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提交 13ec6f99 编写于 作者: W wangmeng28
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Merge remote-tracking branch 'upstream/develop' into factorization_machine_layer

上级 6fed6f20 d5be1d4d
隐藏空白更改
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Showing with 872 addition and 392 deletion
benchmark/paddle/image/googlenet.py
浏览文件 @ 13ec6f99
......@@ -5,6 +5,7 @@ height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128)
use_gpu = get_config_arg('use_gpu', bool, True)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
define_py_data_sources2(
......@@ -16,6 +17,8 @@ settings(
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
conv_projection = conv_projection if use_gpu else img_conv_layer
def inception2(name, input, channels, \
filter1,
filter3R, filter3,
......@@ -138,7 +141,7 @@ def inception(name, input, channels, \
cat = concat_layer(
name=name,
input=[cov1, cov3, cov5, covprj],
bias_attr=True,
bias_attr=True if use_gpu else False,
act=ReluActivation())
return cat
......
benchmark/paddle/image/run_mkldnn.sh
浏览文件 @ 13ec6f99
......@@ -40,6 +40,7 @@ fi
for use_mkldnn in True False; do
for batchsize in 64 128 256; do
train vgg 19 $batchsize $use_mkldnn
train resnet 50 $batchsize $use_mkldnn
train resnet 50 $batchsize $use_mkldnn
train googlenet v1 $batchsize $use_mkldnn
done
done
paddle/gserver/activations/ActivationFunction.cpp
浏览文件 @ 13ec6f99
......@@ -212,6 +212,37 @@ Error __must_check backward(Argument& act) {
}
END_DEFINE_ACTIVATION(sequence_softmax)
/*
* @brief SoftSign Activation.
* \f[
* f(z) = \frac{z}{1 + |z|}
* \f]
*/
BEGIN_DEFINE_ACTIVATION(softsign)
private:
MatrixPtr denominator_;
Error __must_check forward(Argument& act) {
size_t height = act.value->getHeight();
size_t width = act.value->getWidth();
Matrix::resizeOrCreate(
denominator_, height, width, false, useGpu(act.deviceId));
denominator_->assign(*act.value);
denominator_->abs2();
denominator_->add(1.);
act.value->dotDiv(*act.value, *denominator_);
return Error();
}
Error __must_check backward(Argument& act) {
denominator_->square2();
denominator_->scalarDiv(*denominator_, 1.);
act.grad->dotMul(*act.grad, *denominator_);
return Error();
}
END_DEFINE_ACTIVATION(softsign)
/**
* @brief Relu Activation.
* forward. y = max(0, z)
......
paddle/operators/conv_cudnn_op.cc
浏览文件 @ 13ec6f99
......@@ -40,7 +40,8 @@ REGISTER_OP(conv_cudnn, ops::ConvOp, ops::CudnnConvOpMaker, conv_cudnn_grad,
ops::ConvOpGrad);
REGISTER_OP_CPU_KERNEL(conv_cudnn,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv_cudnn_grad,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
conv_cudnn_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/conv_cudnn_op.cu.cc
浏览文件 @ 13ec6f99
......@@ -259,6 +259,8 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
} // namespace operators
} // namespace paddle
REGISTER_OP_GPU_KERNEL(conv_cudnn, paddle::operators::CudnnConvOpKernel<float>);
REGISTER_OP_GPU_KERNEL(conv_cudnn, paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>);
paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
paddle/operators/conv_transpose_cudnn_op.cc
浏览文件 @ 13ec6f99
......@@ -61,10 +61,12 @@ REGISTER_OP(conv2d_transpose_cudnn, ops::ConvTransposeOp,
REGISTER_OP_CPU_KERNEL(
conv2d_transpose_cudnn,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_transpose_cudnn_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
ops::CudnnConv3DTransposeOpMaker, conv3d_transpose_cudnn_grad,
......@@ -72,7 +74,9 @@ REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
REGISTER_OP_CPU_KERNEL(
conv3d_transpose_cudnn,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv3d_transpose_cudnn_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/conv_transpose_cudnn_op.cu.cc
浏览文件 @ 13ec6f99
......@@ -235,11 +235,15 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn,
ops::CudnnConvTransposeOpKernel<float>);
ops::CudnnConvTransposeOpKernel<float>,
ops::CudnnConvTransposeOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn_grad,
ops::CudnnConvTransposeGradOpKernel<float>);
ops::CudnnConvTransposeGradOpKernel<float>,
ops::CudnnConvTransposeGradOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn,
ops::CudnnConvTransposeOpKernel<float>);
ops::CudnnConvTransposeOpKernel<float>,
ops::CudnnConvTransposeOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn_grad,
ops::CudnnConvTransposeGradOpKernel<float>);
ops::CudnnConvTransposeGradOpKernel<float>,
ops::CudnnConvTransposeGradOpKernel<double>);
paddle/operators/math/pooling.cc
浏览文件 @ 13ec6f99
......@@ -498,8 +498,8 @@ template class Pool3dGradFunctor<
* Ksize, strides, paddings are two elements. These two elements represent
* height and width, respectively.
*/
template <typename T>
class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
template <typename T1, typename T2>
class MaxPool2dWithIndexFunctor<platform::CPUPlace, T1, T2> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, std::vector<int>& ksize,
......@@ -520,9 +520,9 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
const int input_stride = input_height * input_width;
const int output_stride = output_height * output_width;
const T* input_data = input.data<T>();
T* output_data = output->mutable_data<T>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace());
const T1* input_data = input.data<T1>();
T1* output_data = output->mutable_data<T1>(context.GetPlace());
T2* mask_data = mask->mutable_data<T2>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -535,7 +535,7 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
int wend = std::min(wstart + ksize_width, input_width);
wstart = std::max(wstart, 0);
T ele = static_cast<T>(-FLT_MAX);
T1 ele = static_cast<T1>(-FLT_MAX);
int index = -1;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
......@@ -563,8 +563,8 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
* Ksize, strides, paddings are two elements. These two elements represent
* height and width, respectively.
*/
template <typename T>
class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> {
template <typename T1, typename T2>
class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T1, T2> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& output_grad,
......@@ -580,9 +580,9 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> {
const int input_stride = input_height * input_width;
const int output_stride = output_height * output_width;
const T* mask_data = mask.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
const T2* mask_data = mask.data<T2>();
const T1* output_grad_data = output_grad.data<T1>();
T1* input_grad_data = input_grad->mutable_data<T1>(context.GetPlace());
for (int n = 0; n < batch_size; ++n) {
for (int c = 0; c < output_channels; ++c) {
......@@ -602,18 +602,18 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> {
}
};
template class MaxPool2dWithIndexFunctor<platform::CPUPlace, float>;
template class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, float>;
template class MaxPool2dWithIndexFunctor<platform::CPUPlace, double>;
template class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, double>;
template class MaxPool2dWithIndexFunctor<platform::CPUPlace, float, int>;
template class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, float, int>;
template class MaxPool2dWithIndexFunctor<platform::CPUPlace, double, int>;
template class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, double, int>;
/*
* All tensors are in NCDHW format.
* Ksize, strides, paddings are three elements. These three elements represent
* depth, height and width, respectively.
*/
template <typename T>
class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
template <typename T1, typename T2>
class MaxPool3dWithIndexFunctor<platform::CPUPlace, T1, T2> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, std::vector<int>& ksize,
......@@ -639,9 +639,9 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
const int input_stride = input_depth * input_height * input_width;
const int output_stride = output_depth * output_height * output_width;
const T* input_data = input.data<T>();
T* output_data = output->mutable_data<T>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace());
const T1* input_data = input.data<T1>();
T1* output_data = output->mutable_data<T1>(context.GetPlace());
T2* mask_data = mask->mutable_data<T2>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -659,7 +659,7 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
wstart = std::max(wstart, 0);
int output_idx = (pd * output_height + ph) * output_width + pw;
T ele = static_cast<T>(-FLT_MAX);
T1 ele = static_cast<T1>(-FLT_MAX);
int index = -1;
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
......@@ -691,8 +691,8 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
* Ksize, strides, paddings are three elements. These three elements represent
* depth, height and width, respectively.
*/
template <typename T>
class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> {
template <typename T1, typename T2>
class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T1, T2> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& output_grad,
......@@ -710,9 +710,9 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> {
const int input_stride = input_depth * input_height * input_width;
const int output_stride = output_depth * output_height * output_width;
const T* mask_data = mask.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
const T2* mask_data = mask.data<T2>();
const T1* output_grad_data = output_grad.data<T1>();
T1* input_grad_data = input_grad->mutable_data<T1>(context.GetPlace());
for (int n = 0; n < batch_size; ++n) {
for (int c = 0; c < output_channels; ++c) {
......@@ -735,10 +735,10 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> {
}
};
template class MaxPool3dWithIndexFunctor<platform::CPUPlace, float>;
template class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, float>;
template class MaxPool3dWithIndexFunctor<platform::CPUPlace, double>;
template class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, double>;
template class MaxPool3dWithIndexFunctor<platform::CPUPlace, float, int>;
template class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, float, int>;
template class MaxPool3dWithIndexFunctor<platform::CPUPlace, double, int>;
template class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, double, int>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/pooling.cu
浏览文件 @ 13ec6f99
......@@ -658,13 +658,13 @@ template class Pool3dGradFunctor<
template class Pool3dGradFunctor<
platform::GPUPlace, paddle::operators::math::AvgPoolGrad<double>, double>;
template <typename T>
template <typename T1, typename T2>
__global__ void KernelMaxPool2dWithIdx(
const int nthreads, const T* input_data, const int channels,
const int nthreads, const T1* input_data, const int channels,
const int input_height, const int input_width, const int output_height,
const int output_width, const int ksize_height, const int ksize_width,
const int stride_height, const int stride_width, const int padding_height,
const int padding_width, T* output_data, T* mask_data) {
const int padding_width, T1* output_data, T2* mask_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -681,7 +681,7 @@ __global__ void KernelMaxPool2dWithIdx(
wstart = max(wstart, 0);
input_data += (batch_idx * channels + c) * input_height * input_width;
T ele = -FLT_MAX;
T1 ele = -FLT_MAX;
int max_index = -1;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
......@@ -697,13 +697,13 @@ __global__ void KernelMaxPool2dWithIdx(
}
}
template <typename T>
template <typename T1, typename T2>
__global__ void KernelMaxPool2DWithIdxGrad(
const int nthreads, const T* output_grad, const T* mask_data,
const int nthreads, const T1* output_grad, const T2* mask_data,
const int channels, const int input_height, const int input_width,
const int output_height, const int output_width, const int ksize_height,
const int ksize_width, const int stride_height, const int stride_width,
const int padding_height, const int padding_width, T* input_grad) {
const int padding_height, const int padding_width, T1* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int w_offset = index % input_width;
......@@ -724,7 +724,7 @@ __global__ void KernelMaxPool2DWithIdxGrad(
int pw_end =
min((w_offset + padding_width) / stride_width + 1, output_width);
T gradient = 0;
T1 gradient = 0;
int input_current_featuremap_idx = h_offset * input_width + w_offset;
int output_idx =
(batch_idx * channels + c_offset) * output_height * output_width;
......@@ -746,8 +746,8 @@ __global__ void KernelMaxPool2DWithIdxGrad(
* Ksize, strides, paddings are two elements. These two elements represent
* height and width, respectively.
*/
template <typename T>
class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
template <typename T1, typename T2>
class MaxPool2dWithIndexFunctor<platform::GPUPlace, T1, T2> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, std::vector<int>& ksize,
......@@ -767,9 +767,9 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
const int padding_height = paddings[0];
const int padding_width = paddings[1];
const T* input_data = input.data<T>();
T* output_data = output->mutable_data<T>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace());
const T1* input_data = input.data<T1>();
T1* output_data = output->mutable_data<T1>(context.GetPlace());
T2* mask_data = mask->mutable_data<T2>(context.GetPlace());
int nthreads = batch_size * output_channels * output_height * output_width;
int blocks = (nthreads + 1024 - 1) / 1024;
......@@ -777,9 +777,9 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
dim3 grid(blocks, 1);
KernelMaxPool2dWithIdx<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
T1, T2><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_data, input_channels, input_height, input_width,
output_height, output_width, ksize_height, ksize_width, stride_height,
stride_width, padding_height, padding_width, output_data, mask_data);
......@@ -791,8 +791,8 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
* Ksize, strides, paddings are two elements. These two elements represent
* height and width, respectively.
*/
template <typename T>
class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> {
template <typename T1, typename T2>
class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T1, T2> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& output_grad,
......@@ -812,9 +812,9 @@ class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> {
const int padding_height = paddings[0];
const int padding_width = paddings[1];
const T* mask_data = mask.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
const T2* mask_data = mask.data<T2>();
const T1* output_grad_data = output_grad.data<T1>();
T1* input_grad_data = input_grad->mutable_data<T1>(context.GetPlace());
int nthreads = batch_size * input_channels * input_height * input_width;
int blocks = (nthreads + 1024 - 1) / 1024;
......@@ -822,30 +822,30 @@ class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> {
dim3 grid(blocks, 1);
KernelMaxPool2DWithIdxGrad<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(nthreads, output_grad_data, mask_data,
input_channels, input_height, input_width,
output_height, output_width, ksize_height,
ksize_width, stride_height, stride_width,
padding_height, padding_width, input_grad_data);
T1, T2><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, output_grad_data, mask_data, input_channels, input_height,
input_width, output_height, output_width, ksize_height, ksize_width,
stride_height, stride_width, padding_height, padding_width,
input_grad_data);
}
};
template class MaxPool2dWithIndexFunctor<platform::GPUPlace, float>;
template class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, float>;
template class MaxPool2dWithIndexFunctor<platform::GPUPlace, double>;
template class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, double>;
template class MaxPool2dWithIndexFunctor<platform::GPUPlace, float, int>;
template class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, float, int>;
template class MaxPool2dWithIndexFunctor<platform::GPUPlace, double, int>;
template class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, double, int>;
template <typename T>
template <typename T1, typename T2>
__global__ void KernelMaxPool3DWithIdx(
const int nthreads, const T* input_data, const int channels,
const int nthreads, const T1* input_data, const int channels,
const int input_depth, const int input_height, const int input_width,
const int output_depth, const int output_height, const int output_width,
const int ksize_depth, const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height, const int stride_width,
const int padding_depth, const int padding_height, const int padding_width,
T* output_data, T* mask_data) {
T1* output_data, T2* mask_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -865,7 +865,7 @@ __global__ void KernelMaxPool3DWithIdx(
hstart = max(hstart, 0);
wstart = max(wstart, 0);
T ele = -FLT_MAX;
T1 ele = -FLT_MAX;
int max_index = -1;
input_data +=
(batch_idx * channels + c) * input_depth * input_height * input_width;
......@@ -885,15 +885,15 @@ __global__ void KernelMaxPool3DWithIdx(
}
}
template <typename T>
template <typename T1, typename T2>
__global__ void KernelMaxPool3DWithIdxGrad(
const int nthreads, const T* output_grad, const T* mask, const int channels,
const int input_depth, const int input_height, const int input_width,
const int output_depth, const int output_height, const int output_width,
const int ksize_depth, const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height, const int stride_width,
const int padding_depth, const int padding_height, const int padding_width,
T* input_grad) {
const int nthreads, const T1* output_grad, const T2* mask,
const int channels, const int input_depth, const int input_height,
const int input_width, const int output_depth, const int output_height,
const int output_width, const int ksize_depth, const int ksize_height,
const int ksize_width, const int stride_depth, const int stride_height,
const int stride_width, const int padding_depth, const int padding_height,
const int padding_width, T1* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int w_offset = index % input_width;
......@@ -922,7 +922,7 @@ __global__ void KernelMaxPool3DWithIdxGrad(
int pw_end =
min((w_offset + padding_width) / stride_width + 1, output_width);
T gradient = 0;
T1 gradient = 0;
int input_current_feature_map_idx =
(d_offset * input_height + h_offset) * input_width + w_offset;
int output_idx = (batch_idx * channels + c_offset) * output_depth *
......@@ -949,8 +949,8 @@ __global__ void KernelMaxPool3DWithIdxGrad(
* Ksize, strides, paddings are three elements. These three elements represent
* depth, height and width, respectively.
*/
template <typename T>
class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
template <typename T1, typename T2>
class MaxPool3dWithIndexFunctor<platform::GPUPlace, T1, T2> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, std::vector<int>& ksize,
......@@ -975,9 +975,9 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
const int padding_height = paddings[1];
const int padding_width = paddings[2];
const T* input_data = input.data<T>();
T* output_data = output->mutable_data<T>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace());
const T1* input_data = input.data<T1>();
T1* output_data = output->mutable_data<T1>(context.GetPlace());
T2* mask_data = mask->mutable_data<T2>(context.GetPlace());
int nthreads = batch_size * output_channels * output_depth * output_height *
output_width;
......@@ -986,9 +986,9 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
dim3 grid(blocks, 1);
KernelMaxPool3DWithIdx<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
T1, T2><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_data, input_channels, input_depth, input_height,
input_width, output_depth, output_height, output_width, ksize_depth,
ksize_height, ksize_width, stride_depth, stride_height, stride_width,
......@@ -1001,8 +1001,8 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
* Ksize, strides, paddings are three elements. These three elements represent
* depth, height and width, respectively.
*/
template <typename T>
class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
template <typename T1, typename T2>
class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T1, T2> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& output_grad,
......@@ -1027,9 +1027,9 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
const int padding_height = paddings[1];
const int padding_width = paddings[2];
const T* output_grad_data = output_grad.data<T>();
const T* mask_data = mask.data<T>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
const T1* output_grad_data = output_grad.data<T1>();
const T2* mask_data = mask.data<T2>();
T1* input_grad_data = input_grad->mutable_data<T1>(context.GetPlace());
int nthreads =
batch_size * input_channels * input_depth * input_height * input_width;
......@@ -1038,9 +1038,9 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
dim3 grid(blocks, 1);
KernelMaxPool3DWithIdxGrad<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
T1, T2><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, output_grad_data, mask_data, input_channels, input_depth,
input_height, input_width, output_depth, output_height, output_width,
ksize_depth, ksize_height, ksize_width, stride_depth, stride_height,
......@@ -1049,10 +1049,10 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
}
};
template class MaxPool3dWithIndexFunctor<platform::GPUPlace, float>;
template class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, float>;
template class MaxPool3dWithIndexFunctor<platform::GPUPlace, double>;
template class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, double>;
template class MaxPool3dWithIndexFunctor<platform::GPUPlace, float, int>;
template class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, float, int>;
template class MaxPool3dWithIndexFunctor<platform::GPUPlace, double, int>;
template class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, double, int>;
} // namespace math
} // namespace operators
......
paddle/operators/math/pooling.h
浏览文件 @ 13ec6f99
......@@ -153,7 +153,7 @@ class MaxPool3dGradFunctor {
* In pool2d, all tensors are in NCHW format. In pool3d, all tensors are in
* NCDHW format.
*/
template <typename Place, typename T>
template <typename Place, typename T1, typename T2>
class MaxPool2dWithIndexFunctor {
public:
void operator()(const platform::DeviceContext& context,
......@@ -162,7 +162,7 @@ class MaxPool2dWithIndexFunctor {
framework::Tensor* output, framework::Tensor* mask);
};
template <typename Place, typename T>
template <typename Place, typename T1, typename T2>
class MaxPool2dWithIndexGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
......@@ -172,7 +172,7 @@ class MaxPool2dWithIndexGradFunctor {
framework::Tensor* input_grad);
};
template <typename Place, typename T>
template <typename Place, typename T1, typename T2>
class MaxPool3dWithIndexFunctor {
public:
void operator()(const platform::DeviceContext& context,
......@@ -181,7 +181,7 @@ class MaxPool3dWithIndexFunctor {
framework::Tensor* output, framework::Tensor* mask);
};
template <typename Place, typename T>
template <typename Place, typename T1, typename T2>
class MaxPool3dWithIndexGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
......
paddle/operators/pool_cudnn_op.cc
浏览文件 @ 13ec6f99
......@@ -20,6 +20,18 @@ REGISTER_OP(pool2d_cudnn, ops::PoolOp, ops::Pool2dOpMaker, pool2d_cudnn_grad,
ops::PoolOpGrad);
REGISTER_OP_CPU_KERNEL(pool2d_cudnn,
ops::PoolKernel<paddle::platform::CPUPlace, float>);
ops::PoolKernel<paddle::platform::CPUPlace, float>,
ops::PoolKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(pool2d_cudnn_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>)
ops::PoolGradKernel<paddle::platform::CPUPlace, float>,
ops::PoolGradKernel<paddle::platform::CPUPlace, double>)
REGISTER_OP(pool3d_cudnn, ops::PoolOp, ops::Pool3dOpMaker, pool3d_cudnn_grad,
ops::PoolOpGrad);
REGISTER_OP_CPU_KERNEL(pool3d_cudnn,
ops::PoolKernel<paddle::platform::CPUPlace, float>,
ops::PoolKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(pool3d_cudnn_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>,
ops::PoolGradKernel<paddle::platform::CPUPlace, double>)
paddle/operators/pool_cudnn_op.cu.cc
浏览文件 @ 13ec6f99
......@@ -52,7 +52,13 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> {
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_desc;
ScopedPoolingDescriptor pool_desc;
DataLayout layout = DataLayout::kNCHW;
DataLayout layout;
if (strides.size() == 2U) {
layout = DataLayout::kNCHW;
} else {
layout = DataLayout::kNCDHW;
}
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()));
......@@ -112,7 +118,13 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_desc;
ScopedPoolingDescriptor pool_desc;
DataLayout layout = DataLayout::kNCHW;
DataLayout layout;
if (strides.size() == 2U) {
layout = DataLayout::kNCHW;
} else {
layout = DataLayout::kNCDHW;
}
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()));
......@@ -150,5 +162,12 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(pool2d_cudnn, ops::PoolCudnnOpKernel<float>);
REGISTER_OP_GPU_KERNEL(pool2d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>);
REGISTER_OP_GPU_KERNEL(pool2d_cudnn, ops::PoolCudnnOpKernel<float>,
ops::PoolCudnnOpKernel<double>);
REGISTER_OP_GPU_KERNEL(pool2d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>,
ops::PoolCudnnGradOpKernel<double>);
REGISTER_OP_GPU_KERNEL(pool3d_cudnn, ops::PoolCudnnOpKernel<float>,
ops::PoolCudnnOpKernel<double>);
REGISTER_OP_GPU_KERNEL(pool3d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>,
ops::PoolCudnnGradOpKernel<double>);
paddle/operators/pool_op.cc
浏览文件 @ 13ec6f99
......@@ -217,14 +217,18 @@ REGISTER_OP(pool2d, ops::PoolOp, ops::Pool2dOpMaker, pool2d_grad,
ops::PoolOpGrad);
REGISTER_OP_CPU_KERNEL(pool2d,
ops::PoolKernel<paddle::platform::CPUPlace, float>);
ops::PoolKernel<paddle::platform::CPUPlace, float>,
ops::PoolKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(pool2d_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>)
ops::PoolGradKernel<paddle::platform::CPUPlace, float>,
ops::PoolGradKernel<paddle::platform::CPUPlace, double>)
REGISTER_OP(pool3d, ops::PoolOp, ops::Pool3dOpMaker, pool3d_grad,
ops::PoolOpGrad);
REGISTER_OP_CPU_KERNEL(pool3d,
ops::PoolKernel<paddle::platform::CPUPlace, float>);
ops::PoolKernel<paddle::platform::CPUPlace, float>,
ops::PoolKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(pool3d_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>);
ops::PoolGradKernel<paddle::platform::CPUPlace, float>,
ops::PoolGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/pool_op.cu.cc
浏览文件 @ 13ec6f99
......@@ -17,11 +17,15 @@ limitations under the License. */
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(pool2d,
ops::PoolKernel<paddle::platform::GPUPlace, float>);
ops::PoolKernel<paddle::platform::GPUPlace, float>,
ops::PoolKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(pool2d_grad,
ops::PoolGradKernel<paddle::platform::GPUPlace, float>);
ops::PoolGradKernel<paddle::platform::GPUPlace, float>,
ops::PoolGradKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(pool3d,
ops::PoolKernel<paddle::platform::GPUPlace, float>);
ops::PoolKernel<paddle::platform::GPUPlace, float>,
ops::PoolKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(pool3d_grad,
ops::PoolGradKernel<paddle::platform::GPUPlace, float>);
ops::PoolGradKernel<paddle::platform::GPUPlace, float>,
ops::PoolGradKernel<paddle::platform::GPUPlace, double>);
paddle/operators/pool_with_index_op.cc
浏览文件 @ 13ec6f99
......@@ -29,11 +29,11 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"X(Input) of Pooling should not be null.");
"Input(X) of Pooling should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Out(Output) of Pooling should not be null.");
"Output(Out) of Pooling should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Mask"),
"Mask(Output) of Pooling should not be null.");
"Output(Mask) of Pooling should not be null.");
auto in_x_dims = ctx->GetInputDim("X");
......@@ -67,6 +67,14 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
ctx->SetOutputDim("Mask", framework::make_ddim(output_shape));
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
};
class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
......@@ -80,6 +88,14 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
"Input(X@GRAD) should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
};
class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
......@@ -116,7 +132,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"global_pooling",
"(bool, default false) Whether to use the global pooling. "
"(bool, default:false) Whether to use the global pooling. "
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
......@@ -126,7 +142,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, defalut {0, 0}), paddings(height, width) of pooling "
"(vector<int>, defalut:{0, 0}), paddings(height, width) of pooling "
"operator. "
"If global_pooling = true, paddings and will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
......@@ -250,10 +266,12 @@ REGISTER_OP(max_pool2d_with_index, ops::MaxPoolWithIndexOp,
REGISTER_OP_CPU_KERNEL(
max_pool2d_with_index,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float>);
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float, int>,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, double, int>);
REGISTER_OP_CPU_KERNEL(
max_pool2d_with_index_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float>)
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float, int>,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, double, int>)
REGISTER_OP(max_pool3d_with_index, ops::MaxPoolWithIndexOp,
ops::MaxPool3dWithIndexOpMaker, max_pool3d_with_index_grad,
......@@ -261,7 +279,9 @@ REGISTER_OP(max_pool3d_with_index, ops::MaxPoolWithIndexOp,
REGISTER_OP_CPU_KERNEL(
max_pool3d_with_index,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float>);
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float, int>,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, double, int>);
REGISTER_OP_CPU_KERNEL(
max_pool3d_with_index_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float>)
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float, int>,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, double, int>)
paddle/operators/pool_with_index_op.cu.cc
浏览文件 @ 13ec6f99
......@@ -18,14 +18,18 @@ namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
max_pool2d_with_index,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float>);
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float, int>,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, double, int>);
REGISTER_OP_GPU_KERNEL(
max_pool2d_with_index_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float>)
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float, int>,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, double, int>)
REGISTER_OP_GPU_KERNEL(
max_pool3d_with_index,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float>);
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float, int>,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, double, int>);
REGISTER_OP_GPU_KERNEL(
max_pool3d_with_index_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float>)
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float, int>,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, double, int>)
paddle/operators/pool_with_index_op.h
浏览文件 @ 13ec6f99
......@@ -24,8 +24,8 @@ namespace operators {
using Tensor = framework::Tensor;
template <typename Place, typename T>
class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
template <typename Place, typename T1, typename T2>
class MaxPoolWithIndexKernel : public framework::OpKernel<T1> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* in_x = context.Input<Tensor>("X");
......@@ -44,13 +44,13 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
switch (ksize.size()) {
case 2: {
paddle::operators::math::MaxPool2dWithIndexFunctor<Place, T>
paddle::operators::math::MaxPool2dWithIndexFunctor<Place, T1, T2>
pool2d_forward;
pool2d_forward(context.device_context(), *in_x, ksize, strides,
paddings, out, mask);
} break;
case 3: {
paddle::operators::math::MaxPool3dWithIndexFunctor<Place, T>
paddle::operators::math::MaxPool3dWithIndexFunctor<Place, T1, T2>
pool3d_forward;
pool3d_forward(context.device_context(), *in_x, ksize, strides,
paddings, out, mask);
......@@ -60,8 +60,8 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
}
};
template <typename Place, typename T>
class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
template <typename Place, typename T1, typename T2>
class MaxPoolWithIndexGradKernel : public framework::OpKernel<T1> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* mask = context.Input<Tensor>("Mask");
......@@ -80,19 +80,19 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
}
if (in_x_grad) {
in_x_grad->mutable_data<T>(context.GetPlace());
in_x_grad->mutable_data<T1>(context.GetPlace());
auto& device_ctx = context.device_context();
math::set_constant(device_ctx, in_x_grad, 0);
switch (ksize.size()) {
case 2: {
paddle::operators::math::MaxPool2dWithIndexGradFunctor<Place, T>
paddle::operators::math::MaxPool2dWithIndexGradFunctor<Place, T1, T2>
pool2d_backward;
pool2d_backward(device_ctx, *out_grad, *mask, ksize, strides,
paddings, in_x_grad);
} break;
case 3: {
paddle::operators::math::MaxPool3dWithIndexGradFunctor<Place, T>
paddle::operators::math::MaxPool3dWithIndexGradFunctor<Place, T1, T2>
pool3d_backward;
pool3d_backward(device_ctx, *out_grad, *mask, ksize, strides,
paddings, in_x_grad);
......
paddle/operators/sequence_slice_op.cc 0 → 100755
浏览文件 @ 13ec6f99
/* 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 "paddle/operators/sequence_slice_op.h"
namespace paddle {
namespace operators {
class SequenceSliceOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceSliceOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Offset"),
"Input(Offset) of SequenceSliceOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Length"),
"Input(Length) of SequenceSliceOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceSliceOp should not be null.");
auto input_dims = ctx->GetInputDim("X");
auto offset_dim = ctx->GetInputDim("Offset");
auto length_dim = ctx->GetInputDim("Length");
PADDLE_ENFORCE_EQ(
offset_dim.size(), 2UL,
"Only support one level sequence now, The rank of offset must be 2.");
PADDLE_ENFORCE_EQ(
length_dim.size(), 2UL,
"Only support one level sequence now, The rank of Length must be 2.");
// Initialize the output's dims to maximum,
// and re-set to real dims by the value of Offset and Length at kernel
ctx->SetOutputDim("Out", input_dims);
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
class SequenceSliceGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"The gradient of Out should not be null.");
PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
"The gradient of X should not be null.");
ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
class SequenceSliceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceSliceOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor), "
"the input of SequenceSliceOp.");
AddInput("Offset",
"(Tensor), "
"a vector<int> to describe the offset of every input sequence for "
"sub sequence item.");
AddInput("Length",
"(Tensor), "
"a vector<int> to describe the length of every input sequence for "
"sub sequence item.");
AddOutput("Out",
"(LoDTensor), the output of SequenceSliceOp.");
AddComment(R"DOC(
Sequence slice operator
The operator crops a subsequence from given sequence with given start offset and subsequence length.
It only supports sequence (LoD Tensor with level number is 1).
- Case:
X = [[a1, a2;
b1, b2;
c1, c2]
[d1, d2;
e1, e2]]
LoD(X) = {{0, 3, 5}}; Dims(X) = (5, 2)
Offset = [[0], [1]]; Length = [[2], [1]]
Out = [[a1, a2;
b1, b2]
[e1, e2]]
LoD(Out) = {{0, 2, 3}}; Dims(Out) = (3, 2)
NOTE: The first dimension size of input, the size of offset and Length, should be equal. The offset start from 0.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sequence_slice, ops::SequenceSliceOp, ops::SequenceSliceOpMaker,
sequence_slice_grad, ops::SequenceSliceGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_slice,
ops::SequenceSliceOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
sequence_slice_grad,
ops::SequenceSliceGradOpKernel<paddle::platform::CPUPlace, float>);
paddle/operators/sequence_slice_op.cu 0 → 100755
浏览文件 @ 13ec6f99
/* 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 "paddle/operators/sequence_slice_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
sequence_slice,
ops::SequenceSliceOpKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
sequence_slice_grad,
ops::SequenceSliceGradOpKernel<paddle::platform::GPUPlace, float>);
paddle/operators/sequence_slice_op.h 0 → 100755
浏览文件 @ 13ec6f99
/* 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 "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/strided_memcpy.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using LoD = framework::LoD;
template <typename T>
inline LoD SequenceSliceLoD(const T& in, const int64_t* offset_data,
const int64_t* length_data) {
auto out_lod = in.lod();
size_t lod_offset = 0;
auto n = in.lod()[0].size() - 1;
out_lod[0][0] = 0;
for (size_t i = 0; i < n; ++i) {
lod_offset += length_data[i];
out_lod[0][i+1] = lod_offset;
}
return out_lod;
}
template <typename Place, typename T>
class SequenceSliceOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<LoDTensor>("X");
auto* offset = ctx.Input<Tensor>("Offset");
auto* length = ctx.Input<Tensor>("Length");
auto* out = ctx.Output<LoDTensor>("Out");
auto lod = in->lod();
auto n = lod[0].size() - 1;
PADDLE_ENFORCE_EQ(lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(
n, static_cast<size_t>(length->dims()[0]),
"The size of input-sequence and length-array should be the same")
PADDLE_ENFORCE_EQ(
n, static_cast<size_t>(offset->dims()[0]),
"The size of input-sequence and offset-array should be the same")
const int64_t* offset_data = offset->data<int64_t>();
const int64_t* length_data = length->data<int64_t>();
framework::Tensor offset_cpu;
framework::Tensor length_cpu;
if (platform::is_gpu_place(ctx.GetPlace())) {
offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
offset_cpu.CopyFrom(*offset, platform::CPUPlace(), ctx.device_context());
offset_data = offset_cpu.data<int64_t>();
length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
length_cpu.CopyFrom(*length, platform::CPUPlace(), ctx.device_context());
length_data = length_cpu.data<int64_t>();
}
for (size_t i = 0; i < n; ++i) {
PADDLE_ENFORCE_LT(0, offset_data[i],
"The offset[%d] must greater than zero.", i)
PADDLE_ENFORCE_LT(0, length_data[i],
"The length[%d] must greater than zero.", i)
PADDLE_ENFORCE_LT(
lod[0][i] + offset_data[i] + length_data[i],
lod[0][i + 1],
"The target tensor's length overflow.")
}
out->mutable_data<T>(ctx.GetPlace());
auto out_lod = SequenceSliceLoD(*in, offset_data, length_data);
auto out_dims = in->dims();
out_dims[0] = out_lod[0][out_lod[0].size() - 1];
out->Resize(out_dims);
out->set_lod(out_lod);
auto in_stride = framework::stride(in->dims());
auto out_stride = framework::stride(out->dims());
size_t out_offset = 0;
for (size_t i = 0; i < n; ++i) {
Tensor in_t =
in->Slice(static_cast<int>(lod[0][i] + offset_data[i]),
static_cast<int>(lod[0][i] + offset_data[i] +
length_data[i]));
StridedMemcpy<T>(ctx.device_context(), in_t.data<T>(),
in_stride, in_t.dims(), out_stride,
out->data<T>() + out_offset);
out_offset += length_data[i] * in_stride[0];
}
}
};
template <typename Place, typename T>
class SequenceSliceGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<LoDTensor>("X");
auto* offset = ctx.Input<Tensor>("Offset");
auto* length = ctx.Input<Tensor>("Length");
auto* out_grad =
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
auto* x_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
const int64_t* offset_data = offset->data<int64_t>();
const int64_t* length_data = length->data<int64_t>();
framework::Tensor offset_cpu;
framework::Tensor length_cpu;
if (platform::is_gpu_place(ctx.GetPlace())) {
offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
offset_cpu.CopyFrom(*offset, platform::CPUPlace(), ctx.device_context());
offset_data = offset_cpu.data<int64_t>();
length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
length_cpu.CopyFrom(*length, platform::CPUPlace(), ctx.device_context());
length_data = length_cpu.data<int64_t>();
}
auto lod = in->lod();
auto out_lod = out_grad->lod();
if (x_grad) {
x_grad->mutable_data<T>(ctx.GetPlace());
x_grad->set_lod(in->lod());
math::SetConstant<Place, T> set_zero;
set_zero(ctx.device_context(), x_grad, static_cast<T>(0));
auto out_grad_stride = framework::stride(out_grad->dims());
for (size_t i = 0; i < out_lod[0].size() - 1; ++i) {
Tensor out_grad_t =
out_grad->Slice(static_cast<int>(out_lod[0][i]),
static_cast<int>(out_lod[0][i + 1]));
auto out_grad_stride = framework::stride(out_grad_t.dims());
auto x_grad_stride = framework::stride(x_grad->dims());
Tensor x_grad_t = x_grad->Slice(
static_cast<int>(lod[0][i] + offset_data[i]),
static_cast<int>(lod[0][i] + offset_data[i] + length_data[i]));
StridedMemcpy<T>(ctx.device_context(), out_grad_t.data<T>(),
out_grad_stride, out_grad_t.dims(), x_grad_stride,
x_grad_t.data<T>());
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/platform/cudnn_helper.h
浏览文件 @ 13ec6f99
......@@ -224,13 +224,15 @@ class ScopedConvolutionDescriptor {
PADDLE_ENFORCE_EQ(pads.size(), strides.size());
PADDLE_ENFORCE_EQ(pads.size(), dilations.size());
#if CUDNN_VERSION < 6000
#if !CUDNN_VERSION_MIN(6, 0, 0)
// cudnn v5 does not support dilation conv, the argument is called upscale
// instead of dilations and it is must be one.
for (size_t i = 0; i < dilations.size(); ++i) {
PADDLE_ENFORCE_EQ(
dilations[i], 1,
"Dilations conv is not supported in this cuDNN version");
"Dilations conv is not supported in this cuDNN version(%d.%d.%d).",
CUDNN_VERSION / 1000, CUDNN_VERSION % 1000 / 100,
CUDNN_VERSION % 100);
}
#endif
......
paddle/platform/cudnn_helper_test.cc
浏览文件 @ 13ec6f99
......@@ -38,6 +38,26 @@ TEST(CudnnHelper, ScopedTensorDescriptor) {
EXPECT_EQ(strides[2], 6);
EXPECT_EQ(strides[1], 36);
EXPECT_EQ(strides[0], 144);
// test tensor5d: ScopedTensorDescriptor
ScopedTensorDescriptor tensor5d_desc;
std::vector<int> shape_5d = {2, 4, 6, 6, 6};
auto desc_5d = tensor5d_desc.descriptor<float>(DataLayout::kNCDHW, shape_5d);
std::vector<int> dims_5d(5);
std::vector<int> strides_5d(5);
paddle::platform::dynload::cudnnGetTensorNdDescriptor(
desc_5d, 5, &type, &nd, dims_5d.data(), strides_5d.data());
EXPECT_EQ(nd, 5);
for (size_t i = 0; i < dims_5d.size(); ++i) {
EXPECT_EQ(dims_5d[i], shape_5d[i]);
}
EXPECT_EQ(strides_5d[4], 1);
EXPECT_EQ(strides_5d[3], 6);
EXPECT_EQ(strides_5d[2], 36);
EXPECT_EQ(strides_5d[1], 216);
EXPECT_EQ(strides_5d[0], 864);
}
TEST(CudnnHelper, ScopedFilterDescriptor) {
......@@ -60,6 +80,20 @@ TEST(CudnnHelper, ScopedFilterDescriptor) {
for (size_t i = 0; i < shape.size(); ++i) {
EXPECT_EQ(kernel[i], shape[i]);
}
ScopedFilterDescriptor filter_desc_4d;
std::vector<int> shape_4d = {2, 3, 3, 3};
auto desc_4d = filter_desc.descriptor<float>(DataLayout::kNCDHW, shape_4d);
std::vector<int> kernel_4d(4);
paddle::platform::dynload::cudnnGetFilterNdDescriptor(
desc_4d, 4, &type, &format, &nd, kernel_4d.data());
EXPECT_EQ(GetCudnnTensorFormat(DataLayout::kNCHW), format);
EXPECT_EQ(nd, 4);
for (size_t i = 0; i < shape_4d.size(); ++i) {
EXPECT_EQ(kernel_4d[i], shape_4d[i]);
}
}
TEST(CudnnHelper, ScopedConvolutionDescriptor) {
......
python/paddle/trainer_config_helpers/activations.py
浏览文件 @ 13ec6f99
......@@ -17,7 +17,8 @@ __all__ = [
"IdentityActivation", "LinearActivation", 'SequenceSoftmaxActivation',
'ExpActivation', "ReluActivation", "BReluActivation", "SoftReluActivation",
"STanhActivation", "AbsActivation", "SquareActivation", "BaseActivation",
"LogActivation", "SqrtActivation", "ReciprocalActivation"
"LogActivation", "SqrtActivation", "ReciprocalActivation",
"SoftSignActivation"
]
......@@ -243,8 +244,20 @@ class ReciprocalActivation(BaseActivation):
Reciprocal Activation.
.. math::
f(z) = 1/z
f(z)=\\frac{1}{z}
"""
def __init__(self):
BaseActivation.__init__(self, 'reciprocal', False)
class SoftSignActivation(BaseActivation):
"""
SoftSign Activation.
.. math::
f(z)=\\frac{z}{1 + |z|}
"""
def __init__(self):
BaseActivation.__init__(self, 'softsign', False)
python/paddle/v2/fluid/layers.py
浏览文件 @ 13ec6f99
......@@ -661,7 +661,7 @@ def conv2d(input,
if groups is None:
num_filter_channels = num_channels
else:
if num_channels % groups is not 0:
if num_channels % groups != 0:
raise ValueError("num_channels must be divisible by groups.")
num_filter_channels = num_channels / groups
......
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
浏览文件 @ 13ec6f99
......@@ -4,6 +4,7 @@ import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.evaluator as evaluator
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.initializer import XavierInitializer
from paddle.v2.fluid.optimizer import AdamOptimizer
......@@ -103,12 +104,13 @@ net = vgg16_bn_drop(images)
predict = layers.fc(input=net, size=classdim, act='softmax')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
accuracy = layers.accuracy(input=predict, label=label)
# optimizer = SGDOptimizer(learning_rate=0.001)
optimizer = AdamOptimizer(learning_rate=0.001)
opts = optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
BATCH_SIZE = 128
PASS_NUM = 1
......@@ -124,6 +126,7 @@ exe.run(framework.default_startup_program())
for pass_id in range(PASS_NUM):
batch_id = 0
accuracy.reset(exe)
for data in train_reader():
img_data = np.array(map(lambda x: x[0].reshape(data_shape),
data)).astype("float32")
......@@ -141,12 +144,14 @@ for pass_id in range(PASS_NUM):
outs = exe.run(framework.default_main_program(),
feed={"pixel": tensor_img,
"label": tensor_y},
fetch_list=[avg_cost, accuracy])
fetch_list=[avg_cost, acc_out])
loss = np.array(outs[0])
acc = np.array(outs[1])
pass_acc = accuracy.eval(exe)
print("pass_id:" + str(pass_id) + " batch_id:" + str(batch_id) +
" loss:" + str(loss) + " acc:" + str(acc))
" loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
batch_id = batch_id + 1
if batch_id > 1:
......
python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
浏览文件 @ 13ec6f99
......@@ -46,7 +46,6 @@ exe = Executor(place)
exe.run(framework.default_startup_program())
for pass_id in range(PASS_NUM):
count = 0
accuracy.reset(exe)
for data in train_reader():
img_data = np.array(map(lambda x: x[0].reshape([1, 28, 28]),
......@@ -66,13 +65,14 @@ for pass_id in range(PASS_NUM):
loss = np.array(outs[0])
acc = np.array(outs[1])
pass_acc = accuracy.eval(exe)
print "pass id : ", pass_id, pass_acc
print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc=" +
str(pass_acc))
# print loss, acc
if loss < 10.0 and acc > 0.9:
if loss < 10.0 and pass_acc > 0.9:
# if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
exit(0)
pass_acc = accuracy.eval(exe)
print "pass id : ", pass_id, pass_acc
print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
exit(1)
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
浏览文件 @ 13ec6f99
......@@ -3,6 +3,7 @@ import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.evaluator as evaluator
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.initializer import UniformInitializer
from paddle.v2.fluid.optimizer import MomentumOptimizer
......@@ -30,11 +31,12 @@ label = layers.data(name='y', shape=[1], data_type='int64')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
accuracy = layers.accuracy(input=predict, label=label)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
opts = optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
......@@ -47,6 +49,7 @@ exe.run(framework.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
......@@ -61,9 +64,13 @@ for pass_id in range(PASS_NUM):
outs = exe.run(framework.default_main_program(),
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost, accuracy])
fetch_list=[avg_cost, acc_out])
out = np.array(outs[0])
acc = np.array(outs[1])
if out[0] < 5.0:
exit(0) # if avg cost less than 5.0, we think our code is good.
pass_acc = accuracy.eval(exe)
if pass_acc > 0.7:
exit(0)
# print("pass_id=" + str(pass_id) + " auc=" +
# str(acc) + " pass_acc=" + str(pass_acc))
exit(1)
python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
浏览文件 @ 13ec6f99
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.evaluator as evaluator
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
......@@ -32,8 +33,8 @@ def convolution_net(input_dim, class_dim=2, emb_dim=32, hid_dim=32):
avg_cost = layers.mean(x=cost)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
acc = layers.accuracy(input=prediction, label=label)
return avg_cost, acc
accuracy, acc_out = evaluator.accuracy(input=prediction, label=label)
return avg_cost, accuracy, acc_out
def to_lodtensor(data, place):
......@@ -59,7 +60,8 @@ def main():
dict_dim = len(word_dict)
class_dim = 2
cost, acc = convolution_net(input_dim=dict_dim, class_dim=class_dim)
cost, accuracy, acc_out = convolution_net(
input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
......@@ -71,6 +73,7 @@ def main():
exe.run(framework.default_startup_program())
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
for data in train_data():
tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
......@@ -83,12 +86,13 @@ def main():
outs = exe.run(framework.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc])
fetch_list=[cost, acc_out])
cost_val = np.array(outs[0])
acc_val = np.array(outs[1])
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if cost_val < 1.0 and acc_val > 0.7:
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
if cost_val < 1.0 and pass_acc > 0.8:
exit(0)
exit(1)
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
浏览文件 @ 13ec6f99
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.evaluator as evaluator
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
......@@ -41,8 +42,8 @@ def stacked_lstm_net(input_dim,
avg_cost = layers.mean(x=cost)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
acc = layers.accuracy(input=prediction, label=label)
return avg_cost, acc
accuracy, acc_out = evaluator.accuracy(input=prediction, label=label)
return avg_cost, accuracy, acc_out
def to_lodtensor(data, place):
......@@ -69,7 +70,8 @@ def main():
dict_dim = len(word_dict)
class_dim = 2
cost, acc = stacked_lstm_net(input_dim=dict_dim, class_dim=class_dim)
cost, accuracy, acc_out = stacked_lstm_net(
input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
......@@ -81,6 +83,7 @@ def main():
exe.run(framework.default_startup_program())
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
for data in train_data():
tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
......@@ -93,12 +96,13 @@ def main():
outs = exe.run(framework.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc])
fetch_list=[cost, acc_out])
cost_val = np.array(outs[0])
acc_val = np.array(outs[1])
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if cost_val < 1.0 and acc_val > 0.7:
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
if cost_val < 1.0 and acc_val > 0.8:
exit(0)
exit(1)
......
python/paddle/v2/fluid/tests/test_pool2d_op.py
浏览文件 @ 13ec6f99
......@@ -3,8 +3,7 @@ import numpy as np
from op_test import OpTest
def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
......@@ -23,8 +22,7 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
return out
def avg_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
def avg_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
......@@ -47,6 +45,7 @@ def avg_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
class TestPool2d_Op(OpTest):
def setUp(self):
self.init_test_case()
self.init_global_pool()
self.init_op_type()
self.init_pool_type()
if self.global_pool:
......@@ -75,8 +74,6 @@ class TestPool2d_Op(OpTest):
self.check_grad(set(['X']), 'Out', max_relative_error=0.07)
def init_test_case(self):
self.global_pool = True
self.pool2D_forward_naive = avg_pool2D_forward_naive
self.shape = [2, 3, 5, 5]
self.ksize = [3, 3]
self.strides = [1, 1]
......@@ -87,12 +84,14 @@ class TestPool2d_Op(OpTest):
def init_pool_type(self):
self.pool_type = "avg"
self.pool2D_forward_naive = avg_pool2D_forward_naive
def init_global_pool(self):
self.global_pool = True
class TestCase1(TestPool2d_Op):
def init_test_case(self):
self.global_pool = False
self.pool2D_forward_naive = avg_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
......@@ -103,12 +102,14 @@ class TestCase1(TestPool2d_Op):
def init_pool_type(self):
self.pool_type = "avg"
self.pool2D_forward_naive = avg_pool2D_forward_naive
def init_global_pool(self):
self.global_pool = False
class TestCase2(TestPool2d_Op):
def init_test_case(self):
self.global_pool = False
self.pool2D_forward_naive = avg_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
......@@ -119,152 +120,69 @@ class TestCase2(TestPool2d_Op):
def init_pool_type(self):
self.pool_type = "avg"
self.pool2D_forward_naive = avg_pool2D_forward_naive
def init_global_pool(self):
self.global_pool = False
class TestCase3(TestPool2d_Op):
def init_test_case(self):
self.global_pool = True
self.pool2D_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 5, 5]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
class TestCase3(TestPool2d_Op):
def init_op_type(self):
self.op_type = "pool2d"
def init_pool_type(self):
self.pool_type = "max"
class TestCase4(TestPool2d_Op):
def init_test_case(self):
self.global_pool = False
self.pool2D_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
class TestCase4(TestCase1):
def init_op_type(self):
self.op_type = "pool2d"
def init_pool_type(self):
self.pool_type = "max"
class TestCase5(TestPool2d_Op):
def init_test_case(self):
self.global_pool = False
self.pool2D_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [1, 1]
class TestCase5(TestCase2):
def init_op_type(self):
self.op_type = "pool2d"
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
#--------------------test pool2d_cudnn--------------------
class TestCaseCudnn1(TestPool2d_Op):
def init_test_case(self):
self.global_pool = True
self.pool2D_forward_naive = avg_pool2D_forward_naive
self.shape = [2, 3, 5, 5]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
class TestCudnnCase1(TestPool2d_Op):
def init_op_type(self):
self.op_type = "pool2d_cudnn"
def init_pool_type(self):
self.pool_type = "avg"
class TestCaseCudnn2(TestPool2d_Op):
def init_test_case(self):
self.global_pool = False
self.pool2D_forward_naive = avg_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
class TestCudnnCase2(TestCase1):
def init_op_type(self):
self.op_type = "pool2d_cudnn"
def init_pool_type(self):
self.pool_type = "avg"
class TestCaseCudnn3(TestPool2d_Op):
def init_test_case(self):
self.global_pool = False
self.pool2D_forward_naive = avg_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [1, 1]
class TestCudnnCase3(TestCase2):
def init_op_type(self):
self.op_type = "pool2d_cudnn"
def init_pool_type(self):
self.pool_type = "avg"
class TestCaseCudnn4(TestPool2d_Op):
def init_test_case(self):
self.global_pool = True
self.pool2D_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 5, 5]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
class TestCudnnCase4(TestCase3):
def init_op_type(self):
self.op_type = "pool2d_cudnn"
def init_pool_type(self):
self.pool_type = "max"
class TestCaseCudnn5(TestPool2d_Op):
def init_test_case(self):
self.global_pool = False
self.pool2D_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
class TestCudnnCase5(TestCase4):
def init_op_type(self):
self.op_type = "pool2d_cudnn"
def init_pool_type(self):
self.pool_type = "max"
class TestCaseCudnn6(TestPool2d_Op):
def init_test_case(self):
self.global_pool = False
self.pool2D_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [1, 1]
class TestCudnnCase6(TestCase5):
def init_op_type(self):
self.op_type = "pool2d_cudnn"
def init_pool_type(self):
self.pool_type = "max"
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_pool3d_op.py
浏览文件 @ 13ec6f99
......@@ -3,8 +3,7 @@ import numpy as np
from op_test import OpTest
def max_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
N, C, D, H, W = x.shape
if global_pool == 1:
ksize = [D, H, W]
......@@ -27,8 +26,7 @@ def max_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
return out
def avg_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
def avg_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
N, C, D, H, W = x.shape
if global_pool == 1:
ksize = [D, H, W]
......@@ -55,6 +53,10 @@ def avg_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
class TestPool3d_Op(OpTest):
def setUp(self):
self.init_test_case()
self.init_global_pool()
self.init_op_type()
self.init_pool_type()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype("float32")
......@@ -81,74 +83,115 @@ class TestPool3d_Op(OpTest):
self.check_grad(set(['X']), 'Out', max_relative_error=0.07)
def init_test_case(self):
self.global_pool = True
self.op_type = "pool3d"
self.pool_type = "avg"
self.pool3D_forward_naive = avg_pool3D_forward_naive
self.shape = [2, 3, 5, 5, 5]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [0, 0, 0]
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "avg"
self.pool3D_forward_naive = avg_pool3D_forward_naive
def init_global_pool(self):
self.global_pool = True
class TestCase1(TestPool3d_Op):
def init_test_case(self):
self.global_pool = False
self.op_type = "pool3d"
self.pool_type = "avg"
self.pool3D_forward_naive = avg_pool3D_forward_naive
self.shape = [2, 3, 7, 7, 7]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [0, 0, 0]
class TestCase2(TestPool3d_Op):
def init_test_case(self):
self.global_pool = False
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "avg"
self.pool3D_forward_naive = avg_pool3D_forward_naive
def init_global_pool(self):
self.global_pool = False
class TestCase2(TestPool3d_Op):
def init_test_case(self):
self.shape = [2, 3, 7, 7, 7]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [1, 1, 1]
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "avg"
self.pool3D_forward_naive = avg_pool3D_forward_naive
def init_global_pool(self):
self.global_pool = False
class TestCase3(TestPool3d_Op):
def init_test_case(self):
self.global_pool = True
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "max"
self.pool3D_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 5, 5, 5]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [0, 0, 0]
class TestCase4(TestPool3d_Op):
def init_test_case(self):
self.global_pool = False
class TestCase4(TestCase1):
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "max"
self.pool3D_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 7, 7, 7]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [0, 0, 0]
class TestCase5(TestPool3d_Op):
def init_test_case(self):
self.global_pool = False
class TestCase5(TestCase2):
def init_op_type(self):
self.op_type = "pool3d"
def init_pool_type(self):
self.pool_type = "max"
self.pool3D_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 7, 7, 7]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [1, 1, 1]
#--------------------test pool3d_cudnn--------------------
class TestCudnnCase1(TestPool3d_Op):
def init_op_type(self):
self.op_type = "pool3d_cudnn"
class TestCudnnCase2(TestCase1):
def init_op_type(self):
self.op_type = "pool3d_cudnn"
class TestCudnnCase3(TestCase2):
def init_op_type(self):
self.op_type = "pool3d_cudnn"
class TestCudnnCase4(TestCase3):
def init_op_type(self):
self.op_type = "pool3d_cudnn"
class TestCudnnCase5(TestCase4):
def init_op_type(self):
self.op_type = "pool3d_cudnn"
class TestCudnnCase6(TestCase5):
def init_op_type(self):
self.op_type = "pool3d_cudnn"
if __name__ == '__main__':
......
python/paddle/v2/fluid/tests/test_pool_max_op.py
浏览文件 @ 13ec6f99
......@@ -3,11 +3,13 @@ import numpy as np
from op_test import OpTest
def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=False):
N, C, D, H, W = x.shape
if global_pool == 1:
if global_pool:
ksize = [D, H, W]
paddings = [0, 0, 0]
D_out = (D - ksize[0] + 2 * paddings[0]) / strides[0] + 1
H_out = (H - ksize[1] + 2 * paddings[1]) / strides[1] + 1
W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1
......@@ -40,11 +42,13 @@ def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
return out, mask
def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=False):
N, C, H, W = x.shape
if global_pool == 1:
if global_pool:
ksize = [H, W]
paddings = [0, 0]
H_out = (H - ksize[0] + 2 * paddings[0]) / strides[0] + 1
W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1
out = np.zeros((N, C, H_out, W_out))
......@@ -74,13 +78,13 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
class TestMaxPoolWithIndex_Op(OpTest):
def setUp(self):
self.init_test_case()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
self.init_global()
input = np.random.random(self.shape).astype("float32")
output, mask = self.pool_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool)
output = output.astype("float32")
mask = mask.astype("float32")
mask = mask.astype("int32")
self.attrs = {
'strides': self.strides,
......@@ -99,41 +103,24 @@ class TestMaxPoolWithIndex_Op(OpTest):
# self.check_grad(set(['X']), ['Out'], max_relative_error=0.07)
def init_test_case(self):
self.global_pool = True
self.index = "max_pool3d_with_index"
self.op_type = "%s" % self.index
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 5, 5, 5]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [1, 1, 1]
def init_global(self):
self.global_pool = False
class TestCase1(TestMaxPoolWithIndex_Op):
def init_test_case(self):
def init_global(self):
self.global_pool = True
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 5, 5, 5]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [1, 1, 1]
class TestCase2(TestMaxPoolWithIndex_Op):
def init_test_case(self):
self.global_pool = False
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 7, 7, 7]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [1, 1, 1]
class TestCase3(TestMaxPoolWithIndex_Op):
def init_test_case(self):
self.global_pool = False
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 7, 7, 7]
......@@ -141,32 +128,18 @@ class TestCase3(TestMaxPoolWithIndex_Op):
self.strides = [2, 2, 2]
self.paddings = [0, 0, 0]
class TestCase4(TestMaxPoolWithIndex_Op):
def init_test_case(self):
def init_global(self):
self.global_pool = True
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 5, 5, 5]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [1, 1, 1]
class TestCase5(TestMaxPoolWithIndex_Op):
def init_test_case(self):
self.global_pool = True
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 5, 5, 5]
self.ksize = [3, 3, 3]
self.strides = [2, 2, 2]
self.paddings = [0, 0, 0]
class TestCase3(TestCase2):
def init_global(self):
self.global_pool = False
class TestCase6(TestMaxPoolWithIndex_Op):
#----------------max_pool2d_with_index----------------
class TestCase4(TestMaxPoolWithIndex_Op):
def init_test_case(self):
self.global_pool = False
self.op_type = "max_pool2d_with_index"
self.pool_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
......@@ -174,10 +147,17 @@ class TestCase6(TestMaxPoolWithIndex_Op):
self.strides = [1, 1]
self.paddings = [1, 1]
def init_global(self):
self.global_pool = True
class TestCase7(TestMaxPoolWithIndex_Op):
def init_test_case(self):
class TestCase5(TestCase4):
def init_global(self):
self.global_pool = False
class TestCase6(TestMaxPoolWithIndex_Op):
def init_test_case(self):
self.op_type = "max_pool2d_with_index"
self.pool_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
......@@ -185,27 +165,13 @@ class TestCase7(TestMaxPoolWithIndex_Op):
self.strides = [2, 2]
self.paddings = [0, 0]
class TestCase8(TestMaxPoolWithIndex_Op):
def init_test_case(self):
def init_global(self):
self.global_pool = True
self.op_type = "max_pool2d_with_index"
self.pool_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 5, 5]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [1, 1]
class TestCase9(TestMaxPoolWithIndex_Op):
def init_test_case(self):
self.global_pool = True
self.op_type = "max_pool2d_with_index"
self.pool_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 5, 5]
self.ksize = [3, 3]
self.strides = [2, 2]
self.paddings = [0, 0]
class TestCase7(TestCase6):
def init_global(self):
self.global_pool = False
if __name__ == '__main__':
......
python/paddle/v2/fluid/tests/test_sequence_slice_op.py 0 → 100644
浏览文件 @ 13ec6f99
import unittest
import numpy as np
import sys
from op_test import OpTest
class TestSequenceSliceOp(OpTest):
def set_data(self):
self.init_test_case()
# only supprot one level LoD
x = np.random.random(self.x_dim).astype('float32')
lod = self.x_lod
offset = np.array(self.offset).astype("int64")
length = np.array(self.length).astype("int64")
self.inputs = {'X': (x, lod), 'Offset': offset, 'Length': length}
outs = [] #np.zeros((100, 3, 2)).astype('float32')
out_lod = [[0]]
out_lod_offset = 0
for i in range(len(offset)):
sub_x = x[lod[0][i] + offset[i, 0]:lod[0][i] + offset[i, 0] +
length[i, 0], :]
out_lod_offset = out_lod_offset + len(sub_x)
outs.append(sub_x)
out_lod[0].append(out_lod_offset)
outs = np.concatenate(outs, axis=0)
self.outputs = {'Out': (outs, out_lod)}
def init_test_case(self):
self.x_dim = (100, 3, 2)
self.x_lod = [[0, 20, 40, 60, 80, 100]]
self.offset = [[1], [2], [3], [4], [5]]
self.length = [[10], [8], [6], [4], [2]]
def setUp(self):
self.op_type = "sequence_slice"
self.set_data()
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == '__main__':
unittest.main()
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