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Merge pull request #6204 from sweetsky0901/my_spp_op

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add spp（Spatial pyramid pooling ） op
del_some_in_makelist
sweetsky0901 8 years ago committed by GitHub
parent e3a9a896bc 5be5afb331
commit 7456d737b2
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99
paddle/operators/spp_op.cc
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/* 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.
Indicesou 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/spp_op.h"
namespace paddle {
namespace operators {
class SppOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SppOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"(Tensor) The input tensor of spp 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 feature.");
AddOutput("Out",
"(Tensor) The output tensor of spp operator."
"N * M."
"M = C * H * W");
AddAttr<int>("pyramid_height", "(int), multi level pooling");
AddAttr<std::string>(
"pooling_type",
"(string), pooling type, can be \"max\" for max-pooling "
"and \"avg\" for average-pooling.")
.InEnum({"max", "avg"});
AddComment(R"DOC(
"With spatial pyramid pooling, the input image can
be of any sizes. This not only allows arbitrary aspect
ratios, but also allows arbitrary scales. We can resize
the input image to any scale (e.g., min(w, h)=180, 224,
...) and apply the same deep network. When the
input image is at different scales, the network (with
the same filter sizes) will extract features at different
scales. The scales play important roles in traditional
methods.
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Output shape: $(H_{out}, W_{out})$
Where
$$
H_{out} = N \\
W_{out} = (((4^pyramid_height) - 1) / (4 - 1))$ * C_{in}
$$
paper https://arxiv.org/pdf/1406.4729v4.pdf
)DOC");
}
};
class SppOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SppOp"
"should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SppOp should not be null.");
auto in_x_dims = ctx->GetInputDim("X");
int pyramid_height = ctx->Attrs().Get<int>("pyramid_height");
PADDLE_ENFORCE(in_x_dims.size() == 4,
"Spping intput must be of 4-dimensional.");
int outlen = ((std::pow(4, pyramid_height) - 1) / (4 - 1)) * in_x_dims[1];
std::vector<int64_t> output_shape({in_x_dims[0], outlen});
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
}
};
class SppOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Input(X@GRAD) should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(spp, ops::SppOp, ops::SppOpMaker, spp_grad, ops::SppOpGrad);
REGISTER_OP_CPU_KERNEL(
spp, ops::SppKernel<paddle::platform::CPUDeviceContext, float>,
ops::SppKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
spp_grad, ops::SppGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::SppGradKernel<paddle::platform::CPUDeviceContext, double>);

23
paddle/operators/spp_op.cu.cc
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/* 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.
Indicesou 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/spp_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
spp, ops::SppKernel<paddle::platform::CUDADeviceContext, float>,
ops::SppKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
spp_grad, ops::SppGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::SppGradKernel<paddle::platform::CUDADeviceContext, double>);

161
paddle/operators/spp_op.h
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/* 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.
Indicesou 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/math/pooling.h"
#include "paddle/operators/strided_memcpy.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class SppKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
int pyramid_height = context.template Attr<int>("pyramid_height");
std::string pooling_type =
context.template Attr<std::string>("pooling_type");
out->mutable_data<T>(context.GetPlace());
auto out_stride = framework::stride(out->dims());
int input_h = in_x->dims()[2];
int input_w = in_x->dims()[3];
size_t output_offset = 0;
for (int p = 0; p < pyramid_height; ++p) {
int bins = std::pow(2, p);
int kernel_size_h = std::ceil(input_h / static_cast<double>(bins));
int kernel_size_w = std::ceil(input_w / static_cast<double>(bins));
int padding_h = (kernel_size_h * bins - input_h + 1) / 2;
int padding_w = (kernel_size_w * bins - input_w + 1) / 2;
std::vector<int> kernel_size({kernel_size_h, kernel_size_w});
std::vector<int> strides({kernel_size_h, kernel_size_w});
std::vector<int> paddings({padding_h, padding_w});
// pooling output shape
framework::Tensor out_level;
std::vector<int64_t> output_shape_vec(
{in_x->dims()[0], in_x->dims()[1], bins, bins});
framework::DDim output_shape(framework::make_ddim(output_shape_vec));
out_level.mutable_data<T>(output_shape, context.GetPlace());
// pooling
if (pooling_type == "max") {
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);
} 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);
}
// flatten pooling output shape
int output_flatten_w = in_x->dims()[1] * bins * bins;
std::vector<int64_t> output_flatten_shape_vec(
{in_x->dims()[0], output_flatten_w});
framework::DDim output_flatten_shape(
framework::make_ddim(output_flatten_shape_vec));
out_level.Resize(output_flatten_shape);
// concat
auto out_level_stride = framework::stride(out_level.dims());
StridedMemcpy<T>(context.template device_context<DeviceContext>(),
out_level.data<T>(), out_level_stride, out_level.dims(),
out_stride, out->data<T>() + output_offset);
output_offset += out_level.dims()[1] * out_level_stride[1];
}
}
};
template <typename DeviceContext, typename T>
class SppGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
const framework::Tensor* out = context.Input<framework::Tensor>("Out");
const framework::Tensor* out_grad =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
framework::Tensor* in_x_grad =
context.Output<framework::Tensor>(framework::GradVarName("X"));
int pyramid_height = context.template Attr<int>("pyramid_height");
std::string pooling_type =
context.template Attr<std::string>("pooling_type");
auto& device_ctx = context.template device_context<DeviceContext>();
math::SetConstant<DeviceContext, T> zero;
in_x_grad->mutable_data<T>(context.GetPlace());
zero(device_ctx, in_x_grad, static_cast<T>(0));
auto out_stride = framework::stride(out->dims());
int input_h = in_x->dims()[2];
int input_w = in_x->dims()[3];
size_t out_offset = 0;
for (int p = 0; p < pyramid_height; ++p) {
int bins = std::pow(2, p);
int kernel_size_h = std::ceil(input_h / static_cast<double>(bins));
int kernel_size_w = std::ceil(input_w / static_cast<double>(bins));
int padding_h = (kernel_size_h * bins - input_h + 1) / 2;
int padding_w = (kernel_size_w * bins - input_w + 1) / 2;
std::vector<int> kernel_size({kernel_size_h, kernel_size_w});
std::vector<int> strides({kernel_size_h, kernel_size_w});
std::vector<int> paddings({padding_h, padding_w});
// split out and outgrad ... to flatten
framework::Tensor out_level;
framework::Tensor outgrad_level;
int out_flatten_w = in_x->dims()[1] * bins * bins;
std::vector<int64_t> out_flatten_shape_vec(
{in_x->dims()[0], out_flatten_w});
framework::DDim out_flatten_shape(
framework::make_ddim(out_flatten_shape_vec));
out_level.mutable_data<T>(out_flatten_shape, context.GetPlace());
outgrad_level.mutable_data<T>(out_flatten_shape, context.GetPlace());
auto flatten_stride = framework::stride(out_level.dims());
// memcpy
StridedMemcpy<T>(context.template device_context<DeviceContext>(),
out->data<T>() + out_offset, out_stride,
out_level.dims(), flatten_stride, out_level.data<T>());
StridedMemcpy<T>(context.template device_context<DeviceContext>(),
out_grad->data<T>() + out_offset, out_stride,
outgrad_level.dims(), flatten_stride,
outgrad_level.data<T>());
out_offset += out_level.dims()[1] * out_stride[1];
// flatten backward to nchw
std::vector<int64_t> out_shape_vec({in_x->dims()[0], in_x->dims()[1]});
out_shape_vec.push_back(
(input_h - kernel_size_h + 2 * padding_h) / kernel_size_h + 1);
out_shape_vec.push_back(
(input_w - kernel_size_w + 2 * padding_w) / kernel_size_w + 1);
framework::DDim out_shape(framework::make_ddim(out_shape_vec));
out_level.ShareDataWith(out_level);
out_level.Resize(out_shape);
outgrad_level.ShareDataWith(outgrad_level);
outgrad_level.Resize(out_shape);
// pooling backward
if (pooling_type == "max") {
math::MaxPool2dGradFunctor<DeviceContext, T> pool2d_backward;
pool2d_backward(context.template device_context<DeviceContext>(), *in_x,
*&out_level, *&outgrad_level, kernel_size, strides,
paddings, in_x_grad);
} else if (pooling_type == "avg") {
math::Pool2dGradFunctor<DeviceContext, math::AvgPoolGrad<T>, T>
pool_backward;
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);
}
}
}
};
} // namespace operators
} // namespace paddle

68
python/paddle/v2/fluid/tests/test_spp_op.py
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import unittest
import numpy as np
from op_test import OpTest
from test_pool2d_op import max_pool2D_forward_naive
from test_pool2d_op import avg_pool2D_forward_naive
class TestSppOp(OpTest):
def setUp(self):
self.op_type = "spp"
self.init_test_case()
input = np.random.random(self.shape).astype("float32")
nsize, csize, hsize, wsize = input.shape
out_level_flatten = []
for i in xrange(self.pyramid_height):
bins = np.power(2, i)
kernel_size = [0, 0]
padding = [0, 0]
kernel_size[0] = np.ceil(hsize /
bins.astype("double")).astype("int32")
padding[0] = (
(kernel_size[0] * bins - hsize + 1) / 2).astype("int32")
kernel_size[1] = np.ceil(wsize /
bins.astype("double")).astype("int32")
padding[1] = (
(kernel_size[1] * bins - wsize + 1) / 2).astype("int32")
out_level = self.pool2D_forward_naive(input, kernel_size,
kernel_size, padding)
out_level_flatten.append(
out_level.reshape(nsize, bins * bins * csize))
if i == 0:
output = out_level_flatten[i]
else:
output = np.concatenate((output, out_level_flatten[i]), 1)
# output = np.concatenate(out_level_flatten.tolist(), 0);
self.inputs = {'X': input.astype('float32'), }
self.attrs = {
'pyramid_height': self.pyramid_height,
'pooling_type': self.pool_type
}
self.outputs = {'Out': output.astype('float32')}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
if self.pool_type != "avg":
self.check_grad(['X'], 'Out', max_relative_error=0.05)
def init_test_case(self):
self.shape = [3, 2, 4, 4]
self.pyramid_height = 3
self.pool2D_forward_naive = max_pool2D_forward_naive
self.pool_type = "max"
class TestCase2(TestSppOp):
def init_test_case(self):
self.shape = [3, 2, 4, 4]
self.pyramid_height = 3
self.pool2D_forward_naive = avg_pool2D_forward_naive
self.pool_type = "avg"
if __name__ == '__main__':
unittest.main()
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