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Merge pull request #4121 from zchen0211/develop

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Prelu with forward, backward and python test passed
update-doc-pybind
Zhuoyuan 8 years ago committed by GitHub
parent 6e9337e301 6d1446ee83
commit f86c1ccdbe
4 changed files with 246 additions and 0 deletions
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94
paddle/operators/prelu_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.
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/prelu_op.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
class PReluOp : public framework::OperatorWithKernel {
public:
PReluOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
auto *in = ctx.Input<framework::Tensor>("X");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Alpha"),
"Input(Alpha) should not be null");
auto *alpha = ctx.Input<framework::Tensor>("Alpha");
PADDLE_ENFORCE(alpha->numel() == 1, "Size of weight Alpha must be one.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) should not be null");
auto *out = ctx.Output<framework::LoDTensor>("Out");
out->Resize(in->dims());
}
};
class PReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input tensor of prelu operator.");
AddInput("Alpha", "The alpha weight of PRelu operator.");
AddOutput("Out", "The output tensor of PRelu operator.");
AddComment(R"DOC(PRelu operator
The equation is:
f(x) = alpha * x , for x < 0
f(x) = x , for x >= 0
)DOC");
}
};
// The operator to calculate gradients of a prelu operator.
class PReluGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto *dx = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto *x = ctx.Input<framework::Tensor>("X");
auto *dalpha =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Alpha"));
auto *alpha = ctx.Input<framework::Tensor>("Alpha");
dx->Resize(x->dims());
dalpha->Resize(alpha->dims());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(prelu, ops::PReluOp, ops::PReluOpMaker, prelu_grad,
ops::PReluGradOp);
REGISTER_OP_CPU_KERNEL(prelu,
ops::PReluKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(prelu_grad,
ops::PReluGradKernel<paddle::platform::CPUPlace, float>);

21
paddle/operators/prelu_op.cu
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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.
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/prelu_op.h"
REGISTER_OP_GPU_KERNEL(
prelu, paddle::operators::PReluKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
prelu_grad,
paddle::operators::PReluGradKernel<paddle::platform::GPUPlace, float>);

103
paddle/operators/prelu_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.
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/platform/transform.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using platform::Transform;
template <typename T>
class PReluFunctor {
public:
explicit PReluFunctor(const T* alpha) : alpha_(alpha) {}
HOSTDEVICE T operator()(const T& x) const {
if (x > 0)
return x;
else
return x * (*alpha_);
}
private:
const T* alpha_;
};
template <typename Place, typename T>
class PReluKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* x = context.Input<Tensor>("X");
auto* alpha = context.Input<Tensor>("Alpha");
auto* out = context.Output<Tensor>("Out");
const T* x_ptr = x->data<T>();
T* o_ptr = out->mutable_data<T>(context.GetPlace());
auto* alpha_ptr = alpha->data<T>();
int numel = x->numel();
auto place = context.GetPlace();
Transform(place, x_ptr, x_ptr + numel, o_ptr, PReluFunctor<T>(alpha_ptr));
}
};
template <typename T>
class PReluGradFunctor {
public:
explicit PReluGradFunctor(const T* alpha) : alpha_(alpha) {}
HOSTDEVICE T operator()(const T& out, const T& dout) const {
if (out > 0)
return dout;
else
return dout * (*alpha_);
}
private:
const T* alpha_;
};
template <typename Place, typename T>
class PReluGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
auto* dout = context.Input<Tensor>(framework::GradVarName("Out"));
auto* out = context.Input<Tensor>("Out");
auto* alpha = context.Input<Tensor>("Alpha");
auto* alpha_ptr = alpha->data<T>();
T* dx_ptr = dx->mutable_data<T>(context.GetPlace());
const T* dout_ptr = dout->data<T>();
const T* out_ptr = out->data<T>();
int numel = dx->numel();
auto place = context.GetPlace();
Transform(place, out_ptr, out_ptr + numel, dout_ptr, dx_ptr,
PReluGradFunctor<T>(alpha_ptr));
// TODO (Zhuoyuan): add dalpha upgrade when GPU kernels ready
}
};
} // namespace operators
} // namespace paddle

28
python/paddle/v2/framework/tests/test_prelu_op.py
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import unittest
import numpy as np
from op_test import OpTest
class PReluTest(OpTest):
def setUp(self):
self.op_type = "prelu"
x_np = np.random.normal(size=(10, 10)).astype("float32")
x_np_sign = np.sign(x_np)
x_np = x_np_sign * np.maximum(x_np, .005)
alpha_np = np.array([.1])
self.inputs = {'X': x_np, 'Alpha': alpha_np}
out_np = np.maximum(self.inputs['X'], 0.)
out_np = out_np + np.minimum(self.inputs['X'],
0.) * self.inputs['Alpha']
assert out_np is not self.inputs['X']
self.outputs = {'Out': out_np}
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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