#include "./opr_impl.h"
#include "./flip.cuh"
#include <cstring>
#include "src/common/utils.h"
#include "src/cuda/handle.h"
#include "src/cuda/utils.h"
namespace megdnn {
namespace cuda {
namespace flip_intl {
template <typename ctype>
void flip_exec(
const ctype* src, ctype* dst, size_t N, size_t IH, size_t IW, size_t IC,
size_t stride1, size_t stride2, size_t stride3, bool vertical, bool horizontal,
cudaStream_t stream) {
if (vertical) {
if (horizontal) {
flip::flip<ctype, true, true>(
src, dst, N, IH, IW, IC, stride1, stride2, stride3, stream);
} else {
flip::flip<ctype, true, false>(
src, dst, N, IH, IW, IC, stride1, stride2, stride3, stream);
}
} else {
if (horizontal) {
flip::flip<ctype, false, true>(
src, dst, N, IH, IW, IC, stride1, stride2, stride3, stream);
} else {
flip::flip<ctype, false, false>(
src, dst, N, IH, IW, IC, stride1, stride2, stride3, stream);
}
}
}
}
void FlipImpl::exec(
_megdnn_tensor_in src, _megdnn_tensor_in dst, _megdnn_workspace workspace) {
check_exec(src.layout, dst.layout, workspace.size);
auto stream = cuda_stream(handle());
size_t N = src.layout.shape[0];
size_t batch_size = 0;
#define cb(DType) \
if (src.layout.dtype.enumv() == DTypeTrait<DType>::enumv) { \
using ctype = typename DTypeTrait<DType>::ctype; \
ctype* src_ptr = src.ptr<ctype>() + curr_batch * src.layout.stride[0]; \
ctype* dst_ptr = dst.ptr<ctype>() + curr_batch * src.layout.stride[0]; \
batch_size = std::min<size_t>(N - curr_batch, max_batch); \
flip_intl::flip_exec<ctype>( \
src_ptr, dst_ptr, batch_size, src.layout.shape[1], \
src.layout.shape[2], src.layout.shape[3], src.layout.stride[0], \
src.layout.stride[1], src.layout.stride[2], param().vertical, \
param().horizontal, stream); \
}
size_t curr_batch = 0;
size_t max_batch = max_batch_x_channel_size();
if (N <= max_batch) {
MEGDNN_FOREACH_COMPUTING_DTYPE(cb)
} else {
while (curr_batch < N) {
MEGDNN_FOREACH_COMPUTING_DTYPE(cb)
curr_batch += max_batch;
}
}
#undef cb
}
} }