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Add max pool with index

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revert-4814-Add_sequence_project_op
chengduoZH 7 years ago
parent 38bca7d347
commit 6326c40d27
9 changed files with 1171 additions and 2 deletions
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6
paddle/operators/CMakeLists.txt
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@ -62,6 +62,12 @@ function(op_library TARGET)
file(APPEND ${pybind_file} "USE_OP(sigmoid);\n")
endif()
if ("${TARGET}" STREQUAL "pool_with_index_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(maxPool2dWithIndex);\n")
endif()
# pybind USE_NO_KERNEL_OP
file(READ ${TARGET}.cc TARGET_CONTENT)
string(REGEX MATCH "OperatorWithKernel" regex_result "${TARGET_CONTENT}")

4
paddle/operators/math/CMakeLists.txt
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@ -1,12 +1,12 @@
if(WITH_GPU)
nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc
im2col.cu DEPS cblas device_context operator)
im2col.cu pooling.cc pooling.cu DEPS cblas device_context operator)
nv_library(softmax_function SRCS softmax.cc softmax.cu
DEPS operator)
nv_library(cross_entropy_function SRCS cross_entropy.cc cross_entropy.cu
DEPS operator)
else()
cc_library(math_function SRCS math_function.cc im2col.cc
cc_library(math_function SRCS math_function.cc im2col.cc pooling.cc
DEPS cblas device_context operator)
cc_library(softmax_function SRCS softmax.cc DEPS operator)
cc_library(cross_entropy_function SRCS cross_entropy.cc DEPS operator)

255
paddle/operators/math/pooling.cc
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387
paddle/operators/math/pooling.cu
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68
paddle/operators/math/pooling.h
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@ -0,0 +1,68 @@
/* 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/eigen.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/hostdevice.h"
namespace paddle {
namespace operators {
namespace math {
//////////////////////
#define FLT_MAX __FLT_MAX__
/////////////////////
template <typename Place, typename T>
class MaxPool2dWithIndexFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
};
template <typename Place, typename T>
class MaxPool2dWithIndexGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& input_grad,
const framework::Tensor& output_grad,
const framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
};
template <typename Place, typename T>
class MaxPool3dWithIndexFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
};
template <typename Place, typename T>
class MaxPool3dWithIndexGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& input_grad,
const framework::Tensor& output_grad,
const framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
};
} // namespace math
} // namespace operators
} // namespace paddle

198
paddle/operators/pool_with_index_op.cc
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@ -0,0 +1,198 @@
/* 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/pool_with_index_op.h"
namespace paddle {
namespace operators {
int OutputSizeMaxPool(int input_size, int filter_size, int padding,
int stride) {
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
return output_size;
}
class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"X(Input) of Pooling should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Out(Output) of Pooling should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Mask"),
"Out(Output) of Pooling should not be null.");
auto in_x_dims = ctx->GetInputDim("X");
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");
PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
"Pooling intput should be 4-D or 5-D");
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]);
}
PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
"Pooling intput size and pooling size should be consistent");
PADDLE_ENFORCE(ksize.size() == 2 || ksize.size() == 3,
"Pooling size size should be 2 elements. or 3 elements.");
PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
"strides size and pooling size should be the same.");
PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
"paddings size and pooling size should be the same.");
std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
for (size_t i = 0; i < ksize.size(); ++i) {
output_shape.push_back(OutputSizeMaxPool(in_x_dims[i + 2], ksize[i],
paddings[i], strides[i]));
}
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
ctx->SetOutputDim("Mask", framework::make_ddim(output_shape));
}
};
class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("X")),
"X(Input) of MaxPoolWithIndexOpGrad should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput(framework::GradVarName("X")),
"X@GRAD(Input@GRAD) of MaxPoolWithIndexOpGrad should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MaxPool2dWithIndexOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"The input tensor of pooling operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of image.");
AddOutput("Out",
"The output tensor of pooling operator."
"The format of output tensor is also NCHW.");
AddOutput("Mask",
"The Mask tensor of pooling operator."
"The format of output tensor is also NCHW.");
AddAttr<std::vector<int>>(
"ksize", "pooling size(height, width) of pooling operator.");
AddAttr<bool>(
"globalPooling",
"whether to use the globalPooling."
"int constant equal to false or true"
"default false"
"If globalPooling = true, ksize is ignored and need not be specified.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"strides(height, width) of pooling operator."
"default {1,1}")
.SetDefault({1, 1});
AddAttr<std::vector<int>>("paddings",
"paddings(height, width) of pooling operator."
"default {0,0}")
.SetDefault({0, 0});
AddComment(R"DOC(
The maxPooling2d with index operation calculates the output and the mask based on
the input and ksize, strides, paddings parameters.
)DOC");
}
};
class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MaxPool3dWithIndexOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"The input tensor of pooling operator. "
"The format of input tensor is NCDHW. Where N is batch size, C is "
"the number of channels, D, H and W is the depth, height and width of "
"image.");
AddOutput("Out",
"The output tensor of pooling operator."
"The format of output tensor is also NCDHW.");
AddOutput("Mask",
"The Mask tensor of pooling operator."
"The format of output tensor is also NCDHW.");
AddAttr<std::vector<int>>(
"ksize", "pooling size(depth, height, width) of pooling operator.");
AddAttr<bool>(
"globalPooling",
"whether to use the globalPooling."
"int constant equal to false or true"
"default false"
"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}")
.SetDefault({1, 1, 1});
AddAttr<std::vector<int>>(
"paddings",
"paddings(depth, height, width) of pooling operator."
"default {0,0,0}")
.SetDefault({0, 0, 0});
AddComment(R"DOC(
The maxpooling3d with index operation calculates the output and the mask based on
the input and ksize, strides, paddings parameters.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(maxPool2dWithIndex, ops::MaxPoolWithIndexOp,
ops::MaxPool2dWithIndexOpMaker, maxPool2dWithIndex_grad,
ops::MaxPoolWithIndexOpGrad);
REGISTER_OP_CPU_KERNEL(
maxPool2dWithIndex,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
maxPool2dWithIndex_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float>)
REGISTER_OP(maxPool3dWithIndex, ops::MaxPoolWithIndexOp,
ops::MaxPool3dWithIndexOpMaker, maxPool3dWithIndex_grad,
ops::MaxPoolWithIndexOpGrad);
REGISTER_OP_CPU_KERNEL(
maxPool3dWithIndex,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
maxPool3dWithIndex_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float>)

31
paddle/operators/pool_with_index_op.cu
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@ -0,0 +1,31 @@
/* 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/pool_with_index_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
maxPool2dWithIndex,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
maxPool2dWithIndex_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float>)
REGISTER_OP_GPU_KERNEL(
maxPool3dWithIndex,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
maxPool3dWithIndex_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float>)

99
paddle/operators/pool_with_index_op.h
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@ -0,0 +1,99 @@
/* 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/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/pooling.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename Place, typename T>
class MaxPoolWithIndexKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* in_x = context.Input<Tensor>("X");
Tensor* out = context.Output<Tensor>("Out");
Tensor* mask = context.Output<Tensor>("Mask");
bool global_pooling = context.Attr<bool>("globalPooling");
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 (global_pooling) {
for (size_t i = 0; i < ksize.size(); ++i) {
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
}
switch (ksize.size()) {
case 2: {
paddle::operators::math::MaxPool2dWithIndexFunctor<Place, T>
pool2d_forward;
pool2d_forward(context.device_context(), *in_x, *out, *mask, ksize,
strides, paddings);
} break;
case 3: {
paddle::operators::math::MaxPool3dWithIndexFunctor<Place, T>
pool3d_forward;
pool3d_forward(context.device_context(), *in_x, *out, *mask, ksize,
strides, paddings);
} break;
}
}
};
template <typename Place, typename T>
class MaxPoolWithIndexGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* mask = context.Input<Tensor>("Maks");
const Tensor* out_grad =
context.Input<Tensor>(framework::GradVarName("Out"));
Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
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 (in_x_grad) {
in_x_grad->mutable_data<T>(context.GetPlace());
auto temp = framework::EigenVector<T>::Flatten(*in_x_grad);
temp.device(context.GetEigenDevice<Place>()) =
temp.constant(static_cast<T>(0));
switch (ksize.size()) {
case 2: {
paddle::operators::math::MaxPool2dWithIndexGradFunctor<Place, T>
pool2d_backward;
pool2d_backward(context.device_context(), *in_x_grad, *out_grad,
*mask, ksize, strides, paddings);
} break;
case 3: {
paddle::operators::math::MaxPool3dWithIndexGradFunctor<Place, T>
pool3d_backward;
pool3d_backward(context.device_context(), *in_x_grad, *out_grad,
*mask, ksize, strides, paddings);
} break;
}
}
}
};
} // namespace operators
} // namespace paddle

125
python/paddle/v2/framework/tests/test_pool_max_op.py
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import unittest
import numpy as np
from op_test import OpTest
def max_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
N, C, D, H, W = x.shape
if global_pool == 1:
ksize = [D, H, W]
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
out = np.zeros((N, C, D_out, H_out, W_out))
mask = np.zeros((N, C, D_out, H_out, W_out))
for k in xrange(D_out):
d_start = np.max((k * strides[0] - paddings[0], 0))
d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D))
for i in xrange(H_out):
h_start = np.max((i * strides[0] - paddings[0], 0))
h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
for j in xrange(W_out):
w_start = np.max((j * strides[1] - paddings[1], 0))
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.max(x_masked, axis=(2, 3, 4))
# mask[:,:, k, i, j] = np.argmax(x_masked, axis=(2, 3, 4))
return out
def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
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))
mask = np.zeros((N, C, H_out, W_out))
for i in xrange(H_out):
for j in xrange(W_out):
r_start = np.max((i * strides[0] - paddings[0], 0))
r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
c_start = np.max((j * strides[1] - paddings[1], 0))
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.max(x_masked, axis=(2, 3))
# mask[:,:, i, j] = np.argmax(x_masked, axis=(2, 3))
return out
class TestMaxPoolWithIndex_Op(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = "maxPool3dWithIndex"
input = np.random.random(self.shape).astype("float32")
output = self.pool_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool)
# mask = np.zeros(output.shape)
self.attrs = {
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'globalPooling': self.global_pool,
}
self.inputs = {'X': input}
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
# def test_check_grad(self):
# self.check_grad(set(['X']), ['Out'], max_relative_error=0.07)
def initTestCase(self):
self.global_pool = 0
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 TestCase1(TestMaxPoolWithIndex_Op):
def initTestCase(self):
self.global_pool = 1
self.op_type = "maxPool3dWithIndex"
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 = [0, 0, 0]
class TestCase2(TestMaxPoolWithIndex_Op):
def initTestCase(self):
self.global_pool = 0
self.op_type = "maxPool2dWithIndex"
self.pool_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 TestCase3(TestMaxPoolWithIndex_Op):
def initTestCase(self):
self.global_pool = 1
self.op_type = "maxPool2dWithIndex"
self.pool_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 5, 5]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
if __name__ == '__main__':
unittest.main()
"""
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