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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.
*/
/* SampleAs by Kaiming He for Mask R-CNN
X.dim32(0) = L.dim32(0)
Y's output samples are the samples of X for which L > 0.
*/
#include <cfloat>
#include "caffe2/core/context_gpu.h"
#include "modules/detectron/sample_as_op.h"
#include <stdio.h>
namespace caffe2 {
template <>
bool SampleAsOp<float, CUDAContext>::RunOnDevice() {
auto& X = Input(0); // Input data to be sliced
auto& L = Input(1); // Target data that provide the identity
CAFFE_ENFORCE(
X.dim32(0) == L.dim32(0),
"X.dim32(0) must be equal to L.dim32(0)",
"(",
X.dim32(0),
" vs. ",
L.dim32(0),
")");
// copy L to CPU:
std::vector<int> labels(L.dim32(0));
context_.CopyBytes<CUDAContext, CPUContext>(
L.dim32(0) * sizeof(int), L.data<int>(), &labels[0]);
// Make sure that the copy is finished
context_.FinishDeviceComputation();
int count = 0;
for (int i = 0; i < L.dim32(0); i++) {
if (labels[i] > 0) {
count++;
}
}
assert(count > 0);
// resize Y
vector<int64_t> out_shape(X.sizes().vec());
out_shape[0] = count;
auto* Y = Output(0, out_shape, at::dtype<float>()); // Sliced data (Y.dim32(0) = num of (L > 0))
const int len = X.size() / X.dim32(0);
float* output = Y->mutable_data<float>();
for (int i = 0; i < L.dim32(0); i++) {
if (labels[i] > 0) {
context_.CopyBytes<CUDAContext, CUDAContext>(
len * sizeof(float), X.data<float>() + i * len, output);
output += len;
} // if
} // i
return true;
}
template <>
bool SampleAsGradientOp<float, CUDAContext>::RunOnDevice() {
auto& X = Input(0);
auto& L = Input(1);
auto& dY = Input(2);
auto* dX = Output(0, X.sizes(), at::dtype<float>());
// copy L to CPU:
std::vector<int> labels(L.dim32(0));
context_.CopyBytes<CUDAContext, CPUContext>(
L.dim32(0) * sizeof(int), L.data<int>(), &labels[0]);
// Make sure that the copy is finished
context_.FinishDeviceComputation();
// zero-out dX
math::Set<float, CUDAContext>(
dX->size(), 0.f, dX->mutable_data<float>(), &context_);
const int len = X.size() / X.dim32(0);
const float* input = dY.data<float>();
for (int i = 0; i < L.dim32(0); i++) {
if (labels[i] > 0) {
context_.CopyBytes<CUDAContext, CUDAContext>(
len * sizeof(float), input, dX->mutable_data<float>() + i * len);
input += len;
} // if
} // i
return true;
}
REGISTER_CUDA_OPERATOR(SampleAs, SampleAsOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(
SampleAsGradient,
SampleAsGradientOp<float, CUDAContext>);
} // namespace caffe2
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