From e1c33a6d69bf8e40a4e204255ea12ab280189541 Mon Sep 17 00:00:00 2001 From: yinhaofeng <66763551+yinhaofeng@users.noreply.github.com> Date: Fri, 12 Mar 2021 16:27:56 +0800 Subject: [PATCH] [NPU] accuracy op (#31492) * accuracy op * fix license * fix * add test and fix bug --- .../operators/metrics/accuracy_op_npu.cc | 124 ++++++++++++++++++ .../unittests/npu/test_accuracy_op_npu.py | 122 +++++++++++++++++ 2 files changed, 246 insertions(+) create mode 100644 paddle/fluid/operators/metrics/accuracy_op_npu.cc create mode 100644 python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py diff --git a/paddle/fluid/operators/metrics/accuracy_op_npu.cc b/paddle/fluid/operators/metrics/accuracy_op_npu.cc new file mode 100644 index 00000000000..10b28b532b1 --- /dev/null +++ b/paddle/fluid/operators/metrics/accuracy_op_npu.cc @@ -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 +#include + +#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 +class AccuracyNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* pred = ctx.Input("Out"); + auto* label = ctx.Input("Label"); + // auto* logits = ctx.Input("Indices"); + + auto* acc = ctx.Output("Accuracy"); + auto* correct = ctx.Output("Correct"); + auto* total = ctx.Output("Total"); + auto stream = + ctx.template device_context() + .stream(); + + // cast pred + Tensor tmp_pred(pred->type()); + tmp_pred.Resize(pred->dims()); + tmp_pred.mutable_data(ctx.GetPlace()); + auto runner_cast_pred = + NpuOpRunner("Cast", {*pred}, {tmp_pred}, + {{"dst_type", static_cast(ACL_INT32)}}); + runner_cast_pred.Run(stream); + + // cast label + Tensor tmp_label(label->type()); + tmp_label.Resize(label->dims()); + tmp_label.mutable_data(ctx.GetPlace()); + auto runner_cast_label = + NpuOpRunner("Cast", {*label}, {tmp_label}, + {{"dst_type", static_cast(ACL_INT32)}}); + runner_cast_label.Run(stream); + + // equal + Tensor tmp_equal(label->type()); + tmp_equal.Resize(label->dims()); + tmp_equal.mutable_data(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(ctx.GetPlace()); + auto runner_cast_equal = + NpuOpRunner("Cast", {tmp_equal}, {tmp_equal_cast}, + {{"dst_type", static_cast(ACL_FLOAT)}}); + runner_cast_equal.Run(stream); + + // acc + acc->mutable_data(ctx.GetPlace()); + std::vector 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(ctx.GetPlace()); + std::vector 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(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(ctx.GetPlace()); + auto runner_ones_cast = + NpuOpRunner("Cast", {ones_tensor}, {ones_tensor_cast}, + {{"dst_type", static_cast(ACL_FLOAT)}}); + runner_ones_cast.Run(stream); + + // total + total->mutable_data(ctx.GetPlace()); + std::vector 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, + ops::AccuracyNPUKernel, + ops::AccuracyNPUKernel); +#endif diff --git a/python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py new file mode 100644 index 00000000000..b5175bdb19c --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py @@ -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() -- GitLab