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未验证 提交 e82efc0c 编写于 作者: T TTerror 提交者： GitHub
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update activation op on kunlun (#29577) (#29717) 

* fix expand && concat/transpose to new api

* update xpu_header

* update activation op on kunlun

* update activation op on kunlun

* update activation op on kunlun

* update activation op on kunlun

* update activation op on kunlun

* add nearest_interp on kunlun

* update error message
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cmake/external/xpu.cmake
浏览文件 @ e82efc0c
......@@ -4,7 +4,7 @@ endif()
INCLUDE(ExternalProject)
SET(XPU_PROJECT "extern_xpu")
SET(XPU_URL "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2020_12_11.tar.gz" CACHE STRING "" FORCE)
SET(XPU_URL "https://baidu-kunlun-public.su.bcebos.com/paddle_depence/xpu_2020_12_15.tar.gz" CACHE STRING "" FORCE)
SET(XPU_SOURCE_DIR "${THIRD_PARTY_PATH}/xpu")
SET(XPU_DOWNLOAD_DIR "${XPU_SOURCE_DIR}/src/${XPU_PROJECT}")
SET(XPU_INSTALL_DIR "${THIRD_PARTY_PATH}/install/xpu")
......
paddle/fluid/operators/activation_op_xpu.cc
浏览文件 @ e82efc0c
......@@ -54,55 +54,27 @@ class XPUActivationGradKernel
};
template <typename DeviceContext, typename T>
void xpu_activation_forward(const framework::ExecutionContext &ctx,
xpu::Activation_t type) {
void xpu_activation_forward(
const framework::ExecutionContext &ctx,
std::function<int(xpu::Context *, const T *, T *, int)> func) {
const auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Out");
const T *x_data = x->data<T>();
T *y_data = y->mutable_data<T>(ctx.GetPlace());
int r = 0;
auto xpu_context = ctx.device_context<DeviceContext>().x_context();
switch (type.type) {
case xpu::Activation_t::HARD_SWISH: {
float threshold = ctx.Attr<float>("threshold");
float scale = ctx.Attr<float>("scale");
float offset = ctx.Attr<float>("offset");
PADDLE_ENFORCE_EQ(threshold, 6.0f,
platform::errors::External(
"Not support threshold [%f] in XPU", threshold));
PADDLE_ENFORCE_EQ(
scale, 6.0f,
platform::errors::External("Not support scale [%f] in XPU", scale));
auto xpu_context = ctx.device_context<DeviceContext>().x_context();
int r = func(xpu_context, x_data, y_data, x->numel());
PADDLE_ENFORCE_EQ(
offset, 3.0f,
platform::errors::External("Not support offset [%f] in XPU", offset));
r = xpu::hard_swish(xpu_context, reinterpret_cast<const float *>(x_data),
reinterpret_cast<float *>(y_data), x->numel());
break;
}
case xpu::Activation_t::ACT_POW: {
type.pow_factor = ctx.Attr<float>("factor");
}
default: {
r = xpu::activation_forward(xpu_context, type, x->numel(),
reinterpret_cast<const float *>(x_data),
reinterpret_cast<float *>(y_data));
break;
}
}
PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
platform::errors::External(
"XPU API return wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed.",
r));
r, xpu::Error_t::SUCCESS,
platform::errors::External("XPU activation op return wrong value[%d %s].",
r, XPUAPIErrorMsg[r]));
}
template <typename DeviceContext, typename T>
void xpu_activation_backward(const framework::ExecutionContext &ctx,
xpu::Activation_t type) {
std::function<int(xpu::Context *, const T *,
const T *, const T *, T *, int)>
func) {
/* TODO: relu tanh sigmoid are inplace */
const auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Input<Tensor>("Out");
......@@ -115,99 +87,248 @@ void xpu_activation_backward(const framework::ExecutionContext &ctx,
if (y != nullptr) y_data = y->data<T>();
if (dOut != nullptr) y_grad = dOut->data<T>();
T *x_grad = dX->mutable_data<T>(ctx.GetPlace());
int r = 0;
auto xpu_context = ctx.device_context<DeviceContext>().x_context();
switch (type.type) {
case xpu::Activation_t::HARD_SWISH: {
float threshold = ctx.Attr<float>("threshold");
float scale = ctx.Attr<float>("scale");
float offset = ctx.Attr<float>("offset");
PADDLE_ENFORCE_EQ(threshold, 6.0f,
int r = func(xpu_context, x_data, y_data, y_grad, x_grad, dX->numel());
PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
platform::errors::External(
"Not support threshold [%f] in XPU", threshold));
PADDLE_ENFORCE_EQ(
scale, 6.0f,
platform::errors::External("Not support scale [%f] in XPU", scale));
PADDLE_ENFORCE_EQ(
offset, 3.0f,
platform::errors::External("Not support offset [%f] in XPU", offset));
r = xpu::hard_swish_grad(xpu_context,
reinterpret_cast<const float *>(x_data),
reinterpret_cast<const float *>(y_data),
reinterpret_cast<const float *>(y_grad),
reinterpret_cast<float *>(x_grad), dX->numel());
break;
}
default: {
r = xpu::activation_backward(xpu_context, type, dX->numel(),
reinterpret_cast<const float *>(x_data),
reinterpret_cast<const float *>(y_data),
reinterpret_cast<const float *>(y_grad),
reinterpret_cast<float *>(x_grad));
break;
}
}
PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
platform::errors::External(
"XPU API return wrong value[%d], please check whether "
"Baidu Kunlun Card is properly installed.",
r));
"XPU activation grad op return wrong value[%d %s].", r,
XPUAPIErrorMsg[r]));
}
template <typename T, xpu::Activation_t::act_enum algorithm>
struct XPUActivationFunc : public BaseActivationFunctor<T> {
template <typename T>
struct XPUReluFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
algorithm);
xpu::relu<T>);
}
};
template <typename T, xpu::Activation_t::act_enum algorithm>
struct XPUActivationGradFunc : public BaseActivationFunctor<T> {
template <typename T>
struct XPUSigmoidFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(ctx,
algorithm);
xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::sigmoid<T>);
}
};
template <typename T>
using XPUReluFunctor = XPUActivationFunc<T, xpu::Activation_t::RELU>;
struct XPUTanhFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
xpu::tanh<T>);
}
};
template <typename T>
using XPUSigmoidFunctor = XPUActivationFunc<T, xpu::Activation_t::SIGMOID>;
struct XPUGeluFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
xpu::gelu<T>);
}
};
template <typename T>
using XPUTanhFunctor = XPUActivationFunc<T, xpu::Activation_t::TANH>;
struct XPULogFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
xpu::log<T>);
}
};
template <typename T>
using XPUGeluFunctor = XPUActivationFunc<T, xpu::Activation_t::GELU>;
struct XPUSquareFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::square<T>);
}
};
template <typename T>
using XPULogFunctor = XPUActivationFunc<T, xpu::Activation_t::LOG>;
struct XPUSqrtFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
xpu::sqrt<T>);
}
};
template <typename T>
using XPUSquareFunctor = XPUActivationFunc<T, xpu::Activation_t::SQUARE>;
struct XPUAbsFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(ctx,
xpu::abs<T>);
}
};
template <typename T>
using XPUHardSwishFunctor = XPUActivationFunc<T, xpu::Activation_t::HARD_SWISH>;
struct XPUPowFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
const auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Out");
auto pow_factor = ctx.Attr<float>("factor");
const T *x_data = x->data<T>();
T *y_data = y->mutable_data<T>(ctx.GetPlace());
T *factor_data = nullptr;
auto xpu_context =
ctx.device_context<paddle::platform::XPUDeviceContext>().x_context();
PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void **>(&factor_data),
x->numel() * sizeof(T)),
XPU_SUCCESS, platform::errors::ResourceExhausted(
"XPU has no enough memory"));
int r = xpu::constant<T>(xpu_context, factor_data, x->numel(), pow_factor);
PADDLE_ENFORCE_EQ(
r, xpu::Error_t::SUCCESS,
platform::errors::External("XPU constant op return"
" wrong value[%d %s] in pow op.",
r, XPUAPIErrorMsg[r]));
r = xpu::pow(xpu_context, x_data, factor_data, y_data, x->numel());
PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
platform::errors::External("XPU pow op return"
" wrong value[%d %s].",
r, XPUAPIErrorMsg[r]));
if (xpu_context->xpu_stream != nullptr) {
xpu_wait(xpu_context->xpu_stream);
}
xpu_free(factor_data);
}
};
template <typename T>
using XPUSuareGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::SQUARE>;
struct XPUHardSwishFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
float threshold = ctx.Attr<float>("threshold");
float scale = ctx.Attr<float>("scale");
float offset = ctx.Attr<float>("offset");
PADDLE_ENFORCE_EQ(threshold, 6.0f,
platform::errors::External(
"Not support threshold [%f] in XPU", threshold));
PADDLE_ENFORCE_EQ(scale, 6.0f, platform::errors::External(
"Not support scale [%f] in XPU", scale));
PADDLE_ENFORCE_EQ(
offset, 3.0f,
platform::errors::External("Not support offset [%f] in XPU", offset));
xpu_activation_forward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::hard_swish<T>);
}
};
template <typename T>
using XPUReluGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::RELU>;
struct XPUReluGradFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::relu_grad<T>);
}
};
template <typename T>
using XPUSigmoidGradFunctor =
XPUActivationGradFunc<T, xpu::Activation_t::SIGMOID>;
struct XPUTanhGradFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::tanh_grad<T>);
}
};
template <typename T>
using XPUTanhGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::TANH>;
struct XPUSigmoidGradFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::sigmoid_grad<T>);
}
};
template <typename T>
using XPUGeluGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::GELU>;
struct XPUGeluGradFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::gelu_grad<T>);
}
};
template <typename T>
using XPUSqrtFunctor = XPUActivationFunc<T, xpu::Activation_t::SQRT>;
struct XPUSqrtGradFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::sqrt_grad<T>);
}
};
template <typename T>
using XPUSqrtGradFunctor = XPUActivationGradFunc<T, xpu::Activation_t::SQRT>;
struct XPUSquareGradFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::square_grad<T>);
}
};
template <typename T>
using XPUHardSwishGradFunctor =
XPUActivationGradFunc<T, xpu::Activation_t::HARD_SWISH>;
struct XPUHardSwishGradFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
float threshold = ctx.Attr<float>("threshold");
float scale = ctx.Attr<float>("scale");
float offset = ctx.Attr<float>("offset");
PADDLE_ENFORCE_EQ(threshold, 6.0f,
platform::errors::External(
"Not support threshold [%f] in XPU", threshold));
PADDLE_ENFORCE_EQ(scale, 6.0f, platform::errors::External(
"Not support scale [%f] in XPU", scale));
PADDLE_ENFORCE_EQ(
offset, 3.0f,
platform::errors::External("Not support offset [%f] in XPU", offset));
xpu_activation_backward<paddle::platform::XPUDeviceContext, T>(
ctx, xpu::hard_swish_grad<T>);
}
};
template <typename T>
using XPUACTPowFunctor = XPUActivationFunc<T, xpu::Activation_t::ACT_POW>;
struct XPULeakyReluFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
const auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Out");
float alpha = ctx.Attr<float>("alpha");
const T *x_data = x->data<T>();
T *y_data = y->mutable_data<T>(ctx.GetPlace());
auto xpu_context =
ctx.device_context<paddle::platform::XPUDeviceContext>().x_context();
int r = xpu::leaky_relu(xpu_context, x_data, y_data, x->numel(), alpha);
PADDLE_ENFORCE_EQ(
r, xpu::Error_t::SUCCESS,
platform::errors::External("XPU leaky_relu return wrong value[%d %s].",
r, XPUAPIErrorMsg[r]));
}
};
template <typename T>
using XPUABSFunctor = XPUActivationFunc<T, xpu::Activation_t::ABS>;
struct XPULeakyReluGradFunctor : public BaseActivationFunctor<T> {
void operator()(const framework::ExecutionContext &ctx) const {
const auto *x = ctx.Input<Tensor>("X");
auto *dOut = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto *dX = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
float alpha = ctx.Attr<float>("alpha");
const T *x_data = nullptr;
const T *y_grad = nullptr;
if (x != nullptr) x_data = x->data<T>();
if (dOut != nullptr) y_grad = dOut->data<T>();
T *x_grad = dX->mutable_data<T>(ctx.GetPlace());
auto xpu_context =
ctx.device_context<paddle::platform::XPUDeviceContext>().x_context();
// The signs of x and y are the same,
// y == nullptr here,
// so we give 2 x to the api
int r = xpu::leaky_relu_grad(
xpu_context, reinterpret_cast<const float *>(x_data),
reinterpret_cast<const float *>(x_data),
reinterpret_cast<const float *>(y_grad),
reinterpret_cast<float *>(x_grad), dX->numel(), alpha);
PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
platform::errors::External(
"XPU leaky_relu_grad return wrong value[%d %s].", r,
XPUAPIErrorMsg[r]));
}
};
} // namespace operators
} // namespace paddle
......@@ -226,14 +347,16 @@ REGISTER_ACTIVATION_XPU_KERNEL(sigmoid, XPUSigmoidFunctor,
XPUSigmoidGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(gelu, XPUGeluFunctor, XPUGeluGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(sqrt, XPUSqrtFunctor, XPUSqrtGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(square, XPUSquareFunctor, XPUSuareGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(square, XPUSquareFunctor, XPUSquareGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(hard_swish, XPUHardSwishFunctor,
XPUHardSwishGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(leaky_relu, XPULeakyReluFunctor,
XPULeakyReluGradFunctor)
REGISTER_OP_XPU_KERNEL(log,
ops::XPUActivationKernel<ops::XPULogFunctor<float>>);
REGISTER_OP_XPU_KERNEL(pow,
ops::XPUActivationKernel<ops::XPUACTPowFunctor<float>>);
ops::XPUActivationKernel<ops::XPUPowFunctor<float>>);
REGISTER_OP_XPU_KERNEL(abs,
ops::XPUActivationKernel<ops::XPUABSFunctor<float>>);
ops::XPUActivationKernel<ops::XPUAbsFunctor<float>>);
#endif // PADDLE_WITH_XPU
paddle/fluid/operators/interpolate_op_xpu.cc
浏览文件 @ e82efc0c
......@@ -229,9 +229,7 @@ class InterpolateGradXPUKernel : public framework::OpKernel<T> {
int trans_mode = (align_corners) ? (0) : ((align_mode == 0) ? (1) : (2));
if (nearest) {
PADDLE_ENFORCE_EQ((data_layout == DataLayout::kNCHW), true,
platform::errors::InvalidArgument(
"XPU nearest is only support NCHW"));
trans_mode = (align_corners) ? (0) : (2);
}
r = xpu::interpolate2d_grad<T>(dev_ctx.x_context(), output_grad->data<T>(),
......@@ -252,7 +250,10 @@ class InterpolateGradXPUKernel : public framework::OpKernel<T> {
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(bilinear_interp, ops::InterpolateXPUKernel<float>);
REGISTER_OP_XPU_KERNEL(nearest_interp, ops::InterpolateXPUKernel<float>);
REGISTER_OP_XPU_KERNEL(bilinear_interp_grad,
ops::InterpolateGradXPUKernel<float>);
REGISTER_OP_XPU_KERNEL(nearest_interp_grad,
ops::InterpolateGradXPUKernel<float>);
#endif
python/paddle/fluid/tests/unittests/xpu/test_activation_op_xpu.py
浏览文件 @ e82efc0c
......@@ -73,8 +73,7 @@ class TestXPUSigmoid(TestXPUActivation):
def test_check_grad(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(
place, ['X'], 'Out', max_relative_error=0.01)
self.check_grad_with_place(place, ['X'], 'Out')
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
......@@ -90,6 +89,11 @@ class TestXPUTanh(TestXPUActivation):
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def test_check_grad(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out')
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
......@@ -105,6 +109,11 @@ class TestXPUSqrt(TestXPUActivation):
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def test_check_grad(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out')
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
......@@ -142,6 +151,11 @@ class TestXPURelu(TestXPUActivation):
self.inputs = {'X': x}
self.outputs = {'Out': out}
def test_check_grad(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out')
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
......@@ -157,6 +171,11 @@ class TestXPUGelu(TestXPUActivation):
self.outputs = {'Out': out}
self.attrs = {"approximate": approximate, 'use_xpu': True}
def test_check_grad(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out')
def gelu(x, approximate):
if approximate:
......@@ -223,6 +242,11 @@ class TestXPUSquare(TestXPUActivation):
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def test_check_grad(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out')
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
......@@ -239,5 +263,36 @@ class TestXPUPow(TestXPUActivation):
self.outputs = {'Out': out}
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestXPULeakyRelu(TestXPUActivation):
def setUp(self):
self.op_type = "leaky_relu"
self.init_dtype()
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
alpha = np.random.uniform(
0,
1, )
out = leaky_relu(x, alpha)
self.inputs = {'X': x}
self.outputs = {'Out': out}
self.attrs = {'use_xpu': True, 'alpha': alpha}
def test_check_grad(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out')
def leaky_relu(x, alpha):
if (alpha < 1):
y_ref = np.maximum(x, alpha * x)
else:
y_ref = np.minimum(x, alpha * x)
return y_ref.astype(x.dtype)
if __name__ == "__main__":
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/xpu/test_nearest_interp_op_xpu.py 0 → 100644
浏览文件 @ e82efc0c
# Copyright (c) 2018 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.
from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid.core as core
import sys
sys.path.append("..")
from op_test_xpu import XPUOpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
paddle.enable_static()
def nearest_neighbor_interp_np(X,
out_h,
out_w,
out_size=None,
actual_shape=None,
align_corners=True,
data_layout='NCHW'):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if data_layout == "NHWC":
X = np.transpose(X, (0, 3, 1, 2)) # NHWC => NCHW
if out_size is not None:
out_h = out_size[0]
out_w = out_size[1]
if actual_shape is not None:
out_h = actual_shape[0]
out_w = actual_shape[1]
n, c, in_h, in_w = X.shape
ratio_h = ratio_w = 0.0
if (out_h > 1):
if (align_corners):
ratio_h = (in_h - 1.0) / (out_h - 1.0)
else:
ratio_h = 1.0 * in_h / out_h
if (out_w > 1):
if (align_corners):
ratio_w = (in_w - 1.0) / (out_w - 1.0)
else:
ratio_w = 1.0 * in_w / out_w
out = np.zeros((n, c, out_h, out_w))
if align_corners:
for i in range(out_h):
in_i = int(ratio_h * i + 0.5)
for j in range(out_w):
in_j = int(ratio_w * j + 0.5)
out[:, :, i, j] = X[:, :, in_i, in_j]
else:
for i in range(out_h):
in_i = int(ratio_h * i)
for j in range(out_w):
in_j = int(ratio_w * j)
out[:, :, i, j] = X[:, :, in_i, in_j]
if data_layout == "NHWC":
out = np.transpose(out, (0, 2, 3, 1)) # NCHW => NHWC
return out.astype(X.dtype)
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestInterpOp(XPUOpTest):
def setUp(self):
self.use_xpu = True
self.out_size = None
self.actual_shape = None
self.data_layout = 'NCHW'
self.init_test_case()
self.op_type = "nearest_interp"
input_np = np.random.random(self.input_shape).astype("float32")
if self.data_layout == "NCHW":
in_h = self.input_shape[2]
in_w = self.input_shape[3]
else:
in_h = self.input_shape[1]
in_w = self.input_shape[2]
if self.scale > 0:
out_h = int(in_h * self.scale)
out_w = int(in_w * self.scale)
else:
out_h = self.out_h
out_w = self.out_w
output_np = nearest_neighbor_interp_np(
input_np, out_h, out_w, self.out_size, self.actual_shape,
self.align_corners, self.data_layout)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
if self.actual_shape is not None:
self.inputs['OutSize'] = self.actual_shape
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'scale': self.scale,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'data_layout': self.data_layout
}
self.outputs = {'Out': output_np}
def test_check_output(self):
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out', in_place=True)
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 3, 4, 5]
self.out_h = 2
self.out_w = 2
self.scale = 0.
self.out_size = np.array([3, 3]).astype("int32")
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpCase1(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.scale = 0.
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpCase2(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpCase3(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [1, 1, 32, 64]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpCase4(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.scale = 0.
self.out_size = np.array([2, 2]).astype("int32")
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpCase5(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.out_size = np.array([11, 11]).astype("int32")
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpCase6(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [1, 1, 32, 64]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.out_size = np.array([65, 129]).astype("int32")
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpSame(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 3, 32, 64]
self.out_h = 32
self.out_w = 64
self.scale = 0.
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpDataLayout(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 4, 4, 5]
self.out_h = 2
self.out_w = 2
self.scale = 0.
self.out_size = np.array([3, 8]).astype("int32")
self.align_corners = True
self.data_layout = "NCHW"
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestInterpWithoutCorners(TestNearestInterpOp):
def set_align_corners(self):
self.align_corners = False
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpScale1(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 7, 5]
self.out_h = 64
self.out_w = 32
self.scale = 2.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpScale2(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 5, 7]
self.out_h = 64
self.out_w = 32
self.scale = 1.5
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestNeighborInterpScale3(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 7, 5]
self.out_h = 64
self.out_w = 32
self.scale = 1.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestInterpOp_attr_tensor(XPUOpTest):
def setUp(self):
self.out_size = None
self.actual_shape = None
self.init_test_case()
self.op_type = "nearest_interp"
self.shape_by_1Dtensor = False
self.scale_by_1Dtensor = False
self.attrs = {
'interp_method': self.interp_method,
'align_corners': self.align_corners,
}
input_np = np.random.random(self.input_shape).astype("float32")
self.inputs = {'X': input_np}
if self.scale_by_1Dtensor:
self.inputs['Scale'] = np.array([self.scale]).astype("float32")
elif self.scale > 0:
out_h = int(self.input_shape[2] * self.scale)
out_w = int(self.input_shape[3] * self.scale)
self.attrs['scale'] = self.scale
else:
out_h = self.out_h
out_w = self.out_w
if self.shape_by_1Dtensor:
self.inputs['OutSize'] = self.out_size
elif self.out_size is not None:
size_tensor = []
for index, ele in enumerate(self.out_size):
size_tensor.append(("x" + str(index), np.ones(
(1)).astype('int32') * ele))
self.inputs['SizeTensor'] = size_tensor
self.attrs['out_h'] = self.out_h
self.attrs['out_w'] = self.out_w
output_np = nearest_neighbor_interp_np(input_np, out_h, out_w,
self.out_size, self.actual_shape,
self.align_corners)
self.outputs = {'Out': output_np}
def test_check_output(self):
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['X'], 'Out', in_place=True)
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 5, 4, 4]
self.out_h = 3
self.out_w = 3
self.scale = 0.
self.out_size = [3, 3]
self.align_corners = True
# out_size is a tensor list
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestInterp_attr_tensor_Case1(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.out_size = [8, 12]
self.align_corners = True
# out_size is a 1-D tensor
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestInterp_attr_tensor_Case2(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
self.shape_by_1Dtensor = True
# scale is a 1-D tensor
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 2.0
self.out_size = None
self.align_corners = True
self.scale_by_1Dtensor = True
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestNearestInterpException(unittest.TestCase):
def test_exception(self):
input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
def attr_data_format():
# for 4-D input, data_format can only be NCHW or NHWC
out = fluid.layers.resize_nearest(
input, out_shape=[4, 8], data_format='NDHWC')
def attr_scale_type():
out = fluid.layers.resize_nearest(input, scale='scale')
def attr_scale_value():
out = fluid.layers.resize_nearest(input, scale=-0.3)
self.assertRaises(ValueError, attr_data_format)
self.assertRaises(TypeError, attr_scale_type)
self.assertRaises(ValueError, attr_scale_value)
if __name__ == "__main__":
unittest.main()
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