-entropy as cost. The derivate with regard to softmax's input
* is simply:
*
* grad_i = softmax_out_i - target_i,
*
* and here hard label is used.
*/
softmaxOut_->getData()[goldIdsInFinalExpansion_] -= 1.;
MatrixPtr tmp = Matrix::create(
softmaxOut_->getData(), softmaxOut_->getWidth(), 1, false, false);
for (size_t i = 0; i < validExpansionCount_; ++i) {
IVectorPtr rowIds = IVector::create(pathRowIdsInEachBeam_[i].data(),
pathRowIdsInEachBeam_[i].size(),
false);
/*
beams_->scoreGrad[i] has been intialized outside this class, this
class only keeps a pointer pointing to the original input gradients,
so here does not need to allocate or initalize the memory.
*/
tmp->addToRows(*beams_->scoreGrad[i], *rowIds);
}
}
REGISTER_LAYER(cross_entropy_over_beam, CrossEntropyOverBeam);
bool CrossEntropyOverBeam::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
CHECK_EQ(0U, inputLayers_.size() % 3) << "Error input number.";
beamExpanCount_ = inputLayers_.size() / 3;
candidateScores_.resize(beamExpanCount_);
candidateScoreGrad_.resize(beamExpanCount_);
candidateInBeam_.resize(beamExpanCount_);
goldSequence_.resize(beamExpanCount_);
gradToInputs_.resize(beamExpanCount_);
setNeedSequenceInfo(false);
return true;
}
void CrossEntropyOverBeam::checkInputs() {
batchSize_ = 0;
for (size_t i = 0; i < beamExpanCount_; ++i) {
const Argument& scores = getInput(i * 3);
const Argument& selCandidates = getInput(i * 3 + 1);
const Argument& goldSeq = getInput(i * 3 + 2);
if (i) {
CHECK(scores.hasSubseq()) << "input " << i << " "
<< inputLayers_[i * 3]->getName()
<< " should be a nested sequence";
CHECK_EQ(getInputValue(i * 3 + 1)->getWidth(), beamSize_);
CHECK_EQ(scores.getNumSequences(), batchSize_);
CHECK_EQ(scores.getNumSubSequences(), selCandidates.getBatchSize());
} else {
CHECK(scores.hasSeq()) << "input " << i << " "
<< inputLayers_[i]->getName()
<< " should be a sequence";
batchSize_ = scores.getNumSequences();
beamSize_ = getInputValue(i * 3 + 1)->getWidth();
CHECK_EQ(batchSize_, selCandidates.getBatchSize());
}
CHECK_EQ(1U, scores.value->getWidth());
CHECK_EQ(batchSize_, goldSeq.getBatchSize());
}
}
void CrossEntropyOverBeam::copyInputsToCpu() {
auto copyValue = [](const MatrixPtr& src, MatrixPtr& trg) {
if (dynamic_cast<GpuMatrix*>(src.get())) {
Matrix::resizeOrCreate(
trg, src->getHeight(), src->getWidth(), false, false);
trg->copyFrom(*src);
} else {
trg = std::move(src);
}
};
auto copyIds = [](const IVectorPtr& src, IVectorPtr& trg) {
if (dynamic_cast<GpuIVector*>(src.get())) {
IVector::resizeOrCreate(trg, src->getSize(), false);
trg->copyFrom(*src);
} else {
trg = std::move(src);
}
};
beamSplitPos_.clear();
beamSplitPos_.resize(batchSize_, std::vector<int>(beamExpanCount_, 0));
for (size_t i = 0; i < beamExpanCount_; ++i) {
copyValue(getInputValue(i * 3), candidateScores_[i]);
copyValue(getInputValue(i * 3 + 1), candidateInBeam_[i]);
copyIds(getInput(i * 3 + 2).ids, goldSequence_[i]);
if (i) {
ICpuGpuVectorPtr seqInfo = getInput(i * 3).sequenceStartPositions;
const int* seqStarts = seqInfo->getMutableData(false);
ICpuGpuVectorPtr subSeqInfo = getInput(i * 3).subSequenceStartPositions;
const int* subSeqStarts = subSeqInfo->getMutableData(false);
size_t seqId = 1;
for (size_t subSeqId = 0; subSeqId < subSeqInfo->getSize() - 1;
++subSeqId) {
CHECK_LT(seqId, seqInfo->getSize());
if (subSeqStarts[subSeqId] == seqStarts[seqId]) {
beamSplitPos_[seqId][i] = beamSplitPos_[seqId - 1][i];
seqId++;
}
beamSplitPos_[seqId - 1][i]++;
}
} else {
for (size_t j = 0; j < batchSize_; ++j) beamSplitPos_[j][i] = j + 1;
}
}
}
void CrossEntropyOverBeam::splitBatchBeams() {
beamCosts_.resize(batchSize_);
beamPerSeq_.resize(batchSize_, BeamExpansion(beamExpanCount_));
for (size_t i = 0; i < beamExpanCount_; ++i) {
int* seqStarts =
getInput(i * 3).sequenceStartPositions->getMutableData(false);
int* subSeqStarts = nullptr;
int maxLen = 0;
if (i) {
subSeqStarts =
getInput(i * 3).subSequenceStartPositions->getMutableData(false);
maxLen = getInput(i * 3).subSequenceStartPositions->getSize() - 1;
} else {
maxLen = getInput(i).sequenceStartPositions->getSize() - 1;
}
for (size_t j = 0; j < batchSize_; ++j) {
beamPerSeq_[j].scores[i] =
Matrix::create(candidateScores_[i]->getData() + seqStarts[j],
seqStarts[j + 1] - seqStarts[j],
1,
false,
false);
beamPerSeq_[j].scoreGrad[i] =
Matrix::create(candidateScoreGrad_[i]->getData() + seqStarts[j],
seqStarts[j + 1] - seqStarts[j],
1,
false,
false);
int offset = j ? beamSplitPos_[j - 1][i] : 0;
int height = beamSplitPos_[j][i] - (j ? beamSplitPos_[j - 1][i] : 0);
CHECK_GE(maxLen, offset + height);
beamPerSeq_[j].seqInfo[i] = IVector::create(
(i ? subSeqStarts : seqStarts) + offset, height + 1, false);
beamPerSeq_[j].candidateIds[i] =
Matrix::create(candidateInBeam_[i]->getData() + offset * beamSize_,
height,
beamSize_,
false,
false);
beamPerSeq_[j].gold[i] = goldSequence_[i]->getData()[j];
CHECK_LE(beamPerSeq_[j].gold[i], seqStarts[j + 1] - seqStarts[j]);
}
}
}
void CrossEntropyOverBeam::resizeOutput() {
Matrix::resizeOrCreate(output_.value, batchSize_, 1, false, false);
output_.value->zeroMem();
for (size_t i = 0; i < beamExpanCount_; ++i) {
MatrixPtr inGrad = getInputGrad(i * 3);
if (dynamic_cast<GpuMatrix*>(inGrad.get())) {
Matrix::resizeOrCreate(candidateScoreGrad_[i],
inGrad->getHeight(),
inGrad->getWidth(),
false,
false);
} else {
candidateScoreGrad_[i] = std::move(inGrad);
}
candidateScoreGrad_[i]->zeroMem();
}
}
void CrossEntropyOverBeam::copyGradToGpu(size_t copyCount) {
for (size_t i = 0; i < beamExpanCount_; ++i) {
if (dynamic_cast<GpuMatrix*>(getInputGrad(i * 3).get()))
getInputGrad(i * 3)->copyFrom(*candidateScoreGrad_[i]);
if (i == copyCount - 1) break;
}
}
void CrossEntropyOverBeam::forward(PassType passType) {
Layer::forward(passType);
checkInputs();
copyInputsToCpu();
resizeOutput();
splitBatchBeams();
MatrixPtr outputValue = getOutputValue();
for (size_t i = 0; i < batchSize_; ++i) {
beamCosts_[i].setData(
std::move(std::make_shared<BeamExpansion>(beamPerSeq_[i])), beamSize_);
outputValue->getData()[i] = beamCosts_[i].forward();
}
}
void CrossEntropyOverBeam::backward(const UpdateCallback& callback) {
for (size_t i = 0; i < batchSize_; ++i) {
beamCosts_[i].backward();
copyGradToGpu(beamCosts_[i].getValidExpansionCount());
}
}
} // namespace paddle
paddle/gserver/layers/CrossEntropyOverBeam.h 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 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. */
#pragma once
#include "CrossEntropyOverBeam.h"
#include "Layer.h"
namespace paddle {
/* This struct stores the beams in all search steps for a single sequence. */
struct BeamExpansion {
std::vector<MatrixPtr> scores;
std::vector<IVectorPtr> seqInfo;
std::vector<MatrixPtr> candidateIds;
std::vector<int> gold;
std::vector<MatrixPtr> scoreGrad;
size_t expansionCount;
explicit BeamExpansion(int n) {
expansionCount = n;
scores.resize(expansionCount);
seqInfo.resize(expansionCount);
candidateIds.resize(expansionCount);
scoreGrad.resize(expansionCount);
gold.resize(expansionCount);
}
};
typedef std::shared_ptr<BeamExpansion> BeamExpansionPtr;
class CostForOneSequence {
public:
CostForOneSequence()
: beamSize_(0), validExpansionCount_(0), goldAsExtraPath_(false) {}
void setData(const BeamExpansionPtr bPtr, size_t beamSize) {
beams_ = bPtr;
beamSize_ = beamSize;
expandedPathScores_.clear();
expandedPathScores_.resize(beams_->expansionCount);
goldRowIds_.clear();
goldRowIds_.resize(beams_->expansionCount, 0);
goldColIds_.clear();
goldColIds_.resize(beams_->expansionCount, -1);
}
size_t getValidExpansionCount() { return validExpansionCount_; }
real forward();
void backward();
private:
void calValidExpandStep();
void constructTotalExpansion();
size_t initLastExpansion();
real globallyNormalizedScore();
int getSeqStartPos(size_t beamId, size_t rowId) {
CHECK_GT(beams_->seqInfo[beamId]->getSize() - 1, rowId);
int* starts = beams_->seqInfo[beamId]->getData();
return starts[rowId] - starts[0];
}
size_t beamSize_;
size_t validExpansionCount_;
bool goldAsExtraPath_;
std::vector<int> goldRowIds_;
std::vector<int> goldColIds_;
BeamExpansionPtr beams_;
std::vector<std::vector<int>> pathRowIdsInEachBeam_;
std::vector<int> parentIdsInBeam_;
size_t goldIdsInFinalExpansion_;
std::vector<MatrixPtr> expandedPathScores_;
MatrixPtr softmaxOut_;
};
class CrossEntropyOverBeam : public Layer {
public:
explicit CrossEntropyOverBeam(const LayerConfig& config) : Layer(config) {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback) override;
private:
void checkInputs();
void copyInputsToCpu();
void resizeOutput();
void copyGradToGpu(size_t copyCount);
void splitBatchBeams();
size_t beamExpanCount_;
size_t batchSize_;
size_t beamSize_;
/*
* the process of constructing beams is not friendly to GPU, currently, this
* layer only runs on CPU, if any of its inputs is on GPU memory, then copy
* it to CPU memory.
*/
std::vector<MatrixPtr> candidateScores_;
std::vector<MatrixPtr> candidateScoreGrad_;
std::vector<MatrixPtr> candidateInBeam_;
std::vector<MatrixPtr> gradToInputs_;
std::vector<IVectorPtr> goldSequence_;
std::vector<std::vector<int>> beamSplitPos_;
/*
* split entire bath of beams into beam per sequnence and store the result
* into this member.
*/
std::vector<BeamExpansion> beamPerSeq_;
/* beamCosts_ is used to propagate error in one sequence. */
std::vector<CostForOneSequence> beamCosts_;
};
} // namespace paddle
paddle/gserver/layers/CudnnConvBaseLayer.cpp
浏览文件 @ e9cc3282
......@@ -46,8 +46,26 @@ bool CudnnConvBaseLayer::init(const LayerMap &layerMap,
projConf_.emplace_back(conf);
projections_.emplace_back(
Projection::create(*projConf_[i], parameters_[i], useGpu_));
// create a new weight
size_t height, width;
height = filterPixels_[i] * filterChannels_[i];
width = (!isDeconv_) ? numFilters_ : channels_[i];
CHECK_EQ(parameters_[i]->getSize(), width * height);
Weight *w = new Weight(height, width, parameters_[i]);
weights_.emplace_back(w);
}
if (biasParameter_.get()) {
if (sharedBiases_) {
CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
biases_ =
std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
} else {
biases_ =
std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
}
}
if (biases_.get() && sharedBiases_) {
hl_create_tensor_descriptor(&biasDesc_);
hl_create_tensor_descriptor(&outputDesc_);
......
paddle/gserver/layers/DeConv3DLayer.cpp 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 Baidu, Inc. 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. */
#include "DeConv3DLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(deconv3d, DeConv3DLayer);
bool DeConv3DLayer::init(const LayerMap &layerMap,
const ParameterMap &parameterMap) {
if (!ConvBaseLayer::init(layerMap, parameterMap)) return false;
// for Deconv, the dimension of Kernel is
// channel * output * depth * height * weigth
// Matrix storage format: (output * depth * height * weigth) x channel
for (int index = 0; index < config_.inputs().size(); ++index) {
M_.push_back(filterChannels_[index]);
K_.push_back(filterPixels_[index] * (numFilters_ / groups_[index]));
// create a new weight
size_t height, width;
height = filterPixels_[index] * numFilters_;
width = filterChannels_[index];
CHECK_EQ(parameters_[index]->getSize(), width * height);
Weight *w = new Weight(height, width, parameters_[index]);
weights_.emplace_back(w);
}
if (biasParameter_.get()) {
if (sharedBiases_) {
CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
biases_ =
std::unique_ptr<Weight>(new Weight(1, numFilters_, biasParameter_));
} else {
biases_ =
std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
}
}
return true;
}
size_t DeConv3DLayer::getSize() {
CHECK_NE(inputLayers_.size(), 0UL);
outputH_.clear();
outputW_.clear();
outputD_.clear();
N_.clear();
NOut_.clear();
size_t layerSize = 0;
for (size_t i = 0; i < inputLayers_.size(); ++i) {
outputW_.push_back(
imageSize(imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true));
outputH_.push_back(imageSize(
imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
outputD_.push_back(imageSize(
imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
NOut_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]);
CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize);
layerSize += NOut_[i] * numFilters_;
}
getOutput().setFrameHeight(outputH_[0]);
getOutput().setFrameWidth(outputW_[0]);
getOutput().setFrameDepth(outputD_[0]);
return layerSize;
}
void DeConv3DLayer::forward(PassType passType) {
Layer::forward(passType);
int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
int outWidth = getSize();
resetOutput(batchSize, outWidth);
const MatrixPtr outMat = getOutputValue();
for (size_t i = 0; i != inputLayers_.size(); ++i) {
REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str());
const MatrixPtr &inMat = getInputValue(i);
int M = M_[i];
int N = N_[i];
int K = K_[i];
MatrixPtr wMat = weights_[i]->getW();
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
for (int n = 0; n < batchSize; ++n) {
real *inData = inMat->getData() + n * inMat->getStride();
for (int g = 0; g < groups_[i]; ++g) {
MatrixPtr inMatSub = Matrix::create(inData, M, N, false, useGpu_);
MatrixPtr wMatSub = wMat->subMatrix(g * K, K);
MatrixPtr colBufDataSub = colBuf_->subMatrix(g * K, K);
colBufDataSub->mul(*wMatSub, *inMatSub, 1.0, 0.0);
inData += M * N;
}
colBuf_->col2Vol(outMat->getData() + n * outMat->getStride(),
numFilters_,
outputD_[i],
outputH_[i],
outputW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
strideZ_[i],
strideY_[i],
stride_[i],
paddingZ_[i],
paddingY_[i],
padding_[i],
1.0,
1.0);
}
}
if (nullptr != this->biasParameter_) {
REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
this->addBias();
}
forwardActivation();
}
void DeConv3DLayer::backward(const UpdateCallback &callback) {
backwardActivation();
int batchSize = getOutputGrad()->getHeight();
if (biases_ && biases_->getWGrad()) {
bpropBiases();
biases_->getParameterPtr()->incUpdate(callback);
}
for (size_t i = 0; i < inputLayers_.size(); ++i) {
if (weights_[i]->getWGrad() || this->needGradient_) {
int M = M_[i];
int N = N_[i];
int K = K_[i];
REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str());
Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
const MatrixPtr &inMat = getInputValue(i);
for (int n = 0; n < batchSize; ++n) {
colBuf_->vol2Col(
getOutputGrad()->getData() + n * getOutputGrad()->getStride(),
numFilters_,
outputD_[i],
outputH_[i],
outputW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
strideZ_[i],
strideY_[i],
stride_[i],
paddingZ_[i],
paddingY_[i],
padding_[i]);
if (weights_[i]->getWGrad()) {
real *inData = inMat->getData() + n * inMat->getStride();
for (int g = 0; g < groups_[i]; ++g) {
MatrixPtr colBufDataSub = colBuf_->subMatrix(g * K, K);
MatrixPtr wGradMatSub =
weights_[i]->getWGrad()->subMatrix(g * K, K);
MatrixPtr inMatSub = Matrix::create(inData, M, N, false, useGpu_);
wGradMatSub->mul(
*colBufDataSub, *(inMatSub->getTranspose()), 1.0, 1.0);
inData += M * N;
}
}
if (getInputGrad(i)) {
real *preGrad =
getInputGrad(i)->getData() + n * getInputGrad(i)->getStride();
for (int g = 0; g < groups_[i]; ++g) {
MatrixPtr w = weights_[i]->getW()->subMatrix(g * K, K);
MatrixPtr outGradMat = colBuf_->subMatrix(g * K, K);
MatrixPtr inGradMatSub =
Matrix::create(preGrad, M, N, false, useGpu_);
inGradMatSub->mul(*(w->getTranspose()), *outGradMat, 1.0, 1.0);
preGrad += M * N;
}
}
}
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
weights_[i]->getParameterPtr()->incUpdate(callback);
}
}
}
void DeConv3DLayer::bpropWeights(int i) {}
void DeConv3DLayer::bpropData(int i) {}
void DeConv3DLayer::bpropBiases() {
const MatrixPtr &outGradMat = getOutputGrad();
if (this->sharedBiases_) {
biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
} else {
biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
}
}
void DeConv3DLayer::addBias() {
MatrixPtr outMat = getOutputValue();
if (this->sharedBiases_) {
outMat->addSharedBias(*(biases_->getW()), 1.0f);
} else {
outMat->addBias(*(biases_->getW()), 1.0f);
}
}
} // namespace paddle
paddle/gserver/layers/DeConv3DLayer.h 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 Baidu, Inc. 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. */
#pragma once
#include <vector>
#include "ConvBaseLayer.h"
#include "paddle/math/MathUtils.h"
#include "paddle/math/Matrix.h"
namespace paddle {
/**
* @brief A subclass of deconvolution3D layer.
* This layer expands input and use matrix multiplication to
* calculate deconvolution3D operation.
*/
class DeConv3DLayer : public ConvBaseLayer {
public:
explicit DeConv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {}
~DeConv3DLayer() {}
bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
void forward(PassType passType);
void addBias();
void backward(const UpdateCallback& callback);
void bpropBiases();
void bpropData(int i);
void bpropWeights(int i);
size_t getSize();
protected:
// Figure out the dimensions for individual gemms.
IntV M_; /// numFilters_ / filter_group_;
IntV N_; /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_
IntV K_; /// outputD_ * outputH_ * outputW_
IntV NOut_;
MatrixPtr colBuf_;
};
} // namespace paddle
paddle/gserver/layers/ExpandConvBaseLayer.cpp
浏览文件 @ e9cc3282
......@@ -22,12 +22,31 @@ bool ExpandConvBaseLayer::init(const LayerMap &layerMap,
/* Initialize the basic convolutional parent class */
ConvBaseLayer::init(layerMap, parameterMap);
int index = 0;
for (auto &inputConfig : config_.inputs()) {
const ConvConfig &conf = inputConfig.conv_conf();
/* Consistent caffe mode for multiple input */
caffeMode_ = conf.caffe_mode();
}
// create a new weight
size_t height, width;
height = filterPixels_[index] * filterChannels_[index];
width = (!isDeconv_) ? numFilters_ : channels_[index];
CHECK_EQ(parameters_[index]->getSize(), width * height);
Weight *w = new Weight(height, width, parameters_[index]);
weights_.emplace_back(w);
index++;
}
if (biasParameter_.get()) {
if (sharedBiases_) {
CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
biases_ =
std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
} else {
biases_ =
std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
}
}
getOutputSize();
return true;
......
paddle/gserver/layers/ExpandConvLayer.cpp
浏览文件 @ e9cc3282
......@@ -29,6 +29,10 @@ namespace paddle {
REGISTER_LAYER(exconv, ExpandConvLayer);
REGISTER_LAYER(exconvt, ExpandConvLayer);
inline bool isDepthwiseConv(int channels, int groups) {
return channels == groups;
}
bool ExpandConvLayer::init(const LayerMap &layerMap,
const ParameterMap &parameterMap) {
/* Initialize the basic convolutional parent class */
......@@ -47,14 +51,27 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
if (useGpu_ && (size_t)groups_[i] == (size_t)channels_[i] && !isDeconv_) {
// Convolution Layer uses the GemmConv function by default.
convType = "GemmConv";
convGradInputType = "GemmConvGradInput";
convGradFilterType = "GemmConvGradFilter";
// If depth wise convolution and useGpu == true
if (useGpu_ && isDepthwiseConv(channels_[i], groups_[i]) && !isDeconv_) {
convType = "DepthwiseConv";
convGradInputType = "DepthwiseConvGradInput";
convGradFilterType = "DepthwiseConvGradFilter";
} else {
convType = "GemmConv";
convGradInputType = "GemmConvGradInput";
convGradFilterType = "GemmConvGradFilter";
}
// If depth wise convolution and useGpu == false and ARM-NEON
if (!useGpu_ && isDepthwiseConv(channels_[i], groups_[i]) && !isDeconv_) {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
if ((filterSize_[i] == filterSizeY_[i]) &&
(filterSize_[i] == 3 || filterSize_[i] == 4) &&
(stride_[i] == strideY_[i]) && (stride_[i] == 1 || stride_[i] == 2)) {
convType = "NeonDepthwiseConv";
}
#endif
}
if (FLAGS_use_nnpack && !isDeconv_) {
......
paddle/gserver/layers/Layer.cpp
浏览文件 @ e9cc3282
......@@ -41,7 +41,7 @@ namespace paddle {
Layer::Layer(const LayerConfig& config, bool useGpu)
: config_(config),
useGpu_(useGpu),
deviceId_(-1),
deviceId_(CPU_DEVICE),
needSequenceInfo_(true) {}
bool Layer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) {
......
paddle/gserver/layers/Layer.h
浏览文件 @ e9cc3282
......@@ -59,7 +59,12 @@ protected:
LayerConfig config_;
/// whether to use GPU
bool useGpu_;
/// Device Id. CPU is -1, and GPU is 0, 1, 2 ...
/// Paddle device ID, MKLDNN is -2, CPU is -1
enum PADDLE_DEVICE_ID {
MKLDNN_DEVICE = -2,
CPU_DEVICE = -1,
};
/// Device Id. MKLDNN is -2, CPU is -1, and GPU is 0, 1, 2 ...
int deviceId_;
/// Input layers
std::vector<LayerPtr> inputLayers_;
......@@ -77,6 +82,7 @@ protected:
Argument output_;
/// Several outputs stored on different devices, used in 'parallel_nn' case,
/// and record them by deviceId_.
/// Also used in 'use_mkldnn' case.
std::vector<Argument> outputOtherDevice_;
/// If there are several outputs, map them by each name.
std::map<std::string, Argument*> outputMap_;
......@@ -172,6 +178,13 @@ protected:
return inputLayer.getOutput(deviceId_);
}
/**
* Get the argument of input layer with deviceId.
*/
const Argument& getInput(size_t inputIndex, int deviceId) const {
return inputLayers_[inputIndex]->getOutput(deviceId);
}
/**
* Get the forward-input value.
*/
......@@ -186,6 +199,13 @@ protected:
return inputLayer.getOutput(deviceId_).value;
}
/**
* Get the forward-input value with deviceId.
*/
const MatrixPtr& getInputValue(int inputIndex, int deviceId) {
return inputLayers_[inputIndex]->getOutput(deviceId).value;
}
/**
* Get the forward-input grad.
*/
......@@ -200,6 +220,13 @@ protected:
return inputLayer.getOutput(deviceId_).grad;
}
/**
* Get the forward-input grad.
*/
const MatrixPtr& getInputGrad(int inputIndex, int deviceId) {
return inputLayers_[inputIndex]->getOutput(deviceId).grad;
}
/**
* Get the forward-input label.
*/
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ e9cc3282
......@@ -61,43 +61,42 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() {
return;
}
// TODO(TJ): dst format should get from wgtVal_
int dstFmt = PARAM_FORMAT_MKLDNN_OI;
int srcFmt = weight_->getParameterPtr()->getHeaderFormat();
if (srcFmt == dstFmt) {
return;
}
// The weight_ is transposed from initial paddle weight
MatrixPtr paddleWgt = Matrix::create(
weight_->getW()->getData(), iLayerSize_, oc_, false, false);
// TODO(TJ): remove this print when do not need differ weights
std::ostringstream ostr;
paddleWgt->print(ostr);
VLOG(MKLDNN_ALL) << "Initial Weight from paddle: " << std::endl << ostr.str();
// The mkldnn weight is transposed from initial paddle matrix
MatrixPtr paddleWgtT;
paddleWgt->transpose(paddleWgtT, true);
weight_->getW()->copyFrom(*paddleWgtT);
weight_->getParameterPtr()->setHeaderFormat(dstFmt);
CHECK(wgtVal_) << "should have been initialized";
bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
auto targetDim = wgtVal_->getDims();
auto srcFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo;
wgtVal_->reorderDataFrom(wgtVal_, srcFmt, targetDim);
hasInitedWgt_ = true;
}
void MKLDNNFcLayer::convertWeightsToPaddle() {
MatrixPtr dnnWgt = weight_->getW();
MatrixPtr paddleWgt;
dnnWgt->transpose(paddleWgt, true);
// copy paddle weight and override on weight_
MatrixPtr dnnWgtT = Matrix::create(
dnnWgt->getData(), dnnWgt->getWidth(), dnnWgt->getHeight(), false, false);
dnnWgtT->copyFrom(*paddleWgt);
CHECK(wgtVal_) << "should have been initialized";
bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
auto targetDim = wgtVal_->getDims();
auto dstFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo;
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
}
void MKLDNNFcLayer::convertOutputToOtherDevice() {
copyOutputInfoToOtherDevice();
// find other cpu device and reorder output to cpu device
int cnt = 0;
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
if (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
// fc cpu output value do not need convert
// just share point
outputOtherDevice_[i].value = output_.value;
++cnt;
}
}
if (cnt > 1) {
LOG(WARNING) << "should not have more than one CPU devie";
}
}
void MKLDNNFcLayer::reshape() {
const Argument& input = getInput(0);
const Argument& input = getInput(0, getPrev(0)->getDeviceId());
int batchSize = input.getBatchSize();
if (bs_ == batchSize) {
return;
......@@ -111,10 +110,6 @@ void MKLDNNFcLayer::reshape() {
if (iw_ == 0) {
iw_ = 1;
}
hasSpatial_ = true;
if (ih_ == 1 && iw_ == 1) {
hasSpatial_ = false;
}
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
ic_ = iLayerSize_ / (ih_ * iw_);
CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
......@@ -135,37 +130,53 @@ void MKLDNNFcLayer::reshape() {
void MKLDNNFcLayer::resetFwd() {
bool hasBias = biases_ && biases_->getW();
real* iData = getInputValue(0)->getData();
real* oData = getOutputValue()->getData();
real* wData = weight_->getW()->getData();
real* bData = hasBias ? biases_->getW()->getData() : NULL;
// TODO(TJ): below create should be covered in MkldnnMatrix
// create memory desc
memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw)
: createMD({bs_, ic_}, format::nc);
memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw)
: createMD({oc_, ic_}, format::oi);
memory::desc bMD = bData != NULL ? createMD({oc_}, format::x)
: createMD({}, format::format_undef);
memory::desc oMD = createMD({bs_, oc_}, format::nc);
// create memory primitive desc and memory self
inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData));
wgtVal_.reset(new memory(memory::primitive_desc(wMD, engine_), wData));
outVal_.reset(new memory(memory::primitive_desc(oMD, engine_), oData));
const MatrixPtr& wgt = weight_->getW();
const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr;
const MatrixPtr& out = output_.value;
if (inputIsOnlyMKLDNN()) {
const MatrixPtr& in = getInputValue(0);
inVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(in);
CHECK(inVal_) << "Input should be MKLDNNMatrix";
} else {
CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet";
const MatrixPtr& in = getInputValue(0, CPU_DEVICE);
inVal_ = MKLDNNMatrix::create(
in, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_);
}
inVal_->downSpatial();
wgtVal_ = MKLDNNMatrix::create(
wgt, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_);
wgtVal_->downSpatial();
biasVal_ =
hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr;
outVal_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_);
// change original output value to mkldnn output value
output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
if (!outputIsOnlyMKLDNN()) {
convertOutputToOtherDevice();
}
// create forward handle
prop_kind pk = prop_kind::forward;
fc_fwd::desc fwdDesc = bData != NULL ? fc_fwd::desc(pk, iMD, wMD, bMD, oMD)
: fc_fwd::desc(pk, iMD, wMD, oMD);
fc_fwd::desc fwdDesc = hasBias ? fc_fwd::desc(pk,
inVal_->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
biasVal_->getMemoryDesc(),
outVal_->getMemoryDesc())
: fc_fwd::desc(pk,
inVal_->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
outVal_->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
if (bData != NULL) {
biasVal_.reset(new memory(memory::primitive_desc(bMD, engine_), bData));
if (hasBias) {
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_));
} else {
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_));
}
printValueFormatFlow();
pipelineFwd_.clear();
pipelineFwd_.push_back(*fwd_);
}
......@@ -175,45 +186,46 @@ void MKLDNNFcLayer::resetBwd() {
return;
}
needResetBwd_ = false;
bool hasBias = biases_ && biases_->getWGrad();
real* iData = getInputValue(0)->getData();
real* iDiff = getInputGrad(0) != nullptr ? getInputGrad(0)->getData() : NULL;
real* oDiff = getOutputGrad()->getData();
real* wDiff = weight_->getWGrad()->getData();
real* bDiff = hasBias ? biases_->getWGrad()->getData() : NULL;
/// backward weight
// create memory desc for backward memory
memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw)
: createMD({bs_, ic_}, format::nc);
memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw)
: createMD({oc_, ic_}, format::oi);
memory::desc oMD = createMD({bs_, oc_}, format::nc);
memory::desc bMD = bDiff != NULL ? createMD({oc_}, format::x)
: createMD({}, format::format_undef);
if (inVal_) {
// update data
inVal_->set_data_handle(iData);
} else {
inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData));
}
// create memory primitive desc and memory self
wgtGrad_.reset(new memory(memory::primitive_desc(wMD, engine_), wDiff));
outGrad_.reset(new memory(memory::primitive_desc(oMD, engine_), oDiff));
fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward, iMD, wMD, oMD);
CHECK(inVal_) << "Should have input value";
const MatrixPtr& wgt = weight_->getWGrad();
const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr;
// TODO(TJ): merge outgrad
int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
// for MKLDNN device:
// can not directly cast outputgrad to mkldnnmatrix,
// since each layer can not write the inputgrad to mkldnn inputgrad.
// So just create from matrix with outputvalue format.
// for CPU device:
// fc do not need to convert from cpu device since output is always nc format
// only need create from cpu device
const MatrixPtr& out = getOutput(device).grad;
outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc());
wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPrimitiveDesc());
biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPrimitiveDesc())
: nullptr;
// create memory primitive desc
fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward,
inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
fc_bwdWgt::desc bwdWgtDesc = bDiff != NULL
? fc_bwdWgt::desc(iMD, wMD, bMD, oMD)
: fc_bwdWgt::desc(iMD, wMD, oMD);
fc_bwdWgt::desc bwdWgtDesc = hasBias
? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
biasGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc())
: fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc());
fc_bwdWgt::primitive_desc bwdWgtPD =
fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD);
if (bDiff != NULL) {
biasGrad_.reset(new memory(memory::primitive_desc(bMD, engine_), bDiff));
if (hasBias) {
bwdWgt_.reset(
new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_));
} else {
......@@ -223,15 +235,26 @@ void MKLDNNFcLayer::resetBwd() {
pipelineBwd_.push_back(*bwdWgt_);
/// backward data
if (iDiff == NULL) {
device = inputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
const MatrixPtr& in = getInputGrad(0, device);
if (in == nullptr) {
return;
}
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(iMD, wMD, oMD);
if (getInput(0, device).getAllCount() > 1) {
// TODO(TJ): use outputMaps_ ways when merge outgrad done
} else {
inGrad_ = MKLDNNMatrix::create(in, inVal_->getPrimitiveDesc());
}
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc());
fc_bwdData::primitive_desc bwdDataPD =
fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD);
inGrad_.reset(new memory(memory::primitive_desc(iMD, engine_), iDiff));
CHECK(wgtVal_) << "Should have weight memory";
bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_));
printGradFormatFlow();
pipelineBwd_.push_back(*bwdData_);
}
......@@ -241,11 +264,7 @@ void MKLDNNFcLayer::forward(PassType passType) {
{
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
// update input data
// since it might be changed if this is after data layer
real* iData = getInputValue(0)->getData();
inVal_->set_data_handle(iData);
syncInputValue();
// just submit forward pipeline
stream_->submit(pipelineFwd_);
......@@ -267,10 +286,7 @@ void MKLDNNFcLayer::backward(const UpdateCallback& callback) {
REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
resetBwd();
// update diff
real* oDiff = getOutputGrad()->getData();
outGrad_->set_data_handle(oDiff);
syncOutputGrad();
// just sumbmit backward pipeline
stream_->submit(pipelineBwd_);
}
......
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @ e9cc3282
......@@ -32,16 +32,13 @@ protected:
// if has already init the weight
bool hasInitedWgt_;
// if input layer has image size info (ih>1 && iw>1)
bool hasSpatial_;
// fc weight and bias
std::unique_ptr<Weight> weight_;
std::unique_ptr<Weight> biases_;
public:
explicit MKLDNNFcLayer(const LayerConfig& config)
: MKLDNNLayer(config), hasInitedWgt_(false), hasSpatial_(true) {}
: MKLDNNLayer(config), hasInitedWgt_(false) {}
~MKLDNNFcLayer() {}
......@@ -75,6 +72,8 @@ protected:
* only would be called when needed
*/
void resetBwd();
void convertOutputToOtherDevice() override;
};
} // namespace paddle
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ e9cc3282
......@@ -18,9 +18,9 @@ limitations under the License. */
#include "Layer.h"
#include "MKLDNNBase.h"
#include "mkldnn.hpp"
#include "paddle/math/MKLDNNMatrix.h"
DECLARE_bool(use_mkldnn);
DECLARE_bool(use_mkldnn_wgt);
namespace paddle {
......@@ -52,15 +52,15 @@ protected:
std::vector<mkldnn::primitive> pipelineFwd_;
std::vector<mkldnn::primitive> pipelineBwd_;
// TODO(TJ): change below memory as MKLDNNMatrixPtr type
std::shared_ptr<mkldnn::memory> inVal_;
std::shared_ptr<mkldnn::memory> inGrad_;
std::shared_ptr<mkldnn::memory> outVal_;
std::shared_ptr<mkldnn::memory> outGrad_;
std::shared_ptr<mkldnn::memory> wgtVal_;
std::shared_ptr<mkldnn::memory> wgtGrad_;
std::shared_ptr<mkldnn::memory> biasVal_;
std::shared_ptr<mkldnn::memory> biasGrad_;
// MKLDNNMatrixPtr
MKLDNNMatrixPtr inVal_;
MKLDNNMatrixPtr inGrad_;
MKLDNNMatrixPtr outVal_;
MKLDNNMatrixPtr outGrad_;
MKLDNNMatrixPtr wgtVal_;
MKLDNNMatrixPtr wgtGrad_;
MKLDNNMatrixPtr biasVal_;
MKLDNNMatrixPtr biasGrad_;
public:
explicit MKLDNNLayer(const LayerConfig& config)
......@@ -83,17 +83,21 @@ public:
virtual bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
<< "and set use_mkldnn=True";
CHECK(!useGpu_) << "Do not support GPU yet";
// set device id before Layer::init
setDevice(MKLDNN_DEVICE);
// change param device to MKLDNN device
setParamsDevice(MKLDNN_DEVICE, parameterMap);
if (!Layer::init(layerMap, parameterMap)) {
return false;
}
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
<< "and set use_mkldnn=True";
stream_.reset(new MKLDNNStream());
engine_ = CPUEngine::Instance().getEngine();
// TODO(TJ): deivecId
return true;
}
......@@ -109,6 +113,12 @@ public:
*/
virtual void convertWeightsToPaddle() {}
/**
* convert MKLDNN output to other device.
* only support CPU device yet
*/
virtual void convertOutputToOtherDevice() {}
/**
* print info about sizes
*/
......@@ -118,14 +128,124 @@ public:
<< ", oh: " << oh_ << ", ow: " << ow_;
}
// TODO(TJ): move to MkldnnMatrix
// create memory desc
inline mkldnn::memory::desc createMD(
mkldnn::memory::dims dims,
mkldnn::memory::format fmt,
mkldnn::memory::data_type type = mkldnn::memory::data_type::f32) {
// TODO(TJ): isFmtSuppoted(fmt)
return mkldnn::memory::desc(dims, type, fmt);
/**
* Print the mkldnn memory format flow of value
*/
virtual void printValueFormatFlow() {
if (inVal_ && outVal_) {
VLOG(MKLDNN_FMTS) << "value format flow --- " << inVal_->getFormat()
<< " >>> " << outVal_->getFormat();
}
}
/**
* Print the mkldnn memory format flow of grad
*/
virtual void printGradFormatFlow() {
if (inGrad_ && outGrad_) {
VLOG(MKLDNN_FMTS) << "grad format flow --- " << inGrad_->getFormat()
<< " <<< " << outGrad_->getFormat();
}
}
protected:
/**
* copy image size and sequence info to other device
* @note: can not directly use Layer::copyOutputToOtherDevice since here only
* copy base info and do not copy data value
*/
void copyOutputInfoToOtherDevice() {
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
outputOtherDevice_[i].setFrameHeight(output_.getFrameHeight());
outputOtherDevice_[i].setFrameWidth(output_.getFrameWidth());
outputOtherDevice_[i].sequenceStartPositions =
output_.sequenceStartPositions;
outputOtherDevice_[i].subSequenceStartPositions =
output_.subSequenceStartPositions;
outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
}
}
/**
* If input only has MKLDNN device.
* Otherwise, only support the previous layer using CPU device.
*/
bool inputIsOnlyMKLDNN(int index = 0) {
int prevDevice = getPrev(index)->getDeviceId();
if (prevDevice == MKLDNN_DEVICE) {
return true;
} else {
// do not support GPU yet
CHECK_EQ(prevDevice, CPU_DEVICE) << "Only support CPU yet";
return false;
}
}
/**
* If output only has MKLDNN device.
* Otherwise, other devices should only using CPU device.
*/
bool outputIsOnlyMKLDNN() {
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
CHECK_EQ(outputOtherDevice_[i].deviceId, CPU_DEVICE)
<< "Only support other device is CPU yet";
}
return outputOtherDevice_.size() == 0;
}
/**
* Sync input value data
*/
void syncInputValue() {
if (inputIsOnlyMKLDNN()) {
return;
}
real* iData = getInputValue(0, CPU_DEVICE)->getData();
// update input data
// since it might be changed if this is after data layer
inVal_->updateData(iData);
}
/**
* Sync output grad data
*/
void syncOutputGrad() {
if (outputIsOnlyMKLDNN()) {
return;
}
// update diff
real* oDiff = getOutput(CPU_DEVICE).grad->getData();
outGrad_->updateData(oDiff);
}
/**
* Set deviceId of this layer.
*/
void setDevice(int id) { deviceId_ = id; }
/**
* Set deviceId of the params used in this layer.
*/
void setParamsDevice(int id, const ParameterMap& parameterMap) {
for (auto& inputConfig : config_.inputs()) {
if (inputConfig.has_input_parameter_name()) {
ParameterPtr parameter;
std::string name = inputConfig.input_parameter_name();
CHECK(mapGet(name, parameterMap, &parameter))
<< "Cannot find input parameter " << name << " for layer "
<< getName();
parameter->setDevice(id);
}
}
if (config_.has_bias_parameter_name()) {
ParameterPtr parameter;
std::string name = config_.bias_parameter_name();
CHECK(mapGet(name, parameterMap, &parameter))
<< "Cannot find bias parameter " << name << " for layer "
<< getName();
parameter->setDevice(id);
}
}
};
......
paddle/gserver/layers/Pool3DLayer.cpp 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 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. */
#include "Pool3DLayer.h"
#include "PoolProjectionLayer.h"
#include "paddle/utils/Logging.h"
namespace paddle {
REGISTER_LAYER(pool3d, Pool3DLayer);
bool Pool3DLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
Layer::init(layerMap, parameterMap);
/* the size of inputs for pool-layer is 1 */
CHECK_EQ(config_.inputs_size(), 1);
const PoolConfig& conf = config_.inputs(0).pool_conf();
poolType_ = conf.pool_type();
channels_ = conf.channels();
sizeX_ = conf.size_x();
sizeY_ = conf.size_y();
sizeZ_ = conf.size_z();
strideW_ = conf.stride();
strideH_ = conf.stride_y();
strideD_ = conf.stride_z();
imgSizeW_ = conf.img_size();
imgSizeH_ = conf.img_size_y();
imgSizeD_ = conf.img_size_z();
paddingW_ = conf.padding();
paddingH_ = conf.padding_y();
paddingD_ = conf.padding_z();
outputW_ = conf.output_x();
outputH_ = conf.output_y();
outputD_ = conf.output_z();
return true;
}
size_t Pool3DLayer::getSize() {
CHECK_EQ(inputLayers_.size(), 1UL);
size_t layerSize = 0;
outputD_ = outputSize(imgSizeD_, sizeZ_, paddingD_, strideD_, false);
outputH_ = outputSize(imgSizeH_, sizeY_, paddingH_, strideH_, false);
outputW_ = outputSize(imgSizeW_, sizeX_, paddingW_, strideW_, false);
layerSize = outputD_ * outputH_ * outputW_ * channels_;
getOutput().setFrameHeight(outputH_);
getOutput().setFrameWidth(outputW_);
getOutput().setFrameDepth(outputD_);
return layerSize;
}
void Pool3DLayer::forward(PassType passType) {
Layer::forward(passType);
const MatrixPtr& inMat = inputLayers_[0]->getOutputValue();
size_t batchSize = inMat->getHeight();
size_t outWidth = getSize();
resetOutput(batchSize, outWidth);
Matrix::resizeOrCreate(maxPoolIdx_, batchSize, outWidth, false, useGpu_);
const MatrixPtr outMat = getOutputValue();
if (poolType_ == "avg") {
outMat->avgPool3DForward(*inMat,
channels_,
imgSizeD_,
imgSizeH_,
imgSizeW_,
outputD_,
outputH_,
outputW_,
sizeZ_,
sizeY_,
sizeX_,
strideD_,
strideH_,
strideW_,
paddingD_,
paddingH_,
paddingW_);
} else if (poolType_ == "max") {
outMat->maxPool3DForward(*inMat,
*maxPoolIdx_,
channels_,
imgSizeD_,
imgSizeH_,
imgSizeW_,
outputD_,
outputH_,
outputW_,
sizeZ_,
sizeY_,
sizeX_,
strideD_,
strideH_,
strideW_,
paddingD_,
paddingH_,
paddingW_);
} else {
LOG(FATAL) << "Unknown pool type: " << poolType_;
}
forwardActivation();
}
void Pool3DLayer::backward(const UpdateCallback& callback) {
backwardActivation();
(void)callback;
if (NULL == getInputGrad(0)) return;
MatrixPtr inMat = inputLayers_[0]->getOutputValue();
MatrixPtr inGradMat = inputLayers_[0]->getOutputGrad();
MatrixPtr outMat = getOutputValue();
MatrixPtr outGradMat = getOutputGrad();
if (poolType_ == "avg") {
inGradMat->avgPool3DBackward(*outGradMat,
imgSizeD_,
imgSizeH_,
imgSizeW_,
outputD_,
outputH_,
outputW_,
sizeZ_,
sizeY_,
sizeZ_,
strideD_,
strideH_,
strideW_,
paddingD_,
paddingH_,
paddingW_,
1.0,
1.0);
} else if (poolType_ == "max") {
inGradMat->maxPool3DBackward(*outGradMat,
*maxPoolIdx_,
imgSizeD_,
imgSizeH_,
imgSizeW_,
outputD_,
outputH_,
outputW_,
sizeZ_,
sizeY_,
sizeZ_,
strideD_,
strideH_,
strideW_,
paddingD_,
paddingH_,
paddingW_,
1.0,
1.0);
} else {
LOG(FATAL) << "Unknown pool type: " << poolType_;
}
}
} // namespace paddle
paddle/gserver/layers/Pool3DLayer.h 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 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. */
#pragma once
#include <vector>
#include "Layer.h"
#include "paddle/math/MathUtils.h"
#include "paddle/math/Matrix.h"
namespace paddle {
/**
* @brief Basic parent layer of pooling
* Pools the input within regions
*/
class Pool3DLayer : public Layer {
public:
explicit Pool3DLayer(const LayerConfig& config) : Layer(config) {}
~Pool3DLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback) override;
size_t getSize();
protected:
int channels_;
int sizeX_, sizeY_, sizeZ_;
int strideW_, strideH_, strideD_;
int paddingW_, paddingH_, paddingD_;
int imgSizeW_, imgSizeH_, imgSizeD_;
int outputW_, outputH_, outputD_;
std::string poolType_;
MatrixPtr maxPoolIdx_;
};
} // namespace paddle
paddle/gserver/tests/CMakeLists.txt
浏览文件 @ e9cc3282
......@@ -34,6 +34,13 @@ add_unittest_without_exec(test_CRFLayerGrad
add_test(NAME test_CRFLayerGrad
COMMAND test_CRFLayerGrad)
################ test_CrossEntropyOverBeam ####################
add_unittest_without_exec(test_CrossEntropyOverBeam
test_CrossEntropyOverBeamGrad.cpp
LayerGradUtil.cpp)
add_test(NAME test_CrossEntropyOverBeam
COMMAND test_CrossEntropyOverBeam)
################ test_SeqSliceLayerGrad ####################
add_unittest_without_exec(test_SeqSliceLayerGrad
test_SeqSliceLayerGrad.cpp
......
paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2016 Baidu, Inc. 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. */
#include <random>
#include <sstream>
#include <gtest/gtest.h>
#include "ModelConfig.pb.h"
#include "paddle/gserver/layers/DataLayer.h"
#include "paddle/trainer/Trainer.h"
#include "LayerGradUtil.h"
#include "paddle/testing/TestUtil.h"
using namespace paddle; // NOLINT
DECLARE_int32(gpu_id);
DECLARE_bool(thread_local_rand_use_global_seed);
const size_t MAX_SEQ_NUM = 23;
const size_t MAX_SEQ_LEN = 50;
const size_t MAX_BEAM_SIZE = 27;
const size_t SEED = (size_t)(time(NULL));
struct SingleBeamExpansion {
vector<int> seqStartPos;
vector<int> subSeqStartPos;
vector<real> candidateScores;
// TODO(caoying): store this into Argument.ids
vector<real> selectedIndices;
vector<int> groundTruth;
vector<size_t> inBeam;
vector<int> rowIdxInBeam;
vector<int> colIdxInBeam;
void resetGroundTruth(size_t n) {
groundTruth.clear();
groundTruth.resize(n, -1);
inBeam.clear();
inBeam.resize(n, 0);
rowIdxInBeam.clear();
rowIdxInBeam.resize(n, -1);
colIdxInBeam.clear();
colIdxInBeam.resize(n, -1);
}
};
inline float randFloat() {
return static_cast<float>(rand()) / static_cast<float>(RAND_MAX);
}
void genRand(real* numbers, size_t n) {
default_random_engine generator;
uniform_real_distribution<real> distribution(0.0, 1.0);
for (size_t i = 0; i < n; ++i) numbers[i] = distribution(generator);
}
vector<real> randSampling(real range, int n) {
CHECK_GE(range, n);
vector<real> num(range);
iota(begin(num), end(num), 0.);
if (range == n) return num;
random_shuffle(begin(num), end(num));
num.resize(n);
sort(begin(num), end(num));
return num;
}
void genCandidateScores(bool hasSubseq,
size_t beamSize,
SingleBeamExpansion& prevBeam,
SingleBeamExpansion& curBeam) {
vector<int>& seqStartPos = curBeam.seqStartPos;
seqStartPos.resize(1, 0);
vector<int>& subSeqStartPos = curBeam.subSeqStartPos;
subSeqStartPos.resize(1, 0);
srand(SEED);
if (prevBeam.selectedIndices.size()) {
if (prevBeam.subSeqStartPos.size() > 1) {
int seqIdx = 1;
// samples in previous beam are nested sequences.
for (size_t i = 1; i < prevBeam.subSeqStartPos.size(); ++i) {
for (size_t j = 0; j < beamSize; ++j) {
if (prevBeam.selectedIndices[(i - 1) * beamSize + j] == -1.) break;
subSeqStartPos.push_back(1 + (rand() % MAX_SEQ_LEN) +
subSeqStartPos.back());
}
if (prevBeam.seqStartPos[seqIdx] == prevBeam.subSeqStartPos[i]) {
seqStartPos.push_back(subSeqStartPos.back());
seqIdx++;
}
}
} else {
for (size_t i = 0; i <= prevBeam.selectedIndices.size(); ++i) {
if (i && i % beamSize == 0) {
seqStartPos.push_back(subSeqStartPos.back());
if (i == prevBeam.selectedIndices.size()) break;
}
if (prevBeam.selectedIndices[i] == -1.) continue;
subSeqStartPos.push_back(subSeqStartPos.back() +
(1 + (rand() % MAX_SEQ_LEN)));
}
}
} else {
// the first beam expansion
int seqNum = 1 + (rand() % MAX_SEQ_NUM);
for (int i = 0; i < seqNum; ++i) {
if (hasSubseq) {
for (size_t j = 0; j < 1 + (rand() % MAX_SEQ_NUM); ++j)
subSeqStartPos.push_back(subSeqStartPos.back() +
(1 + (rand() % MAX_SEQ_LEN)));
seqStartPos.push_back(subSeqStartPos.back());
} else {
seqStartPos.push_back(seqStartPos.back() +
(1 + (rand() % MAX_SEQ_LEN)));
}
}
}
size_t totalSeqNum = hasSubseq ? subSeqStartPos.back() : seqStartPos.back();
curBeam.candidateScores.resize(totalSeqNum, 0.);
genRand(curBeam.candidateScores.data(), totalSeqNum);
}
void genSelectedIndices(size_t beamSize,
vector<int>& seqStartPos,
vector<real>& selectedIndices) {
size_t selectedIdsCount = beamSize * (seqStartPos.size() - 1);
selectedIndices.resize(selectedIdsCount, -1.);
for (size_t i = 0; i < seqStartPos.size() - 1; ++i) {
int seqLen = seqStartPos[i + 1] - seqStartPos[i];
int n = min(seqLen, static_cast<int>(beamSize));
vector<real> ids = randSampling(seqLen, n);
memcpy(selectedIndices.data() + i * beamSize,
ids.data(),
sizeof(real) * ids.size());
}
}
void genGroundTruth(vector<SingleBeamExpansion>& beamExpansions,
size_t beamSize) {
SingleBeamExpansion& beam = beamExpansions[1];
size_t seqNum = beam.seqStartPos.size() - 1;
for (size_t i = 2; i < beamExpansions.size(); ++i)
CHECK_EQ(seqNum, beamExpansions[i].seqStartPos.size() - 1);
srand(SEED);
// initialize the first beam.
beam.resetGroundTruth(seqNum);
for (size_t i = 0; i < seqNum; ++i) {
if (randFloat() > 0.5) {
/*
* force the randomly generated label falls in the beam by chance 0.5.
* otherwise, when sequence length is relatively long and beam size is
* relatively small, the gold sequences falls off the beam at in the
* first search.
*/
real* begPos = beam.selectedIndices.data() + i * beamSize;
beam.colIdxInBeam[i] =
rand() % count_if(begPos, begPos + beamSize, [](const real& val) {
return val != -1.;
});
beam.groundTruth[i] =
beam.selectedIndices[i * beamSize + beam.colIdxInBeam[i]];
beam.inBeam[i] = 1;
} else {
int label = rand() % (beam.seqStartPos[i + 1] - beam.seqStartPos[i]);
beam.groundTruth[i] = label;
real* begPos = beam.selectedIndices.data() + i * beamSize;
real* endPos = begPos + beamSize;
real* lblPos = find(begPos, endPos, real(label));
if (lblPos != endPos) {
beam.inBeam[i] = 1;
beam.colIdxInBeam[i] = lblPos - begPos;
}
}
beam.rowIdxInBeam[i] = i;
}
// iterate over each beam expansions
for (size_t i = 2; i < beamExpansions.size(); ++i) {
SingleBeamExpansion& curBeam = beamExpansions[i];
SingleBeamExpansion& prevBeam = beamExpansions[i - 1];
curBeam.resetGroundTruth(seqNum);
// iterate over each sequence
for (size_t j = 0; j < seqNum; ++j) {
if (!prevBeam.inBeam[j]) continue;
// gold sequence falls in the beam in previous search.
real* begPos = prevBeam.selectedIndices.data();
int offset =
prevBeam.rowIdxInBeam[j] * beamSize + prevBeam.colIdxInBeam[j];
curBeam.rowIdxInBeam[j] = count_if(
begPos, begPos + offset, [](const real& val) { return val != -1.; });
if (randFloat() > 0.5) {
// force the randomly generated label falls in the beam by chance 0.5.
real* start =
curBeam.selectedIndices.data() + curBeam.rowIdxInBeam[j] * beamSize;
int n = rand() % count_if(start, start + beamSize, [](const real& val) {
return val != -1.;
});
curBeam.colIdxInBeam[j] = n;
curBeam.groundTruth[j] = *(start + n);
curBeam.inBeam[j] = 1;
} else {
CHECK_LE(curBeam.rowIdxInBeam[j] + 1,
curBeam.subSeqStartPos.size() - 1);
int start = curBeam.subSeqStartPos[curBeam.rowIdxInBeam[j]];
int end = curBeam.subSeqStartPos[curBeam.rowIdxInBeam[j] + 1];
CHECK_GT(size_t(end), size_t(start));
int label = rand() % (end - start);
curBeam.groundTruth[j] = label;
real* findBeg =
curBeam.selectedIndices.data() + curBeam.rowIdxInBeam[j] * beamSize;
real* lblPos =
find(findBeg, findBeg + beamSize, static_cast<real>(label));
if (lblPos != (findBeg + beamSize)) {
curBeam.inBeam[j] = 1;
curBeam.colIdxInBeam[j] = lblPos - findBeg;
}
}
}
}
}
void genOneBeam(size_t beamSize,
bool hasSubseq,
SingleBeamExpansion& prevBeam,
SingleBeamExpansion& curBeam) {
genCandidateScores(hasSubseq, beamSize, prevBeam, curBeam);
genSelectedIndices(beamSize,
hasSubseq ? curBeam.subSeqStartPos : curBeam.seqStartPos,
curBeam.selectedIndices);
}
void genRandomBeamExpansion(size_t expansionCount,
size_t beamSize,
vector<SingleBeamExpansion>& beamExpansions) {
beamExpansions.clear();
beamExpansions.resize(expansionCount + 1);
// beamExpansions[0] is reserved.
for (size_t i = 1; i <= expansionCount; ++i)
genOneBeam(beamSize, bool(i - 1), beamExpansions[i - 1], beamExpansions[i]);
genGroundTruth(beamExpansions, beamSize);
}
void testCrossEntropyOverBeam(bool useGpu,
size_t beamSize,
vector<SingleBeamExpansion>& beams) {
TestConfig config;
config.layerConfig.set_type("cross_entropy_over_beam");
size_t seqNum = 0;
for (size_t i = 1; i < beams.size(); ++i) {
const SingleBeamExpansion& beam = beams[i];
// create scores for all the candidates
MatrixPtr candidateScorePtr =
Matrix::create(beam.candidateScores.size(), 1, false, false);
candidateScorePtr->copyFrom(beam.candidateScores.data(),
beam.candidateScores.size());
ostringstream paramName;
paramName << "candidate_scores_" << i;
if (beam.subSeqStartPos.size() > 1) {
seqNum = beam.subSeqStartPos.size() - 1;
config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
paramName.str(),
candidateScorePtr,
beam.seqStartPos,
beam.subSeqStartPos});
} else {
seqNum = beam.seqStartPos.size() - 1;
config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
paramName.str(),
candidateScorePtr,
beam.seqStartPos});
}
config.layerConfig.add_inputs();
// create indices for the selected candidates
MatrixPtr selectedCandidates =
Matrix::create(seqNum, beamSize, false, false);
selectedCandidates->copyFrom(beam.selectedIndices.data(),
beam.selectedIndices.size());
paramName.clear();
paramName << "selected_candidates_" << i;
config.inputDefs.push_back(
{INPUT_SELF_DEFINE_DATA, paramName.str(), selectedCandidates});
config.layerConfig.add_inputs();
// create the ground truth
paramName.clear();
paramName << "label_" << i;
config.inputDefs.push_back(
{INPUT_SELF_DEFINE_DATA, paramName.str(), beam.groundTruth});
config.layerConfig.add_inputs();
}
testLayerGrad(
config, "cross_entropy_over_beam", seqNum, false, useGpu, false);
}
TEST(Layer, CrossEntropyOverBeam) {
LOG(INFO) << "SEED = " << SEED;
const size_t beamSize = 1 + rand() % MAX_BEAM_SIZE;
LOG(INFO) << "beamSize = " << beamSize;
// TODO(caoying): test with random beam expansions.
const size_t expansionCount = 3;
vector<SingleBeamExpansion> beams;
genRandomBeamExpansion(expansionCount, beamSize, beams);
for (bool useGpu : {false, true})
testCrossEntropyOverBeam(useGpu, beamSize, beams);
}
int main(int argc, char** argv) {
initMain(argc, argv);
hl_start();
hl_init(FLAGS_gpu_id);
FLAGS_thread_local_rand_use_global_seed = true;
srand(SEED);
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ e9cc3282
......@@ -850,9 +850,27 @@ TEST(Layer, square_error_weighted) {
}
}
TEST(Layer, huber_regression_loss) {
TestConfig config;
config.layerConfig.set_type("huber_regression");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
for (auto delta : {1, 3, 5}) {
config.layerConfig.set_delta(delta);
testLayerGrad(config, "huber_regression", 100, /* trans */ false, useGpu);
}
}
}
TEST(Layer, huber_two_class) {
TestConfig config;
config.layerConfig.set_type("huber");
config.layerConfig.set_type("huber_classification");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
......@@ -861,7 +879,7 @@ TEST(Layer, huber_two_class) {
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "huber", 100, /* trans */ false, useGpu);
testLayerGrad(config, "huber_two_class", 100, /* trans */ false, useGpu);
}
}
......@@ -1228,6 +1246,75 @@ TEST(Layer, PoolLayer) {
#endif
}
void setPool3DConfig(TestConfig* config,
PoolConfig* pool,
const string& poolType) {
// filter size
const int NUM_FILTERS = 16;
const int FILTER_SIZE = 3;
const int FILTER_SIZE_Y = 3;
const int FILTER_SIZE_Z = 3;
const int CHANNELS = 16;
(*config).biasSize = 0;
(*config).layerConfig.set_type("pool3d");
(*config).layerConfig.set_num_filters(NUM_FILTERS);
int kw = FILTER_SIZE, kh = FILTER_SIZE_Y, kd = FILTER_SIZE_Z;
int pw = 0, ph = 0, pd = 0;
int sw = 2, sh = 2, sd = 2;
pool->set_pool_type(poolType);
pool->set_pool_type("avg");
pool->set_channels(CHANNELS);
pool->set_size_x(kw);
pool->set_size_y(kh);
pool->set_size_z(kd);
pool->set_padding(0);
pool->set_padding_y(0);
pool->set_padding_z(0);
pool->set_stride(sw);
pool->set_stride_y(sh);
pool->set_stride_z(sd);
pool->set_start(0);
int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
int od = outputSize(pool->img_size_z(), kd, pd, sd, /* caffeMode */ false);
pool->set_output_x(ow);
pool->set_output_y(oh);
pool->set_output_z(od);
}
void testPool3DLayer(const string& poolType, bool trans, bool useGpu) {
TestConfig config;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 11664, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
PoolConfig* pool = input->mutable_pool_conf();
const int IMAGE_SIZE = 9;
const int IMAGE_SIZE_Y = 9;
const int IMAGE_SIZE_Z = 9;
pool->set_img_size(IMAGE_SIZE);
pool->set_img_size_y(IMAGE_SIZE_Y);
pool->set_img_size_z(IMAGE_SIZE_Z);
setPool3DConfig(&config, pool, poolType);
config.layerConfig.set_size(pool->output_x() * pool->output_y() *
pool->channels());
testLayerGrad(config, "pool3d", 100, trans, useGpu);
}
TEST(Layer, Pool3DLayer) {
testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ false);
testPool3DLayer("max", /* trans= */ false, /* useGpu= */ false);
#ifndef PADDLE_ONLY_CPU
testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ true);
testPool3DLayer("max", /* trans= */ false, /* useGpu= */ true);
#endif
}
void testSppLayer(const string& poolType,
const int pyramidHeight,
bool trans,
......@@ -2029,6 +2116,159 @@ TEST(Layer, RowL2NormLayer) {
}
}
void test3DConvLayer(const string& type, bool trans, bool useGpu) {
// filter size
const int NUM_FILTERS = 6;
// const int CHANNELS = 3;
const int FILTER_SIZE = 3;
const int FILTER_SIZE_Y = 3;
const int FILTER_SIZE_Z = 3;
// input image
const int CHANNELS = 3;
const int IMAGE_SIZE = 9;
const int IMAGE_SIZE_Y = 9;
const int IMAGE_SIZE_Z = 9;
TestConfig config;
config.biasSize = NUM_FILTERS;
config.layerConfig.set_type(type);
config.layerConfig.set_num_filters(NUM_FILTERS);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
// Setting up conv3D-trans layer
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_channels(CHANNELS);
conv->set_filter_size(FILTER_SIZE);
conv->set_filter_size_y(FILTER_SIZE_Y);
conv->set_filter_size_z(FILTER_SIZE_Z);
conv->set_padding(0);
conv->set_padding_y(0);
conv->set_padding_z(0);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_stride_z(2);
conv->set_img_size(IMAGE_SIZE);
conv->set_img_size_y(IMAGE_SIZE_Y);
conv->set_img_size_z(IMAGE_SIZE_Z);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true));
conv->set_output_y(outputSize(conv->img_size_y(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true));
conv->set_output_z(outputSize(conv->img_size_z(),
conv->filter_size_z(),
conv->padding_z(),
conv->stride_z(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->output_x() * conv->output_y() *
conv->output_z() * NUM_FILTERS);
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
config.inputDefs.push_back(
{INPUT_DATA,
"layer_0",
CHANNELS * IMAGE_SIZE * IMAGE_SIZE_Y * IMAGE_SIZE_Z,
conv->filter_channels() * FILTER_SIZE * FILTER_SIZE_Y * FILTER_SIZE_Z *
NUM_FILTERS});
testLayerGrad(config, "conv3D", 10, trans, useGpu);
// Use small batch_size and useWeight=true to test biasGrad
testLayerGrad(config, "conv3D", 2, trans, useGpu, true, 0.02);
}
TEST(Layer, test3DConvLayer) {
test3DConvLayer("conv3d", /* trans= */ false, /* useGpu= */ false);
#ifndef PADDLE_ONLY_CPU
test3DConvLayer("conv3d", /* trans= */ false, /* useGpu= */ true);
#endif
}
void test3DDeConvLayer(const string& type, bool trans, bool useGpu) {
// filter size
const int NUM_FILTERS = 6;
// const int CHANNELS = 3;
const int FILTER_SIZE = 3;
const int FILTER_SIZE_Y = 3;
const int FILTER_SIZE_Z = 3;
// input image
const int CHANNELS = 3;
const int IMAGE_SIZE = 4;
const int IMAGE_SIZE_Y = 6;
const int IMAGE_SIZE_Z = 6;
// Setting up conv-trans layer
TestConfig config;
config.biasSize = NUM_FILTERS;
config.layerConfig.set_type("deconv3d");
config.layerConfig.set_num_filters(NUM_FILTERS);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_channels(CHANNELS);
conv->set_filter_size(FILTER_SIZE);
conv->set_filter_size_y(FILTER_SIZE_Y);
conv->set_filter_size_z(FILTER_SIZE_Z);
conv->set_padding(0);
conv->set_padding_y(0);
conv->set_padding_z(0);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_stride_z(2);
conv->set_img_size(IMAGE_SIZE);
conv->set_img_size_y(IMAGE_SIZE_Y);
conv->set_img_size_z(IMAGE_SIZE_Z);
conv->set_output_x(imageSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
true));
conv->set_output_y(imageSize(conv->img_size_y(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
true));
conv->set_output_z(imageSize(conv->img_size_z(),
conv->filter_size_z(),
conv->padding_z(),
conv->stride_z(),
true));
config.layerConfig.set_size(conv->output_x() * conv->output_y() *
conv->output_z() * NUM_FILTERS);
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
config.inputDefs.push_back(
{INPUT_DATA,
"layer_0",
CHANNELS * IMAGE_SIZE * IMAGE_SIZE_Y * IMAGE_SIZE_Z,
conv->filter_channels() * FILTER_SIZE * FILTER_SIZE_Y * FILTER_SIZE_Z *
NUM_FILTERS});
testLayerGrad(config, "deconv3D", 10, trans, useGpu);
// Use small batch_size and useWeight=true to test biasGrad
testLayerGrad(config, "deconv3D", 2, trans, useGpu, true, 0.02);
}
TEST(Layer, test3DDeConvLayer) {
test3DDeConvLayer("deconv3d", /* trans= */ false, /* useGpu= */ false);
#ifndef PADDLE_ONLY_CPU
test3DDeConvLayer("deconv3d", /* trans= */ false, /* useGpu= */ true);
#endif
}
TEST(Layer, ScaleShiftLayer) {
const size_t batchSize = 16;
const size_t size = 32;
......
paddle/math/Allocator.h
浏览文件 @ e9cc3282
......@@ -48,7 +48,13 @@ public:
*/
virtual void* alloc(size_t size) {
void* ptr;
#ifdef PADDLE_USE_MKLDNN
// refer to https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp
// memory alignment
CHECK_EQ(posix_memalign(&ptr, 4096ul, size), 0);
#else
CHECK_EQ(posix_memalign(&ptr, 32ul, size), 0);
#endif
CHECK(ptr) << "Fail to allocate CPU memory: size=" << size;
return ptr;
}
......
paddle/math/CMakeLists.txt
浏览文件 @ e9cc3282
......@@ -14,6 +14,17 @@
#
file(GLOB MATH_HEADERS . *.h)
file(GLOB MATH_SOURCES . *.cpp)
if(NOT WITH_MKLDNN)
set(DNN_HEADER "${CMAKE_CURRENT_SOURCE_DIR}/MKLDNNMatrix.h")
set(DNN_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/MKLDNNMatrix.cpp")
list(REMOVE_ITEM MATH_HEADERS "${DNN_HEADER}")
list(REMOVE_ITEM MATH_SOURCES "${DNN_SOURCE}")
message(STATUS "Skip compiling with MKLDNNMatrix")
else()
message(STATUS "Compile with MKLDNNMatrix")
endif()
set(MATH_SOURCES
"${PADDLE_SOURCE_DIR}/paddle/math/BaseMatrix.cu"
"${PADDLE_SOURCE_DIR}/paddle/math/TrainingAlgorithmOp.cu"
......
paddle/math/MKLDNNMatrix.cpp 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2017 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. */
#include "MKLDNNMatrix.h"
using namespace mkldnn; // NOLINT
namespace paddle {
MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) {
memory::desc md = pd.desc();
size_t ndims = md.data.ndims;
int* dims = md.data.dims;
CHECK(ndims > 0) << "Input dims should not be empty";
size_t cnts = 1;
for (size_t i = 0; i < ndims; ++i) {
cnts *= dims[i];
}
if (m == nullptr) {
size_t height = dims[0];
size_t width = cnts / dims[0];
m = Matrix::create(height, width, false, false);
}
CHECK(m) << " Matrix should not be empty";
CpuMatrixPtr cpuMatrix = std::dynamic_pointer_cast<CpuMatrix>(m);
CHECK(cpuMatrix) << "Only support create from CPU matrix yet";
CHECK_EQ(cnts, m->getElementCnt()) << "Count size does not match";
return std::make_shared<MKLDNNMatrix>(
m->getData(), m->getHeight(), m->getWidth(), pd);
}
MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m,
memory::dims dims,
memory::format fmt,
engine& eg,
mkldnn::memory::data_type dtype) {
return create(m, memory::primitive_desc(memory::desc(dims, dtype, fmt), eg));
}
void MKLDNNMatrix::reorderDataFrom(const MKLDNNMatrixPtr& m,
memory::format srcFmt,
memory::dims targetDim) {
memory::format dstFmt = getFormat();
if (srcFmt == dstFmt) {
return;
}
CHECK_EQ(getElementCnt(), m->getElementCnt()) << "size should equal";
reorderOnce(getData(), m->getData(), srcFmt, dstFmt, targetDim);
}
void MKLDNNMatrix::reorderDataTo(const MKLDNNMatrixPtr& m,
memory::format dstFmt,
memory::dims targetDim) {
memory::format srcFmt = getFormat();
if (srcFmt == dstFmt) {
return;
}
CHECK_EQ(getElementCnt(), m->getElementCnt()) << "size should equal";
reorderOnce(getData(), m->getData(), srcFmt, dstFmt, targetDim);
}
void MKLDNNMatrix::reorderOnce(void* srcData,
void* dstData,
memory::format srcFmt,
memory::format dstFmt,
memory::dims dm) {
CHECK(srcData);
CHECK(dstData);
MatrixPtr tmpSrc;
if (dstData == srcData) {
// inplace data
size_t sz = 1;
for (size_t i = 0; i < dm.size(); ++i) {
sz *= dm[i];
}
tmpSrc = Matrix::create(sz, 1, false, false);
tmpSrc->copyFrom((real*)srcData, sz);
srcData = tmpSrc->getData();
}
auto dtype = this->getDtype();
auto srcMD = memory::desc(dm, dtype, srcFmt);
auto dstMD = memory::desc(dm, dtype, dstFmt);
auto eg = this->getEngine();
auto src = memory(memory::primitive_desc(srcMD, eg), srcData);
auto dst = memory(memory::primitive_desc(dstMD, eg), dstData);
auto r = reorder(src, dst);
stream(stream::kind::eager).submit({r}).wait();
}
void MKLDNNMatrix::downSpatial() {
int fmt = getFormat();
if (!(fmt == memory::format::nchw || fmt == memory::format::oihw)) {
// only support nchw and oihw yet, later can support more like nhwc, ihwo
return;
}
// TODO(TJ): change H(height) and W(width) if support nhwc or more
const int H = 2, W = 3;
memory::dims srcDims = getDims();
if (srcDims[H] != 1 || srcDims[W] != 1) {
// can not down spatial
return;
}
memory::dims dstDims = memory::dims{srcDims[0], srcDims[1]};
memory::format dstFmt;
switch (fmt) {
case memory::format::nchw:
dstFmt = memory::format::nc;
break;
case memory::format::oihw:
dstFmt = memory::format::oi;
break;
default:
LOG(FATAL) << "unsupported format";
}
memory::desc md = memory::desc(dstDims, getDtype(), dstFmt);
memory::primitive_desc pd = memory::primitive_desc(md, getEngine());
mkldnn_primitive_t result;
mkldnn::error::wrap_c_api(
mkldnn_primitive_create(&result, pd.get(), nullptr, nullptr),
"could not create a memory primitive");
reset(result);
set_data_handle(getData());
}
} // namespace paddle
paddle/math/MKLDNNMatrix.h 0 → 100644
浏览文件 @ e9cc3282
/* Copyright (c) 2017 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. */
#pragma once
#include <vector>
#include "Matrix.h"
#include "mkldnn.hpp"
#include "paddle/parameter/Parameter.h"
namespace paddle {
class MKLDNNMatrix;
typedef std::shared_ptr<MKLDNNMatrix> MKLDNNMatrixPtr;
/**
* @brief MKLDNN Matrix.
*
*/
class MKLDNNMatrix : public CpuMatrix, public mkldnn::memory {
public:
MKLDNNMatrix(real* data,
size_t height,
size_t width,
mkldnn::memory::primitive_desc pd)
: CpuMatrix(data, height, width, false), mkldnn::memory(pd, data) {}
~MKLDNNMatrix() {}
/**
* Create MKLDNNMatrix from a MatrixPtr and memory primitive_desc
*/
static MKLDNNMatrixPtr create(MatrixPtr m, mkldnn::memory::primitive_desc pd);
/**
* Create MKLDNNMatrix from a MatrixPtr and memory details info
*/
static MKLDNNMatrixPtr create(
MatrixPtr m,
mkldnn::memory::dims dims,
mkldnn::memory::format fmt,
mkldnn::engine& eg,
mkldnn::memory::data_type dtype = mkldnn::memory::data_type::f32);
public:
/**
* Reorder this MKLDNNMatrix from other format.
* Support inplace reorder.
* @note: this function would only reorder the data layout.
* will NOT change this original dim or format info
*/
void reorderDataFrom(const MKLDNNMatrixPtr& m,
memory::format srcFmt,
memory::dims targetDim);
/**
* Reorder this MKLDNNMatrix to other format.
* Support inplace reorder.
* @note: this function would only reorder the data layout.
* will NOT change the dst dim or format info
*/
void reorderDataTo(const MKLDNNMatrixPtr& m,
memory::format dstFmt,
memory::dims targetDim);
/**
* Dimensionality reduction.
* Change format "nchw --> nc" or "oihw --> oi" if the h and w are both 1
*/
void downSpatial();
/**
* Update the memory data handle.
* Caution: This will not check the buffer size of the data,
* it should be coverd by user.
*/
void updateData(void* data) { set_data_handle(data); }
/**
* Get primitive descriptor.
*/
mkldnn::memory::primitive_desc getPrimitiveDesc() {
return this->get_primitive_desc();
}
/**
* Get memory descriptor.
*/
mkldnn::memory::desc getMemoryDesc() { return getPrimitiveDesc().desc(); }
/**
* Get dimensions.
*/
mkldnn::memory::dims getDims() {
mkldnn::memory::desc md = getMemoryDesc();
const int* src = md.data.dims;
int ndims = md.data.ndims;
mkldnn::memory::dims dst;
dst.resize(ndims);
for (int i = 0; i < ndims; ++i) {
dst[i] = src[i];
}
return dst;
}
/**
* Get format.
*/
mkldnn::memory::format getFormat() {
return (mkldnn::memory::format)(getMemoryDesc().data.format);
}
/**
* Get memory data type.
*/
mkldnn::memory::data_type getDtype() {
return (mkldnn::memory::data_type)(getMemoryDesc().data.data_type);
}
/**
* Get engine.
*/
mkldnn::engine getEngine() { return getPrimitiveDesc().get_engine(); }
protected:
/**
* Do reorder once.
* Can support inplace.
*/
void reorderOnce(void* srcData,
void* dstData,
memory::format srcFmt,
memory::format dstFmt,
memory::dims dm);
};
} // namespace paddle
paddle/math/Matrix.cpp
浏览文件 @ e9cc3282
......@@ -1190,6 +1190,221 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad,
outGrad.getStride());
}
void GpuMatrix::maxPool3DForward(Matrix& inputMat,
Matrix& maxPoolIdx,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW) {
CHECK(inputMat.useGpu_) << "Matrix type are not correct";
real* inputData = inputMat.getData();
real* maxPoolIdxData = maxPoolIdx.getData();
size_t num = inputMat.getHeight();
size_t width = imgSizeW;
size_t height = imgSizeH;
size_t depth = imgSizeD;
CHECK(depth * height * width * channels == inputMat.getWidth());
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == outputD * outputH * outputW * channels);
hl_maxpool3D_forward(num,
inputData,
channels,
depth,
height,
width,
outputD,
outputH,
outputW,
sizeZ,
sizeY,
sizeX,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
getData(),
maxPoolIdxData,
getStride());
}
void GpuMatrix::maxPool3DBackward(Matrix& outGrad,
Matrix& maxPoolIdx,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput) {
CHECK(outGrad.useGpu_ && maxPoolIdx.useGpu_) << "Matrix type are not equal";
real* outDiff = outGrad.getData();
real* maxPoolIdxData = maxPoolIdx.getData();
size_t frameNum = getHeight();
size_t channels = outGrad.getWidth() / outputD / outputH / outputW;
size_t width = imgSizeW;
size_t height = imgSizeH;
size_t depth = imgSizeD;
CHECK(depth * height * width * channels == getWidth());
CHECK(width_ == depth * width * height * channels);
CHECK(outGrad.getHeight() == maxPoolIdx.getHeight() &&
outGrad.getWidth() == maxPoolIdx.getWidth());
hl_maxpool3D_backward(frameNum,
outDiff,
channels,
depth,
height,
width,
outputD,
outputH,
outputW,
sizeZ,
sizeY,
sizeX,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
scaleTargets,
scaleOutput,
getData(),
maxPoolIdxData,
outGrad.getStride());
}
void GpuMatrix::avgPool3DForward(Matrix& inputMat,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW) {
CHECK(inputMat.useGpu_) << "Matrix type are not equal";
real* inputData = inputMat.getData();
size_t frameNum = inputMat.getHeight();
size_t height = imgSizeH;
size_t width = imgSizeW;
size_t depth = imgSizeD;
CHECK(depth * height * width * channels == inputMat.getWidth());
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == outputD * outputH * outputW * channels);
hl_avgpool3D_forward(frameNum,
inputData,
channels,
depth,
height,
width,
outputD,
outputH,
outputW,
sizeZ,
sizeY,
sizeX,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
getData(),
getStride());
}
void GpuMatrix::avgPool3DBackward(Matrix& outGrad,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput) {
CHECK(outGrad.useGpu_) << "Matrix type are not equal";
real* outDiff = outGrad.getData();
size_t frameNum = outGrad.getHeight();
size_t channels = outGrad.getWidth() / outputD / outputH / outputW;
size_t height = imgSizeH;
size_t width = imgSizeW;
size_t depth = imgSizeD;
CHECK(depth * height * width * channels == width_);
CHECK(height_ == outGrad.getHeight());
CHECK(outGrad.getWidth() == outputD * outputH * outputW * channels);
hl_avgpool3D_backward(frameNum,
outDiff,
channels,
depth,
height,
width,
outputD,
outputH,
outputW,
sizeZ,
sizeY,
sizeX,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
scaleTargets,
scaleOutput,
getData(),
outGrad.getStride());
}
void GpuMatrix::maxSequenceForward(Matrix& input,
const IVector& sequence,
IVector& index) {
......@@ -1389,6 +1604,72 @@ void GpuMatrix::multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label) {
output_d, grad_d, mat_d, height_, width_);
}
void GpuMatrix::vol2Col(real* dataSrc,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW) {
hl_matrix_vol2Col(dataSrc,
channels,
depth,
height,
width,
filterD,
filterH,
filterW,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
getData());
}
void GpuMatrix::col2Vol(real* dataDst,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
real alpha,
real beta) {
hl_matrix_col2Vol(dataDst,
channels,
depth,
height,
width,
filterD,
filterH,
filterW,
strideD,
strideH,
strideW,
paddingD,
paddingH,
paddingW,
getData(),
alpha,
beta);
}
/**
* CpuMatrix
*/
......@@ -1930,6 +2211,276 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
}
}
void CpuMatrix::maxPool3DForward(Matrix& inputMat,
Matrix& maxPoolIdx,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW) {
real* inputData = inputMat.getData();
real* outData = getData();
real* maxPoolIdxData = maxPoolIdx.getData();
size_t num = inputMat.getHeight();
size_t inWidth = imgSizeW;
size_t inHeight = imgSizeH;
size_t inDepth = imgSizeD;
CHECK(inHeight * inWidth * inDepth == inputMat.getWidth() / channels);
CHECK_EQ(num, this->getHeight());
CHECK_EQ(channels * outputH * outputW * outputD, this->getWidth());
size_t outStride = getStride();
/* initialize the data_ */
for (size_t i = 0; i < height_; i++) {
for (size_t j = 0; j < width_; j++) {
outData[(i)*outStride + j] = -(real)FLT_MAX;
maxPoolIdxData[(i)*outStride + j] = -1;
}
}
/* pool max one by one */
for (size_t n = 0; n < num; ++n) { // frame by frame
if (!isContiguous()) {
outData = getData() + n * outStride;
maxPoolIdxData = maxPoolIdx.getData() + n * outStride;
}
for (size_t c = 0; c < channels; ++c) { // channel by channel
for (size_t pd = 0; pd < outputD; ++pd) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int dstart = pd * strideD - paddingD;
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int dend = std::min(dstart + sizeZ, inDepth);
int hend = std::min(hstart + sizeY, inHeight);
int wend = std::min(wstart + sizeX, inWidth);
dstart = std::max(dstart, 0);
hstart = std::max(hstart, 0);
wstart = std::max(wstart, 0);
int maxIdx = -1;
real maxOutData = outData[(pd * outputH + ph) * outputW + pw];
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
if (maxOutData <
inputData[(d * inHeight + h) * inWidth + w]) {
maxOutData = inputData[(d * inHeight + h) * inWidth + w];
maxIdx = (d * inHeight + h) * inWidth + w;
}
}
}
}
outData[(pd * outputH + ph) * outputW + pw] = maxOutData;
maxPoolIdxData[(pd * outputH + ph) * outputW + pw] = maxIdx;
}
}
}
// compute offset
inputData += inDepth * inHeight * inWidth;
outData += outputD * outputH * outputW;
maxPoolIdxData += outputD * outputH * outputW;
}
}
}
void CpuMatrix::maxPool3DBackward(Matrix& outGrad,
Matrix& maxPoolIdx,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput) {
size_t num = getHeight();
size_t channels = size_t(width_ / imgSizeD / imgSizeH / imgSizeW);
CHECK(maxPoolIdx.getHeight() == outGrad.getHeight() &&
maxPoolIdx.getWidth() == outGrad.getWidth());
real* tgtGrad = getData();
real* otGrad = outGrad.getData();
real* maxPoolIdxData = maxPoolIdx.getData();
size_t outStride = outGrad.getStride();
for (size_t n = 0; n < num; ++n) {
if (!outGrad.isContiguous()) {
otGrad = outGrad.getData() + n * outStride;
maxPoolIdxData = maxPoolIdx.getData() + n * outStride;
}
for (size_t c = 0; c < channels; ++c) {
for (size_t pd = 0; pd < outputD; ++pd) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
const size_t index = (pd * outputH + ph) * outputW + pw;
const size_t tgtIdx = static_cast<size_t>(maxPoolIdxData[index]);
tgtGrad[tgtIdx] =
scaleTargets * tgtGrad[tgtIdx] + scaleOutput * otGrad[index];
}
}
}
// offset
tgtGrad += imgSizeD * imgSizeH * imgSizeW;
otGrad += outputD * outputH * outputW;
maxPoolIdxData += outputD * outputH * outputW;
}
}
}
void CpuMatrix::avgPool3DForward(Matrix& input,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW) {
// The main loop
size_t num = input.getHeight();
size_t inDepth = imgSizeD;
size_t inHeight = imgSizeH;
size_t inWidth = imgSizeW;
CHECK(inDepth * inHeight * inWidth * channels == input.getWidth());
CHECK(outputD * outputH * outputW * channels * num == height_ * width_);
real* tgtData = getData();
real* inData = input.getData();
for (size_t n = 0; n < num; ++n) {
if (!isContiguous()) {
tgtData = data_ + n * getStride();
}
for (size_t c = 0; c < channels; ++c) {
for (size_t pd = 0; pd < outputD; ++pd) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int dstart = pd * strideD - paddingD;
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int dend = std::min(dstart + sizeZ, inDepth + paddingD);
int hend = std::min(hstart + sizeY, inHeight + paddingH);
int wend = std::min(wstart + sizeX, inWidth + paddingW);
int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
dstart = std::max(dstart, 0);
hstart = std::max(hstart, 0);
wstart = std::max(wstart, 0);
dend = std::min(dend, static_cast<int>(inDepth));
hend = std::min(hend, static_cast<int>(inHeight));
wend = std::min(wend, static_cast<int>(inWidth));
CHECK(poolSize);
tgtData[(pd * outputH + ph) * outputW + pw] = 0; // clear
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
tgtData[(pd * outputH + ph) * outputW + pw] +=
inData[(d * inHeight + h) * inWidth + w];
}
}
}
tgtData[(pd * outputH + ph) * outputW + pw] /= poolSize;
}
}
}
// compute offset
inData += inDepth * inHeight * inWidth;
tgtData += outputD * outputH * outputW;
}
}
}
void CpuMatrix::avgPool3DBackward(Matrix& input,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput) {
size_t num = input.getHeight();
size_t channels = input.getWidth() / outputD / outputH / outputW;
CHECK(imgSizeD * imgSizeH * imgSizeW * channels == getWidth());
real* inData = input.getData();
real* outData = getData();
for (size_t n = 0; n < num; ++n) {
if (!input.isContiguous()) {
inData = input.getData() + n * input.getStride();
}
for (size_t c = 0; c < channels; ++c) {
for (size_t pd = 0; pd < outputD; ++pd) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
int dstart = pd * strideD - paddingD;
int hstart = ph * strideH - paddingH;
int wstart = pw * strideW - paddingW;
int dend = std::min(dstart + sizeZ, imgSizeD + paddingD);
int hend = std::min(hstart + sizeY, imgSizeH + paddingH);
int wend = std::min(wstart + sizeX, imgSizeW + paddingW);
int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart);
dstart = std::max(dstart, 0);
hstart = std::max(hstart, 0);
wstart = std::max(wstart, 0);
dend = std::min(dend, static_cast<int>(imgSizeD));
hend = std::min(hend, static_cast<int>(imgSizeH));
wend = std::min(wend, static_cast<int>(imgSizeW));
CHECK(poolSize);
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
outData[(d * imgSizeH + h) * imgSizeW + w] +=
inData[(pd * outputH + ph) * outputW + pw] / poolSize;
}
}
}
}
}
}
// offset
outData += imgSizeD * imgSizeH * imgSizeW;
inData += outputD * outputH * outputW;
}
}
}
/**
* Input: one or more sequences. Each sequence contains some instances.
* Output: output size is the number of input sequences (NOT input instances).
......@@ -3975,6 +4526,95 @@ void CpuMatrix::bilinearBackward(const Matrix& out,
}
}
void CpuMatrix::vol2Col(real* data,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW) {
real* outData = getData();
int outHeight = (height + 2 * paddingH - filterH) / strideH + 1;
int outWidth = (width + 2 * paddingW - filterW) / strideW + 1;
int outDepth = (depth + 2 * paddingD - filterD) / strideD + 1;
int channelsCol = channels * filterD * filterH * filterW;
for (int c = 0; c < channelsCol; ++c) {
int wOffset = c % filterW;
int hOffset = (c / filterW) % filterH;
int dOffset = (c / filterW / filterH) % filterD;
int cIn = c / filterW / filterH / filterD;
for (int d = 0; d < outDepth; ++d) {
for (int h = 0; h < outHeight; ++h) {
for (int w = 0; w < outWidth; ++w) {
int dPad = d * strideD - paddingD + dOffset;
int hPad = h * strideH - paddingH + hOffset;
int wPad = w * strideW - paddingW + wOffset;
if (hPad >= 0 && hPad < height && wPad >= 0 && wPad < width &&
dPad >= 0 && dPad < depth)
outData[((c * outDepth + d) * outHeight + h) * outWidth + w] =
data[((cIn * depth + dPad) * height + hPad) * width + wPad];
else
outData[((c * outDepth + d) * outHeight + h) * outWidth + w] = 0;
}
}
}
}
}
void CpuMatrix::col2Vol(real* trg,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
real alpha,
real beta) {
real* src = getData();
int outDepth = (depth + 2 * paddingD - filterD) / strideD + 1;
int outHeight = (height + 2 * paddingH - filterH) / strideH + 1;
int outWidth = (width + 2 * paddingW - filterW) / strideW + 1;
int channelsCol = channels * filterD * filterH * filterW;
for (int c = 0; c < channelsCol; ++c) {
int wOffset = c % filterW;
int hOffset = (c / filterW) % filterH;
int dOffset = (c / filterW / filterH) % filterD;
int cIm = c / filterW / filterH / filterD;
for (int d = 0; d < outDepth; ++d) {
for (int h = 0; h < outHeight; ++h) {
for (int w = 0; w < outWidth; ++w) {
int dPad = d * strideD - paddingD + dOffset;
int hPad = h * strideH - paddingH + hOffset;
int wPad = w * strideW - paddingW + wOffset;
if (hPad >= 0 && hPad < height && wPad >= 0 && wPad < width &&
dPad >= 0 && dPad < depth)
trg[((cIm * depth + dPad) * height + hPad) * width + wPad] =
alpha *
src[((c * outDepth + d) * outHeight + h) * outWidth + w] +
beta *
trg[((cIm * depth + dPad) * height + hPad) * width + wPad];
}
}
}
}
}
////////////////////////////////////////////////////////////////
// functions executed via cpu //
////////////////////////////////////////////////////////////////
......
paddle/math/Matrix.h
浏览文件 @ e9cc3282
......@@ -928,15 +928,102 @@ public:
size_t paddingW) {
LOG(FATAL) << "Not implemeted";
}
/**
* Input: one or more sequences. Each sequence contains some instances.
*
* Output: output size is the number of input sequences (NOT input
* instances).
*
* output[i] is set to max_input[i].
* Pooling 3D forward operation, pick out the largest element
* in the sizeX of value
*/
virtual void maxPool3DForward(Matrix& inputMat,
Matrix& maxPoolIdx,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW) {
LOG(FATAL) << "Not implemeted";
}
virtual void maxPool3DBackward(Matrix& outGrad,
Matrix& maxPoolIdx,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput) {
LOG(FATAL) << "Not implemeted";
}
virtual void avgPool3DForward(Matrix& input,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW) {
LOG(FATAL) << "Not implemeted";
}
virtual void avgPool3DBackward(Matrix& input,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput) {
LOG(FATAL) << "Not implemeted";
}
/**
* Input: one or more sequences. Each sequence contains some instances.
*
* Output: output size is the number of input sequences (NOT input
* instances).
*
* output[i] is set to max_input[i].
*/
virtual void maxSequenceForward(Matrix& input,
const IVector& sequence,
IVector& index) {
......@@ -1039,6 +1126,42 @@ public:
LOG(FATAL) << "Not implemented";
}
virtual void vol2Col(real* data,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW) {
LOG(FATAL) << "Not implemeted";
}
virtual void col2Vol(real* trg,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
real alpha,
real beta) {
LOG(FATAL) << "Not implemeted";
}
virtual void bilinearForward(const Matrix& in,
const size_t inImgH,
const size_t inImgW,
......@@ -1348,6 +1471,82 @@ public:
size_t paddingH,
size_t paddingW);
void maxPool3DForward(Matrix& inputMat,
Matrix& maxPoolIdx,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW);
void maxPool3DBackward(Matrix& outGrad,
Matrix& maxPoolIdx,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput);
void avgPool3DForward(Matrix& input,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW);
void avgPool3DBackward(Matrix& input,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput);
void maxSequenceForward(Matrix& input,
const IVector& sequence,
IVector& index);
......@@ -1374,6 +1573,38 @@ public:
const real ratioH,
const real ratioW);
void vol2Col(real* data,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW);
void col2Vol(real* trg,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
real alpha,
real beta);
void multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label);
void multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label);
......@@ -1507,6 +1738,82 @@ public:
size_t paddingH,
size_t paddingW);
void maxPool3DForward(Matrix& inputMat,
Matrix& maxPoolIdx,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW);
void maxPool3DBackward(Matrix& outGrad,
Matrix& maxPoolIdx,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput);
void avgPool3DForward(Matrix& input,
size_t channels,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW);
void avgPool3DBackward(Matrix& input,
size_t imgSizeD,
size_t imgSizeH,
size_t imgSizeW,
size_t outputD,
size_t outputH,
size_t outputW,
size_t sizeZ,
size_t sizeY,
size_t sizeX,
size_t strideD,
size_t strideH,
size_t strideW,
size_t paddingD,
size_t paddingH,
size_t paddingW,
real scaleTargets,
real scaleOutput);
void maxSequenceForward(Matrix& input,
const IVector& sequence,
IVector& index);
......@@ -1715,6 +2022,38 @@ public:
const real ratioH,
const real ratioW);
void vol2Col(real* data,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW);
void col2Vol(real* trg,
int channels,
int depth,
int height,
int width,
int filterD,
int filterH,
int filterW,
int strideD,
int strideH,
int strideW,
int paddingD,
int paddingH,
int paddingW,
real alpha,
real beta);
template <typename ExpressionType>
void operator=(const ExpressionType& expr) {
TensorCpuApply<real>(*this, expr);
......
paddle/math/tests/test_matrixCompare.cpp
浏览文件 @ e9cc3282
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <gtest/gtest.h>
#include "TensorCheck.h"
#include "paddle/math/MathUtils.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/testing/TestUtil.h"
......@@ -1203,4 +1204,497 @@ TEST(Matrix, warpCTC) {
}
}
void testMaxPool3DFwdBwd(int numSamples,
int channels,
int imgSizeD,
int imgSizeH,
int imgSizeW,
int ksizeD,
int ksizeH,
int ksizeW,
int strideD,
int strideH,
int strideW,
int padD,
int padH,
int padW) {
int outD = outputSize(imgSizeD, ksizeD, padD, strideD, true);
int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true);
int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true);
int inWidth = channels * imgSizeD * imgSizeH * imgSizeW;
MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);
MatrixPtr inputGpu = GpuMatrix::create(numSamples, inWidth, false, true);
int outWidth = channels * outD * outH * outW;
MatrixPtr target = CpuMatrix::create(numSamples, outWidth, false, false);
MatrixPtr targetGpu = GpuMatrix::create(numSamples, outWidth, false, true);
MatrixPtr maxIdx = CpuMatrix::create(numSamples, outWidth, false, false);
MatrixPtr maxIdxGpu = GpuMatrix::create(numSamples, outWidth, false, true);
input->randomizeUniform();
target->randomizeUniform();
inputGpu->copyFrom(*input);
targetGpu->copyFrom(*target);
target->maxPool3DForward(*input,
*maxIdx,
channels,
imgSizeD,
imgSizeH,
imgSizeW,
outD,
outH,
outW,
ksizeD,
ksizeH,
ksizeW,
strideD,
strideH,
strideW,
padD,
padH,
padW);
targetGpu->maxPool3DForward(*inputGpu,
*maxIdxGpu,
channels,
imgSizeD,
imgSizeH,
imgSizeW,
outD,
outH,
outW,
ksizeD,
ksizeH,
ksizeW,
strideD,
strideH,
strideW,
padD,
padH,
padW);
MatrixPtr targetCheck = CpuMatrix::create(numSamples, outWidth, false, false);
targetCheck->copyFrom(*targetGpu);
checkMatrixEqual(target, targetCheck);
MatrixPtr inputGrad = CpuMatrix::create(numSamples, inWidth, false, false);
MatrixPtr inputGpuGrad = GpuMatrix::create(numSamples, inWidth, false, true);
MatrixPtr targetGrad = CpuMatrix::create(numSamples, outWidth, false, false);
MatrixPtr targetGpuGrad =
GpuMatrix::create(numSamples, outWidth, false, true);
inputGrad->randomizeUniform();
targetGrad->randomizeUniform();
inputGpuGrad->copyFrom(*inputGrad);
targetGpuGrad->copyFrom(*targetGrad);
inputGrad->maxPool3DBackward(*targetGrad,
*maxIdx,
imgSizeD,
imgSizeH,
imgSizeW,
outD,
outH,
outW,
ksizeD,
ksizeH,
ksizeW,
strideD,
strideH,
strideW,
padD,
padH,
padW,
1.0,
1.0);
inputGpuGrad->maxPool3DBackward(*targetGpuGrad,
*maxIdxGpu,
imgSizeD,
imgSizeH,
imgSizeW,
outD,
outH,
outW,
ksizeD,
ksizeH,
ksizeW,
strideD,
strideH,
strideW,
padD,
padH,
padW,
1.0,
1.0);
MatrixPtr targetBwdCheck =
CpuMatrix::create(numSamples, inWidth, false, false);
targetBwdCheck->copyFrom(*inputGpuGrad);
checkMatrixEqual(inputGrad, targetBwdCheck);
}
void testAvgPool3DFwdBwd(int numSamples,
int channels,
int imgSizeD,
int imgSizeH,
int imgSizeW,
int ksizeD,
int ksizeH,
int ksizeW,
int strideD,
int strideH,
int strideW,
int padD,
int padH,
int padW) {
int outD = outputSize(imgSizeD, ksizeD, padD, strideD, true);
int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true);
int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true);
int inWidth = imgSizeD * imgSizeH * imgSizeW * channels;
MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);
MatrixPtr inputGpu = GpuMatrix::create(numSamples, inWidth, false, true);
int outWidth = channels * outD * outH * outW;
MatrixPtr target = CpuMatrix::create(numSamples, outWidth, false, false);
MatrixPtr targetGpu = GpuMatrix::create(numSamples, outWidth, false, true);
input->randomizeUniform();
target->randomizeUniform();
inputGpu->copyFrom(*input);
targetGpu->copyFrom(*target);
target->avgPool3DForward(*input,
channels,
imgSizeD,
imgSizeH,
imgSizeW,
outD,
outH,
outW,
ksizeD,
ksizeH,
ksizeW,
strideD,
strideH,
strideW,
padD,
padH,
padW);
targetGpu->avgPool3DForward(*inputGpu,
channels,
imgSizeD,
imgSizeH,
imgSizeW,
outD,
outH,
outW,
ksizeD,
ksizeH,
ksizeW,
strideD,
strideH,
strideW,
padD,
padH,
padW);
TensorCheckErr(*target, *targetGpu);
MatrixPtr inputGrad = CpuMatrix::create(numSamples, inWidth, false, false);
MatrixPtr inputGpuGrad = GpuMatrix::create(numSamples, inWidth, false, true);
MatrixPtr targetGrad = CpuMatrix::create(numSamples, outWidth, false, false);
MatrixPtr targetGpuGrad =
GpuMatrix::create(numSamples, outWidth, false, true);
inputGrad->randomizeUniform();
targetGrad->randomizeUniform();
inputGpuGrad->copyFrom(*inputGrad);
targetGpuGrad->copyFrom(*targetGrad);
inputGrad->avgPool3DBackward(*targetGrad,
imgSizeD,
imgSizeH,
imgSizeW,
outD,
outH,
outW,
ksizeD,
ksizeH,
ksizeW,
strideD,
strideH,
strideW,
padD,
padH,
padW,
1.0,
1.0);
inputGpuGrad->avgPool3DBackward(*targetGpuGrad,
imgSizeD,
imgSizeH,
imgSizeW,
outD,
outH,
outW,
ksizeD,
ksizeH,
ksizeW,
strideD,
strideH,
strideW,
padD,
padH,
padW,
1.0,
1.0);
TensorCheckErr(*inputGrad, *inputGpuGrad);
}
// TODO(yi): I noticed many such blindly combinatorial tests in this
// file. They are no help to locate defects at all.
TEST(Matrix, Pool3DFwdBwd) {
for (auto numSamples : {1, 3}) {
for (auto channels : {3}) {
for (auto imgSizeD : {9, 16}) {
for (auto imgSizeH : {9, 32}) {
for (auto imgSizeW : {9, 32}) {
for (auto sizeX : {3}) {
for (auto sizeY : {3}) {
for (auto sizeZ : {3}) {
for (auto sD : {2}) {
for (auto sH : {2}) {
for (auto sW : {2}) {
for (auto pD : {0, (sizeZ - 1) / 2}) {
for (auto pH : {0, (sizeY - 1) / 2}) {
for (auto pW : {0, (sizeX - 1) / 2}) {
VLOG(3) << " numSamples=" << numSamples
<< " channels=" << channels
<< " imgSizeD=" << imgSizeD
<< " imgSizeH=" << imgSizeH
<< " imgSizeW=" << imgSizeW
<< " sizeX=" << sizeX
<< " sizeY=" << sizeY
<< " sizeZ=" << sizeZ << " strideD=" << sD
<< " strideH=" << sH << " strideW=" << sW
<< " padingD=" << pD << " padingH=" << pH
<< " padingW=" << pW;
testMaxPool3DFwdBwd(numSamples,
channels,
imgSizeD,
imgSizeH,
imgSizeW,
sizeX,
sizeY,
sizeZ,
sD,
sH,
sW,
pD,
pH,
pW);
testAvgPool3DFwdBwd(numSamples,
channels,
imgSizeD,
imgSizeH,
imgSizeW,
sizeX,
sizeY,
sizeZ,
sD,
sH,
sW,
pD,
pH,
pW);
}
}
}
}
}
}
}
}
}
}
}
}
}
}
// for (auto numSamples : {1, 3}) {
// for (auto channels : {1, 3}) {
// for (auto imgSizeD : {9,16}) {
// for (auto imgSizeH : {9, 32}) {
// for (auto imgSizeW : {9, 32}) {
// for (auto sizeX : {2, 3}) {
// for (auto sizeY : {2, 3}) {
// for (auto sizeZ : {2,3}){
// for (auto sD : {1, 2}) {
// for (auto sH : {1, 2}) {
// for (auto sW : {1, 2}) {
// for (auto pD : {0, (sizeZ - 1) / 2}){
// for (auto pH : {0, (sizeY - 1) / 2}) {
// for (auto pW : {0, (sizeX - 1) / 2}) {
// VLOG(3) << " numSamples=" << numSamples
// << " channels=" << channels
// << " imgSizeD=" << imgSizeD
// << " imgSizeH=" << imgSizeH
// << " imgSizeW=" << imgSizeW
// << " sizeX=" << sizeX
// << " sizeY=" << sizeY
// << " sizeZ=" << sizeZ
// << " strideD=" << sD
// << " strideH=" << sH
// << " strideW=" << sW
// << " padingD=" << pD
// << " padingH=" << pH
// << " padingW=" << pW;
//
// testMaxPool3DFwdBwd(numSamples,
// channels,
// imgSizeD,
// imgSizeH,
// imgSizeW,
// sizeX,
// sizeY,
// sizeZ,
// sD,
// sH,
// sW,
// pD,
// pH,
// pW);
// testAvgPool3DFwdBwd(numSamples,
// channels,
// imgSizeD,
// imgSizeH,
// imgSizeW,
// sizeX,
// sizeY,
// sizeZ,
// sD,
// sH,
// sW,
// pD,
// pH,
// pW);
// }
// }
// }
// }
// }
// }
// }
// }
// }
// }
// }
// }
// }
// }
}
void testMatrixCol2Vol(int depth, int height, int width) {
int channel = 3;
int filterX = 3, filterY = 4, filterZ = 5;
int strideX = 2, strideY = 2, strideZ = 2;
int padX = 1, padY = 1, padZ = 1;
MatrixPtr cpuImage =
std::make_shared<CpuMatrix>(channel, depth * height * width);
MatrixPtr gpuImage =
std::make_shared<GpuMatrix>(channel, depth * height * width);
cpuImage->randomizeUniform();
gpuImage->copyFrom(*cpuImage);
int outD = outputSize(depth, filterZ, padZ, strideZ, true);
int outH = outputSize(height, filterY, padY, strideY, true);
int outW = outputSize(width, filterX, padX, strideX, true);
int colBufHeight = channel * filterZ * filterY * filterX;
int colBufWidth = outD * outH * outW;
MatrixPtr cpuColBuf = std::make_shared<CpuMatrix>(colBufHeight, colBufWidth);
MatrixPtr gpuColBuf = std::make_shared<GpuMatrix>(colBufHeight, colBufWidth);
cpuColBuf->vol2Col(cpuImage->getData(),
channel,
depth,
height,
width,
filterZ,
filterY,
filterX,
strideZ,
strideY,
strideX,
padZ,
padY,
padX);
gpuColBuf->vol2Col(gpuImage->getData(),
channel,
depth,
height,
width,
filterZ,
filterY,
filterX,
strideZ,
strideY,
strideX,
padZ,
padY,
padX);
TensorCheckEqual(*cpuColBuf, *gpuColBuf);
cpuColBuf->randomizeUniform();
gpuColBuf->copyFrom(*cpuColBuf);
cpuColBuf->col2Vol(cpuImage->getData(),
channel,
depth,
height,
width,
filterZ,
filterY,
filterX,
strideZ,
strideY,
strideX,
padZ,
padY,
padX,
1.0,
1.0);
gpuColBuf->col2Vol(gpuImage->getData(),
channel,
depth,
height,
width,
filterZ,
filterY,
filterX,
strideZ,
strideY,
strideX,
padZ,
padY,
padX,
1.0,
1.0);
TensorCheckErr(*cpuImage, *gpuImage);
}
TEST(Matrix, col2Vol) {
for (auto depth : {9, 16, 64}) {
for (auto height : {9, 11, 128}) {
for (auto width : {9, 32, 128}) {
VLOG(3) << "depth=" << depth << " height=" << height
<< " width=" << width;
testMatrixCol2Vol(depth, height, width);
}
}
}
}
#endif
paddle/parameter/Argument.cpp
浏览文件 @ e9cc3282
......@@ -186,6 +186,7 @@ void Argument::resizeAndCopyFrom(const Argument& src,
resizeAndCopy(strs, src.strs, useGpu, stream);
frameWidth = src.frameWidth;
frameHeight = src.frameHeight;
frameDepth = src.frameDepth;
}
int32_t Argument::resizeAndCopyFrom(const Argument& src,
......@@ -206,6 +207,7 @@ int32_t Argument::resizeAndCopyFrom(const Argument& src,
dataId = src.dataId;
frameWidth = src.frameWidth;
frameHeight = src.frameHeight;
frameDepth = src.frameDepth;
if (!src.sequenceStartPositions) {
// non-sequence input, copy samples directly
......@@ -677,6 +679,7 @@ void Argument::reorganizeSeqInfo(
const ICpuGpuVectorPtr subSeqStartPos,
std::vector<std::vector<int>>& reorganizedSeqInfo) {
CHECK(seqStartPos);
reorganizedSeqInfo.clear();
int seqNum = seqStartPos->getSize() - 1;
int* seqStarts = seqStartPos->getMutableData(false);
......
paddle/parameter/Argument.h
浏览文件 @ e9cc3282
/* Copyright (c) 2016 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.
......@@ -35,6 +32,7 @@ struct Argument {
strs(nullptr),
frameHeight(0),
frameWidth(0),
frameDepth(0),
sequenceStartPositions(nullptr),
subSequenceStartPositions(nullptr),
cpuSequenceDims(nullptr),
......@@ -64,6 +62,7 @@ struct Argument {
allCount = argument.allCount;
frameHeight = argument.frameHeight;
frameWidth = argument.frameWidth;
frameDepth = argument.frameDepth;
dataId = argument.dataId;
}
......@@ -76,6 +75,7 @@ struct Argument {
// A dataBatch includes batchSize frames, one frame maybe not only vector
size_t frameHeight;
size_t frameWidth;
size_t frameDepth;
// If NULL, each position is treated independently.
// Otherwise, its size should be #NumberOfSequences + 1.
......@@ -136,8 +136,10 @@ struct Argument {
}
size_t getFrameHeight() const { return frameHeight; }
size_t getFrameWidth() const { return frameWidth; }
size_t getFrameDepth() const { return frameDepth; }
void setFrameHeight(size_t h) { frameHeight = h; }
void setFrameWidth(size_t w) { frameWidth = w; }
void setFrameDepth(size_t d) { frameDepth = d; }
int64_t getNumSequences() const {
return sequenceStartPositions ? sequenceStartPositions->getSize() - 1
......
paddle/parameter/Parameter.h
浏览文件 @ e9cc3282
......@@ -281,7 +281,11 @@ public:
/**
* @brief Set the format in header.
*/
void setHeaderFormat(int32_t fmt) { headerFormat_ = fmt; }
void setHeaderFormat(int32_t fmt) {
CHECK(isHeaderFormatSupported(fmt)) << "Unsupported format version: "
<< fmt;
headerFormat_ = fmt;
}
/**
* @brief Parameter Update Hook.
......
paddle/pserver/LightNetwork.cpp
浏览文件 @ e9cc3282
......@@ -22,7 +22,6 @@ limitations under the License. */
#include <arpa/inet.h>
#include <net/if.h>
#include <net/if_arp.h>
#include <sys/ioctl.h>
#include <sstream>
......
proto/ModelConfig.proto
浏览文件 @ e9cc3282
......@@ -85,6 +85,12 @@ message ConvConfig {
optional uint32 dilation = 15 [ default = 1 ];
optional uint32 dilation_y = 16 [ default = 1 ];
optional uint32 filter_size_z = 17 [ default = 1 ];
optional uint32 padding_z = 18 [ default = 1 ];
optional uint32 stride_z = 19 [ default = 1 ];
optional uint32 output_z = 20 [ default = 1 ];
optional uint32 img_size_z = 21 [ default = 1 ];
}
message PoolConfig {
......@@ -127,6 +133,12 @@ message PoolConfig {
// if not set, use padding
optional uint32 padding_y = 13;
optional uint32 size_z = 14 [ default = 1 ];
optional uint32 stride_z = 15 [ default = 1 ];
optional uint32 output_z = 16 [ default = 1 ];
optional uint32 img_size_z = 17 [ default = 1 ];
optional uint32 padding_z = 18 [ default = 1 ];
}
message SppConfig {
......@@ -499,6 +511,11 @@ message LayerConfig {
optional int32 axis = 54 [ default = 2 ];
repeated uint32 offset = 55;
repeated uint32 shape = 56;
// for HuberRegressionLoss
optional double delta = 57 [ default = 1.0 ];
optional uint64 depth = 58 [ default = 1 ];
}
message EvaluatorConfig {
......
python/paddle/trainer/config_parser.py
浏览文件 @ e9cc3282
......@@ -886,6 +886,36 @@ class Conv(Cfg):
config_assert(output_x <= 0)
# please refer to the comments in proto/ModelConfig.proto
@config_class
class Conv3D(Cfg):
def __init__(self,
filter_size,
channels,
padding=None,
stride=None,
groups=None,
filter_channels=None,
output_x=None,
img_size=None,
caffe_mode=True,
filter_size_y=None,
padding_y=None,
stride_y=None,
filter_size_z=None,
padding_z=None,
stride_z=None):
self.add_keys(locals())
self.filter_size_y = filter_size_y if filter_size_y else filter_size
self.filter_size_z = filter_size_z if filter_size_z else filter_size
self.padding_y = padding_y if padding_y else padding
self.padding_z = padding_z if padding_z else padding
self.stride_y = stride_y if stride_y else stride
self.stride_z = stride_z if stride_z else stride
if output_x is not None:
config_assert(output_x <= 0)
@config_class
class BilinearInterp(Cfg):
def __init__(self, out_size_x=None, out_size_y=None, channels=None):
......@@ -908,6 +938,31 @@ class Pool(Cfg):
self.add_keys(locals())
@config_class
class Pool3d(Cfg):
def __init__(
self,
pool_type,
channels,
size_x,
size_y=None,
size_z=None,
start=None,
stride=None, # 1 by defalut in protobuf
stride_y=None,
stride_z=None,
padding=None, # 0 by defalut in protobuf
padding_y=None,
padding_z=None):
self.add_keys(locals())
self.filter_size_y = size_y if size_y else size_x
self.filter_size_z = size_z if size_z else size_x
self.padding_y = padding_y if padding_y else padding
self.padding_z = padding_z if padding_z else padding
self.stride_y = stride_y if stride_y else stride
self.stride_z = stride_z if stride_z else stride
@config_class
class SpatialPyramidPool(Cfg):
def __init__(self, pool_type, pyramid_height, channels):
......@@ -1172,6 +1227,20 @@ def get_img_size(input_layer_name, channels):
return img_size, img_size_y
def get_img3d_size(input_layer_name, channels):
input = g_layer_map[input_layer_name]
img_pixels = input.size / channels
img_size = input.width
img_size_y = input.height
img_size_z = input.depth
config_assert(
img_size * img_size_y * img_size_z == img_pixels,
"Input layer %s: Incorrect input image size %d * %d * %d for input image pixels %d"
% (input_layer_name, img_size, img_size_y, img_size_z, img_pixels))
return img_size, img_size_y, img_size_z
def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
parse_image(bilinear, input_layer_name, bilinear_conf.image_conf)
bilinear_conf.out_size_x = bilinear.out_size_x
......@@ -1209,6 +1278,45 @@ def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
pool_conf.stride_y, not ceil_mode)
def parse_pool3d(pool, input_layer_name, pool_conf, ceil_mode):
pool_conf.pool_type = pool.pool_type
config_assert(pool.pool_type in ['max-projection', 'avg-projection'],
"pool-type %s is not in "
"['max-projection', 'avg-projection']" % pool.pool_type)
pool_conf.channels = pool.channels
pool_conf.size_x = pool.size_x
pool_conf.stride = pool.stride
pool_conf.padding = pool.padding
pool_conf.size_y = default(pool.size_y, pool_conf.size_x)
pool_conf.size_z = default(pool.size_z, pool_conf.size_x)
pool_conf.stride_y = default(pool.stride_y, pool_conf.stride)
pool_conf.stride_z = default(pool.stride_z, pool_conf.stride)
pool_conf.padding_y = default(pool.padding_y, pool_conf.padding)
pool_conf.padding_z = default(pool.padding_z, pool_conf.padding)
pool_conf.img_size, pool_conf.img_size_y, pool_conf.img_size_z = \
get_img3d_size(input_layer_name, pool.channels)
config_assert(not pool.start, "start is deprecated in pooling.")
if pool.padding is not None:
pool_conf.padding = pool.padding
pool_conf.padding_y = default(pool.padding_y, pool_conf.padding)
pool_conf.padding_z = default(pool.padding_z, pool_conf.padding)
pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x,
pool_conf.padding, pool_conf.stride,
not ceil_mode)
pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y,
pool_conf.padding_y,
pool_conf.stride_y, not ceil_mode)
pool_conf.output_z = cnn_output_size(pool_conf.img_size_z, pool_conf.size_z,
pool_conf.padding_z,
pool_conf.stride_z, not ceil_mode)
def parse_spp(spp, input_layer_name, spp_conf):
parse_image(spp, input_layer_name, spp_conf.image_conf)
spp_conf.pool_type = spp.pool_type
......@@ -1282,6 +1390,50 @@ def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False):
conv_conf.stride_y, conv_conf.caffe_mode)
#caffe_mode: compute the output size using floor instead of ceil,
# which is consistent of caffe and CuDNN's convention.
def parse_conv3d(conv, input_layer_name, conv_conf, num_filters, trans=False):
conv_conf.filter_size = conv.filter_size
conv_conf.filter_size_y = conv.filter_size_y
conv_conf.filter_size_z = conv.filter_size_z
conv_conf.channels = conv.channels
conv_conf.padding = conv.padding
conv_conf.padding_y = conv.padding_y
conv_conf.padding_z = conv.padding_z
conv_conf.stride = conv.stride
conv_conf.stride_y = conv.stride_y
conv_conf.stride_z = conv.stride_z
conv_conf.groups = conv.groups
conv_conf.caffe_mode = conv.caffe_mode
if not trans:
conv_conf.filter_channels = conv.channels / conv.groups
conv_conf.img_size, conv_conf.img_size_y, conv_conf.img_size_z = \
get_img3d_size(input_layer_name, conv.channels)
conv_conf.output_x = cnn_output_size(
conv_conf.img_size, conv_conf.filter_size, conv_conf.padding,
conv_conf.stride, conv_conf.caffe_mode)
conv_conf.output_y = cnn_output_size(
conv_conf.img_size_y, conv_conf.filter_size_y, conv_conf.padding_y,
conv_conf.stride_y, conv_conf.caffe_mode)
conv_conf.output_z = cnn_output_size(
conv_conf.img_size_z, conv_conf.filter_size_z, conv_conf.padding_z,
conv_conf.stride_z, conv_conf.caffe_mode)
else:
conv_conf.filter_channels = num_filters / conv.groups
conv_conf.output_x, conv_conf.output_y, conv_conf.output_z = \
get_img3d_size(input_layer_name, conv.channels)
conv_conf.img_size = cnn_image_size(
conv_conf.output_x, conv_conf.filter_size, conv_conf.padding,
conv_conf.stride, conv_conf.caffe_mode)
conv_conf.img_size_y = cnn_image_size(
conv_conf.output_y, conv_conf.filter_size_y, conv_conf.padding_y,
conv_conf.stride_y, conv_conf.caffe_mode)
conv_conf.img_size_z = cnn_image_size(
conv_conf.output_z, conv_conf.filter_size_z, conv_conf.padding_z,
conv_conf.stride_z, conv_conf.caffe_mode)
def parse_block_expand(block_expand, input_layer_name, block_expand_conf):
block_expand_conf.channels = block_expand.channels
block_expand_conf.stride_x = block_expand.stride_x
......@@ -1585,6 +1737,9 @@ class LayerBase(object):
self.config.height = height
self.config.width = width
def set_layer_depth(self, depth):
self.config.depth = depth
def set_cnn_layer(self,
input_layer_name,
height,
......@@ -1607,6 +1762,21 @@ class MultiClassCrossEntropySelfNormCostLayer(LayerBase):
self.config.softmax_selfnorm_alpha = softmax_selfnorm_alpha
@config_layer('cross_entropy_over_beam')
class CrossEntropyOverBeamLayer(LayerBase):
def __init__(self, name, inputs, **xargs):
config_assert(len(inputs) % 3 == 0, "Error input number.")
super(CrossEntropyOverBeamLayer, self).__init__(
name, 'cross_entropy_over_beam', 0, inputs, **xargs)
input_num = len(inputs) / 3
for i in range(input_num):
input_layer = self.get_input_layer(i * 3)
config_assert(input_layer.size == 1, (
"Inputs for this layer are made up of "
"several triples, in which the first one is scores over "
"all candidate paths, whose size should be equal to 1."))
@config_layer('fc')
class FCLayer(LayerBase):
layer_type = 'fc'
......@@ -1788,11 +1958,19 @@ class DetectionOutputLayer(LayerBase):
@config_layer('data')
class DataLayer(LayerBase):
def __init__(self, name, size, height=None, width=None, device=None):
def __init__(self,
name,
size,
depth=None,
height=None,
width=None,
device=None):
super(DataLayer, self).__init__(
name, 'data', size, inputs=[], device=device)
if height and width:
self.set_layer_height_width(height, width)
if depth:
self.set_layer_depth(depth)
'''
......@@ -1907,7 +2085,7 @@ class ConvLayerBase(LayerBase):
def calc_parameter_size(self, conv_conf):
return self.config.num_filters * conv_conf.filter_channels \
* (conv_conf.filter_size * conv_conf.filter_size_y)
* (conv_conf.filter_size * conv_conf.filter_size_y)
@config_layer('exconv')
......@@ -1991,6 +2169,87 @@ class ConvTransLayer(ConvTransLayerBase):
layer_type = 'cudnn_convt'
@config_layer('conv_3d')
class Conv3DLayerBase(LayerBase):
def __init__(self,
name,
inputs=[],
bias=True,
num_filters=None,
shared_biases=True,
**xargs):
super(Conv3DLayerBase, self).__init__(
name, self.layer_type, 0, inputs=inputs, **xargs)
if num_filters is not None:
self.config.num_filters = num_filters
# need to specify layer in config
self.config.type = self.layer_type
trans = False
if self.config.type == "deconv3d":
trans = True
if shared_biases is not None:
self.config.shared_biases = shared_biases
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
conv_conf = self.config.inputs[input_index].conv_conf
parse_conv3d(
self.inputs[input_index].conv,
input_layer.name,
conv_conf,
num_filters,
trans=trans
) # for z-axis pad:0, strid:1, filter_size:1, img_size:1
psize = self.calc_parameter_size(conv_conf)
self.create_input_parameter(input_index, psize)
if trans:
self.set_cnn_layer(name, conv_conf.img_size_z,
conv_conf.img_size_y, conv_conf.img_size,
self.config.num_filters)
else:
self.set_cnn_layer(name, conv_conf.output_z, conv_conf.output_y,
conv_conf.output_x, self.config.num_filters)
psize = self.config.size
if shared_biases:
psize = self.config.num_filters
self.create_bias_parameter(bias, psize, [psize, 1])
def calc_parameter_size(self, conv_conf):
return self.config.num_filters * conv_conf.filter_channels \
* (conv_conf.filter_size * conv_conf.filter_size_y \
* conv_conf.filter_size_z)
def set_cnn_layer(self,
input_layer_name,
depth,
height,
width,
channels,
is_print=True):
size = depth * height * width * channels
self.set_layer_size(size)
self.set_layer_height_width(height, width)
self.set_layer_depth(depth)
if is_print:
print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" %
(input_layer_name, channels, depth, height, width, size))
@config_layer('conv3d')
class Conv3DLayer(Conv3DLayerBase):
layer_type = 'conv3d'
@config_layer('deconv3d')
class Conv3DLayer(Conv3DLayerBase):
layer_type = 'deconv3d'
@config_layer('norm')
class NormLayer(LayerBase):
def __init__(self, name, inputs, **xargs):
......@@ -2020,6 +2279,35 @@ class PoolLayer(LayerBase):
pool_conf.channels)
@config_layer('pool3d')
class Pool3DLayer(LayerBase):
def __init__(self, name, inputs, ceil_mode=True, **xargs):
super(Pool3DLayer, self).__init__(
name, 'pool3d', 0, inputs=inputs, **xargs)
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
pool_conf = self.config.inputs[input_index].pool_conf
parse_pool3d(self.inputs[input_index].pool, input_layer.name,
pool_conf, ceil_mode)
self.set_cnn_layer(name, pool_conf.output_z, pool_conf.output_y,
pool_conf.output_x, pool_conf.channels)
def set_cnn_layer(self,
input_layer_name,
depth,
height,
width,
channels,
is_print=True):
size = depth * height * width * channels
self.set_layer_size(size)
self.set_layer_height_width(height, width)
self.set_layer_depth(depth)
if is_print:
print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" %
(input_layer_name, channels, depth, height, width, size))
@config_layer('spp')
class SpatialPyramidPoolLayer(LayerBase):
def __init__(self, name, inputs, **xargs):
......@@ -2268,13 +2556,14 @@ def define_cost(class_name, cost_type):
define_cost('MultiClassCrossEntropy', 'multi-class-cross-entropy')
define_cost('CrossEntropyOverBeamCostLayer', 'cross_entropy_over_beam')
define_cost('RankingCost', 'rank-cost')
define_cost('AucValidation', 'auc-validation')
define_cost('PnpairValidation', 'pnpair-validation')
define_cost('SumOfSquaresCostLayer', 'square_error')
define_cost('MultiBinaryLabelCrossEntropy', 'multi_binary_label_cross_entropy')
define_cost('SoftBinaryClassCrossEntropy', 'soft_binary_class_cross_entropy')
define_cost('HuberTwoClass', 'huber')
define_cost('HuberTwoClassification', 'huber_classification')
define_cost('SumCost', 'sum_cost')
define_cost('SmoothL1Cost', 'smooth_l1')
......@@ -2336,6 +2625,17 @@ class LambdaCost(LayerBase):
self.config.max_sort_size = max_sort_size
@config_layer('huber_regression')
class HuberRegressionLoss(LayerBase):
def __init__(self, name, inputs, delta=1., coeff=1., device=None):
super(HuberRegressionLoss, self).__init__(
name, 'huber_regression', 1, inputs=inputs, device=device)
config_assert(
len(self.inputs) == 2, 'HuberRegression must have 2 inputs')
self.config.delta = delta
self.config.coeff = coeff
@config_layer('nce')
class NCELayer(LayerBase):
def __init__(self,
......
python/paddle/trainer/recurrent_units.py 100755 → 100644
浏览文件 @ e9cc3282
文件模式从 100755 更改为 100644
python/paddle/trainer_config_helpers/layers.py 100755 → 100644
浏览文件 @ e9cc3282
......@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import collections
import inspect
......@@ -106,11 +105,14 @@ __all__ = [
'nce_layer',
'cross_entropy_with_selfnorm',
'cross_entropy',
'BeamInput',
'cross_entropy_over_beam',
'multi_binary_label_cross_entropy',
'sum_cost',
'rank_cost',
'lambda_cost',
'huber_cost',
'huber_regression_cost',
'huber_classification_cost',
'block_expand_layer',
'maxout_layer',
'out_prod_layer',
......@@ -136,7 +138,9 @@ __all__ = [
'slice_projection',
'seq_slice_layer',
'kmax_sequence_score_layer',
'img_pool3d_layer',
'scale_shift_layer',
'img_conv3d_layer',
]
......@@ -165,6 +169,7 @@ class LayerType(object):
EXCONVTRANS_LAYER = 'exconvt'
CUDNNCONV_LAYER = 'cudnn_conv'
POOL_LAYER = 'pool'
POOL3D_LAYER = 'pool3d'
BATCH_NORM_LAYER = 'batch_norm'
NORM_LAYER = 'norm'
SUM_TO_ONE_NORM_LAYER = 'sum_to_one_norm'
......@@ -218,11 +223,16 @@ class LayerType(object):
CRF_DECODING_LAYER = 'crf_decoding'
NCE_LAYER = 'nce'
CONV3D_LAYER = 'conv3d'
DECONV3D_LAYER = 'deconv3d'
RANK_COST = 'rank-cost'
LAMBDA_COST = 'lambda_cost'
HUBER = 'huber'
HUBER_REGRESSION = 'huber_regression'
HUBER_CLASSIFICATION = 'huber_classification'
CROSS_ENTROPY = 'multi-class-cross-entropy'
CROSS_ENTROPY_WITH_SELFNORM = 'multi_class_cross_entropy_with_selfnorm'
CROSS_ENTROPY_OVER_BEAM = 'cross_entropy_over_beam'
SOFT_BIN_CLASS_CROSS_ENTROPY = 'soft_binary_class_cross_entropy'
MULTI_BIN_LABEL_CROSS_ENTROPY = 'multi_binary_label_cross_entropy'
SUM_COST = 'sum_cost'
......@@ -892,7 +902,8 @@ def mixed_layer(size=0,
@layer_support()
def data_layer(name, size, height=None, width=None, layer_attr=None):
def data_layer(name, size, depth=None, height=None, width=None,
layer_attr=None):
"""
Define DataLayer For NeuralNetwork.
......@@ -919,15 +930,18 @@ def data_layer(name, size, height=None, width=None, layer_attr=None):
type=LayerType.DATA,
name=name,
size=size,
depth=depth,
height=height,
width=width,
**ExtraLayerAttribute.to_kwargs(layer_attr))
if depth is None:
depth = 1
num_filters = None
if height is not None and width is not None:
num_filters = size / (width * height)
assert num_filters * width * height == size, \
"size=%s width=%s height=%s" % (size, width, height)
num_filters = size / (width * height * depth)
assert num_filters * width * height * depth == size, \
"size=%s width=%s height=%s depth=%s" % (size, width, height, depth)
return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters)
......@@ -2651,6 +2665,146 @@ def img_pool_layer(input,
size=l.config.size)
@wrap_name_default("pool3d")
@layer_support()
def img_pool3d_layer(input,
pool_size,
name=None,
num_channels=None,
pool_type=None,
stride=1,
padding=0,
layer_attr=None,
pool_size_y=None,
stride_y=None,
padding_y=None,
pool_size_z=None,
stride_z=None,
padding_z=None,
ceil_mode=True):
"""
Image pooling Layer.
The details of pooling layer, please refer ufldl's pooling_ .
.. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/
- ceil_mode=True:
.. math::
w = 1 + int(ceil(input\_width + 2 * padding - pool\_size) / float(stride))
h = 1 + int(ceil(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))
d = 1 + int(ceil(input\_depth + 2 * padding\_z - pool\_size\_z) / float(stride\_z))
- ceil_mode=False:
.. math::
w = 1 + int(floor(input\_width + 2 * padding - pool\_size) / float(stride))
h = 1 + int(floor(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))
d = 1 + int(floor(input\_depth + 2 * padding\_z - pool\_size\_z) / float(stride\_z))
The example usage is:
.. code-block:: python
maxpool = img_pool3d_layer(input=conv,
pool_size=3,
num_channels=8,
stride=1,
padding=1,
pool_type=MaxPooling())
:param padding: pooling padding width.
:type padding: int|tuple|list
:param name: name of pooling layer
:type name: basestring.
:param input: layer's input
:type input: LayerOutput
:param pool_size: pooling window width
:type pool_size: int|tuple|list
:param num_channels: number of input channel.
:type num_channels: int
:param pool_type: pooling type. MaxPooling or AvgPooling. Default is
MaxPooling.
:type pool_type: BasePoolingType
:param stride: stride width of pooling.
:type stride: int|tuple|list
:param layer_attr: Extra Layer attribute.
:type layer_attr: ExtraLayerAttribute
:param ceil_mode: Wether to use ceil mode to calculate output height and with.
Defalut is True. If set false, Otherwise use floor.
:type ceil_mode: bool
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if pool_type is None:
pool_type = MaxPooling()
elif isinstance(pool_type, AvgPooling):
pool_type.name = 'avg'
type_name = pool_type.name + '-projection' \
if (
isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \
else pool_type.name
if isinstance(pool_size, collections.Sequence):
assert len(pool_size) == 3
pool_size, pool_size_y, pool_size_z = pool_size
else:
pool_size_y = pool_size
pool_size_z = pool_size
if isinstance(stride, collections.Sequence):
assert len(stride) == 3
stride, stride_y, stride_z = stride
else:
stride_y = stride
stride_z = stride
if isinstance(padding, collections.Sequence):
assert len(padding) == 3
padding, padding_y, padding_y = padding
else:
padding_y = padding
padding_z = padding
l = Layer(
name=name,
type=LayerType.POOL3D_LAYER,
inputs=[
Input(
input.name,
pool=Pool3d(
pool_type=type_name,
channels=num_channels,
size_x=pool_size,
start=None,
stride=stride,
padding=padding,
size_y=pool_size_y,
stride_y=stride_y,
padding_y=padding_y,
size_z=pool_size_z,
stride_z=stride_z,
padding_z=padding_z))
],
ceil_mode=ceil_mode,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.POOL_LAYER,
parents=[input],
num_filters=num_channels,
size=l.config.size)
@wrap_name_default("spp")
@layer_support()
def spp_layer(input,
......@@ -4069,8 +4223,12 @@ def __cost_input__(input, label, weight=None):
"""
inputs and parents for cost layers.
"""
ipts = [Input(input.name), Input(label.name)]
parents = [input, label]
if isinstance(input, LayerOutput):
input = [input]
if isinstance(label, LayerOutput):
label = [label]
ipts = [Input(ipt.name) for ipt in (input + label)]
parents = [ipt for ipt in (input + label)]
if weight is not None:
assert weight.size == 1
ipts.append(Input(weight.name))
......@@ -5057,17 +5215,6 @@ def warp_ctc_layer(input,
building process, PaddlePaddle will clone the source codes, build and
install it to :code:`third_party/install/warpctc` directory.
To use warp_ctc layer, you need to specify the path of :code:`libwarpctc.so`,
using following methods:
1. Set it in :code:`paddle.init` (python api) or :code:`paddle_init` (c api),
such as :code:`paddle.init(use_gpu=True,
warpctc_dir=your_paddle_source_dir/third_party/install/warpctc/lib)`.
2. Set environment variable LD_LIBRARY_PATH on Linux or DYLD_LIBRARY_PATH
on Mac OS. For instance, :code:`export
LD_LIBRARY_PATH=your_paddle_source_dir/third_party/install/warpctc/lib:$LD_LIBRARY_PATH`.
More details of CTC can be found by referring to `Connectionist Temporal
Classification: Labelling Unsegmented Sequence Data with Recurrent
Neural Networks <http://machinelearning.wustl.edu/mlpapers/paper_files/
......@@ -5644,16 +5791,77 @@ def sum_cost(input, name=None, layer_attr=None):
@wrap_name_default()
@layer_support()
def huber_cost(input, label, name=None, coeff=1.0, layer_attr=None):
def huber_regression_cost(input,
label,
name=None,
delta=1.0,
coeff=1.0,
layer_attr=None):
"""
A loss layer for huber loss.
In statistics, the Huber loss is a loss function used in robust regression,
that is less sensitive to outliers in data than the squared error loss.
Given a prediction f(x), a label y and :math:`\delta`, the loss function
is defined as:
.. math:
loss = 0.5*\left ( y-f(x) \right )^2, \left | y-f(x) \right |\leq \delta
loss = \delta \left | y-f(x) \right |-0.5\delta ^2, otherwise
The example usage is:
.. code-block:: python
cost = huber_cost(input=input_layer,
label=label_layer)
cost = huber_regression_cost(input=input_layer, label=label_layer)
:param input: The first input layer.
:type input: LayerOutput.
:param label: The input label.
:type input: LayerOutput.
:param name: The name of this layers. It is not necessary.
:type name: None|basestring.
:param delta: The difference between the observed and predicted values.
:type delta: float.
:param coeff: The coefficient affects the gradient in the backward.
:type coeff: float.
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput.
"""
assert isinstance(input, LayerOutput)
Layer(
name=name,
type=LayerType.HUBER_REGRESSION,
inputs=[input.name, label.name],
delta=delta,
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.HUBER_REGRESSION, parents=[input, label], size=1)
@wrap_name_default()
@layer_support()
def huber_classification_cost(input,
label,
name=None,
coeff=1.0,
layer_attr=None):
"""
For classification purposes, a variant of the Huber loss called modified Huber
is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
a true binary class label :math:`y\in \left \{-1, 1 \right \}`, the modified Huber
loss is defined as:
.. math:
loss = \max \left ( 0, 1-yf(x) \right )^2, yf(x)\geq 1
loss = -4yf(x), \text{otherwise}
The example usage is:
.. code-block:: python
cost = huber_classification_cost(input=input_layer, label=label_layer)
:param input: The first input layer.
:type input: LayerOutput.
......@@ -5673,11 +5881,12 @@ def huber_cost(input, label, name=None, coeff=1.0, layer_attr=None):
assert input.size == 1
Layer(
name=name,
type=LayerType.HUBER,
type=LayerType.HUBER_CLASSIFICATION,
inputs=[input.name, label.name],
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(name, LayerType.HUBER, parents=[input, label], size=1)
return LayerOutput(
name, LayerType.HUBER_CLASSIFICATION, parents=[input, label], size=1)
@wrap_name_default()
......@@ -5713,10 +5922,10 @@ def multi_binary_label_cross_entropy(input,
if input.activation is None or \
not isinstance(input.activation, SigmoidActivation):
logger.log(
logging.WARN,
"%s is not recommend for multi_binary_label_cross_entropy's activation, "
"maybe the sigmoid is better" % repr(input.activation))
logger.log(logging.WARN,
("%s is not a recommended activation for "
"multi_binary_label_cross_entropy, sigmoid is better") %
repr(input.activation))
Layer(
name=name,
......@@ -5731,6 +5940,113 @@ def multi_binary_label_cross_entropy(input,
size=1)
class BeamInput(object):
"""
Define the input for cross_entropy_over_beam layer.
A beam is made up of a triple: the first one is scores over all
candidates; the second one is indices of top k selected candidates; the
third one is the index of ground truth, which is also always called
gold.
"""
def __init__(self, candidate_scores, selected_candidates, gold):
assert isinstance(candidate_scores, LayerOutput)
self.candidate_scores = candidate_scores
assert candidate_scores.size == 1
assert isinstance(selected_candidates, LayerOutput)
self.selected_candidates = selected_candidates
assert isinstance(gold, LayerOutput)
self.gold = gold
@wrap_name_default()
@layer_support()
def cross_entropy_over_beam(input, name=None):
"""
This layer is used in learning to search models, which is to solve complex
joint prediction problems based on learning to search through a
problem-defined search space.
Specifically, the learning to search process for this layer begins with
searching a target sequence from a nested sequence. In the first search
step, top beam size sequences with highest scores, indices of these top k
sequences in the original nested sequence, and the ground truth (also
called gold) altogether (a triple) make up of the first beam.
Then, several special positions, for example, start and end positions
that define meaningful segments are searched. In these searches, top k
positions with highest scores are selected, and then sequence, starting
from the selected starts till ends of the sequences (or a fixed position)
are taken to search next.
We call the possible top k results returned in one search the beam. This
search process can be repeated for pre-defined turns and leads to several
beam expansions.
Finally, the layer cross_entropy_over_beam takes all the beam expansions
which contain several candidate targets found along the multi-step search.
cross_entropy_over_beam calculates cross entropy over the expanded beams
which all the candidates in the beam as the normalized factor.
Note that, if gold falls off the beam at search step t, then the cost is
calculated over the beam at step t.
This cost layer always works together with kmax_sequence_score_layer,
sub_nested_seq_layer, and sequence_slice_layer to trim the input to form a
sub-search space.
The example usage is:
.. code-block:: python
cost = cross_entropy_over_beam(input=[
BeamInput(
candidate_scores=beam1_candidates,
selected_candidates=beam1_topk,
gold=gold1),
BeamInput(
candidate_scores=beam2_candidates,
selected_candidates=beam2_topk,
gold=gold2),
])
:param input: input beams for this layer.
:type input: BeamInput
:param name: input beams for this layer.
:type name: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, BeamInput):
input = [input]
else:
assert isinstance(input, list), (
'input for cross_entropy_over_beam shold be a python list '
'of BeamInput object.')
for ipt in input:
assert isinstance(ipt, BeamInput), (
'input for cross_entropy_over_beam '
'should be a BeamInput object.')
ipts = []
parents = []
for beam in input:
parents += [beam.candidate_scores, beam.selected_candidates, beam.gold]
ipts += [
beam.candidate_scores.name, beam.selected_candidates.name,
beam.gold.name
]
Layer(name=name, type=LayerType.CROSS_ENTROPY_OVER_BEAM, inputs=ipts)
return LayerOutput(name, LayerType.CROSS_ENTROPY, parents=parents, size=1)
@wrap_name_default()
@layer_support()
def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
......@@ -6317,6 +6633,149 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1):
name, LayerType.KMAX_SEQ_SCORE, parents=[input], size=input.size)
@wrap_name_default("conv3d")
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default(act=ReluActivation())
@layer_support(DROPOUT)
def img_conv3d_layer(input,
filter_size,
num_filters,
name=None,
num_channels=None,
act=None,
groups=1,
stride=1,
padding=0,
bias_attr=None,
param_attr=None,
shared_biases=True,
layer_attr=None,
trans=False,
layer_type=None):
"""
The example usage is:
.. code-block:: python
conv = img_conv3d_layer(input=data, filter_size=1,
num_channels=8,
num_filters=16, stride=1,
bias_attr=False,
act=ReluActivation())
:param name: Layer name.
:type name: basestring
:param input: Layer Input.
:type input: LayerOutput
:param filter_size: The x dimension of a filter kernel. Or input a list.
:type filter_size: int|tuple|list
:param num_filters: Each filter group's number of filter
:param act: Activation type. Default is tanh
:type act: BaseActivation
:param groups: Group size of filters.
:type groups: int
:param stride: The x dimension of the stride. Or input a tuple for two image
dimension.
:type stride: int|tuple|list
:param padding: The x dimension of the padding. Or input a tuple for two
image dimension
:type padding: int|tuple|list
:param bias_attr: Convolution bias attribute. None means default bias.
False means no bias.
:type bias_attr: ParameterAttribute|False
:param num_channels: number of input channels. If None will be set
automatically from previous output.
:type num_channels: int
:param param_attr: Convolution param attribute. None means default attribute
:type param_attr: ParameterAttribute
:param shared_biases: Is biases will be shared between filters or not.
:type shared_biases: bool
:param layer_attr: Layer Extra Attribute.
:type layer_attr: ExtraLayerAttribute
:param trans: true if it is a convTransLayer, false if it is a convLayer
:type trans: bool
:param layer_type: specify the layer_type, default is None. If trans=True,
layer_type has to be "exconvt" or "cudnn_convt",
otherwise layer_type has to be either "exconv" or
"cudnn_conv"
:type layer_type: String
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if isinstance(filter_size, collections.Sequence):
assert len(filter_size) == 3
filter_size, filter_size_y, filter_size_z = filter_size
else:
filter_size_y = filter_size
filter_size_z = filter_size
if isinstance(stride, collections.Sequence):
assert len(stride) == 3
stride, stride_y, stride_z = stride
else:
stride_y = stride
stride_z = stride
if isinstance(padding, collections.Sequence):
assert len(padding) == 3
padding, padding_y, padding_z = padding
else:
padding_y = padding
padding_z = padding
if param_attr.attr.get('initial_smart'):
# special initial for conv layers.
init_w = (2.0 / (filter_size**2 * num_channels))**0.5
param_attr.attr["initial_mean"] = 0.0
param_attr.attr["initial_std"] = init_w
param_attr.attr["initial_strategy"] = 0
param_attr.attr["initial_smart"] = False
if layer_type:
if trans:
assert layer_type in ["deconv3d"]
lt = layer_type
else:
lt = LayerType.DECONV3D_LAYER if trans else LayerType.CONV3D_LAYER
l = Layer(
name=name,
inputs=Input(
input.name,
conv=Conv3D(
filter_size=filter_size,
padding=padding,
stride=stride,
channels=num_channels,
groups=groups,
filter_size_y=filter_size_y,
padding_y=padding_y,
stride_y=stride_y,
filter_size_z=filter_size_z,
padding_z=padding_z,
stride_z=stride_z),
**param_attr.attr),
active_type=act.name,
num_filters=num_filters,
bias=ParamAttr.to_bias(bias_attr),
shared_biases=shared_biases,
type=lt,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
lt,
parents=[input],
activation=act,
num_filters=num_filters,
size=l.config.size)
@wrap_name_default("scale_shift")
@wrap_param_attr_default()
@wrap_bias_attr_default()
......
python/paddle/trainer_config_helpers/networks.py 100755 → 100644
浏览文件 @ e9cc3282
文件模式从 100755 更改为 100644
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @ e9cc3282
......@@ -9,6 +9,7 @@ test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer
test_seq_slice_layer)
test_seq_slice_layer test_cross_entropy_over_beam test_pooling3D_layer
test_conv3d_layer test_deconv3d_layer)
export whole_configs=(test_split_datasource)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_conv3d_layer.protostr 0 → 100644
浏览文件 @ e9cc3282
type: "nn"
layers {
name: "data"
type: "data"
size: 36288
active_type: ""
height: 48
width: 42
depth: 6
}
layers {
name: "conv3d_1"
type: "conv3d"
size: 24192
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "_conv3d_1.w0"
conv_conf {
filter_size: 3
channels: 3
stride: 2
padding: 1
groups: 1
filter_channels: 3
output_x: 21
img_size: 42
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 2
output_y: 24
img_size_y: 48
filter_size_z: 3
padding_z: 1
stride_z: 2
output_z: 3
img_size_z: 6
}
}
bias_parameter_name: "_conv3d_1.wbias"
num_filters: 16
shared_biases: true
height: 24
width: 21
depth: 3
}
layers {
name: "conv3d_2"
type: "conv3d"
size: 24192
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "_conv3d_2.w0"
conv_conf {
filter_size: 3
channels: 3
stride: 2
padding: 1
groups: 1
filter_channels: 3
output_x: 21
img_size: 42
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 2
output_y: 24
img_size_y: 48
filter_size_z: 3
padding_z: 1
stride_z: 2
output_z: 3
img_size_z: 6
}
}
bias_parameter_name: "_conv3d_2.wbias"
num_filters: 16
shared_biases: true
height: 24
width: 21
depth: 3
}
parameters {
name: "_conv3d_1.w0"
size: 1296
initial_mean: 0.0
initial_std: 0.272165526976
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_conv3d_1.wbias"
size: 16
initial_mean: 0.0
initial_std: 0.0
dims: 16
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_conv3d_2.w0"
size: 1296
initial_mean: 0.0
initial_std: 0.272165526976
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_conv3d_2.wbias"
size: 16
initial_mean: 0.0
initial_std: 0.0
dims: 16
dims: 1
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "conv3d_2"
sub_models {
name: "root"
layer_names: "data"
layer_names: "conv3d_1"
layer_names: "conv3d_2"
input_layer_names: "data"
output_layer_names: "conv3d_2"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr
浏览文件 @ e9cc3282
......@@ -167,6 +167,20 @@ layers {
softmax_selfnorm_alpha: 0.1
coeff: 1.0
}
layers {
name: "__huber_regression_cost_0__"
type: "huber_regression"
size: 1
active_type: ""
inputs {
input_layer_name: "input"
}
inputs {
input_layer_name: "labels"
}
coeff: 1.0
delta: 1.0
}
layers {
name: "huber_probs"
type: "data"
......@@ -180,8 +194,8 @@ layers {
active_type: ""
}
layers {
name: "__huber_cost_0__"
type: "huber"
name: "__huber_classification_cost_0__"
type: "huber_classification"
size: 1
active_type: ""
inputs {
......@@ -300,7 +314,8 @@ output_layer_names: "__rank_cost_0__"
output_layer_names: "__lambda_cost_0__"
output_layer_names: "__cross_entropy_0__"
output_layer_names: "__cross_entropy_with_selfnorm_0__"
output_layer_names: "__huber_cost_0__"
output_layer_names: "__huber_regression_cost_0__"
output_layer_names: "__huber_classification_cost_0__"
output_layer_names: "__multi_binary_label_cross_entropy_0__"
output_layer_names: "__sum_cost_0__"
output_layer_names: "__nce_layer_0__"
......@@ -324,9 +339,10 @@ sub_models {
layer_names: "__lambda_cost_0__"
layer_names: "__cross_entropy_0__"
layer_names: "__cross_entropy_with_selfnorm_0__"
layer_names: "__huber_regression_cost_0__"
layer_names: "huber_probs"
layer_names: "huber_label"
layer_names: "__huber_cost_0__"
layer_names: "__huber_classification_cost_0__"
layer_names: "__multi_binary_label_cross_entropy_0__"
layer_names: "__sum_cost_0__"
layer_names: "__nce_layer_0__"
......@@ -349,7 +365,8 @@ sub_models {
output_layer_names: "__lambda_cost_0__"
output_layer_names: "__cross_entropy_0__"
output_layer_names: "__cross_entropy_with_selfnorm_0__"
output_layer_names: "__huber_cost_0__"
output_layer_names: "__huber_regression_cost_0__"
output_layer_names: "__huber_classification_cost_0__"
output_layer_names: "__multi_binary_label_cross_entropy_0__"
output_layer_names: "__sum_cost_0__"
output_layer_names: "__nce_layer_0__"
......
python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr 0 → 100644
浏览文件 @ e9cc3282
type: "nn"
layers {
name: "sentence_states"
type: "data"
size: 32
active_type: ""
}
layers {
name: "sentence_scores"
type: "data"
size: 1
active_type: ""
}
layers {
name: "__kmax_sequence_score_layer_0__"
type: "kmax_seq_score"
active_type: ""
inputs {
input_layer_name: "sentence_scores"
}
beam_size: 5
}
layers {
name: "__sub_nested_seq_layer_0__"
type: "sub_nested_seq"
size: 32
active_type: ""
inputs {
input_layer_name: "sentence_states"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_0__"
}
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 1
active_type: ""
inputs {
input_layer_name: "__sub_nested_seq_layer_0__"
input_parameter_name: "___fc_layer_0__.w0"
}
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "__kmax_sequence_score_layer_1__"
type: "kmax_seq_score"
active_type: ""
inputs {
input_layer_name: "sentence_scores"
}
beam_size: 5
}
layers {
name: "__seq_slice_layer_0__"
type: "seq_slice"
size: 32
active_type: ""
inputs {
input_layer_name: "__sub_nested_seq_layer_0__"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_1__"
}
select_first: true
}
layers {
name: "__fc_layer_1__"
type: "fc"
size: 1
active_type: ""
inputs {
input_layer_name: "__seq_slice_layer_0__"
input_parameter_name: "___fc_layer_1__.w0"
}
bias_parameter_name: "___fc_layer_1__.wbias"
}
layers {
name: "__kmax_sequence_score_layer_2__"
type: "kmax_seq_score"
active_type: ""
inputs {
input_layer_name: "__fc_layer_1__"
}
beam_size: 5
}
layers {
name: "sentences_ids"
type: "data"
size: 1
active_type: ""
}
layers {
name: "start_ids"
type: "data"
size: 1
active_type: ""
}
layers {
name: "end_ids"
type: "data"
size: 1
active_type: ""
}
layers {
name: "__cross_entropy_over_beam_0__"
type: "cross_entropy_over_beam"
active_type: ""
inputs {
input_layer_name: "sentence_scores"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_0__"
}
inputs {
input_layer_name: "sentences_ids"
}
inputs {
input_layer_name: "__fc_layer_0__"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_1__"
}
inputs {
input_layer_name: "start_ids"
}
inputs {
input_layer_name: "__fc_layer_1__"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_2__"
}
inputs {
input_layer_name: "end_ids"
}
}
parameters {
name: "___fc_layer_0__.w0"
size: 32
initial_mean: 0.0
initial_std: 0.176776695297
dims: 32
dims: 1
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_0__.wbias"
size: 1
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_1__.w0"
size: 32
initial_mean: 0.0
initial_std: 0.176776695297
dims: 32
dims: 1
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_1__.wbias"
size: 1
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: false
}
input_layer_names: "sentence_scores"
input_layer_names: "sentences_ids"
input_layer_names: "sentence_states"
input_layer_names: "start_ids"
input_layer_names: "end_ids"
output_layer_names: "__cross_entropy_over_beam_0__"
sub_models {
name: "root"
layer_names: "sentence_states"
layer_names: "sentence_scores"
layer_names: "__kmax_sequence_score_layer_0__"
layer_names: "__sub_nested_seq_layer_0__"
layer_names: "__fc_layer_0__"
layer_names: "__kmax_sequence_score_layer_1__"
layer_names: "__seq_slice_layer_0__"
layer_names: "__fc_layer_1__"
layer_names: "__kmax_sequence_score_layer_2__"
layer_names: "sentences_ids"
layer_names: "start_ids"
layer_names: "end_ids"
layer_names: "__cross_entropy_over_beam_0__"
input_layer_names: "sentence_scores"
input_layer_names: "sentences_ids"
input_layer_names: "sentence_states"
input_layer_names: "start_ids"
input_layer_names: "end_ids"
output_layer_names: "__cross_entropy_over_beam_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_deconv3d_layer.protostr 0 → 100644
浏览文件 @ e9cc3282
type: "nn"
layers {
name: "data"
type: "data"
size: 36288
active_type: ""
height: 48
width: 42
depth: 6
}
layers {
name: "deconv3d_1"
type: "deconv3d"
size: 1387760
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "_deconv3d_1.w0"
conv_conf {
filter_size: 3
channels: 3
stride: 2
padding: 1
groups: 1
filter_channels: 16
output_x: 42
img_size: 83
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 2
output_y: 48
img_size_y: 95
filter_size_z: 3
padding_z: 1
stride_z: 2
output_z: 6
img_size_z: 11
}
}
bias_parameter_name: "_deconv3d_1.wbias"
num_filters: 16
shared_biases: true
height: 95
width: 83
depth: 11
}
layers {
name: "deconv3d_2"
type: "deconv3d"
size: 1387760
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "_deconv3d_2.w0"
conv_conf {
filter_size: 3
channels: 3
stride: 2
padding: 1
groups: 1
filter_channels: 16
output_x: 42
img_size: 83
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 2
output_y: 48
img_size_y: 95
filter_size_z: 3
padding_z: 1
stride_z: 2
output_z: 6
img_size_z: 11
}
}
bias_parameter_name: "_deconv3d_2.wbias"
num_filters: 16
shared_biases: true
height: 95
width: 83
depth: 11
}
parameters {
name: "_deconv3d_1.w0"
size: 6912
initial_mean: 0.0
initial_std: 0.272165526976
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_deconv3d_1.wbias"
size: 16
initial_mean: 0.0
initial_std: 0.0
dims: 16
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_deconv3d_2.w0"
size: 6912
initial_mean: 0.0
initial_std: 0.272165526976
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_deconv3d_2.wbias"
size: 16
initial_mean: 0.0
initial_std: 0.0
dims: 16
dims: 1
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "deconv3d_2"
sub_models {
name: "root"
layer_names: "data"
layer_names: "deconv3d_1"
layer_names: "deconv3d_2"
input_layer_names: "data"
output_layer_names: "deconv3d_2"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_pooling3D_layer.protostr 0 → 100644
浏览文件 @ e9cc3282
type: "nn"
layers {
name: "data_2d"
type: "data"
size: 6000
active_type: ""
height: 20
width: 10
}
layers {
name: "pool___2d"
type: "pool"
size: 840
active_type: ""
inputs {
input_layer_name: "data_2d"
pool_conf {
pool_type: "avg-projection"
channels: 30
size_x: 5
stride: 3
output_x: 4
img_size: 10
padding: 1
size_y: 5
stride_y: 3
output_y: 7
img_size_y: 20
padding_y: 1
}
}
height: 7
width: 4
}
layers {
name: "data_3d_1"
type: "data"
size: 60000
active_type: ""
height: 20
width: 10
depth: 10
}
layers {
name: "pool_3d_1"
type: "pool3d"
size: 3360
active_type: ""
inputs {
input_layer_name: "data_3d_1"
pool_conf {
pool_type: "avg-projection"
channels: 30
size_x: 5
stride: 3
output_x: 4
img_size: 10
padding: 1
size_y: 5
stride_y: 3
output_y: 7
img_size_y: 20
padding_y: 1
size_z: 5
stride_z: 3
output_z: 4
img_size_z: 10
padding_z: 1
}
}
height: 7
width: 4
depth: 4
}
layers {
name: "pool_3d_2"
type: "pool3d"
size: 3360
active_type: ""
inputs {
input_layer_name: "data_3d_1"
pool_conf {
pool_type: "max-projection"
channels: 30
size_x: 5
stride: 3
output_x: 4
img_size: 10
padding: 1
size_y: 5
stride_y: 3
output_y: 7
img_size_y: 20
padding_y: 1
size_z: 5
stride_z: 3
output_z: 4
img_size_z: 10
padding_z: 1
}
}
height: 7
width: 4
depth: 4
}
input_layer_names: "data_2d"
output_layer_names: "pool___2d"
output_layer_names: "pool_3d_1"
output_layer_names: "pool_3d_2"
sub_models {
name: "root"
layer_names: "data_2d"
layer_names: "pool___2d"
layer_names: "data_3d_1"
layer_names: "pool_3d_1"
layer_names: "pool_3d_2"
input_layer_names: "data_2d"
output_layer_names: "pool___2d"
output_layer_names: "pool_3d_1"
output_layer_names: "pool_3d_2"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_conv3d_layer.py 0 → 100644
浏览文件 @ e9cc3282
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
num_channels = 3
filter_size = 3
filter_size_y = 3
filter_size_z = 3
stride = 2
stride_y = 2
stride_z = 2
padding = 1
padding_y = 1
padding_z = 1
groups = 1
data = data_layer(
name='data', size=12096 * num_channels, height=48, width=42, depth=6)
# first
conv3d_1 = img_conv3d_layer(
input=data,
name='conv3d_1',
num_filters=16,
num_channels=num_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
bias_attr=True,
shared_biases=True,
trans=False,
layer_type="conv3d",
act=LinearActivation())
# second
conv3d_2 = img_conv3d_layer(
input=data,
name='conv3d_2',
num_filters=16,
num_channels=num_channels,
filter_size=[filter_size, filter_size_y, filter_size_z],
stride=[stride, stride_y, stride_z],
padding=[padding, padding_y, padding_z],
groups=groups,
bias_attr=True,
shared_biases=True,
trans=False,
layer_type="conv3d",
act=LinearActivation())
outputs(conv3d_2)
python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py
浏览文件 @ e9cc3282
......@@ -33,7 +33,9 @@ outputs(
input=probs, label=xe_label),
cross_entropy_with_selfnorm(
input=probs, label=xe_label),
huber_cost(
huber_regression_cost(
input=seq_in, label=labels),
huber_classification_cost(
input=data_layer(
name='huber_probs', size=1),
label=data_layer(
......
python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py 0 → 100644
浏览文件 @ e9cc3282
#!/usr/bin/env python
#coding=utf-8
from paddle.trainer_config_helpers import *
beam_size = 5
# the first beam expansion.
sentence_states = data_layer(name="sentence_states", size=32)
sentence_scores = data_layer(name="sentence_scores", size=1)
topk_sentence_ids = kmax_sequence_score_layer(
input=sentence_scores, beam_size=beam_size)
# the second beam expansion.
topk_sen = sub_nested_seq_layer(
input=sentence_states, selected_indices=topk_sentence_ids)
start_pos_scores = fc_layer(input=topk_sen, size=1, act=LinearActivation())
topk_start_pos_ids = kmax_sequence_score_layer(
input=sentence_scores, beam_size=beam_size)
# the final beam expansion.
topk_start_spans = seq_slice_layer(
input=topk_sen, starts=topk_start_pos_ids, ends=None)
end_pos_scores = fc_layer(
input=topk_start_spans, size=1, act=LinearActivation())
topk_end_pos_ids = kmax_sequence_score_layer(
input=end_pos_scores, beam_size=beam_size)
# define the cost
sentence_idx = data_layer(name="sentences_ids", size=1)
start_idx = data_layer(name="start_ids", size=1)
end_idx = data_layer(name="end_ids", size=1)
cost = cross_entropy_over_beam(input=[
BeamInput(
candidate_scores=sentence_scores,
selected_candidates=topk_sentence_ids,
gold=sentence_idx), BeamInput(
candidate_scores=start_pos_scores,
selected_candidates=topk_start_pos_ids,
gold=start_idx), BeamInput(
candidate_scores=end_pos_scores,
selected_candidates=topk_end_pos_ids,
gold=end_idx)
])
outputs(cost)
python/paddle/trainer_config_helpers/tests/configs/test_deconv3d_layer.py 0 → 100644
浏览文件 @ e9cc3282
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
num_channels = 3
filter_size = 3
filter_size_y = 3
filter_size_z = 3
stride = 2
stride_y = 2
stride_z = 2
padding = 1
padding_y = 1
padding_z = 1
groups = 1
data = data_layer(
name='data', size=12096 * num_channels, height=48, width=42, depth=6)
# first
deconv3d_1 = img_conv3d_layer(
input=data,
name='deconv3d_1',
num_filters=16,
num_channels=num_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
bias_attr=True,
shared_biases=True,
trans=True,
layer_type="deconv3d",
act=LinearActivation())
# second
deconv3d_2 = img_conv3d_layer(
input=data,
name='deconv3d_2',
num_filters=16,
num_channels=num_channels,
filter_size=[filter_size, filter_size_y, filter_size_z],
stride=[stride, stride_y, stride_z],
padding=[padding, padding_y, padding_z],
groups=groups,
bias_attr=True,
shared_biases=True,
trans=True,
layer_type="deconv3d",
act=LinearActivation())
outputs(deconv3d_2)
python/paddle/trainer_config_helpers/tests/configs/test_pooling3D_layer.py 0 → 100644
浏览文件 @ e9cc3282
from paddle.trainer_config_helpers import *
settings(batch_size=100, learning_rate=1e-5)
data_2d = data_layer(name='data_2d', size=6000, height=20, width=10)
pool_2d = img_pool_layer(
name="pool___2d",
input=data_2d,
num_channels=30,
pool_size=5,
stride=3,
padding=1,
pool_type=AvgPooling())
outputs(pool_2d)
data_3d = data_layer(
name='data_3d_1', size=60000, depth=10, height=20, width=10)
pool_3d_1 = img_pool3d_layer(
name="pool_3d_1",
input=data_3d,
num_channels=30,
pool_size=5,
stride=3,
padding=1,
pool_type=AvgPooling())
outputs(pool_3d_1)
pool_3d_2 = img_pool3d_layer(
name="pool_3d_2",
input=data_3d,
num_channels=30,
pool_size=[5, 5, 5],
stride=[3, 3, 3],
padding=[1, 1, 1],
pool_type=MaxPooling())
outputs(pool_3d_2)
python/paddle/trainer_config_helpers/tests/layers_test.py
浏览文件 @ e9cc3282
......@@ -17,3 +17,4 @@ from paddle.trainer.config_parser import parse_config_and_serialize
if __name__ == '__main__':
parse_config_and_serialize(
'trainer_config_helpers/tests/layers_test_config.py', '')
# layers_test_config.py
python/paddle/v2/framework/tests/gradient_checker.py
浏览文件 @ e9cc3282
......@@ -268,7 +268,7 @@ class GradientChecker(unittest.TestCase):
:param input_vars: numpy value of input variable. The following
computation will use these variables.
:param inputs_to_check: inputs var names that should check gradient.
:param output_name: output name that used to
:param output_name: the output variable name of forward network.
:param max_relative_error: The relative tolerance parameter.
:param no_grad_set: used when create backward ops
:param only_cpu: only compute and check gradient on cpu kernel.
......
python/paddle/v2/inference.py
浏览文件 @ e9cc3282
......@@ -70,7 +70,7 @@ class Inference(object):
item = [each_result[each_field] for each_field in field]
yield item
def infer(self, input, field='value', **kwargs):
def infer(self, input, field='value', flatten_result=True, **kwargs):
"""
Infer a data by model.
:param input: input data batch. Should be python iterable object.
......@@ -83,7 +83,10 @@ class Inference(object):
retv = [[] for i in xrange(len(result))]
for i, item in enumerate(result):
retv[i].append(item)
retv = [numpy.concatenate(out) for out in retv]
if flatten_result:
retv = [numpy.concatenate(out) for out in retv]
if len(retv) == 1:
return retv[0]
else:
......
python/paddle/v2/tests/test_layer.py
浏览文件 @ e9cc3282
......@@ -141,12 +141,13 @@ class CostLayerTest(unittest.TestCase):
cost8 = layer.rank_cost(left=score, right=score, label=score)
cost9 = layer.lambda_cost(input=inference, score=score)
cost10 = layer.sum_cost(input=inference)
cost11 = layer.huber_cost(input=score, label=label)
cost11 = layer.huber_regression_cost(input=score, label=label)
cost12 = layer.huber_classification_cost(input=score, label=label)
print layer.parse_network([cost1, cost2])
print layer.parse_network([cost3, cost4])
print layer.parse_network([cost5, cost6])
print layer.parse_network([cost7, cost8, cost9, cost10, cost11])
print layer.parse_network([cost7, cost8, cost9, cost10, cost11, cost12])
crf = layer.crf(input=inference, label=label)
crf_decoding = layer.crf_decoding(input=inference, size=3)
......
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