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提交 0ab012cf 编写于 作者: C chengduoZH
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fix doc

上级 25df8929
隐藏空白更改
内联 并排
Showing with 75 addition and 101 deletion
paddle/operators/pool_cudnn_op.cc
浏览文件 @ 0ab012cf
......@@ -23,12 +23,3 @@ REGISTER_OP_CPU_KERNEL(pool2d_cudnn,
ops::PoolKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(pool2d_cudnn_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>)
// 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>);
// REGISTER_OP_CPU_KERNEL(pool3d_cudnn_grad,
// ops::PoolGradKernel<paddle::platform::CPUPlace,
// float>);
paddle/operators/pool_cudnn_op.cu
浏览文件 @ 0ab012cf
......@@ -46,11 +46,11 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> {
const T *input_data = input->data<T>();
T *output_data = output->mutable_data<T>(ctx.GetPlace());
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::string pooling_type = ctx.Attr<std::string>("poolingType");
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");
if (ctx.Attr<bool>("global_pooling")) {
if (ctx.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
ksize[i] = static_cast<int>(input->dims()[i + 2]);
}
......@@ -100,12 +100,12 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
ctx.Input<Tensor>(framework::GradVarName("Out"));
Tensor *input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::string pooling_type = ctx.Attr<std::string>("poolingType");
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");
if (ctx.Attr<bool>("global_pooling")) {
if (ctx.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i)
ksize[i] = static_cast<int>(input->dims()[i + 2]);
}
......@@ -169,6 +169,3 @@ 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(pool3d_cudnn, ops::PoolCudnnOpKernel<float>);
// REGISTER_OP_GPU_KERNEL(pool3d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>);
paddle/operators/pool_op.cc
浏览文件 @ 0ab012cf
......@@ -29,7 +29,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
auto in_x_dims = ctx->GetInputDim("X");
std::string pooling_type = ctx->Attrs().Get<std::string>("pooling_type");
std::string pooling_type = ctx->Attrs().Get<std::string>("poolingType");
std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
......@@ -37,7 +37,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
"Pooling intput should be 4-D or 5-D tensor.");
if (ctx->Attrs().Get<bool>("global_pooling")) {
if (ctx->Attrs().Get<bool>("globalPooling")) {
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
for (size_t i = 0; i < ksize.size(); ++i)
ksize[i] = static_cast<int>(in_x_dims[i + 2]);
......@@ -80,34 +80,31 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
"the number of channels, H and W is the height and "
"width of feature.");
AddAttr<std::string>("pooling_type",
"Pooling_type of pooling operator."
AddAttr<std::string>("poolingType",
"(string), poolingType of pooling operator."
"Str constant equal to 'max' or 'avg'.")
.InEnum({"max", "avg"});
AddAttr<std::vector<int>>(
"ksize",
"The pooling window size(height, width) of pooling operator."
"If global_pooling = true, ksize is ignored and need not be "
"(vector ), the pooling window size(height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"global_pooling",
"Whether to use the global_pooling."
"Bool constant equal to false or true."
"Default false."
"If global_pooling = true, ksize is ignored and need not be specified.")
"globalPooling",
"(bool default: false), whether to use the global pooling."
"If globalPooling = true, ksize is ignored and need not be specified.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"The strides(height, width) of pooling window."
"Default {1,1}.")
AddAttr<std::vector<int>>(
"strides",
"(vector, default:{1, 1}), strides(height, width) of pooling operator.")
.SetDefault({1, 1}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>("paddings",
"The zero padding(height, width) size on both sides"
"Default {0,0}.")
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector defalut:{0,0}), paddings(height, width) of pooling operator.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
// TypedAttrChecker don't support vector type.)
AddComment(R"DOC(
The pooling2d operation calculates the output based on
......@@ -123,7 +120,6 @@ Example:
X shape: (N, C, H_in, W_in)
Output:
Out shape: (N, C, H_out, W_out)
Mask shape: (N, C, H_out, W_out)
where
H_out = (H_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
W_out = (W_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
......@@ -146,33 +142,30 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
"the number of channels, D, H and W is the depth, height and "
"width of feature.");
AddAttr<std::string>("pooling_type",
"PoolingType of pooling operator."
AddAttr<std::string>("poolingType",
"(string), poolingType of pooling operator."
"Str constant equal to 'max' or 'avg'.")
.InEnum({"max", "avg"});
AddAttr<std::vector<int>>(
"ksize",
"The pooling window size(depth, height, width) of pooling operator."
"If global_pooling = true, ksize is ignored and need not be "
"(vector ), the pooling window size(depth, height, width) of pooling "
"operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"global_pooling",
"Whether to use the global_pooling."
"Bool constant equal to false or true."
"Default false."
"If global_pooling = true, ksize is ignored and need not be specified.")
"globalPooling",
"(bool default: false), whether to use the global pooling."
"If globalPooling = true, ksize is ignored and need not be specified.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"Strides(depth, height, width) of pooling operator."
"Default {1,1,1}.")
"(vector, default:{1,1,1}), strides(depth, height, "
"width) of pooling operator.")
.SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"Paddings(depth, height, width) of pooling operator."
"Default {0,0,0}.")
AddAttr<std::vector<int>>("paddings",
"(vector defalut:{0,0,0}), paddings(depth, height, "
"width) of pooling operator.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
......@@ -190,7 +183,6 @@ Example:
X shape: (N, C, D_in, H_in, W_in)
Output:
Out shape: (N, C, D_out, H_out, W_out)
Mask shape: (N, C, D_out, H_out, W_out)
where
D_out = (D_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
H_out = (H_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
......
paddle/operators/pool_op.h
浏览文件 @ 0ab012cf
......@@ -57,11 +57,11 @@ class PoolKernel : public framework::OpKernel<T> {
const Tensor* in_x = context.Input<Tensor>("X");
Tensor* out = context.Output<Tensor>("Out");
std::string pooling_type = context.Attr<std::string>("pooling_type");
std::string pooling_type = context.Attr<std::string>("poolingType");
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");
if (context.Attr<bool>("global_pooling")) {
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
......@@ -117,12 +117,12 @@ class PoolGradKernel : public framework::OpKernel<T> {
context.Input<Tensor>(framework::GradVarName("Out"));
Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
std::string pooling_type = context.Attr<std::string>("pooling_type");
std::string pooling_type = context.Attr<std::string>("poolingType");
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");
if (context.Attr<bool>("global_pooling")) {
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i)
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
......
paddle/operators/pool_with_index_op.cc
浏览文件 @ 0ab012cf
......@@ -44,7 +44,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
"Pooling intput should be 4-D or 5-D tensor.");
if (ctx->Attrs().Get<bool>("global_pooling")) {
if (ctx->Attrs().Get<bool>("globalPooling")) {
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
for (size_t i = 0; i < ksize.size(); ++i)
ksize[i] = static_cast<int>(in_x_dims[i + 2]);
......@@ -105,28 +105,25 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>(
"ksize",
"The pooling window size(height, width) of pooling operator."
"If global_pooling = true, ksize is ignored and need not be "
"(vector ), the pooling window size(height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"global_pooling",
"Whether to use the global_pooling."
"Bool constant equal to false or true."
"Default false."
"If global_pooling = true, ksize is ignored and need not be specified.")
"globalPooling",
"(bool default: false), whether to use the global pooling."
"If globalPooling = true, ksize is ignored and need not be specified.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"The strides(height, width) of pooling window."
"Default {1,1}.")
AddAttr<std::vector<int>>(
"strides",
"(vector, default:{1, 1}), strides(height, width) of pooling operator.")
.SetDefault({1, 1}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"The zero padding(height, width) size on both sides"
"Default {0,0}.")
"(vector defalut:{0,0}), paddings(height, width) of pooling operator.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
// TypedAttrChecker don't support vector type.)
AddComment(R"DOC(
The maxPooling2d with index operation calculates the output and the mask
......@@ -176,29 +173,26 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>(
"ksize",
"The pooling window size(depth, height, width) of pooling operator."
"If global_pooling = true, ksize is ignored and need not be "
"(vector ), the pooling window size(depth, height, width) of pooling "
"operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"global_pooling",
"Whether to use the global_pooling."
"Bool constant equal to false or true."
"Default false."
"If global_pooling = true, ksize is ignored and need not be specified.")
"globalPooling",
"(bool default: false), whether to use the global pooling."
"If globalPooling = true, ksize is ignored and need not be specified.")
.SetDefault(false);
AddAttr<std::vector<int>>(
"strides",
"Strides(depth, height, width) of pooling operator."
"Default {1,1,1}.")
AddAttr<std::vector<int>>("strides",
"(vector, default:{1,1,1}), strides(depth, "
"height, width) of pooling operator.")
.SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"Paddings(depth, height, width) of pooling operator."
"Default {0,0,0}.")
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>("paddings",
"(vector defalut:{0,0,0}), paddings(depth, "
"height, width) of pooling operator.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
// TypedAttrChecker don't support vector type.)
AddComment(R"DOC(
The maxpooling3d with index operation calculates the output and the mask
......
paddle/operators/pool_with_index_op.h
浏览文件 @ 0ab012cf
......@@ -35,7 +35,7 @@ class MaxPoolWithIndexKernel : 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");
if (context.Attr<bool>("global_pooling")) {
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
......@@ -70,7 +70,7 @@ class MaxPoolWithIndexGradKernel : 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");
if (context.Attr<bool>("global_pooling")) {
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
ksize[i] = static_cast<int>(in_x_grad->dims()[i + 2]);
}
......
python/paddle/v2/framework/tests/test_pool2d_cudnn_op.py
浏览文件 @ 0ab012cf
......@@ -56,8 +56,8 @@ class TestPool2d_cudnn_Op(OpTest):
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'poolingType': self.pool_type,
'globalPooling': self.global_pool,
}
self.outputs = {'Out': output}
......
python/paddle/v2/framework/tests/test_pool2d_op.py
浏览文件 @ 0ab012cf
......@@ -56,8 +56,8 @@ class TestPool2d_Op(OpTest):
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'poolingType': self.pool_type,
'globalPooling': self.global_pool,
}
self.outputs = {'Out': output}
......
python/paddle/v2/framework/tests/test_pool3d_op.py
浏览文件 @ 0ab012cf
......@@ -64,8 +64,8 @@ class TestPool3d_Op(OpTest):
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'poolingType': self.pool_type,
'globalPooling': self.global_pool,
}
self.outputs = {'Out': output}
......
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