-
Notifications
You must be signed in to change notification settings - Fork 5.6k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
* accuracy op * fix license * fix * add test and fix bug
- Loading branch information
1 parent
3bf8a34
commit e1c33a6
Showing
2 changed files
with
246 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,124 @@ | ||
/* 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. */ | ||
|
||
#ifdef PADDLE_WITH_ASCEND_CL | ||
#include <memory> | ||
#include <string> | ||
|
||
#include "paddle/fluid/operators/controlflow/compare_op.h" | ||
#include "paddle/fluid/operators/metrics/accuracy_op.h" | ||
#include "paddle/fluid/operators/npu_op_runner.h" | ||
|
||
namespace paddle { | ||
namespace operators { | ||
|
||
template <typename DeviceContext, typename T> | ||
class AccuracyNPUKernel : public framework::OpKernel<T> { | ||
public: | ||
void Compute(const framework::ExecutionContext& ctx) const override { | ||
auto* pred = ctx.Input<Tensor>("Out"); | ||
auto* label = ctx.Input<Tensor>("Label"); | ||
// auto* logits = ctx.Input<Tensor>("Indices"); | ||
|
||
auto* acc = ctx.Output<Tensor>("Accuracy"); | ||
auto* correct = ctx.Output<Tensor>("Correct"); | ||
auto* total = ctx.Output<Tensor>("Total"); | ||
auto stream = | ||
ctx.template device_context<paddle::platform::NPUDeviceContext>() | ||
.stream(); | ||
|
||
// cast pred | ||
Tensor tmp_pred(pred->type()); | ||
tmp_pred.Resize(pred->dims()); | ||
tmp_pred.mutable_data<int>(ctx.GetPlace()); | ||
auto runner_cast_pred = | ||
NpuOpRunner("Cast", {*pred}, {tmp_pred}, | ||
{{"dst_type", static_cast<int>(ACL_INT32)}}); | ||
runner_cast_pred.Run(stream); | ||
|
||
// cast label | ||
Tensor tmp_label(label->type()); | ||
tmp_label.Resize(label->dims()); | ||
tmp_label.mutable_data<int>(ctx.GetPlace()); | ||
auto runner_cast_label = | ||
NpuOpRunner("Cast", {*label}, {tmp_label}, | ||
{{"dst_type", static_cast<int>(ACL_INT32)}}); | ||
runner_cast_label.Run(stream); | ||
|
||
// equal | ||
Tensor tmp_equal(label->type()); | ||
tmp_equal.Resize(label->dims()); | ||
tmp_equal.mutable_data<bool>(ctx.GetPlace()); | ||
auto runner_equal = | ||
NpuOpRunner("Equal", {tmp_pred, tmp_label}, {tmp_equal}, {}); | ||
runner_equal.Run(stream); | ||
|
||
// cast equal | ||
Tensor tmp_equal_cast(label->type()); | ||
tmp_equal_cast.Resize(label->dims()); | ||
tmp_equal_cast.mutable_data<float>(ctx.GetPlace()); | ||
auto runner_cast_equal = | ||
NpuOpRunner("Cast", {tmp_equal}, {tmp_equal_cast}, | ||
{{"dst_type", static_cast<float>(ACL_FLOAT)}}); | ||
runner_cast_equal.Run(stream); | ||
|
||
// acc | ||
acc->mutable_data<float>(ctx.GetPlace()); | ||
std::vector<int> axes_vec_1; | ||
auto runner_acc = NpuOpRunner("ReduceMeanD", {tmp_equal_cast}, {*acc}, | ||
{{"keep_dims", false}, {"axes", axes_vec_1}}); | ||
runner_acc.Run(stream); | ||
|
||
// correct | ||
correct->mutable_data<float>(ctx.GetPlace()); | ||
std::vector<int> axes_vec_2; | ||
auto runner_correct = | ||
NpuOpRunner("ReduceSumD", {tmp_equal_cast}, {*correct}, | ||
{{"keep_dims", false}, {"axes", axes_vec_2}}); | ||
runner_correct.Run(stream); | ||
|
||
// ones_tensor | ||
Tensor ones_tensor(label->type()); | ||
ones_tensor.Resize(label->dims()); | ||
ones_tensor.mutable_data<int>(ctx.GetPlace()); | ||
auto runner_oneslike = | ||
NpuOpRunner("OnesLike", {tmp_label}, {ones_tensor}, {}); | ||
runner_oneslike.Run(stream); | ||
|
||
// ones_tensor_cast | ||
Tensor ones_tensor_cast(label->type()); | ||
ones_tensor_cast.Resize(label->dims()); | ||
ones_tensor_cast.mutable_data<float>(ctx.GetPlace()); | ||
auto runner_ones_cast = | ||
NpuOpRunner("Cast", {ones_tensor}, {ones_tensor_cast}, | ||
{{"dst_type", static_cast<float>(ACL_FLOAT)}}); | ||
runner_ones_cast.Run(stream); | ||
|
||
// total | ||
total->mutable_data<float>(ctx.GetPlace()); | ||
std::vector<int> axes_vec_3; | ||
auto runner_total = | ||
NpuOpRunner("ReduceSumD", {ones_tensor_cast}, {*total}, | ||
{{"keep_dims", false}, {"axes", axes_vec_3}}); | ||
runner_total.Run(stream); | ||
} | ||
}; | ||
|
||
} // namespace operators | ||
} // namespace paddle | ||
|
||
namespace ops = paddle::operators; | ||
|
||
REGISTER_OP_NPU_KERNEL( | ||
accuracy, ops::AccuracyNPUKernel<paddle::platform::NPUDeviceContext, float>, | ||
ops::AccuracyNPUKernel<paddle::platform::NPUDeviceContext, int>, | ||
ops::AccuracyNPUKernel<paddle::platform::NPUDeviceContext, int64_t>); | ||
#endif |
122 changes: 122 additions & 0 deletions
122
python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,122 @@ | ||
# 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. | ||
|
||
from __future__ import print_function | ||
|
||
import numpy as np | ||
import unittest | ||
import sys | ||
sys.path.append("..") | ||
from op_test import OpTest | ||
import paddle | ||
import paddle.fluid as fluid | ||
|
||
paddle.enable_static() | ||
|
||
SEED = 2021 | ||
|
||
|
||
@unittest.skipIf(not paddle.is_compiled_with_npu(), | ||
"core is not compiled with NPU") | ||
class TestAccuracy(OpTest): | ||
def setUp(self): | ||
self.op_type = "accuracy" | ||
self.set_npu() | ||
self.init_dtype() | ||
np.random.seed(SEED) | ||
pred = np.random.uniform(1, 2, [11, 1]).astype(self.dtype) | ||
label = pred.copy() | ||
accuracy = np.array([1]).astype(self.dtype) | ||
correct = np.array([11 * 1]).astype(self.dtype) | ||
total = np.array([11 * 1]).astype(self.dtype) | ||
|
||
self.inputs = { | ||
"Out": OpTest.np_dtype_to_fluid_dtype(pred), | ||
"Label": OpTest.np_dtype_to_fluid_dtype(label), | ||
"Indices": OpTest.np_dtype_to_fluid_dtype(pred) | ||
} | ||
self.outputs = { | ||
"Accuracy": accuracy, | ||
"Correct": correct, | ||
"Total": total | ||
} | ||
|
||
def set_npu(self): | ||
self.__class__.use_npu = True | ||
self.place = paddle.NPUPlace(0) | ||
|
||
def init_dtype(self): | ||
self.dtype = np.float32 | ||
|
||
def test_check_output(self): | ||
self.check_output_with_place(self.place, check_dygraph=False) | ||
|
||
|
||
class TestAccuracy2(TestAccuracy): | ||
def setUp(self): | ||
self.op_type = "accuracy" | ||
self.set_npu() | ||
self.init_dtype() | ||
np.random.seed(SEED) | ||
pred = np.random.uniform(1, 2, [11, 1]).astype(self.dtype) | ||
label = np.random.uniform(4, 5, [11, 1]).astype(self.dtype) | ||
accuracy = np.array([0]).astype(self.dtype) | ||
correct = np.array([11 * 0]).astype(self.dtype) | ||
total = np.array([11 * 1]).astype(self.dtype) | ||
|
||
self.inputs = { | ||
"Out": OpTest.np_dtype_to_fluid_dtype(pred), | ||
"Label": OpTest.np_dtype_to_fluid_dtype(label), | ||
"Indices": OpTest.np_dtype_to_fluid_dtype(pred) | ||
} | ||
self.outputs = { | ||
"Accuracy": accuracy, | ||
"Correct": correct, | ||
"Total": total | ||
} | ||
|
||
|
||
class TestAccuracy3(TestAccuracy): | ||
def setUp(self): | ||
self.op_type = "accuracy" | ||
self.set_npu() | ||
self.init_dtype() | ||
np.random.seed(SEED) | ||
a = np.random.randint(1, 2, [5, 1]) | ||
b = np.random.randint(0, 1, [5, 1]) | ||
pred = np.row_stack((a, b)).astype(self.dtype) | ||
label = np.random.randint(1, 2, [10, 1]).astype(self.dtype) | ||
accuracy = np.array([0.5]).astype(self.dtype) | ||
correct = np.array([5]).astype(self.dtype) | ||
total = np.array([10 * 1]).astype(self.dtype) | ||
|
||
self.inputs = { | ||
"Out": OpTest.np_dtype_to_fluid_dtype(pred), | ||
"Label": OpTest.np_dtype_to_fluid_dtype(label), | ||
"Indices": OpTest.np_dtype_to_fluid_dtype(pred) | ||
} | ||
self.outputs = { | ||
"Accuracy": accuracy, | ||
"Correct": correct, | ||
"Total": total | ||
} | ||
|
||
|
||
class TestAccuracyInt(TestAccuracy): | ||
def init_dtype(self): | ||
self.dtype = np.int | ||
|
||
|
||
if __name__ == '__main__': | ||
unittest.main() |