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Merge pull request #5571 from sweetsky0901/my_maxout_op
Add maxout operator.
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | ||
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. */ | ||
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#include "paddle/operators/math/maxouting.h" | ||
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namespace paddle { | ||
namespace operators { | ||
namespace math { | ||
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// All tensors are in NCHW format, and the groups must be greater than 1 | ||
template <typename T> | ||
class MaxOutFunctor<platform::CPUPlace, T> { | ||
public: | ||
void operator()(const platform::DeviceContext& context, | ||
const framework::Tensor& input, | ||
framework::Tensor * output, | ||
int groups) { | ||
const int batch_size = input.dims()[0]; | ||
const int input_height = input.dims()[2]; | ||
const int input_width = input.dims()[3]; | ||
const int output_channels = output->dims()[1]; | ||
int fea_size = input_height * input_width; | ||
// c_size means the output size of each sample | ||
int c_size = fea_size * output_channels; | ||
const T* input_data = input.data<T>(); | ||
T* output_data = output->mutable_data<T>(context.GetPlace()); | ||
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for (int i = 0; i < batch_size; ++i) { | ||
int new_bindex = c_size * i; | ||
for (int c = 0; c < output_channels; ++c) { | ||
int new_cindex = fea_size * c; | ||
for (int f = 0; f < fea_size; ++f) { | ||
T ele = static_cast<T>(-FLT_MAX); | ||
for (int ph = 0; ph < groups; ++ph) { | ||
T x = input_data[(new_bindex + new_cindex) * groups | ||
+ ph * fea_size + f]; | ||
ele = ele > x ? ele : x; | ||
} | ||
output_data[(new_bindex+new_cindex+f)] = ele; | ||
} | ||
} | ||
} | ||
} | ||
}; | ||
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template <class T> | ||
class MaxOutGradFunctor<platform::CPUPlace, T> { | ||
public: | ||
void operator()(const platform::DeviceContext& context, | ||
const framework::Tensor& input, | ||
framework::Tensor * input_grad, | ||
const framework::Tensor& output, | ||
const framework::Tensor& output_grad, | ||
int groups) { | ||
const int batch_size = input.dims()[0]; | ||
const int input_height = input.dims()[2]; | ||
const int input_width = input.dims()[3]; | ||
const int output_channels = output.dims()[1]; | ||
int fea_size = input_height * input_width; | ||
const T* input_data = input.data<T>(); | ||
const T* output_data = output.data<T>(); | ||
const T* output_grad_data = output_grad.data<T>(); | ||
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace()); | ||
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for (int i = 0; i < batch_size; ++i) { | ||
int blen = fea_size * output_channels * i; | ||
for (int c = 0; c < output_channels; ++c) { | ||
int clen = fea_size * c; | ||
for (int f = 0; f < fea_size; ++f) { | ||
int input_idx0 = (blen + clen) * groups + f; | ||
bool continue_match = true; | ||
int output_idx = blen + clen + f; | ||
for (int g = 0; g < groups && continue_match; ++g) { | ||
int input_idx = input_idx0 + fea_size * g; | ||
if (input_data[input_idx] == output_data[output_idx]) { | ||
input_grad_data[input_idx] += output_grad_data[output_idx]; | ||
continue_match = false; | ||
} | ||
} | ||
} | ||
} | ||
} | ||
} | ||
}; | ||
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template class MaxOutGradFunctor<platform::CPUPlace, float>; | ||
template class MaxOutGradFunctor<platform::CPUPlace, double>; | ||
template class MaxOutFunctor<platform::CPUPlace, float>; | ||
template class MaxOutFunctor<platform::CPUPlace, double>; | ||
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} // namespace math | ||
} // namespace operators | ||
} // namespace paddle |
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/* Copyright (c) 2016 paddlepaddle Authors. All Rights Reserve. | ||
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. */ | ||
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#include "paddle/operators/math/maxouting.h" | ||
#include "paddle/platform/cuda_helper.h" | ||
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namespace paddle { | ||
namespace operators { | ||
namespace math { | ||
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template <typename T> | ||
__global__ void KernelMaxOut(const int nthreads, const T* input_data, | ||
const int channels, | ||
const int input_height, const int input_width, | ||
int groups, T* output_data ) { | ||
const int size = input_height * input_width * channels / groups; | ||
const int feat_len = input_height * input_width; | ||
int index = blockIdx.x * blockDim.x + threadIdx.x; | ||
int offset = blockDim.x * gridDim.x; | ||
for (int i = index; i < nthreads; i += offset) { | ||
int batch_idx = i / size; | ||
int batch_offset = i % size; | ||
int channel_idx = batch_offset / feat_len; | ||
int feat_idx = batch_offset % feat_len; | ||
int data_idx = | ||
(batch_idx * size + channel_idx * feat_len) * groups + feat_idx; | ||
T ele = static_cast<T>(-FLT_MAX); | ||
for (int g = 0; g < groups; ++g) { | ||
T x = input_data[data_idx + g * feat_len]; | ||
ele = ele > x ? ele : x; | ||
} | ||
output_data[i] = ele; | ||
} | ||
} | ||
template <typename T> | ||
__global__ void KernelMaxoutGrad( | ||
const int nthreads, const T* input_data, const T* output_data, | ||
const T* output_grad, T* input_grad, const int channels, | ||
const int input_height, const int input_width, int groups) { | ||
const int size = input_height * input_width * channels / groups; | ||
const int feat_len = input_height * input_width; | ||
int index = blockIdx.x * blockDim.x + threadIdx.x; | ||
int offset = blockDim.x * gridDim.x; | ||
for (int i = index; i < nthreads; i += offset) { | ||
int batch_idx = i / size; | ||
int batch_offset = i % size; | ||
int channel_idx = batch_offset / feat_len; | ||
int feat_idx = batch_offset % feat_len; | ||
int data_idx = | ||
(batch_idx * size + channel_idx * feat_len) * groups + feat_idx; | ||
int max_index = -1; | ||
bool continue_match = true; | ||
for (int g = 0; g < groups && continue_match; ++g) { | ||
if (input_data[data_idx + g * feat_len] == output_data[i]) { | ||
max_index = data_idx + g * feat_len; | ||
continue_match = false; | ||
break; | ||
} | ||
} | ||
if (max_index != -1) { | ||
input_grad[max_index] += output_grad[index]; | ||
} | ||
} | ||
} | ||
/* | ||
* All tensors are in NCHW format. | ||
*/ | ||
template <typename T> | ||
class MaxOutFunctor<platform::GPUPlace, T> { | ||
public: | ||
void operator()(const platform::DeviceContext& context, | ||
const framework::Tensor& input, framework::Tensor * output, | ||
int groups) { | ||
const int batch_size = input.dims()[0]; | ||
const int input_channels = input.dims()[1]; | ||
const int input_height = input.dims()[2]; | ||
const int input_width = input.dims()[3]; | ||
const int output_channels = output->dims()[1]; | ||
const int output_height = output->dims()[2]; | ||
const int output_width = output->dims()[3]; | ||
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const T* input_data = input.data<T>(); | ||
T* output_data = output->mutable_data<T>(context.GetPlace()); | ||
int nthreads = output->numel(); | ||
int blocks = (nthreads + 1024 - 1) / 1024; | ||
dim3 threads(1024, 1); | ||
dim3 grid(blocks, 1); | ||
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KernelMaxOut< | ||
T><<<grid, threads, 0, | ||
reinterpret_cast<const platform::CUDADeviceContext&>(context) | ||
.stream()>>>(nthreads, input_data, input_channels, | ||
input_height, input_width, groups, | ||
output_data); | ||
} | ||
}; | ||
/* | ||
* All tensors are in NCHW format. | ||
*/ | ||
template <typename T> | ||
class MaxOutGradFunctor<platform::GPUPlace, T> { | ||
public: | ||
void operator()(const platform::DeviceContext& context, | ||
const framework::Tensor& input, | ||
framework::Tensor * input_grad, | ||
const framework::Tensor& output, | ||
const framework::Tensor& output_grad, | ||
int groups) { | ||
const int batch_size = input.dims()[0]; | ||
const int input_channels = input.dims()[1]; | ||
const int input_height = input.dims()[2]; | ||
const int input_width = input.dims()[3]; | ||
const int output_channels = output.dims()[1]; | ||
const int output_height = output.dims()[2]; | ||
const int output_width = output.dims()[3]; | ||
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const T* input_data = input.data<T>(); | ||
const T* output_data = output.data<T>(); | ||
const T* output_grad_data = output_grad.data<T>(); | ||
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace()); | ||
int nthreads = output.numel(); | ||
int blocks = (nthreads + 1024 - 1) / 1024; | ||
dim3 threads(1024, 1); | ||
dim3 grid(blocks, 1); | ||
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KernelMaxoutGrad< | ||
T><<<grid, threads, 0, | ||
reinterpret_cast<const platform::CUDADeviceContext&>(context) | ||
.stream()>>>( | ||
nthreads, input_data, output_data, output_grad_data, input_grad_data, | ||
input_channels, input_height, input_width, groups); | ||
} | ||
}; | ||
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template class MaxOutGradFunctor<platform::GPUPlace, float>; | ||
template class MaxOutGradFunctor<platform::GPUPlace, double>; | ||
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template class MaxOutFunctor<platform::GPUPlace, float>; | ||
template class MaxOutFunctor<platform::GPUPlace, double>; | ||
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} // namespace math | ||
} // namespace operators | ||
} // namespace paddle |
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@@ -0,0 +1,47 @@ | ||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | ||
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. */ | ||
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#pragma once | ||
#include "paddle/framework/tensor.h" | ||
#include "paddle/platform/device_context.h" | ||
#include "paddle/platform/hostdevice.h" | ||
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namespace paddle { | ||
namespace operators { | ||
namespace math { | ||
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#define FLT_MAX \ | ||
__FLT_MAX__ | ||
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template <typename Place, typename T> | ||
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class MaxOutFunctor { | ||
public: | ||
void operator()(const platform::DeviceContext& context, | ||
const framework::Tensor& input, framework::Tensor * output, | ||
int groups); | ||
}; | ||
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template <typename Place, class T> | ||
class MaxOutGradFunctor { | ||
public: | ||
void operator()(const platform::DeviceContext& context, | ||
const framework::Tensor& input, | ||
framework::Tensor * input_grad, | ||
const framework::Tensor& output, | ||
const framework::Tensor& output_grad, int groups); | ||
}; | ||
} // namespace math | ||
} // namespace operators | ||
} // namespace paddle |
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