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100 lines
3.5 KiB
100 lines
3.5 KiB
/* Copyright (c) 2016 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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#include <thrust/execution_policy.h>
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#include <thrust/reduce.h>
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#include "paddle/fluid/operators/accuracy_op.h"
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#include "paddle/fluid/platform/cuda_primitives.h"
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#include "paddle/fluid/platform/gpu_info.h"
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namespace paddle {
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namespace operators {
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using platform::PADDLE_CUDA_NUM_THREADS;
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template <int BlockSize>
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__global__ void AccuracyCudaKernel(const int N, const int D,
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const int64_t* Xdata,
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const int64_t* labeldata, int* correct_data,
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float* accuracy, int* total_data) {
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int count = 0;
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__shared__ int total[BlockSize];
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// support only 1 block
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for (int i = threadIdx.x; i < (N); i += BlockSize) {
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for (int j = 0; j < D; ++j) {
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if (Xdata[i * D + j] == labeldata[i]) {
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++count;
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break;
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}
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}
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}
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total[threadIdx.x] = count;
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__syncthreads();
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// reduce the count with init value 0, and output accuracy.
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int result = thrust::reduce(thrust::device, total, total + BlockSize, 0);
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if (threadIdx.x == 0) {
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*correct_data = result;
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*accuracy = static_cast<float>(result) / static_cast<float>(N);
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*total_data = N;
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}
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}
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template <typename T>
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class AccuracyOpCUDAKernel : 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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PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
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"It must use CUDAPlace.");
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auto* inference = ctx.Input<Tensor>("Out");
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auto* indices = ctx.Input<Tensor>("Indices");
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auto* label = ctx.Input<Tensor>("Label");
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auto* accuracy = 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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// FIXME(typhoonzero): only support indices currently
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// if add support for output values, how to detect the data type?
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const int64_t* indices_data = indices->data<int64_t>();
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const int64_t* label_data = label->data<int64_t>();
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int* correct_data = correct->mutable_data<int>(ctx.GetPlace());
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int* total_data = total->mutable_data<int>(ctx.GetPlace());
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float* accuracy_data = accuracy->mutable_data<float>(ctx.GetPlace());
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int num_samples = static_cast<int>(inference->dims()[0]);
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size_t infer_width = inference->dims()[1];
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auto stream = ctx.cuda_device_context().stream();
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platform::GpuMemsetAsync(accuracy_data, 0, sizeof(float), stream);
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if (num_samples == 0) {
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return;
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}
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AccuracyCudaKernel<
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PADDLE_CUDA_NUM_THREADS><<<1, PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
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num_samples, infer_width, indices_data, label_data, correct_data,
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accuracy_data, total_data);
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}
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};
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} // namespace operators
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} // namespace paddle
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// FIXME(typhoonzero): types of T is for inference data.
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// label data is always int64
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REGISTER_OP_CUDA_KERNEL(accuracy,
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paddle::operators::AccuracyOpCUDAKernel<float>,
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paddle::operators::AccuracyOpCUDAKernel<double>);
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