未验证 提交 918aeb71 编写于 作者: R ronnywang 提交者: GitHub

Add atan2 op and test (#33067)

* add atan2_op

* fix
上级 b0984c7c
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/operators/atan2_op.h"
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
namespace paddle {
namespace operators {
class Atan2Op : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X1"), "Input", "X1", "atan2");
OP_INOUT_CHECK(ctx->HasInput("X2"), "Input", "X2", "atan2");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "atan2");
auto in_dims = ctx->GetInputDim("X1");
ctx->SetOutputDim("Out", in_dims);
}
};
class Atan2OpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X1", "(Tensor), The input tensor of atan2 op.");
AddInput("X2", "(Tensor), The input tensor of atan2 op.");
AddOutput("Out", "(Tensor), The output tensor of atan2 op.");
AddComment(R"DOC(
Atan2 Operator.
This operator is used to perform elementwise atan2 for input $X1$, $X2$.
$$out = atan2(x1, x2)$$
)DOC");
}
};
class Atan2GradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X1"), "Input", "X1", "Atan2Grad");
OP_INOUT_CHECK(ctx->HasInput("X2"), "Input", "X2", "Atan2Grad");
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
"Out@Grad", "Atan2Grad");
auto x1_grad_name = framework::GradVarName("X1");
auto x2_grad_name = framework::GradVarName("X2");
auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
if (ctx->HasOutput(x1_grad_name)) {
ctx->SetOutputDim(framework::GradVarName("X1"), dout_dims);
}
if (ctx->HasOutput(x2_grad_name)) {
ctx->SetOutputDim(framework::GradVarName("X2"), dout_dims);
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto dtype = OperatorWithKernel::IndicateVarDataType(ctx, "X1");
return framework::OpKernelType(dtype, ctx.GetPlace());
}
};
template <typename T>
class Atan2GradMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
void Apply(GradOpPtr<T> retv) const override {
retv->SetType("atan2_grad");
retv->SetInput("X1", this->Input("X1"));
retv->SetInput("X2", this->Input("X2"));
retv->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
retv->SetAttrMap(this->Attrs());
retv->SetOutput(framework::GradVarName("X1"), this->InputGrad("X1"));
retv->SetOutput(framework::GradVarName("X2"), this->InputGrad("X2"));
}
};
class Atan2OpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(framework::InferVarTypeContext* ctx) const override {
auto type = ctx->GetInputDataType("X1");
if (ctx->GetInputDataType("X1") == framework::proto::VarType::INT32 ||
ctx->GetInputDataType("X1") == framework::proto::VarType::INT64 ||
ctx->GetInputDataType("X2") == framework::proto::VarType::INT32 ||
ctx->GetInputDataType("X2") == framework::proto::VarType::INT64) {
type = framework::proto::VarType::FP64;
}
ctx->SetOutputDataType("Out", type);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(atan2, ops::Atan2Op, ops::Atan2OpMaker,
ops::Atan2GradMaker<paddle::framework::OpDesc>,
ops::Atan2GradMaker<paddle::imperative::OpBase>,
ops::Atan2OpVarTypeInference);
REGISTER_OPERATOR(atan2_grad, ops::Atan2GradOp);
REGISTER_OP_CPU_KERNEL(
atan2, ops::Atan2Kernel<paddle::platform::CPUDeviceContext, int32_t>,
ops::Atan2Kernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::Atan2Kernel<paddle::platform::CPUDeviceContext, float>,
ops::Atan2Kernel<paddle::platform::CPUDeviceContext, double>,
ops::Atan2Kernel<paddle::platform::CPUDeviceContext,
paddle::platform::float16>);
REGISTER_OP_CPU_KERNEL(
atan2_grad, ops::Atan2GradKernel<paddle::platform::CPUDeviceContext, float>,
ops::Atan2GradKernel<paddle::platform::CPUDeviceContext, double>,
ops::Atan2GradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::float16>);
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/operators/atan2_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
atan2, ops::Atan2Kernel<paddle::platform::CUDADeviceContext, int32_t>,
ops::Atan2Kernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::Atan2Kernel<paddle::platform::CUDADeviceContext, float>,
ops::Atan2Kernel<paddle::platform::CUDADeviceContext, double>,
ops::Atan2Kernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>);
REGISTER_OP_CUDA_KERNEL(
atan2_grad,
ops::Atan2GradKernel<paddle::platform::CUDADeviceContext, float>,
ops::Atan2GradKernel<paddle::platform::CUDADeviceContext, double>,
ops::Atan2GradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>);
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using framework::To32BitIndex;
template <typename T>
struct Atan2Out {
using type = T;
};
template <>
struct Atan2Out<int32_t> {
using type = double;
};
template <>
struct Atan2Out<int64_t> {
using type = double;
};
template <typename T>
struct Atan2Functor {
Atan2Functor(const T* x1, const T* x2, typename Atan2Out<T>::type* out,
int64_t numel)
: x1_(x1), x2_(x2), out_(out), numel_(numel) {}
HOSTDEVICE void operator()(int64_t idx) const {
out_[idx] = static_cast<typename Atan2Out<T>::type>(
::atan2f(static_cast<float>(x1_[idx]), static_cast<float>(x2_[idx])));
}
const T* x1_;
const T* x2_;
typename Atan2Out<T>::type* out_;
int64_t numel_;
};
template <>
struct Atan2Functor<double> {
Atan2Functor(const double* x1, const double* x2, double* out, int64_t numel)
: x1_(x1), x2_(x2), out_(out), numel_(numel) {}
HOSTDEVICE void operator()(int64_t idx) const {
out_[idx] = ::atan2(x1_[idx], x2_[idx]);
}
const double* x1_;
const double* x2_;
double* out_;
int64_t numel_;
};
// dx1 = dout * x2 / ((x1)^2 + (x2)^2)
// dx2 = - dout * x1 / ((x1)^2 + (x2)^2)
template <typename T>
struct Atan2GradFunctor {
Atan2GradFunctor(const T* x1, const T* x2, const T* dout, T* dx1, T* dx2,
int64_t numel)
: x1_(x1), x2_(x2), dout_(dout), dx1_(dx1), dx2_(dx2), numel_(numel) {}
HOSTDEVICE void operator()(int64_t idx) const {
float x1 = static_cast<float>(x1_[idx]);
float x2 = static_cast<float>(x2_[idx]);
float x = x1 * x1 + x2 * x2;
dx1_[idx] = static_cast<T>(static_cast<float>(dout_[idx]) * x2 / x);
dx2_[idx] = static_cast<T>(-static_cast<float>(dout_[idx]) * x1 / x);
}
const T* x1_;
const T* x2_;
const T* dout_;
T* dx1_;
T* dx2_;
int64_t numel_;
};
template <>
struct Atan2GradFunctor<double> {
Atan2GradFunctor(const double* x1, const double* x2, const double* dout,
double* dx1, double* dx2, int64_t numel)
: x1_(x1), x2_(x2), dout_(dout), dx1_(dx1), dx2_(dx2), numel_(numel) {}
HOSTDEVICE void operator()(int64_t idx) const {
auto x = x1_[idx] * x1_[idx] + x2_[idx] * x2_[idx];
dx1_[idx] = dout_[idx] * x2_[idx] / x;
dx2_[idx] = -dout_[idx] * x1_[idx] / x;
}
const double* x1_;
const double* x2_;
const double* dout_;
double* dx1_;
double* dx2_;
int64_t numel_;
};
template <typename DeviceContext, typename T>
class Atan2Kernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* X1 = context.Input<Tensor>("X1");
const Tensor* X2 = context.Input<Tensor>("X2");
Tensor* Out = context.Output<Tensor>("Out");
auto numel = X1->numel();
auto x1 = X1->data<T>();
auto x2 = X2->data<T>();
auto out = Out->mutable_data<typename Atan2Out<T>::type>(
context.GetPlace(), size_t(numel * sizeof(typename Atan2Out<T>::type)));
auto& dev_ctx = context.template device_context<DeviceContext>();
platform::ForRange<DeviceContext> for_range(dev_ctx, numel);
Atan2Functor<T> functor(x1, x2, out, numel);
for_range(functor);
}
};
template <typename DeviceContext, typename T>
class Atan2GradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const {
const Tensor* X1 = context.Input<Tensor>("X1");
const Tensor* X2 = context.Input<Tensor>("X2");
const Tensor* dOut = context.Input<Tensor>(framework::GradVarName("Out"));
Tensor* dX1 = context.Output<Tensor>(framework::GradVarName("X1"));
Tensor* dX2 = context.Output<Tensor>(framework::GradVarName("X2"));
auto numel = X1->numel();
auto x1 = X1->data<T>();
auto x2 = X2->data<T>();
auto dout = dOut->data<T>();
auto dx1 =
dX1->mutable_data<T>(context.GetPlace(), size_t(numel * sizeof(T)));
auto dx2 =
dX2->mutable_data<T>(context.GetPlace(), size_t(numel * sizeof(T)));
auto& dev_ctx = context.template device_context<DeviceContext>();
platform::ForRange<DeviceContext> for_range(dev_ctx, numel);
Atan2GradFunctor<T> functor(x1, x2, dout, dx1, dx2, numel);
for_range(functor);
}
};
} // namespace operators
} // namespace paddle
...@@ -152,6 +152,7 @@ from .tensor.math import abs # noqa: F401 ...@@ -152,6 +152,7 @@ from .tensor.math import abs # noqa: F401
from .tensor.math import acos # noqa: F401 from .tensor.math import acos # noqa: F401
from .tensor.math import asin # noqa: F401 from .tensor.math import asin # noqa: F401
from .tensor.math import atan # noqa: F401 from .tensor.math import atan # noqa: F401
from .tensor.math import atan2 # noqa: F401
from .tensor.math import ceil # noqa: F401 from .tensor.math import ceil # noqa: F401
from .tensor.math import cos # noqa: F401 from .tensor.math import cos # noqa: F401
from .tensor.math import tan # noqa: F401 from .tensor.math import tan # noqa: F401
...@@ -434,6 +435,7 @@ __all__ = [ # noqa ...@@ -434,6 +435,7 @@ __all__ = [ # noqa
'divide', 'divide',
'ceil', 'ceil',
'atan', 'atan',
'atan2',
'expand', 'expand',
'broadcast_to', 'broadcast_to',
'ones_like', 'ones_like',
......
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import numpy as np
import unittest
from op_test import OpTest
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import compiler, Program, program_guard
paddle.enable_static()
np.random.seed(0)
def atan2_grad(x1, x2, dout):
dx1 = dout * x2 / (x1 * x1 + x2 * x2)
dx2 = -dout * x1 / (x1 * x1 + x2 * x2)
return dx1, dx2
class TestAtan2(OpTest):
def setUp(self):
self.op_type = "atan2"
self.init_dtype()
x1 = np.random.uniform(-1, -0.1, [15, 17]).astype(self.dtype)
x2 = np.random.uniform(0.1, 1, [15, 17]).astype(self.dtype)
out = np.arctan2(x1, x2)
self.inputs = {'X1': x1, 'X2': x2}
self.outputs = {'Out': out}
def test_check_grad(self):
self.check_grad(['X1', 'X2'], 'Out')
def test_check_output(self):
self.check_output()
def init_dtype(self):
self.dtype = np.float64
class TestAtan2_float(TestAtan2):
def init_dtype(self):
self.dtype = np.float32
def test_check_grad(self):
if self.dtype not in [np.int32, np.int64]:
self.check_grad(
['X1', 'X2'],
'Out',
user_defined_grads=atan2_grad(self.inputs['X1'],
self.inputs['X2'],
1 / self.inputs['X1'].size))
class TestAtan2_float16(TestAtan2_float):
def init_dtype(self):
self.dtype = np.float16
class TestAtan2_int32(TestAtan2_float):
def init_dtype(self):
self.dtype = np.int32
class TestAtan2_int64(TestAtan2_float):
def init_dtype(self):
self.dtype = np.int64
class TestAtan2API(unittest.TestCase):
def init_dtype(self):
self.dtype = 'float64'
self.shape = [11, 17]
def setUp(self):
self.init_dtype()
self.x1 = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
self.x2 = np.random.uniform(-1, -0.1, self.shape).astype(self.dtype)
self.place = [paddle.CPUPlace()]
if core.is_compiled_with_cuda():
self.place.append(paddle.CUDAPlace(0))
def test_static_api(self):
paddle.enable_static()
def run(place):
with paddle.static.program_guard(paddle.static.Program()):
X1 = paddle.fluid.data('X1', self.shape, dtype=self.dtype)
X2 = paddle.fluid.data('X2', self.shape, dtype=self.dtype)
out = paddle.atan2(X1, X2)
exe = paddle.static.Executor(place)
res = exe.run(feed={'X1': self.x1, 'X2': self.x2})
out_ref = np.arctan2(self.x1, self.x2)
for r in res:
self.assertEqual(np.allclose(out_ref, r), True)
for place in self.place:
run(place)
def test_dygraph_api(self):
def run(place):
paddle.disable_static(place)
X1 = paddle.to_tensor(self.x1)
X2 = paddle.to_tensor(self.x2)
out = paddle.atan2(X1, X2)
out_ref = np.arctan2(self.x1, self.x2)
self.assertEqual(np.allclose(out_ref, out.numpy()), True)
paddle.enable_static()
for place in self.place:
run(place)
if __name__ == '__main__':
unittest.main()
...@@ -147,6 +147,7 @@ from .math import add # noqa: F401 ...@@ -147,6 +147,7 @@ from .math import add # noqa: F401
from .math import add_ # noqa: F401 from .math import add_ # noqa: F401
from .math import subtract # noqa: F401 from .math import subtract # noqa: F401
from .math import subtract_ # noqa: F401 from .math import subtract_ # noqa: F401
from .math import atan2 # noqa: F401
from .math import logsumexp # noqa: F401 from .math import logsumexp # noqa: F401
from .math import inverse # noqa: F401 from .math import inverse # noqa: F401
from .math import log2 # noqa: F401 from .math import log2 # noqa: F401
......
...@@ -2386,3 +2386,59 @@ def neg(x, name=None): ...@@ -2386,3 +2386,59 @@ def neg(x, name=None):
""" """
return layers.scale(x, scale=-1.0, bias=0.0, bias_after_scale=True, act=None, name=name) return layers.scale(x, scale=-1.0, bias=0.0, bias_after_scale=True, act=None, name=name)
def atan2(y, x, name=None):
r"""
Element-wise arctangent of y/x with consideration of the quadrant.
Equation:
.. math::
atan2(y,x)=\left\{\begin{matrix}
& tan^{-1}(\frac{y}{x}) & x > 0 \\
& tan^{-1}(\frac{y}{x}) + \pi & y>=0, x < 0 \\
& tan^{-1}(\frac{y}{x}) - \pi & y<0, x < 0 \\
& +\frac{\pi}{2} & y>0, x = 0 \\
& -\frac{\pi}{2} & y<0, x = 0 \\
&\text{undefined} & y=0, x = 0
\end{matrix}\right.
Args:
y (Tensor): An N-D Tensor, the data type is int32, int64, float16, float32, float64.
x (Tensor): An N-D Tensor, must have the same type as `x`.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
out (Tensor): An N-D Tensor, the shape and data type is the same with input (The output data type is float64 when the input data type is int).
Examples:
.. code-block:: python
import paddle
y = paddle.to_tensor([-1, +1, +1, -1]).astype('float32')
#Tensor(shape=[4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [-1, 1, 1, -1])
x = paddle.to_tensor([-1, -1, +1, +1]).astype('float32')
#Tensor(shape=[4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [-1, -1, 1, 1])
out = paddle.atan2(y, x)
#Tensor(shape=[4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [-2.35619450, 2.35619450, 0.78539819, -0.78539819])
"""
if in_dygraph_mode():
return core.ops.atan2(y, x)
else:
check_variable_and_dtype(y, 'y', ['int32', 'int64', 'float16', 'float32', 'float64'], 'atan2')
check_variable_and_dtype(x, 'x', ['int32', 'int64', 'float16', 'float32', 'float64'], 'atan2')
helper = LayerHelper('atan2', **locals())
inputs = {'X1' : y, 'X2' : x}
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='atan2', inputs=inputs, outputs={'Out': out})
return out
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