Skip to content

P
Paddle

Crayon鑫 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 c93e044a 编写于 作者: D dengkaipeng 提交者： dengkaipeng
浏览文件
操作

add inclusive/exclusive mode in PoolOp avg pool type

上级 0a80f06e
隐藏空白更改
内联 并排
Showing with 145 addition and 73 deletion
paddle/fluid/operators/math/pooling.cc
浏览文件 @ c93e044a
......@@ -29,8 +29,8 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
const std::vector<int>& strides, const std::vector<int>& paddings,
PoolProcess pool_process, bool exclusive,
framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
......@@ -68,7 +68,8 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
pool_process.compute(input_data[h * input_width + w], &ele);
}
}
int pool_size = (hend - hstart) * (wend - wstart);
int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
: ksize_height * ksize_width;
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[ph * output_width + pw] = ele;
}
......@@ -93,7 +94,7 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const framework::Tensor& output, const framework::Tensor& output_grad,
const std::vector<int>& ksize, const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_grad_process,
framework::Tensor* input_grad) {
bool exclusive, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
......@@ -124,7 +125,8 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
int wstart = pw * stride_width - padding_width;
int wend = std::min(wstart + ksize_width, input_width);
wstart = std::max(wstart, 0);
int pool_size = (hend - hstart) * (wend - wstart);
int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
: ksize_height * ksize_width;
float scale = 1.0 / pool_size;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
......@@ -247,9 +249,9 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* output) {
const std::vector<int>& strides, const std::vector<int>& paddings,
PoolProcess pool_process,
bool exclusive, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
......@@ -299,8 +301,9 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
}
}
}
int pool_size =
(dend - dstart) * (hend - hstart) * (wend - wstart);
int pool_size = exclusive ?
(dend - dstart) * (hend - hstart) * (wend - wstart)
: ksize_depth * ksize_height * ksize_width;
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[output_idx] = ele;
}
......@@ -326,7 +329,7 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const framework::Tensor& output, const framework::Tensor& output_grad,
const std::vector<int>& ksize, const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_grad_process,
framework::Tensor* input_grad) {
bool exclusive, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
......@@ -368,8 +371,9 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
int wend = std::min(wstart + ksize_width, input_width);
wstart = std::max(wstart, 0);
int pool_size =
(dend - dstart) * (hend - hstart) * (wend - wstart);
int pool_size = exclusive ?
(dend - dstart) * (hend - hstart) * (wend - wstart)
: ksize_depth * ksize_height * ksize_width;
float scale = 1.0 / pool_size;
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
......
paddle/fluid/operators/math/pooling.cu
浏览文件 @ c93e044a
......@@ -29,7 +29,7 @@ __global__ void KernelPool2D(const int nthreads, const T* input_data,
const int ksize_width, const int stride_height,
const int stride_width, const int padding_height,
const int padding_width, PoolProcess pool_process,
T* output_data) {
bool exclusive, T* output_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -52,7 +52,8 @@ __global__ void KernelPool2D(const int nthreads, const T* input_data,
pool_process.compute(input_data[h * input_width + w], &ele);
}
}
int pool_size = (hend - hstart) * (wend - wstart);
int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
: ksize_height * ksize_width;
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[index] = ele;
}
......@@ -65,7 +66,7 @@ __global__ void KernelPool2DGrad(
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,
PoolProcess pool_process, T* input_grad) {
PoolProcess pool_process, bool exclusive, T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int offsetW = index % input_width + padding_width;
......@@ -95,7 +96,8 @@ __global__ void KernelPool2DGrad(
int wend = min(wstart + ksize_width, input_width);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
int pool_size = (hend - hstart) * (wend - wstart);
int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
: ksize_height * ksize_width;
int output_sub_idx = ph * output_width + pw;
pool_process.compute(input, output_data[output_sub_idx],
output_grad[output_sub_idx],
......@@ -163,7 +165,7 @@ class Pool2dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
const framework::Tensor& input, const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* output) {
bool exclusive, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_height = input.dims()[2];
......@@ -189,7 +191,8 @@ class Pool2dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
KernelPool2D<PoolProcess, T><<<grid, threads, 0, 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, pool_process, output_data);
stride_width, padding_height, padding_width, pool_process, exclusive,
output_data);
}
};
......@@ -208,7 +211,7 @@ class Pool2dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* input_grad) {
bool exclusive, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_height = input.dims()[2];
......@@ -236,7 +239,7 @@ class Pool2dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
nthreads, input_data, output_data, output_grad_data, input_channels,
input_height, input_width, output_height, output_width, ksize_height,
ksize_width, stride_height, stride_width, padding_height, padding_width,
pool_process, input_grad_data);
pool_process, exclusive, input_grad_data);
}
};
......@@ -313,16 +316,14 @@ template class Pool2dGradFunctor<platform::CUDADeviceContext,
double>;
template <typename PoolProcess, typename T>
__global__ void KernelPool3D(const int nthreads, const T* 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,
PoolProcess pool_process, T* output_data) {
__global__ void KernelPool3D(
const int nthreads, const T* 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,
PoolProcess pool_process, bool exclusive, T* output_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -351,7 +352,9 @@ __global__ void KernelPool3D(const int nthreads, const T* input_data,
}
}
}
int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
int pool_size = exclusive ?
(dend - dstart) * (hend - hstart) * (wend - wstart)
: ksize_depth * ksize_height * ksize_width;
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[index] = ele;
}
......@@ -366,7 +369,7 @@ __global__ void KernelPool3DGrad(
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, PoolProcess pool_process,
T* input_grad) {
bool exclusive, T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int offsetW = index % input_width + padding_width;
......@@ -409,7 +412,9 @@ __global__ void KernelPool3DGrad(
dstart = max(dstart, 0);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
int pool_size = exclusive ?
(dend - dstart) * (hend - hstart) * (wend - wstart)
: ksize_depth * ksize_height * ksize_width;
int output_sub_idx = (pd * output_height + ph) * output_width + pw;
pool_process.compute(input, output_data[output_sub_idx],
output_grad[output_sub_idx],
......@@ -484,7 +489,7 @@ class Pool3dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
const framework::Tensor& input, const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* output) {
bool exclusive, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_depth = input.dims()[2];
......@@ -518,7 +523,7 @@ class Pool3dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
input_width, output_depth, output_height, output_width, ksize_depth,
ksize_height, ksize_width, stride_depth, stride_height, stride_width,
padding_depth, padding_height, padding_width, pool_process,
output_data);
exclusive, output_data);
}
};
......@@ -537,7 +542,7 @@ class Pool3dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* input_grad) {
bool exclusive, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_depth = input.dims()[2];
......@@ -573,7 +578,7 @@ class Pool3dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
input_depth, input_height, input_width, output_depth, output_height,
output_width, ksize_depth, ksize_height, ksize_width, stride_depth,
stride_height, stride_width, padding_depth, padding_height,
padding_width, pool_process, input_grad_data);
padding_width, pool_process, exclusive, input_grad_data);
}
};
......
paddle/fluid/operators/math/pooling.h
浏览文件 @ c93e044a
......@@ -89,7 +89,7 @@ class Pool2dFunctor {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_compute,
framework::Tensor* output);
bool exclusive, framework::Tensor* output);
};
template <typename DeviceContext, typename PoolProcess, typename T>
......@@ -101,7 +101,7 @@ class Pool2dGradFunctor {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_compute,
framework::Tensor* input_grad);
bool exclusive, framework::Tensor* input_grad);
};
template <typename DeviceContext, class T>
......@@ -123,7 +123,7 @@ class Pool3dFunctor {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_compute,
framework::Tensor* output);
bool exclusive, framework::Tensor* output);
};
template <typename DeviceContext, typename PoolProcess, typename T>
......@@ -135,7 +135,7 @@ class Pool3dGradFunctor {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_compute,
framework::Tensor* input_grad);
bool exclusive, framework::Tensor* input_grad);
};
template <typename DeviceContext, class T>
......
paddle/fluid/operators/pool_cudnn_op.cu.cc
浏览文件 @ c93e044a
......@@ -41,6 +41,7 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
T *output_data = output->mutable_data<T>(ctx.GetPlace());
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
bool exclusive = ctx.Attr<bool>("exclusive");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
......@@ -72,7 +73,7 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
if (pooling_type == "max") {
pooling_mode = PoolingMode::kMaximum;
} else {
pooling_mode = PoolingMode::kAverage;
pooling_mode = exclusive ? PoolingMode::kAverageExclusive : PoolingMode::kAverageInclusive;
}
cudnnPoolingDescriptor_t cudnn_pool_desc =
......@@ -101,6 +102,7 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
Tensor *input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
bool exclusive = ctx.Attr<bool>("exclusive");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
......@@ -141,7 +143,7 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
pooling_mode = PoolingMode::kMaximum;
}
} else {
pooling_mode = PoolingMode::kAverage;
pooling_mode = exclusive ? PoolingMode::kAverageExclusive : PoolingMode::kAverageInclusive;
}
cudnnPoolingDescriptor_t cudnn_pool_desc =
......
paddle/fluid/operators/pool_op.cc
浏览文件 @ c93e044a
......@@ -180,6 +180,12 @@ void Pool2dOpMaker::Make() {
"operator."
"If global_pooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0});
AddAttr<bool>(
"exclusive",
"(bool, default True) When true, will exclude the zero-padding in the "
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True.")
.SetDefault(true);
AddAttr<bool>(
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
......@@ -283,6 +289,12 @@ void Pool3dOpMaker::Make() {
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"exclusive",
"(bool, default True) When true, will exclude the zero-padding in the "
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True.")
.SetDefault(true);
AddAttr<bool>(
"use_cudnn",
......
paddle/fluid/operators/pool_op.h
浏览文件 @ c93e044a
......@@ -69,6 +69,7 @@ class PoolKernel : public framework::OpKernel<T> {
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
bool exclusive = context.Attr<bool>("exclusive");
if (context.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
......@@ -84,7 +85,7 @@ class PoolKernel : public framework::OpKernel<T> {
pool2d_forward;
paddle::operators::math::MaxPool<T> pool_process;
pool2d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
out);
true, out);
} else if (pooling_type == "avg") {
paddle::operators::math::Pool2dFunctor<
......@@ -92,7 +93,7 @@ class PoolKernel : public framework::OpKernel<T> {
pool2d_forward;
paddle::operators::math::AvgPool<T> pool_process;
pool2d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
out);
exclusive, out);
}
} break;
case 3: {
......@@ -102,14 +103,14 @@ class PoolKernel : public framework::OpKernel<T> {
pool3d_forward;
paddle::operators::math::MaxPool<T> pool_process;
pool3d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
out);
true, out);
} else if (pooling_type == "avg") {
paddle::operators::math::Pool3dFunctor<
DeviceContext, paddle::operators::math::AvgPool<T>, T>
pool3d_forward;
paddle::operators::math::AvgPool<T> pool_process;
pool3d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
out);
exclusive, out);
}
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
......@@ -131,6 +132,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
bool exclusive = context.Attr<bool>("exclusive");
if (context.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
......@@ -157,7 +159,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
pool2d_backward;
paddle::operators::math::AvgPoolGrad<T> pool_process;
pool2d_backward(dev_ctx, *in_x, *out, *out_grad, ksize, strides,
paddings, pool_process, in_x_grad);
paddings, pool_process, exclusive, in_x_grad);
}
} break;
case 3: {
......@@ -172,7 +174,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
pool3d_backward;
paddle::operators::math::AvgPoolGrad<T> pool_process;
pool3d_backward(dev_ctx, *in_x, *out, *out_grad, ksize, strides,
paddings, pool_process, in_x_grad);
paddings, pool_process, exclusive, in_x_grad);
}
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
......
paddle/fluid/operators/spp_op.h
浏览文件 @ c93e044a
......@@ -56,12 +56,14 @@ class SppKernel : public framework::OpKernel<T> {
math::Pool2dFunctor<DeviceContext, math::MaxPool<T>, T> pool_forward;
math::MaxPool<T> max_process;
pool_forward(context.template device_context<DeviceContext>(), *in_x,
kernel_size, strides, paddings, max_process, &out_level);
kernel_size, strides, paddings, max_process, true,
&out_level);
} else if (pooling_type == "avg") {
math::Pool2dFunctor<DeviceContext, math::AvgPool<T>, T> pool_forward;
math::AvgPool<T> avg_process;
pool_forward(context.template device_context<DeviceContext>(), *in_x,
kernel_size, strides, paddings, avg_process, &out_level);
kernel_size, strides, paddings, avg_process, true,
&out_level);
}
// flatten pooling output shape
int output_flatten_w = in_x->dims()[1] * bins * bins;
......@@ -154,7 +156,7 @@ class SppGradKernel : public framework::OpKernel<T> {
math::AvgPoolGrad<T> avg_process;
pool_backward(context.template device_context<DeviceContext>(), *in_x,
*&out_level, *&outgrad_level, kernel_size, strides,
paddings, avg_process, in_x_grad);
paddings, avg_process, true, in_x_grad);
}
}
}
......
paddle/fluid/platform/cudnn_helper.h
浏览文件 @ c93e044a
......@@ -76,8 +76,9 @@ enum class DataLayout { // Not use
enum class PoolingMode {
kMaximum,
kAverage,
kMaximumDeterministic,
kAverageExclusive,
kAverageInclusive,
};
#if CUDNN_VERSION < 6000
......@@ -91,8 +92,10 @@ inline cudnnPoolingMode_t GetPoolingMode(const PoolingMode& mode) {
switch (mode) {
case PoolingMode::kMaximumDeterministic:
return CUDNN_POOLING_MAX;
case PoolingMode::kAverage:
case PoolingMode::kAverageExclusive:
return CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
case PoolingMode::kAverageInclusive:
return CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
case PoolingMode::kMaximum:
return CUDNN_POOLING_MAX;
default:
......@@ -105,8 +108,10 @@ inline cudnnPoolingMode_t GetPoolingMode(const PoolingMode& mode) {
switch (mode) {
case PoolingMode::kMaximumDeterministic:
return CUDNN_POOLING_MAX_DETERMINISTIC;
case PoolingMode::kAverage:
case PoolingMode::kAverageExclusive:
return CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
case PoolingMode::kAverageInclusive:
return CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
case PoolingMode::kMaximum:
return CUDNN_POOLING_MAX;
default:
......
python/paddle/fluid/layers/nn.py
浏览文件 @ c93e044a
......@@ -2067,6 +2067,7 @@ def pool2d(input,
global_pooling=False,
use_cudnn=True,
ceil_mode=False,
exclusive=True,
name=None):
"""
${comment}
......@@ -2081,9 +2082,11 @@ def pool2d(input,
pool_type: ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling: ${global_pooling_comment}
use_cudnn: ${use_cudnn_comment}
ceil_mode: ${ceil_mode_comment}
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is true
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
......@@ -2143,7 +2146,8 @@ def pool2d(input,
"paddings": pool_padding,
"use_cudnn": use_cudnn,
"ceil_mode": ceil_mode,
"use_mkldnn": False
"use_mkldnn": False,
"exclusive": exclusive,
})
return pool_out
......@@ -2157,6 +2161,7 @@ def pool3d(input,
global_pooling=False,
use_cudnn=True,
ceil_mode=False,
exclusive=True,
name=None):
"""
This function adds the operator for pooling in 3-dimensions, using the
......@@ -2171,6 +2176,8 @@ def pool3d(input,
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is true
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
......@@ -2211,7 +2218,8 @@ def pool3d(input,
"paddings": pool_padding,
"use_cudnn": use_cudnn,
"ceil_mode": ceil_mode,
"use_mkldnn": False
"use_mkldnn": False,
"exclusive": exclusive,
})
return pool_out
......
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @ c93e044a
......@@ -26,7 +26,8 @@ def max_pool2D_forward_naive(x,
strides,
paddings,
global_pool=0,
ceil_mode=False):
ceil_mode=False,
exclusive=True):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
......@@ -54,7 +55,8 @@ def avg_pool2D_forward_naive(x,
strides,
paddings,
global_pool=0,
ceil_mode=False):
ceil_mode=False,
exclusive=True):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
......@@ -73,8 +75,9 @@ def avg_pool2D_forward_naive(x,
c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
x_masked = x[:, :, r_start:r_end, c_start:c_end]
out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / (
(r_end - r_start) * (c_end - c_start))
field_size = ((r_end - r_start) * (c_end - c_start)) if exclusive \
else (ksize[0] * ksize[1])
out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
return out
......@@ -89,12 +92,13 @@ class TestPool2d_Op(OpTest):
self.init_kernel_type()
self.init_pool_type()
self.init_ceil_mode()
self.init_exclusive()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype(self.dtype)
output = self.pool2D_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool,
self.ceil_mode).astype(self.dtype)
self.ceil_mode, self.exclusive).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
self.attrs = {
......@@ -106,7 +110,8 @@ class TestPool2d_Op(OpTest):
'use_cudnn': self.use_cudnn,
'use_mkldnn': self.use_mkldnn,
'ceil_mode': self.ceil_mode,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
'data_format': 'AnyLayout', # TODO(dzhwinter) : should be fix latter
'exclusive': self.exclusive
}
self.outputs = {'Out': output}
......@@ -150,6 +155,9 @@ class TestPool2d_Op(OpTest):
def init_ceil_mode(self):
self.ceil_mode = False
def init_exclusive(self):
self.exclusive = True
class TestCase1(TestPool2d_Op):
def init_test_case(self):
......@@ -321,6 +329,14 @@ class TestCeilModeCase4(TestCase2):
def init_ceil_mode(self):
self.ceil_mode = True
class TestAvgInclude(TestCase2):
def init_exclusive(self):
self.exclusive = False
class TestCUDNNAvgInclude(TestCUDNNCase3):
def init_exclusive(self):
self.exclusive = False
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_pool3d_op.py
浏览文件 @ c93e044a
......@@ -26,7 +26,8 @@ def max_pool3D_forward_naive(x,
strides,
paddings,
global_pool=0,
ceil_mode=False):
ceil_mode=False,
exclusive=True):
N, C, D, H, W = x.shape
if global_pool == 1:
ksize = [D, H, W]
......@@ -60,7 +61,8 @@ def avg_pool3D_forward_naive(x,
strides,
paddings,
global_pool=0,
ceil_mode=False):
ceil_mode=False,
exclusive=True):
N, C, D, H, W = x.shape
if global_pool == 1:
ksize = [D, H, W]
......@@ -85,8 +87,9 @@ def avg_pool3D_forward_naive(x,
w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end]
out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3, 4)) / (
(d_end - d_start) * (h_end - h_start) * (w_end - w_start))
field_size = (d_end - d_start) * (h_end - h_start) * (w_end - w_start) \
if exclusive else ksize[0] * ksize[1] * ksize[2]
out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3, 4)) / field_size
return out
......@@ -100,13 +103,14 @@ class TestPool3d_Op(OpTest):
self.init_kernel_type()
self.init_pool_type()
self.init_ceil_mode()
self.init_exclusive()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype(self.dtype)
output = self.pool3D_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool,
self.ceil_mode).astype(self.dtype)
self.ceil_mode, self.exclusive).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
self.attrs = {
......@@ -117,7 +121,8 @@ class TestPool3d_Op(OpTest):
'global_pooling': self.global_pool,
'use_cudnn': self.use_cudnn,
'ceil_mode': self.ceil_mode,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
'data_format': 'AnyLayout', # TODO(dzhwinter) : should be fix latter
'exclusive': self.exclusive
}
self.outputs = {'Out': output}
......@@ -161,6 +166,9 @@ class TestPool3d_Op(OpTest):
def init_ceil_mode(self):
self.ceil_mode = False
def init_exclusive(self):
self.exclusive = True
class TestCase1(TestPool3d_Op):
def init_test_case(self):
......@@ -332,6 +340,14 @@ class TestCeilModeCase4(TestCase2):
def init_ceil_mode(self):
self.ceil_mode = True
class TestAvgInclude(TestCase2):
def init_exclusive(self):
self.exclusive = False
class TestCUDNNAvgInclude(TestCUDNNCase3):
def init_exclusive(self):
self.exclusive = False
if __name__ == '__main__':
unittest.main()
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或