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[NPU] accuracy op (#31492)

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* accuracy op

* fix license

* fix

* add test and fix bug
revert-31562-mean
yinhaofeng 4 years ago committed by GitHub
parent 3bf8a34c69
commit e1c33a6d69
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124
paddle/fluid/operators/metrics/accuracy_op_npu.cc
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/* 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
python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py
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# 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()
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