[NPU] accuracy op (#31492)
* accuracy op * fix license * fix * add test and fix bugrevert-31562-mean
yinhaofeng
5 years ago
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GitHub
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3bf8a34c69
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e1c33a6d69
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/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#ifdef PADDLE_WITH_ASCEND_CL
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#include <memory>
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#include <string>
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#include "paddle/fluid/operators/controlflow/compare_op.h"
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#include "paddle/fluid/operators/metrics/accuracy_op.h"
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#include "paddle/fluid/operators/npu_op_runner.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class AccuracyNPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* pred = ctx.Input<Tensor>("Out");
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auto* label = ctx.Input<Tensor>("Label");
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// auto* logits = ctx.Input<Tensor>("Indices");
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auto* acc = ctx.Output<Tensor>("Accuracy");
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auto* correct = ctx.Output<Tensor>("Correct");
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auto* total = ctx.Output<Tensor>("Total");
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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// cast pred
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Tensor tmp_pred(pred->type());
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tmp_pred.Resize(pred->dims());
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tmp_pred.mutable_data<int>(ctx.GetPlace());
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auto runner_cast_pred =
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NpuOpRunner("Cast", {*pred}, {tmp_pred},
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{{"dst_type", static_cast<int>(ACL_INT32)}});
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runner_cast_pred.Run(stream);
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// cast label
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Tensor tmp_label(label->type());
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tmp_label.Resize(label->dims());
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tmp_label.mutable_data<int>(ctx.GetPlace());
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auto runner_cast_label =
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NpuOpRunner("Cast", {*label}, {tmp_label},
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{{"dst_type", static_cast<int>(ACL_INT32)}});
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runner_cast_label.Run(stream);
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// equal
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Tensor tmp_equal(label->type());
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tmp_equal.Resize(label->dims());
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tmp_equal.mutable_data<bool>(ctx.GetPlace());
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auto runner_equal =
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NpuOpRunner("Equal", {tmp_pred, tmp_label}, {tmp_equal}, {});
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runner_equal.Run(stream);
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// cast equal
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Tensor tmp_equal_cast(label->type());
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tmp_equal_cast.Resize(label->dims());
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tmp_equal_cast.mutable_data<float>(ctx.GetPlace());
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auto runner_cast_equal =
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NpuOpRunner("Cast", {tmp_equal}, {tmp_equal_cast},
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{{"dst_type", static_cast<float>(ACL_FLOAT)}});
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runner_cast_equal.Run(stream);
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// acc
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acc->mutable_data<float>(ctx.GetPlace());
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std::vector<int> axes_vec_1;
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auto runner_acc = NpuOpRunner("ReduceMeanD", {tmp_equal_cast}, {*acc},
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{{"keep_dims", false}, {"axes", axes_vec_1}});
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runner_acc.Run(stream);
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// correct
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correct->mutable_data<float>(ctx.GetPlace());
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std::vector<int> axes_vec_2;
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auto runner_correct =
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NpuOpRunner("ReduceSumD", {tmp_equal_cast}, {*correct},
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{{"keep_dims", false}, {"axes", axes_vec_2}});
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runner_correct.Run(stream);
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// ones_tensor
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Tensor ones_tensor(label->type());
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ones_tensor.Resize(label->dims());
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ones_tensor.mutable_data<int>(ctx.GetPlace());
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auto runner_oneslike =
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NpuOpRunner("OnesLike", {tmp_label}, {ones_tensor}, {});
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runner_oneslike.Run(stream);
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// ones_tensor_cast
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Tensor ones_tensor_cast(label->type());
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ones_tensor_cast.Resize(label->dims());
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ones_tensor_cast.mutable_data<float>(ctx.GetPlace());
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auto runner_ones_cast =
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NpuOpRunner("Cast", {ones_tensor}, {ones_tensor_cast},
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{{"dst_type", static_cast<float>(ACL_FLOAT)}});
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runner_ones_cast.Run(stream);
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// total
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total->mutable_data<float>(ctx.GetPlace());
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std::vector<int> axes_vec_3;
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auto runner_total =
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NpuOpRunner("ReduceSumD", {ones_tensor_cast}, {*total},
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{{"keep_dims", false}, {"axes", axes_vec_3}});
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runner_total.Run(stream);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_NPU_KERNEL(
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accuracy, ops::AccuracyNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::AccuracyNPUKernel<paddle::platform::NPUDeviceContext, int>,
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ops::AccuracyNPUKernel<paddle::platform::NPUDeviceContext, int64_t>);
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#endif
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@ -0,0 +1,122 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import numpy as np
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import unittest
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import sys
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sys.path.append("..")
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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paddle.enable_static()
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SEED = 2021
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestAccuracy(OpTest):
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def setUp(self):
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self.op_type = "accuracy"
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self.set_npu()
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self.init_dtype()
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np.random.seed(SEED)
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pred = np.random.uniform(1, 2, [11, 1]).astype(self.dtype)
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label = pred.copy()
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accuracy = np.array([1]).astype(self.dtype)
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correct = np.array([11 * 1]).astype(self.dtype)
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total = np.array([11 * 1]).astype(self.dtype)
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self.inputs = {
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"Out": OpTest.np_dtype_to_fluid_dtype(pred),
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"Label": OpTest.np_dtype_to_fluid_dtype(label),
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"Indices": OpTest.np_dtype_to_fluid_dtype(pred)
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}
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self.outputs = {
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"Accuracy": accuracy,
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"Correct": correct,
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"Total": total
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}
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def set_npu(self):
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self.__class__.use_npu = True
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self.place = paddle.NPUPlace(0)
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def init_dtype(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(self.place, check_dygraph=False)
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class TestAccuracy2(TestAccuracy):
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def setUp(self):
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self.op_type = "accuracy"
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self.set_npu()
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self.init_dtype()
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np.random.seed(SEED)
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pred = np.random.uniform(1, 2, [11, 1]).astype(self.dtype)
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label = np.random.uniform(4, 5, [11, 1]).astype(self.dtype)
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accuracy = np.array([0]).astype(self.dtype)
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correct = np.array([11 * 0]).astype(self.dtype)
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total = np.array([11 * 1]).astype(self.dtype)
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self.inputs = {
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"Out": OpTest.np_dtype_to_fluid_dtype(pred),
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"Label": OpTest.np_dtype_to_fluid_dtype(label),
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"Indices": OpTest.np_dtype_to_fluid_dtype(pred)
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}
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self.outputs = {
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"Accuracy": accuracy,
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"Correct": correct,
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"Total": total
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}
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class TestAccuracy3(TestAccuracy):
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def setUp(self):
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self.op_type = "accuracy"
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self.set_npu()
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self.init_dtype()
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np.random.seed(SEED)
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a = np.random.randint(1, 2, [5, 1])
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b = np.random.randint(0, 1, [5, 1])
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pred = np.row_stack((a, b)).astype(self.dtype)
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label = np.random.randint(1, 2, [10, 1]).astype(self.dtype)
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accuracy = np.array([0.5]).astype(self.dtype)
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correct = np.array([5]).astype(self.dtype)
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total = np.array([10 * 1]).astype(self.dtype)
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self.inputs = {
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"Out": OpTest.np_dtype_to_fluid_dtype(pred),
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"Label": OpTest.np_dtype_to_fluid_dtype(label),
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"Indices": OpTest.np_dtype_to_fluid_dtype(pred)
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}
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self.outputs = {
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"Accuracy": accuracy,
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"Correct": correct,
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"Total": total
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}
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class TestAccuracyInt(TestAccuracy):
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def init_dtype(self):
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self.dtype = np.int
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if __name__ == '__main__':
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unittest.main()
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