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/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* 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.
*/
/**
* Adapted from https://github.com/torch/cunn/blob/master/lib/THCUNN/SpatialUpSamplingNearest.cu
*
* Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
* Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
* Copyright (c) 2011-2013 NYU (Clement Farabet)
* Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert,
* Leon Bottou, Iain Melvin, Jason Weston)
* Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
* Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert,
* Samy Bengio, Johnny Mariethoz)
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* 3. Neither the names of NEC Laboratories American and IDIAP Research
* Institute nor the names of its contributors may be used to endorse or
* promote products derived from this software without specific prior
* written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#include "caffe2/core/context_gpu.h"
#include "modules/detectron/upsample_nearest_op.h"
namespace caffe2 {
namespace {
__device__ int translate_idx(int ii, int d1, int d2, int d3, int scale_factor) {
int x, y, z, w;
w = ii % d3;
ii = ii/d3;
z = ii % d2;
ii = ii/d2;
y = ii % d1;
ii = ii/d1;
x = ii;
w = w/scale_factor;
z = z/scale_factor;
d2 /= scale_factor;
d3 /= scale_factor;
return (((x*d1+y)*d2)+z)*d3+w;
}
__device__ int translate_idx_inv(
int ii, int d1, int d2, int d3, int scale_factor, int off_x, int off_y) {
int x, y, z, w;
w = ii % d3;
ii = ii/d3;
z = ii % d2;
ii = ii/d2;
y = ii % d1;
ii = ii/d1;
x = ii;
w = w*scale_factor+off_x;
z = z*scale_factor+off_y;
d2 *= scale_factor;
d3 *= scale_factor;
return (((x*d1+y)*d2)+z)*d3+w;
}
__global__ void upscale(const float *input, float *output, long no_elements,
int scale_factor, int d1, int d2, int d3) {
long ii = threadIdx.x + blockDim.x * blockIdx.x;
ii += threadIdx.y + blockDim.y * (blockDim.x * gridDim.x) * blockIdx.y;
if (ii >= no_elements) return;
int ipidx = translate_idx(ii, d1, d2, d3, scale_factor);
output[ii]=input[ipidx];
}
__global__ void downscale(float *gradInput_data, const float *gradOutput_data,
long no_elements, int scale_factor, int d1, int d2,
int d3) {
long ii = threadIdx.x + blockDim.x * blockIdx.x;
ii += threadIdx.y + blockDim.y * (blockDim.x * gridDim.x) * blockIdx.y;
if (ii >= no_elements) return;
for (int i=0; i < scale_factor; i++){
for(int j=0; j < scale_factor; j++){
int ipidx = translate_idx_inv(ii, d1, d2, d3, scale_factor, i, j);
gradInput_data[ii] += gradOutput_data[ipidx];
}
}
}
} // namespace
template<>
bool UpsampleNearestOp<float, CUDAContext>::RunOnDevice() {
auto& X = Input(0);
auto* Y = Output(0);
vector<int64_t> out_shape;
for (int i = 0; i < X.ndim(); ++i) {
out_shape.push_back(X.dim32(i));
}
out_shape[X.ndim() - 1] *= scale_;
out_shape[X.ndim() - 2] *= scale_;
Y->Resize(out_shape);
int d1;
int d2;
int d3;
if (X.ndim() == 3) {
d1 = Y->dim32(0);
d2 = Y->dim32(1);
d3 = Y->dim32(2);
} else {
d1 = Y->dim32(1);
d2 = Y->dim32(2);
d3 = Y->dim32(3);
}
long no_elements = Y->size();
const float *input_data = X.data<float>();
float *output_data = Y->mutable_data<float>();
// cuda blocks & threads:
long nthreads = 256;
// Max number of blocks: http://en.wikipedia.org/wiki/CUDA
// 65535 for SM 2.x, 2^32 -1 for >= 3.0
// TODO: When we move to SM 3.5 we should update this
long n_xblocks = min(max((int)ceil((float)no_elements / nthreads), 1), 65535);
long n_yblocks = (long)ceil(
(float)no_elements / (float)(n_xblocks * nthreads));
CAFFE_ENFORCE(n_yblocks <= 65535);
dim3 blocks(n_xblocks, n_yblocks);
dim3 threads(nthreads);
upscale<<<blocks, threads, 0, context_.cuda_stream()>>>(
input_data, output_data, no_elements, scale_, d1, d2, d3);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
template<>
bool UpsampleNearestGradientOp<float, CUDAContext>::RunOnDevice() {
auto& X = Input(0); // Original input to "forward" op
auto& dY = Input(1); // Gradient of net w.r.t. output of "forward" op
// (aka "gradOutput")
auto* dX = Output(0); // Gradient of net w.r.t. input to "forward" op
// (aka "gradInput")
dX->ResizeLike(X);
float *gradInput_data = dX->mutable_data<float>();
const float *gradOutput_data = dY.data<float>();
int d1;
int d2;
int d3;
if (dX->ndim() == 3) {
d1 = dX->dim32(0);
d2 = dX->dim32(1);
d3 = dX->dim32(2);
} else {
d1 = dX->dim32(1);
d2 = dX->dim32(2);
d3 = dX->dim32(3);
}
long no_elements = dX->size();
// cuda blocks & threads:
long nthreads = 256;
// Max number of blocks: http://en.wikipedia.org/wiki/CUDA
// 65535 for SM 2.x, 2^32 -1 for >= 3.0
// TODO: When we move to SM 3.5 we should update this
long n_xblocks = min(max((int)ceil((float)no_elements / nthreads), 1), 65535);
long n_yblocks = (long)ceil(
(float)no_elements / (float)(n_xblocks * nthreads));
CAFFE_ENFORCE(n_yblocks <= 65535);
dim3 blocks(n_xblocks, n_yblocks);
dim3 threads(nthreads);
math::Set<float, CUDAContext>(no_elements, 0.f, gradInput_data, &context_);
downscale<<<blocks, threads, 0, context_.cuda_stream()>>>(
gradInput_data, gradOutput_data, no_elements, scale_, d1, d2, d3);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
REGISTER_CUDA_OPERATOR(UpsampleNearest,
UpsampleNearestOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(UpsampleNearestGradient,
UpsampleNearestGradientOp<float, CUDAContext>);
} // namespace caffe2
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