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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.
*/
#include "caffe2/core/context_gpu.h"
#include "caffe2/core/operator.h"
#include "modules/detectron/spatial_narrow_as_op.h"
namespace caffe2 {
namespace {
template <typename T>
__global__ void CopyKernel(
const int N,
const int C,
const int in_H,
const int in_W,
const int out_H,
const int out_W,
const T* in_data,
T* out_data) {
CUDA_1D_KERNEL_LOOP(index, N * C * out_H * out_W) {
int w = index % out_W;
int h = (index / out_W) % out_H;
int c = (index / out_W / out_H) % C;
int n = (index / out_W / out_H / C);
int in_index = n * C * in_H * in_W + c * in_H * in_W + h * in_W + w;
int out_index = n * C * out_H * out_W + c * out_H * out_W + h * out_W + w;
out_data[out_index] = in_data[in_index];
}
}
template <typename T>
__global__ void CopyGradientKernel(
const int N,
const int C,
const int in_H,
const int in_W,
const int out_H,
const int out_W,
const T* in_data,
T* out_data) {
CUDA_1D_KERNEL_LOOP(index, N * C * in_H * in_W) {
int w = index % in_W;
int h = (index / in_W) % in_H;
int c = (index / in_W / in_H) % C;
int n = (index / in_W / in_H / C);
int in_index = n * C * in_H * in_W + c * in_H * in_W + h * in_W + w;
int out_index = n * C * out_H * out_W + c * out_H * out_W + h * out_W + w;
out_data[out_index] = in_data[in_index];
}
}
} // namespace
template <>
bool SpatialNarrowAsOp<CUDAContext>::RunOnDevice() {
return DispatchHelper<TensorTypes<float_t, int32_t>>::call(this, Input(0));
}
template <>
template <typename T>
bool SpatialNarrowAsOp<CUDAContext>::DoRunWithType() {
// Narrows input 0 (A) spatially to match input 1 (B)
auto& A = Input(0);
auto& B = Input(1);
CAFFE_ENFORCE_EQ(A.dim32(0), B.dim32(0), "Input dim 0 must be equal.");
std::vector<int64_t> sizes;
if (A.ndim() == B.ndim()) {
CAFFE_ENFORCE_EQ(A.dim32(1), B.dim32(1), "Input dim 1 must be equal.");
CAFFE_ENFORCE_GE(
A.dim32(2), B.dim32(2), "Input 0 height must be >= input 1 height.");
CAFFE_ENFORCE_GE(
A.dim32(3), B.dim32(3), "Input 0 width must be >= input 1 width.");
sizes = B.sizes().vec();
} else {
// For (N, H, W) case
CAFFE_ENFORCE_EQ(A.ndim() - 1, B.ndim(), "Dimension mismatch.");
CAFFE_ENFORCE_GE(
A.dim32(2), B.dim32(1), "Input 0 height must be >= input 1 height.");
CAFFE_ENFORCE_GE(
A.dim32(3), B.dim32(2), "Input 0 width must be >= input 1 width.");
sizes = {A.dim32(0), A.dim32(1), B.dim32(1), B.dim32(2)};
}
auto* C = Output(0, sizes, at::dtype<T>());
int out_width = C->dim32(3);
int out_height = C->dim32(2);
int in_width = A.dim32(3);
int in_height = A.dim32(2);
CopyKernel<T><<<
CAFFE_GET_BLOCKS(C->size()),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
C->dim32(0),
C->dim32(1),
in_height,
in_width,
out_height,
out_width,
A.template data<T>(),
C->template mutable_data<T>());
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
template <>
bool SpatialNarrowAsGradientOp<CUDAContext>::RunOnDevice() {
return DispatchHelper<TensorTypes<float_t, int32_t>>::call(this, Input(0));
}
template <>
template <typename T>
bool SpatialNarrowAsGradientOp<CUDAContext>::DoRunWithType() {
auto& A = Input(0);
auto& B = Input(1);
auto& dC = Input(2); // Gradient of net w.r.t. output of forward op
auto* dA = Output(0, A.sizes(), at::dtype<T>()); // Gradient of net w.r.t. input to forward op
math::Set<T, CUDAContext>(
dA->size(), 0.f, dA->template mutable_data<T>(), &context_);
int out_width = dA->dim32(3);
int out_height = dA->dim32(2);
int in_width = dC.dim32(3);
int in_height = dC.dim32(2);
CopyGradientKernel<T><<<
CAFFE_GET_BLOCKS(dC.size()),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
dA->dim32(0),
dA->dim32(1),
in_height,
in_width,
out_height,
out_width,
dC.template data<T>(),
dA->template mutable_data<T>());
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
REGISTER_CUDA_OPERATOR(SpatialNarrowAs, SpatialNarrowAsOp<CUDAContext>);
REGISTER_CUDA_OPERATOR(
SpatialNarrowAsGradient,
SpatialNarrowAsGradientOp<CUDAContext>);
} // namespace caffe2
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