提交 c7b6ef35 编写于 作者: M Megvii Engine Team 提交者: Xinran Xu

feat(dnn/cuda): add warp perspective backward mat idx

GitOrigin-RevId: b4b494bb69eeffed564fb86e587e59fa409f7426
上级 a773d076
...@@ -105,15 +105,32 @@ class WarpPerspectiveBackwardData: public WarpPerspectiveBase { ...@@ -105,15 +105,32 @@ class WarpPerspectiveBackwardData: public WarpPerspectiveBase {
* \param[out] grad the backpropagated gradient wrt. src * \param[out] grad the backpropagated gradient wrt. src
* \param[out] workspace temporary workspace to perform backward * \param[out] workspace temporary workspace to perform backward
*/ */
void exec(_megdnn_tensor_in mat,
_megdnn_tensor_in diff,
_megdnn_tensor_out grad,
_megdnn_workspace workspace) {
exec(mat, {}, diff, grad, workspace);
}
virtual void exec(_megdnn_tensor_in mat, virtual void exec(_megdnn_tensor_in mat,
_megdnn_tensor_in mat_idx,
_megdnn_tensor_in diff, _megdnn_tensor_in diff,
_megdnn_tensor_out grad, _megdnn_tensor_out grad,
_megdnn_workspace workspace) = 0; _megdnn_workspace workspace) = 0;
size_t get_workspace_in_bytes(const TensorLayout &mat,
const TensorLayout &diff,
const TensorLayout &grad) {
return get_workspace_in_bytes(mat, {}, diff, grad);
}
virtual size_t get_workspace_in_bytes(const TensorLayout &mat, virtual size_t get_workspace_in_bytes(const TensorLayout &mat,
const TensorLayout &mat_idx,
const TensorLayout &diff, const TensorLayout &diff,
const TensorLayout &grad) = 0; const TensorLayout &grad) = 0;
protected: protected:
void check_exec(const TensorLayout &mat, void check_exec(const TensorLayout &mat,
const TensorLayout &mat_idx,
const TensorLayout &diff, const TensorLayout &diff,
const TensorLayout &grad, const TensorLayout &grad,
size_t workspace_in_bytes); size_t workspace_in_bytes);
...@@ -129,18 +146,37 @@ class WarpPerspectiveBackwardMat: public WarpPerspectiveBase { ...@@ -129,18 +146,37 @@ class WarpPerspectiveBackwardMat: public WarpPerspectiveBase {
* \param[out] grad the backpropagated gradient wrt. mat * \param[out] grad the backpropagated gradient wrt. mat
* \param[out] workspace temporary workspace to perform backward * \param[out] workspace temporary workspace to perform backward
*/ */
void exec(_megdnn_tensor_in src,
_megdnn_tensor_in mat,
_megdnn_tensor_in diff,
_megdnn_tensor_out grad,
_megdnn_workspace workspace) {
exec(src, mat, {}, diff, grad, workspace);
}
virtual void exec(_megdnn_tensor_in src, virtual void exec(_megdnn_tensor_in src,
_megdnn_tensor_in mat, _megdnn_tensor_in mat,
_megdnn_tensor_in mat_idx,
_megdnn_tensor_in diff, _megdnn_tensor_in diff,
_megdnn_tensor_out grad, _megdnn_tensor_out grad,
_megdnn_workspace workspace) = 0; _megdnn_workspace workspace) = 0;
size_t get_workspace_in_bytes(const TensorLayout &src,
const TensorLayout &mat,
const TensorLayout &diff,
const TensorLayout &grad) {
return get_workspace_in_bytes(src, mat, {}, diff, grad);
}
virtual size_t get_workspace_in_bytes(const TensorLayout &src, virtual size_t get_workspace_in_bytes(const TensorLayout &src,
const TensorLayout &mat, const TensorLayout &mat,
const TensorLayout &mat_idx,
const TensorLayout &diff, const TensorLayout &diff,
const TensorLayout &grad) = 0; const TensorLayout &grad) = 0;
protected: protected:
void check_exec(const TensorLayout &src, void check_exec(const TensorLayout &src,
const TensorLayout &mat, const TensorLayout &mat,
const TensorLayout &mat_idx,
const TensorLayout &diff, const TensorLayout &diff,
const TensorLayout &grad, const TensorLayout &grad,
size_t workspace_in_bytes); size_t workspace_in_bytes);
......
...@@ -255,29 +255,31 @@ void WarpPerspectiveForward::check_exec_allow_nhwc_mat_idx( ...@@ -255,29 +255,31 @@ void WarpPerspectiveForward::check_exec_allow_nhwc_mat_idx(
} }
void WarpPerspectiveBackwardData::check_exec(const TensorLayout& mat, void WarpPerspectiveBackwardData::check_exec(const TensorLayout& mat,
const TensorLayout& mat_idx,
const TensorLayout& diff, const TensorLayout& diff,
const TensorLayout& grad, const TensorLayout& grad,
size_t workspace_in_bytes) { size_t workspace_in_bytes) {
check_layout_fwd(grad, mat, diff); check_layout_fwd(grad, mat, mat_idx, diff);
megdnn_assert(grad.dtype == dtype::Float32() MEGDNN_INC_FLOAT16( megdnn_assert(grad.dtype == dtype::Float32() MEGDNN_INC_FLOAT16(
|| grad.dtype == dtype::BFloat16()), || grad.dtype == dtype::BFloat16()),
"Backward WarpPerspective only supports Float32/BFloat16."); "Backward WarpPerspective only supports Float32/BFloat16.");
auto required_workspace_in_bytes = get_workspace_in_bytes(mat, diff, grad); auto required_workspace_in_bytes = get_workspace_in_bytes(mat, mat_idx, diff, grad);
megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes); megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes);
} }
void WarpPerspectiveBackwardMat::check_exec(const TensorLayout& src, void WarpPerspectiveBackwardMat::check_exec(const TensorLayout& src,
const TensorLayout& mat, const TensorLayout& mat,
const TensorLayout& mat_idx,
const TensorLayout& diff, const TensorLayout& diff,
const TensorLayout& grad, const TensorLayout& grad,
size_t workspace_in_bytes) { size_t workspace_in_bytes) {
check_layout_fwd(src, mat, diff); check_layout_fwd(src, mat, mat_idx, diff);
megdnn_assert_eq_layout(mat, grad); megdnn_assert_eq_layout(mat, grad);
megdnn_assert(grad.dtype == dtype::Float32() MEGDNN_INC_FLOAT16( megdnn_assert(grad.dtype == dtype::Float32() MEGDNN_INC_FLOAT16(
|| grad.dtype == dtype::BFloat16()), || grad.dtype == dtype::BFloat16()),
"Backward WarpPerspective only supports Float32/BFloat16."); "Backward WarpPerspective only supports Float32/BFloat16.");
auto required_workspace_in_bytes = auto required_workspace_in_bytes =
get_workspace_in_bytes(src, mat, diff, grad); get_workspace_in_bytes(src, mat, mat_idx, diff, grad);
megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes); megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes);
} }
......
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#include "src/cuda/warp_perspective/opr_impl.h" #include "src/cuda/warp_perspective/opr_impl.h"
...@@ -18,8 +19,8 @@ namespace megdnn { ...@@ -18,8 +19,8 @@ namespace megdnn {
namespace cuda { namespace cuda {
WorkspaceBundle WarpPerspectiveBackwardDataImpl::get_workspace_bundle( WorkspaceBundle WarpPerspectiveBackwardDataImpl::get_workspace_bundle(
void* ptr, const TensorLayout& mat, const TensorLayout& diff, void* ptr, const TensorLayout& mat, const TensorLayout& mat_idx,
const TensorLayout& grad) const { const TensorLayout& diff, const TensorLayout& grad) const {
SmallVector<size_t> sizes; SmallVector<size_t> sizes;
TensorLayout fmat = mat; TensorLayout fmat = mat;
TensorLayout fdiff = diff; TensorLayout fdiff = diff;
...@@ -33,20 +34,24 @@ WorkspaceBundle WarpPerspectiveBackwardDataImpl::get_workspace_bundle( ...@@ -33,20 +34,24 @@ WorkspaceBundle WarpPerspectiveBackwardDataImpl::get_workspace_bundle(
get_workspace(fmat); get_workspace(fmat);
get_workspace(fdiff); get_workspace(fdiff);
get_workspace(fgrad); get_workspace(fgrad);
sizes.push_back(get_float32_workspace_in_bytes(fmat, fdiff, fgrad)); sizes.push_back(
get_float32_workspace_in_bytes(fmat, mat_idx, fdiff, fgrad));
return {ptr, std::move(sizes)}; return {ptr, std::move(sizes)};
} }
void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat, void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat,
_megdnn_tensor_in mat_idx,
_megdnn_tensor_in sdiff, _megdnn_tensor_in sdiff,
_megdnn_tensor_out sgrad, _megdnn_tensor_out sgrad,
_megdnn_workspace sworkspace) { _megdnn_workspace sworkspace) {
check_exec(smat.layout, sdiff.layout, sgrad.layout, sworkspace.size); check_exec(smat.layout, mat_idx.layout, sdiff.layout, sgrad.layout,
sworkspace.size);
TensorND mat = smat; TensorND mat = smat;
TensorND diff = sdiff; TensorND diff = sdiff;
TensorND grad = sgrad; TensorND grad = sgrad;
auto bundle = get_workspace_bundle(sworkspace.raw_ptr, smat.layout, auto bundle =
sdiff.layout, sgrad.layout); get_workspace_bundle(sworkspace.raw_ptr, smat.layout,
mat_idx.layout, sdiff.layout, sgrad.layout);
auto ctypecvt = CompTypeCvter<dtype::BFloat16, dtype::Float32>( auto ctypecvt = CompTypeCvter<dtype::BFloat16, dtype::Float32>(
concrete_handle(this->handle()), &bundle); concrete_handle(this->handle()), &bundle);
if (sgrad.layout.dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) { if (sgrad.layout.dtype.enumv() == DTypeTrait<dtype::BFloat16>::enumv) {
...@@ -60,6 +65,15 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat, ...@@ -60,6 +65,15 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat,
auto N = grad.layout.shape[0], C = grad.layout.shape[1], auto N = grad.layout.shape[0], C = grad.layout.shape[1],
IH = grad.layout.shape[2], IW = grad.layout.shape[3], IH = grad.layout.shape[2], IW = grad.layout.shape[3],
OH = diff.layout.shape[2], OW = diff.layout.shape[3]; OH = diff.layout.shape[2], OW = diff.layout.shape[3];
int* midx_ptr = nullptr;
if (mat_idx.raw_ptr) {
megdnn_assert(mat_idx.layout.ndim == 1);
N = mat_idx.layout.shape[0];
midx_ptr = mat_idx.ptr<int>();
} else {
megdnn_assert(mat_idx.layout.ndim == 0);
}
auto bval = param().border_val; auto bval = param().border_val;
auto bmode = warp_perspective::get_bmode(param().bmode); auto bmode = warp_perspective::get_bmode(param().bmode);
...@@ -67,10 +81,11 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat, ...@@ -67,10 +81,11 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat,
size_t max_batch_x_channel = max_batch_x_channel_size(); size_t max_batch_x_channel = max_batch_x_channel_size();
if (batch_x_channel_size <= max_batch_x_channel) { if (batch_x_channel_size <= max_batch_x_channel) {
warp_perspective::backward_data_proxy( warp_perspective::backward_data_proxy(
mat.ptr<dt_float32>(), diff.ptr<dt_float32>(), mat.ptr<dt_float32>(), midx_ptr, diff.ptr<dt_float32>(),
grad.ptr<dt_float32>(), grad.ptr<dt_float32>(),
reinterpret_cast<float*>(workspace.raw_ptr), N, C, IH, IW, reinterpret_cast<float*>(workspace.raw_ptr), N,
OH, OW, bval, bmode, stream); grad.layout.shape[0], C, IH, IW, OH, OW, bval, bmode,
stream);
} else { } else {
dt_float32* mat_ptr = mat.ptr<dt_float32>(); dt_float32* mat_ptr = mat.ptr<dt_float32>();
dt_float32* diff_ptr = diff.ptr<dt_float32>(); dt_float32* diff_ptr = diff.ptr<dt_float32>();
...@@ -80,10 +95,10 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat, ...@@ -80,10 +95,10 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat,
size_t curr_batch_size = size_t curr_batch_size =
N > max_batch_size ? max_batch_size : N; N > max_batch_size ? max_batch_size : N;
warp_perspective::backward_data_proxy( warp_perspective::backward_data_proxy(
mat_ptr, diff_ptr, grad_ptr, mat_ptr, midx_ptr, diff_ptr, grad_ptr,
reinterpret_cast<float*>(workspace.raw_ptr), reinterpret_cast<float*>(workspace.raw_ptr),
curr_batch_size, C, IH, IW, OH, OW, bval, bmode, curr_batch_size, grad.layout.shape[0], C, IH, IW, OH,
stream); OW, bval, bmode, stream);
if (N <= max_batch_size) { if (N <= max_batch_size) {
break; break;
...@@ -91,7 +106,11 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat, ...@@ -91,7 +106,11 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat,
N -= max_batch_size; N -= max_batch_size;
mat_ptr += curr_batch_size * mat.layout.stride[0]; mat_ptr += curr_batch_size * mat.layout.stride[0];
diff_ptr += curr_batch_size * diff.layout.stride[0]; diff_ptr += curr_batch_size * diff.layout.stride[0];
grad_ptr += curr_batch_size * grad.layout.stride[0]; if (midx_ptr == nullptr) {
grad_ptr += curr_batch_size * grad.layout.stride[0];
} else {
midx_ptr += curr_batch_size;
}
} }
} }
} }
...@@ -102,8 +121,8 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat, ...@@ -102,8 +121,8 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in smat,
} }
size_t WarpPerspectiveBackwardDataImpl::get_float32_workspace_in_bytes( size_t WarpPerspectiveBackwardDataImpl::get_float32_workspace_in_bytes(
const TensorLayout& /* mat */, const TensorLayout& diff, const TensorLayout& /* mat */, const TensorLayout& mat_idx,
const TensorLayout& grad) const { const TensorLayout& diff, const TensorLayout& grad) const {
auto N = grad.shape[0], C = grad.shape[1], IH = grad.shape[2], auto N = grad.shape[0], C = grad.shape[1], IH = grad.shape[2],
IW = grad.shape[3]; IW = grad.shape[3];
auto OH = diff.shape[2], OW = diff.shape[3]; auto OH = diff.shape[2], OW = diff.shape[3];
...@@ -112,6 +131,9 @@ size_t WarpPerspectiveBackwardDataImpl::get_float32_workspace_in_bytes( ...@@ -112,6 +131,9 @@ size_t WarpPerspectiveBackwardDataImpl::get_float32_workspace_in_bytes(
size_t max_batch_size = N; size_t max_batch_size = N;
size_t max_batch_x_channel = max_batch_x_channel_size(); size_t max_batch_x_channel = max_batch_x_channel_size();
if (N * C > max_batch_x_channel) { if (N * C > max_batch_x_channel) {
/* when batch size is too large, the workspace only contains part of grad,
this will cause out of range with mat idx */
megdnn_assert(mat_idx.ndim == 0, "batch size is too large, it's unsupported with mat idx backward.");
max_batch_size = max_batch_x_channel / C; max_batch_size = max_batch_x_channel / C;
} }
......
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#include "src/cuda/warp_perspective/common.h" #include "src/cuda/warp_perspective/common.h"
...@@ -20,16 +21,21 @@ namespace warp_perspective { ...@@ -20,16 +21,21 @@ namespace warp_perspective {
const int factor = 4; const int factor = 4;
template <typename Getter, int factor> template <typename Getter, int factor>
__global__ void warp_perspective_bwd_data_kernel(const float *hidden, __global__ void warp_perspective_bwd_data_kernel(const float* hidden,
const float *mat, float *dst, const float* mat,
int N, int C, int IH, int IW, int OH, int OW) const int* midx, float* dst,
{ int N, int C, int IH, int IW,
int OH, int OW) {
Getter getter; Getter getter;
int n = blockIdx.z; int n = blockIdx.z;
int ow = blockIdx.x * blockDim.x + threadIdx.x; int ow = blockIdx.x * blockDim.x + threadIdx.x;
int oh = blockIdx.y * blockDim.y + threadIdx.y; int oh = blockIdx.y * blockDim.y + threadIdx.y;
hidden += n * C*OH*OW; hidden += n * C*OH*OW;
dst += n * C*factor*IH*IW; if (midx) {
dst += midx[n] * C * factor * IH * IW;
} else {
dst += n * C * factor * IH * IW;
}
mat += n * 3*3; mat += n * 3*3;
if (ow < OW && oh < OH) { if (ow < OW && oh < OH) {
float denominator = mat[6]*ow + mat[7]*oh + mat[8]; float denominator = mat[6]*ow + mat[7]*oh + mat[8];
...@@ -72,15 +78,19 @@ __global__ void add_up_kernel(const float *src, float *dst, ...@@ -72,15 +78,19 @@ __global__ void add_up_kernel(const float *src, float *dst,
} }
template <int factor> template <int factor>
__global__ void warp_perspective_bwd_data_constant_kernel(const float *hidden, __global__ void warp_perspective_bwd_data_constant_kernel(
const float *mat, float *dst, const float* hidden, const float* mat, const int* midx, float* dst,
int N, int C, int IH, int IW, int OH, int OW) int N, int C, int IH, int IW, int OH, int OW) {
{ int n = blockIdx.z;
int ow = blockIdx.x * blockDim.x + threadIdx.x; int ow = blockIdx.x * blockDim.x + threadIdx.x;
int oh = blockIdx.y * blockDim.y + threadIdx.y; int oh = blockIdx.y * blockDim.y + threadIdx.y;
hidden += blockIdx.z * C*OH*OW; hidden += n * C * OH * OW;
dst += blockIdx.z * C*factor*IH*IW; if (midx) {
mat += blockIdx.z * 3*3; dst += midx[n] * C * factor * IH * IW;
} else {
dst += n * C * factor * IH * IW;
}
mat += n * 3 * 3;
if (ow < OW && oh < OH) { if (ow < OW && oh < OH) {
float denominator = mat[6]*ow + mat[7]*oh + mat[8]; float denominator = mat[6]*ow + mat[7]*oh + mat[8];
float iw = (mat[0]*ow + mat[1]*oh + mat[2]) / denominator; float iw = (mat[0]*ow + mat[1]*oh + mat[2]) / denominator;
...@@ -119,30 +129,35 @@ __global__ void warp_perspective_bwd_data_constant_kernel(const float *hidden, ...@@ -119,30 +129,35 @@ __global__ void warp_perspective_bwd_data_constant_kernel(const float *hidden,
} }
} }
size_t get_backward_data_workspace_in_bytes( size_t get_backward_data_workspace_in_bytes(int N, int C, int IH, int IW,
int N, int C, int IH, int IW, int /* OH */, int /* OW */, int /* OH */, int /* OW */,
BorderMode /* bmode */) BorderMode /* bmode */) {
{
return N*C*IH*IW*factor * sizeof(float); return N*C*IH*IW*factor * sizeof(float);
} }
void backward_data_proxy(const float *mat, const float *diff, void backward_data_proxy(const float* mat, const int* midx, const float* diff,
float *grad, float *workspace, float* grad, float* workspace, int N, int N_SRC, int C,
int N, int C, int IH, int IW, int OH, int OW, float bval, int IH, int IW, int OH, int OW, float bval,
BorderMode mode, cudaStream_t stream) BorderMode mode, cudaStream_t stream) {
{
(void)bval; (void)bval;
(void)grad; (void)grad;
const int BY = 16, BX = 32; const int BY = 16, BX = 32;
{ {
dim3 threads(BX, BY); dim3 threads(BX, BY);
dim3 blocks((OW+BX-1)/BX, (OH+BY-1)/BY, N); dim3 blocks((OW+BX-1)/BX, (OH+BY-1)/BY, N);
cuda_check(cudaMemsetAsync(workspace, 0, sizeof(float) * factor*N*C*IH*IW, if (midx) {
cuda_check(cudaMemsetAsync(
workspace, 0, sizeof(float) * factor * N_SRC * C * IH * IW,
stream)); stream));
#define DISPATCH(Getter) \ } else {
warp_perspective_bwd_data_kernel<Getter, factor><<<blocks, threads, \ cuda_check(cudaMemsetAsync(workspace, 0,
0, stream>>>(diff, mat, workspace, N, C, IH, IW, OH, OW); sizeof(float) * factor * N * C * IH * IW,
stream));
}
#define DISPATCH(Getter) \
warp_perspective_bwd_data_kernel<Getter, factor> \
<<<blocks, threads, 0, stream>>>(diff, mat, midx, workspace, N, C, \
IH, IW, OH, OW);
switch (mode) { switch (mode) {
case BORDER_REPLICATE: case BORDER_REPLICATE:
DISPATCH(ReplicateGetter); DISPATCH(ReplicateGetter);
...@@ -158,8 +173,9 @@ void backward_data_proxy(const float *mat, const float *diff, ...@@ -158,8 +173,9 @@ void backward_data_proxy(const float *mat, const float *diff,
break; break;
case BORDER_CONSTANT: case BORDER_CONSTANT:
warp_perspective_bwd_data_constant_kernel<factor> warp_perspective_bwd_data_constant_kernel<factor>
<<<blocks, threads, 0, stream>>> <<<blocks, threads, 0, stream>>>(diff, mat, midx,
(diff, mat, workspace, N, C, IH, IW, OH, OW); workspace, N, C, IH,
IW, OH, OW);
break; break;
default: default:
break; break;
...@@ -169,9 +185,15 @@ void backward_data_proxy(const float *mat, const float *diff, ...@@ -169,9 +185,15 @@ void backward_data_proxy(const float *mat, const float *diff,
{ {
int THREADS = 512; int THREADS = 512;
dim3 threads(THREADS); dim3 threads(THREADS);
dim3 blocks((IH*IW+THREADS-1)/THREADS, N*C); if (midx) {
add_up_kernel<factor><<<blocks, threads, 0, stream>>>(workspace, grad, dim3 blocks((IH * IW + THREADS - 1) / THREADS, N_SRC * C);
IH*IW); add_up_kernel<factor>
<<<blocks, threads, 0, stream>>>(workspace, grad, IH * IW);
} else {
dim3 blocks((IH * IW + THREADS - 1) / THREADS, N * C);
add_up_kernel<factor>
<<<blocks, threads, 0, stream>>>(workspace, grad, IH * IW);
}
} }
after_kernel_launch(); after_kernel_launch();
} }
...@@ -181,4 +203,3 @@ void backward_data_proxy(const float *mat, const float *diff, ...@@ -181,4 +203,3 @@ void backward_data_proxy(const float *mat, const float *diff,
} // namespace megdnn } // namespace megdnn
// vim: syntax=cpp.doxygen // vim: syntax=cpp.doxygen
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#include "src/cuda/warp_perspective/opr_impl.h" #include "src/cuda/warp_perspective/opr_impl.h"
...@@ -40,15 +41,17 @@ WorkspaceBundle WarpPerspectiveBackwardMatImpl::get_workspace_bundle( ...@@ -40,15 +41,17 @@ WorkspaceBundle WarpPerspectiveBackwardMatImpl::get_workspace_bundle(
void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc, void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc,
_megdnn_tensor_in smat, _megdnn_tensor_in smat,
_megdnn_tensor_in smat_idx,
_megdnn_tensor_in sdiff, _megdnn_tensor_in sdiff,
_megdnn_tensor_out sgrad, _megdnn_tensor_out sgrad,
_megdnn_workspace sworkspace) { _megdnn_workspace sworkspace) {
check_exec(ssrc.layout, smat.layout, sdiff.layout, sgrad.layout, check_exec(ssrc.layout, smat.layout, smat_idx.layout, sdiff.layout,
sworkspace.size); sgrad.layout, sworkspace.size);
TensorND src = ssrc; TensorND src = ssrc;
TensorND mat = smat; TensorND mat = smat;
TensorND diff = sdiff; TensorND diff = sdiff;
TensorND grad = sgrad; TensorND grad = sgrad;
TensorND mat_idx = smat_idx;
auto bundle = get_workspace_bundle(sworkspace.raw_ptr, ssrc.layout, auto bundle = get_workspace_bundle(sworkspace.raw_ptr, ssrc.layout,
smat.layout, sdiff.layout, sgrad.layout); smat.layout, sdiff.layout, sgrad.layout);
auto ctypecvt = CompTypeCvter<dtype::BFloat16, dtype::Float32>( auto ctypecvt = CompTypeCvter<dtype::BFloat16, dtype::Float32>(
...@@ -64,6 +67,15 @@ void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc, ...@@ -64,6 +67,15 @@ void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc,
auto N = src.layout.shape[0], C = src.layout.shape[1], auto N = src.layout.shape[0], C = src.layout.shape[1],
IH = src.layout.shape[2], IW = src.layout.shape[3], IH = src.layout.shape[2], IW = src.layout.shape[3],
OH = diff.layout.shape[2], OW = diff.layout.shape[3]; OH = diff.layout.shape[2], OW = diff.layout.shape[3];
int* midx_ptr = nullptr;
if (mat_idx.raw_ptr) {
megdnn_assert(mat_idx.layout.ndim == 1);
N = mat_idx.layout.shape[0];
midx_ptr = mat_idx.ptr<int>();
} else {
megdnn_assert(mat_idx.layout.ndim == 0);
}
auto bval = param().border_val; auto bval = param().border_val;
auto bmode = warp_perspective::get_bmode(param().bmode); auto bmode = warp_perspective::get_bmode(param().bmode);
...@@ -71,7 +83,7 @@ void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc, ...@@ -71,7 +83,7 @@ void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc,
size_t max_batch_x_channel = max_batch_x_channel_size(); size_t max_batch_x_channel = max_batch_x_channel_size();
if (batch_x_channel_size <= max_batch_x_channel) { if (batch_x_channel_size <= max_batch_x_channel) {
warp_perspective::backward_mat_proxy( warp_perspective::backward_mat_proxy(
src.ptr<dt_float32>(), mat.ptr<dt_float32>(), src.ptr<dt_float32>(), mat.ptr<dt_float32>(), midx_ptr,
diff.ptr<dt_float32>(), grad.ptr<dt_float32>(), N, C, IH, diff.ptr<dt_float32>(), grad.ptr<dt_float32>(), N, C, IH,
IW, OH, OW, bval, bmode, stream); IW, OH, OW, bval, bmode, stream);
} else { } else {
...@@ -84,14 +96,19 @@ void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc, ...@@ -84,14 +96,19 @@ void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc,
size_t curr_batch_size = size_t curr_batch_size =
N > max_batch_size ? max_batch_size : N; N > max_batch_size ? max_batch_size : N;
warp_perspective::backward_mat_proxy( warp_perspective::backward_mat_proxy(
src_ptr, mat_ptr, diff_ptr, grad_ptr, curr_batch_size, src_ptr, mat_ptr, midx_ptr, diff_ptr, grad_ptr,
C, IH, IW, OH, OW, bval, bmode, stream); curr_batch_size, C, IH, IW, OH, OW, bval, bmode,
stream);
if (N <= max_batch_size) { if (N <= max_batch_size) {
break; break;
} else { } else {
N -= max_batch_size; N -= max_batch_size;
src_ptr += curr_batch_size * src.layout.stride[0]; if (midx_ptr == nullptr) {
src_ptr += curr_batch_size * src.layout.stride[0];
} else {
midx_ptr += curr_batch_size;
}
mat_ptr += curr_batch_size * mat.layout.stride[0]; mat_ptr += curr_batch_size * mat.layout.stride[0];
diff_ptr += curr_batch_size * diff.layout.stride[0]; diff_ptr += curr_batch_size * diff.layout.stride[0];
grad_ptr += curr_batch_size * grad.layout.stride[0]; grad_ptr += curr_batch_size * grad.layout.stride[0];
...@@ -109,4 +126,3 @@ void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc, ...@@ -109,4 +126,3 @@ void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in ssrc,
} // namespace megdnn } // namespace megdnn
// vim: syntax=cpp.doxygen // vim: syntax=cpp.doxygen
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#include "src/cuda/warp_perspective/common.h" #include "src/cuda/warp_perspective/common.h"
...@@ -20,17 +21,21 @@ namespace cuda { ...@@ -20,17 +21,21 @@ namespace cuda {
namespace warp_perspective { namespace warp_perspective {
template <typename Getter> template <typename Getter>
__global__ void warp_perspective_bwd_mat_kernel(const float *hidden, __global__ void warp_perspective_bwd_mat_kernel(
const float *in, const float *mat, float *grad, const float* hidden, const float* in, const float* mat, const int* midx,
int N, int C, int IH, int IW, int OH, int OW) float* grad, int N, int C, int IH, int IW, int OH, int OW) {
{
Getter getter; Getter getter;
int n = blockIdx.z;
int ow = blockIdx.x * blockDim.x + threadIdx.x; int ow = blockIdx.x * blockDim.x + threadIdx.x;
int oh = blockIdx.y * blockDim.y + threadIdx.y; int oh = blockIdx.y * blockDim.y + threadIdx.y;
hidden += blockIdx.z * C*OH*OW; hidden += blockIdx.z * C*OH*OW;
in += blockIdx.z * C*IH*IW; if (midx) {
mat += blockIdx.z * 3*3; in += midx[n] * C * IH * IW;
grad += blockIdx.z * 3*3; } else {
in += n * C * IH * IW;
}
mat += n * 3*3;
grad += n * 3*3;
float grad_local[3*3]; float grad_local[3*3];
memset(grad_local, 0, sizeof(grad_local)); memset(grad_local, 0, sizeof(grad_local));
if (ow < OW && oh < OH) { if (ow < OW && oh < OH) {
...@@ -83,9 +88,8 @@ __global__ void warp_perspective_bwd_mat_kernel(const float *hidden, ...@@ -83,9 +88,8 @@ __global__ void warp_perspective_bwd_mat_kernel(const float *hidden,
dh[8] = 1.0f * ddenominatorh; dh[8] = 1.0f * ddenominatorh;
#pragma unroll #pragma unroll
for (int i = 0; i < 9; ++i) { for (int i = 0; i < 9; ++i) {
grad_local[i] += grad_local[i] += hidden[oh * OW + ow] * dalpha * dh[i] +
hidden[oh*OW+ow] * dalpha * dh[i] + hidden[oh * OW + ow] * dbeta * dw[i];
hidden[oh*OW+ow] * dbeta * dw[i];
} }
hidden += OH*OW; hidden += OH*OW;
in += IH*IW; in += IH*IW;
...@@ -125,17 +129,21 @@ __global__ void warp_perspective_bwd_mat_kernel(const float *hidden, ...@@ -125,17 +129,21 @@ __global__ void warp_perspective_bwd_mat_kernel(const float *hidden,
} }
} }
__global__ void warp_perspective_bwd_mat_constant_kernel(const float *hidden, __global__ void warp_perspective_bwd_mat_constant_kernel(
const float *in, const float *mat, float *grad, const float* hidden, const float* in, const float* mat, const int* midx,
int N, int C, int IH, int IW, int OH, int OW, float bval) float* grad, int N, int C, int IH, int IW, int OH, int OW, float bval) {
{ int n = blockIdx.z;
int ow = blockIdx.x * blockDim.x + threadIdx.x; int ow = blockIdx.x * blockDim.x + threadIdx.x;
int oh = blockIdx.y * blockDim.y + threadIdx.y; int oh = blockIdx.y * blockDim.y + threadIdx.y;
hidden += blockIdx.z * C*OH*OW; hidden += blockIdx.z * C * OH * OW;
in += blockIdx.z * C*IH*IW; if (midx) {
mat += blockIdx.z * 3*3; in += midx[n] * C * IH * IW;
grad += blockIdx.z * 3*3; } else {
float grad_local[3*3]; in += n * C * IH * IW;
}
mat += n * 3 * 3;
grad += n * 3 * 3;
float grad_local[3 * 3];
memset(grad_local, 0, sizeof(grad_local)); memset(grad_local, 0, sizeof(grad_local));
if (ow < OW && oh < OH) { if (ow < OW && oh < OH) {
float numeratorw = mat[0]*ow + mat[1]*oh + mat[2]; float numeratorw = mat[0]*ow + mat[1]*oh + mat[2];
...@@ -199,10 +207,10 @@ __global__ void warp_perspective_bwd_mat_constant_kernel(const float *hidden, ...@@ -199,10 +207,10 @@ __global__ void warp_perspective_bwd_mat_constant_kernel(const float *hidden,
dh[8] = 1.0f * ddenominatorh; dh[8] = 1.0f * ddenominatorh;
#pragma unroll #pragma unroll
for (int i = 0; i < 9; ++i) { for (int i = 0; i < 9; ++i) {
float delta = float delta = hidden[oh * OW + ow] * dalpha * dh[i] +
hidden[oh*OW+ow] * dalpha * dh[i] + hidden[oh * OW + ow] * dbeta * dw[i];
hidden[oh*OW+ow] * dbeta * dw[i]; if (isfinite(delta))
if (isfinite(delta)) grad_local[i] += delta; grad_local[i] += delta;
} }
hidden += OH*OW; hidden += OH*OW;
in += IH*IW; in += IH*IW;
...@@ -227,8 +235,9 @@ __global__ void warp_perspective_bwd_mat_constant_kernel(const float *hidden, ...@@ -227,8 +235,9 @@ __global__ void warp_perspective_bwd_mat_constant_kernel(const float *hidden,
for (int k = 16; k >= 1; k >>= 1) { for (int k = 16; k >= 1; k >>= 1) {
if (tidx < k) { if (tidx < k) {
#pragma unroll #pragma unroll
for (int i = 0; i < 9; ++i) for (int i = 0; i < 9; ++i)
grad_shared[tidy][tidx][i] += grad_shared[tidy][tidx+k][i]; grad_shared[tidy][tidx][i] +=
grad_shared[tidy][tidx + k][i];
} }
cub::WARP_SYNC(0xffffffff); cub::WARP_SYNC(0xffffffff);
} }
...@@ -240,18 +249,17 @@ __global__ void warp_perspective_bwd_mat_constant_kernel(const float *hidden, ...@@ -240,18 +249,17 @@ __global__ void warp_perspective_bwd_mat_constant_kernel(const float *hidden,
} }
} }
void backward_mat_proxy(const float *src, const float *mat, void backward_mat_proxy(const float* src, const float* mat, const int* midx,
const float *diff, float *grad, const float* diff, float* grad, int N, int C, int IH,
int N, int C, int IH, int IW, int OH, int OW, float bval, int IW, int OH, int OW, float bval, BorderMode mode,
BorderMode mode, cudaStream_t stream) cudaStream_t stream) {
{
const int BY = 16, BX = 32; const int BY = 16, BX = 32;
dim3 threads(BX, BY); dim3 threads(BX, BY);
dim3 blocks((OW+BX-1)/BX, (OH+BY-1)/BY, N); dim3 blocks((OW+BX-1)/BX, (OH+BY-1)/BY, N);
cuda_check(cudaMemsetAsync(grad, 0, sizeof(float) * N*3*3, stream)); cuda_check(cudaMemsetAsync(grad, 0, sizeof(float) * N*3*3, stream));
#define DISPATCH(Getter) \ #define DISPATCH(Getter) \
warp_perspective_bwd_mat_kernel<Getter><<<blocks, threads, 0, stream>>>( \ warp_perspective_bwd_mat_kernel<Getter><<<blocks, threads, 0, stream>>>( \
diff, src, mat, grad, N, C, IH, IW, OH, OW); diff, src, mat, midx, grad, N, C, IH, IW, OH, OW);
switch (mode) { switch (mode) {
case BORDER_REPLICATE: case BORDER_REPLICATE:
DISPATCH(ReplicateGetter); DISPATCH(ReplicateGetter);
...@@ -266,8 +274,9 @@ void backward_mat_proxy(const float *src, const float *mat, ...@@ -266,8 +274,9 @@ void backward_mat_proxy(const float *src, const float *mat,
DISPATCH(WrapGetter); DISPATCH(WrapGetter);
break; break;
case BORDER_CONSTANT: case BORDER_CONSTANT:
warp_perspective_bwd_mat_constant_kernel<<<blocks, threads, 0, stream>>>( warp_perspective_bwd_mat_constant_kernel<<<blocks, threads, 0,
diff, src, mat, grad, N, C, IH, IW, OH, OW, bval); stream>>>(
diff, src, mat, midx, grad, N, C, IH, IW, OH, OW, bval);
break; break;
default: default:
break; break;
...@@ -281,4 +290,3 @@ void backward_mat_proxy(const float *src, const float *mat, ...@@ -281,4 +290,3 @@ void backward_mat_proxy(const float *src, const float *mat,
} // namespace megdnn } // namespace megdnn
// vim: syntax=cpp.doxygen // vim: syntax=cpp.doxygen
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#pragma once #pragma once
#include <cuda_runtime_api.h> #include <cuda_runtime_api.h>
...@@ -19,40 +20,34 @@ namespace warp_perspective { ...@@ -19,40 +20,34 @@ namespace warp_perspective {
// all these kernels use bilinear interpolation // all these kernels use bilinear interpolation
template<typename ctype> template <typename ctype>
void forward_proxy( void forward_proxy(bool is_nhwc, const ctype* src, const float* mat,
bool is_nhwc, const int* mat_idx, ctype* dst, int N_SRC, int N_MAT, int C,
const ctype *src, const float *mat, const int *mat_idx, int IH, int IW, int OH, int OW, ctype bval, BorderMode bmode,
ctype *dst, int N_SRC, int N_MAT, megcore::AsyncErrorInfo* error_info, void* error_tracker,
int C, int IH, int IW, int OH, int OW, ctype bval, cudaStream_t stream);
BorderMode bmode,
megcore::AsyncErrorInfo* error_info, void* error_tracker,
cudaStream_t stream);
template <typename ctype> template <typename ctype>
void forward_proxy_nchw4( void forward_proxy_nchw4(const ctype* src, const float* mat, const int* mat_idx,
const ctype *src, const float *mat, const int *mat_idx, ctype* dst, int N_SRC, int N_MAT, int C, int IH,
ctype *dst, int N_SRC, int N_MAT, int IW, int OH, int OW, ctype bval, BorderMode bmode,
int C, int IH, int IW, int OH, int OW, ctype bval, megcore::AsyncErrorInfo* error_info,
BorderMode bmode, void* error_tracker, cudaStream_t stream);
megcore::AsyncErrorInfo* error_info, void* error_tracker,
cudaStream_t stream); void backward_data_proxy(const float* mat, const int* midx, const float* diff,
float* grad, float* workspace, int N, int N_SRC, int C,
void backward_data_proxy(const float *mat, const float *diff, float *grad, int IH, int IW, int OH, int OW, float bval,
float *workspace, BorderMode bmode, cudaStream_t stream);
int N, int C, int IH, int IW, int OH, int OW, float bval, size_t get_backward_data_workspace_in_bytes(int N, int C, int IH, int IW,
BorderMode bmode, cudaStream_t stream); int OH, int OW, BorderMode bmode);
size_t get_backward_data_workspace_in_bytes(
int N, int C, int IH, int IW, int OH, int OW, void backward_mat_proxy(const float* src, const float* mat, const int* midx,
BorderMode bmode); const float* diff, float* grad, int N, int C, int IH,
int IW, int OH, int OW, float bval, BorderMode bmode,
void backward_mat_proxy( cudaStream_t stream);
const float *src, const float *mat, const float *diff, float *grad,
int N, int C, int IH, int IW, int OH, int OW, float bval, } // namespace warp_perspective
BorderMode bmode, cudaStream_t stream); } // namespace cuda
} // namespace megdnn
} // namespace warp_perspective
} // namespace cuda
} // namespace megdnn
// vim: syntax=cpp.doxygen // vim: syntax=cpp.doxygen
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#pragma once #pragma once
#include "megdnn/oprs.h" #include "megdnn/oprs.h"
...@@ -48,20 +49,24 @@ class WarpPerspectiveBackwardDataImpl final ...@@ -48,20 +49,24 @@ class WarpPerspectiveBackwardDataImpl final
: public WarpPerspectiveBackwardData { : public WarpPerspectiveBackwardData {
public: public:
using WarpPerspectiveBackwardData::WarpPerspectiveBackwardData; using WarpPerspectiveBackwardData::WarpPerspectiveBackwardData;
void exec(_megdnn_tensor_in mat, _megdnn_tensor_in diff, void exec(_megdnn_tensor_in mat, _megdnn_tensor_in mat_idx,
_megdnn_tensor_out grad, _megdnn_workspace workspace) override; _megdnn_tensor_in diff, _megdnn_tensor_out grad,
_megdnn_workspace workspace) override;
size_t get_workspace_in_bytes(const TensorLayout& mat, size_t get_workspace_in_bytes(const TensorLayout& mat,
const TensorLayout& mat_idx,
const TensorLayout& diff, const TensorLayout& diff,
const TensorLayout& grad) override { const TensorLayout& grad) override {
return get_workspace_bundle(nullptr, mat, diff, grad) return get_workspace_bundle(nullptr, mat, mat_idx, diff, grad)
.total_size_in_bytes(); .total_size_in_bytes();
} }
private: private:
WorkspaceBundle get_workspace_bundle(void* ptr, const TensorLayout& mat, WorkspaceBundle get_workspace_bundle(void* ptr, const TensorLayout& mat,
const TensorLayout& mat_idx,
const TensorLayout& diff, const TensorLayout& diff,
const TensorLayout& grad) const; const TensorLayout& grad) const;
size_t get_float32_workspace_in_bytes(const TensorLayout& mat, size_t get_float32_workspace_in_bytes(const TensorLayout& mat,
const TensorLayout& mat_idx,
const TensorLayout& diff, const TensorLayout& diff,
const TensorLayout& grad) const; const TensorLayout& grad) const;
}; };
...@@ -70,10 +75,11 @@ class WarpPerspectiveBackwardMatImpl final : public WarpPerspectiveBackwardMat { ...@@ -70,10 +75,11 @@ class WarpPerspectiveBackwardMatImpl final : public WarpPerspectiveBackwardMat {
public: public:
using WarpPerspectiveBackwardMat::WarpPerspectiveBackwardMat; using WarpPerspectiveBackwardMat::WarpPerspectiveBackwardMat;
void exec(_megdnn_tensor_in src, _megdnn_tensor_in mat, void exec(_megdnn_tensor_in src, _megdnn_tensor_in mat,
_megdnn_tensor_in diff, _megdnn_tensor_out grad, _megdnn_tensor_in mat_idx, _megdnn_tensor_in diff,
_megdnn_workspace workspace) override; _megdnn_tensor_out grad, _megdnn_workspace workspace) override;
size_t get_workspace_in_bytes(const TensorLayout& src, size_t get_workspace_in_bytes(const TensorLayout& src,
const TensorLayout& mat, const TensorLayout& mat,
const TensorLayout& /* mat_idx */,
const TensorLayout& diff, const TensorLayout& diff,
const TensorLayout& grad) override { const TensorLayout& grad) override {
return get_workspace_bundle(nullptr, src, mat, diff, grad) return get_workspace_bundle(nullptr, src, mat, diff, grad)
......
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#include "src/naive/warp_perspective/opr_impl.h" #include "src/naive/warp_perspective/opr_impl.h"
#include "src/naive/warp_perspective/warp_perspective_cv.h" #include "src/naive/warp_perspective/warp_perspective_cv.h"
...@@ -358,18 +359,29 @@ void WarpPerspectiveForwardImpl::exec(_megdnn_tensor_in src, ...@@ -358,18 +359,29 @@ void WarpPerspectiveForwardImpl::exec(_megdnn_tensor_in src,
} }
template <typename ctype, typename mtype> template <typename ctype, typename mtype>
void WarpPerspectiveBackwardDataImpl::kern_naive(const KernParam<ctype, mtype>& kern_param) { void WarpPerspectiveBackwardDataImpl::kern_naive(
const int N = kern_param.n, C = kern_param.c, const KernParam<ctype, mtype>& kern_param) {
IH = kern_param.ih, IW = kern_param.iw; const int N = kern_param.n_mat, C = kern_param.c, IH = kern_param.ih,
IW = kern_param.iw;
const int OH = kern_param.oh, OW = kern_param.ow; const int OH = kern_param.oh, OW = kern_param.ow;
const ctype* hptr_ = kern_param.hptr; const ctype* hptr_ = kern_param.hptr;
const mtype* mptr_ = kern_param.mptr; const mtype* mptr_ = kern_param.mptr;
ctype* sptr_ = kern_param.sptr; ctype* sptr_ = kern_param.sptr;
int* midx_ptr = kern_param.midx_ptr;
auto hptr = hptr_; auto hptr = hptr_;
auto mptr = mptr_; auto mptr = mptr_;
auto sptr = sptr_; auto sptr = sptr_;
std::memset(sptr, 0, sizeof(ctype) * N * C * IH * IW); if (midx_ptr) {
std::memset(sptr, 0, sizeof(ctype) * kern_param.n_src * C * IH * IW);
} else {
std::memset(sptr, 0, sizeof(ctype) * N * C * IH * IW);
}
rep(n, N) { rep(n, N) {
if (midx_ptr) {
sptr = sptr_ + midx_ptr[n] * C * IH * IW;
} else {
sptr = sptr_ + n * C * IH * IW;
}
rep(oh, OH) rep(ow, OW) { rep(oh, OH) rep(ow, OW) {
float numeratorw = mptr[0] * ow + mptr[1] * oh + mptr[2]; float numeratorw = mptr[0] * ow + mptr[1] * oh + mptr[2];
float numeratorh = mptr[3] * ow + mptr[4] * oh + mptr[5]; float numeratorh = mptr[3] * ow + mptr[4] * oh + mptr[5];
...@@ -404,27 +416,30 @@ void WarpPerspectiveBackwardDataImpl::kern_naive(const KernParam<ctype, mtype>& ...@@ -404,27 +416,30 @@ void WarpPerspectiveBackwardDataImpl::kern_naive(const KernParam<ctype, mtype>&
} }
} }
} }
sptr += C * IH * IW;
hptr += C * OH * OW; hptr += C * OH * OW;
mptr += 3 * 3; mptr += 3 * 3;
} }
} }
void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in mat, void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in mat,
_megdnn_tensor_in mat_idx,
_megdnn_tensor_in diff, _megdnn_tensor_in diff,
_megdnn_tensor_out grad, _megdnn_tensor_out grad,
_megdnn_workspace workspace) { _megdnn_workspace workspace) {
check_exec(mat.layout, diff.layout, grad.layout, workspace.size); check_exec(mat.layout, mat_idx.layout, diff.layout, grad.layout,
workspace.size);
megdnn_assert(param().format == param::WarpPerspective::Format::NCHW, megdnn_assert(param().format == param::WarpPerspective::Format::NCHW,
"invalid warp_perspective format"); "invalid warp_perspective format");
#define DISPATCH_ST_MT(dt, ct) \ #define DISPATCH_ST_MT(dt, ct) \
if (diff.layout.dtype.enumv() == DTypeTrait<dt>::enumv) { \ if (diff.layout.dtype.enumv() == DTypeTrait<dt>::enumv) { \
if (mat.layout.dtype.enumv() == DTypeTrait<dtype::Float32>::enumv) { \ if (mat.layout.dtype.enumv() == DTypeTrait<dtype::Float32>::enumv) { \
auto kparam = KernParam<ct, float>::from_tensors(mat, diff, grad); \ auto kparam = KernParam<ct, float>::from_tensors(mat, mat_idx, \
diff, grad); \
MEGDNN_DISPATCH_CPU_KERN_OPR(kern_naive(kparam)); \ MEGDNN_DISPATCH_CPU_KERN_OPR(kern_naive(kparam)); \
return; \ return; \
} else { \ } else { \
auto kparam = KernParam<ct, ct>::from_tensors(mat, diff, grad); \ auto kparam = \
KernParam<ct, ct>::from_tensors(mat, mat_idx, diff, grad); \
MEGDNN_DISPATCH_CPU_KERN_OPR(kern_naive(kparam)); \ MEGDNN_DISPATCH_CPU_KERN_OPR(kern_naive(kparam)); \
return; \ return; \
} \ } \
...@@ -441,7 +456,7 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in mat, ...@@ -441,7 +456,7 @@ void WarpPerspectiveBackwardDataImpl::exec(_megdnn_tensor_in mat,
template <typename ctype, typename mtype> template <typename ctype, typename mtype>
void WarpPerspectiveBackwardMatImpl::kern_naive( void WarpPerspectiveBackwardMatImpl::kern_naive(
const KernParam<ctype, mtype>& kern_param) { const KernParam<ctype, mtype>& kern_param) {
const int N = kern_param.n, C = kern_param.c, IH = kern_param.ih, const int N = kern_param.n_mat, C = kern_param.c, IH = kern_param.ih,
IW = kern_param.iw; IW = kern_param.iw;
const int OH = kern_param.oh, OW = kern_param.ow; const int OH = kern_param.oh, OW = kern_param.ow;
...@@ -449,9 +464,15 @@ void WarpPerspectiveBackwardMatImpl::kern_naive( ...@@ -449,9 +464,15 @@ void WarpPerspectiveBackwardMatImpl::kern_naive(
auto sptr = kern_param.sptr; auto sptr = kern_param.sptr;
auto mptr = kern_param.mptr; auto mptr = kern_param.mptr;
auto res = kern_param.res; auto res = kern_param.res;
auto midx_ptr = kern_param.midx_ptr;
auto border_val = kern_param.border_val; auto border_val = kern_param.border_val;
std::memset(res, 0, sizeof(float) * N * 3 * 3); std::memset(res, 0, sizeof(float) * N * 3 * 3);
rep(n, N) { rep(n, N) {
if (midx_ptr) {
sptr = kern_param.sptr + midx_ptr[n] * C * IH * IW;
} else {
sptr = kern_param.sptr + n * C * IH * IW;
}
rep(oh, OH) rep(ow, OW) { rep(oh, OH) rep(ow, OW) {
float numeratorw = mptr[0] * ow + mptr[1] * oh + mptr[2]; float numeratorw = mptr[0] * ow + mptr[1] * oh + mptr[2];
float numeratorh = mptr[3] * ow + mptr[4] * oh + mptr[5]; float numeratorh = mptr[3] * ow + mptr[4] * oh + mptr[5];
...@@ -537,7 +558,6 @@ void WarpPerspectiveBackwardMatImpl::kern_naive( ...@@ -537,7 +558,6 @@ void WarpPerspectiveBackwardMatImpl::kern_naive(
} }
} }
hptr += C * OH * OW; hptr += C * OH * OW;
sptr += C * IH * IW;
mptr += 3 * 3; mptr += 3 * 3;
res += 3 * 3; res += 3 * 3;
} }
...@@ -545,21 +565,22 @@ void WarpPerspectiveBackwardMatImpl::kern_naive( ...@@ -545,21 +565,22 @@ void WarpPerspectiveBackwardMatImpl::kern_naive(
void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in src, void WarpPerspectiveBackwardMatImpl::exec(_megdnn_tensor_in src,
_megdnn_tensor_in mat, _megdnn_tensor_in mat,
_megdnn_tensor_in mat_idx,
_megdnn_tensor_in diff, _megdnn_tensor_in diff,
_megdnn_tensor_out grad, _megdnn_tensor_out grad,
_megdnn_workspace workspace) { _megdnn_workspace workspace) {
check_exec(src.layout, mat.layout, diff.layout, grad.layout, check_exec(src.layout, mat.layout, mat_idx.layout, diff.layout, grad.layout,
workspace.size); workspace.size);
#define DISPATCH_ST_MT(dt, ct) \ #define DISPATCH_ST_MT(dt, ct) \
if (src.layout.dtype.enumv() == DTypeTrait<dt>::enumv) { \ if (src.layout.dtype.enumv() == DTypeTrait<dt>::enumv) { \
if (mat.layout.dtype.enumv() == DTypeTrait<dtype::Float32>::enumv) { \ if (mat.layout.dtype.enumv() == DTypeTrait<dtype::Float32>::enumv) { \
auto kparam = KernParam<ct, float>::from_tensors( \ auto kparam = KernParam<ct, float>::from_tensors( \
param().border_val, src, mat, diff, grad); \ param().border_val, src, mat, mat_idx, diff, grad); \
MEGDNN_DISPATCH_CPU_KERN_OPR(kern_naive(kparam)); \ MEGDNN_DISPATCH_CPU_KERN_OPR(kern_naive(kparam)); \
return; \ return; \
} else { \ } else { \
auto kparam = KernParam<ct, ct>::from_tensors( \ auto kparam = KernParam<ct, ct>::from_tensors( \
param().border_val, src, mat, diff, grad); \ param().border_val, src, mat, mat_idx, diff, grad); \
MEGDNN_DISPATCH_CPU_KERN_OPR(kern_naive(kparam)); \ MEGDNN_DISPATCH_CPU_KERN_OPR(kern_naive(kparam)); \
return; \ return; \
} \ } \
......
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#pragma once #pragma once
#include "megdnn/oprs.h" #include "megdnn/oprs.h"
...@@ -15,144 +16,158 @@ ...@@ -15,144 +16,158 @@
namespace megdnn { namespace megdnn {
namespace naive { namespace naive {
class WarpPerspectiveForwardImpl: public WarpPerspectiveForward { class WarpPerspectiveForwardImpl : public WarpPerspectiveForward {
protected: protected:
using Format = Param::Format; using Format = Param::Format;
template <typename ctype, typename mtype> template <typename ctype, typename mtype>
struct KernParam { struct KernParam {
Format format; Format format;
BorderMode bmode; BorderMode bmode;
float border_val; float border_val;
size_t n_src, n_mat, c, ih, iw, oh, ow; size_t n_src, n_mat, c, ih, iw, oh, ow;
ctype *sptr, *dptr; ctype *sptr, *dptr;
mtype *mptr; mtype* mptr;
int *midx_ptr; //!< can be null int* midx_ptr; //!< can be null
Workspace workspace; Workspace workspace;
static KernParam from_tensors( static KernParam from_tensors(Format format, BorderMode bmode,
Format format, BorderMode bmode, float border_val, float border_val, _megdnn_tensor_in src,
_megdnn_tensor_in src, _megdnn_tensor_in mat, _megdnn_tensor_in mat,
_megdnn_tensor_in mat_idx, _megdnn_tensor_out dst, _megdnn_tensor_in mat_idx,
_megdnn_workspace workspace) { _megdnn_tensor_out dst,
KernParam ret; _megdnn_workspace workspace) {
ret.format = format; KernParam ret;
ret.bmode = bmode; ret.format = format;
ret.border_val = border_val; ret.bmode = bmode;
ret.n_src = src.layout.shape[0]; ret.border_val = border_val;
if (mat_idx.raw_ptr) { ret.n_src = src.layout.shape[0];
megdnn_assert(mat_idx.layout.ndim == 1); if (mat_idx.raw_ptr) {
ret.n_mat = mat_idx.layout.shape[0]; megdnn_assert(mat_idx.layout.ndim == 1);
ret.midx_ptr = mat_idx.ptr<int>(); ret.n_mat = mat_idx.layout.shape[0];
} else { ret.midx_ptr = mat_idx.ptr<int>();
megdnn_assert(mat_idx.layout.ndim == 0); } else {
ret.n_mat = ret.n_src; megdnn_assert(mat_idx.layout.ndim == 0);
ret.midx_ptr = nullptr; ret.n_mat = ret.n_src;
} ret.midx_ptr = nullptr;
if (format == Format::NCHW) { }
ret.c = src.layout.shape[1]; if (format == Format::NCHW) {
ret.ih = src.layout.shape[2]; ret.c = src.layout.shape[1];
ret.iw = src.layout.shape[3]; ret.ih = src.layout.shape[2];
ret.oh = dst.layout.shape[2]; ret.iw = src.layout.shape[3];
ret.ow = dst.layout.shape[3]; ret.oh = dst.layout.shape[2];
} else if (format == Format::NHWC) { ret.ow = dst.layout.shape[3];
ret.c = src.layout.shape[3]; } else if (format == Format::NHWC) {
ret.ih = src.layout.shape[1]; ret.c = src.layout.shape[3];
ret.iw = src.layout.shape[2]; ret.ih = src.layout.shape[1];
ret.oh = dst.layout.shape[1]; ret.iw = src.layout.shape[2];
ret.ow = dst.layout.shape[2]; ret.oh = dst.layout.shape[1];
} else if (format == Format::NCHW4) { ret.ow = dst.layout.shape[2];
ret.c = src.layout.shape[1] * 4; } else if (format == Format::NCHW4) {
ret.ih = src.layout.shape[2]; ret.c = src.layout.shape[1] * 4;
ret.iw = src.layout.shape[3]; ret.ih = src.layout.shape[2];
ret.oh = dst.layout.shape[2]; ret.iw = src.layout.shape[3];
ret.ow = dst.layout.shape[3]; ret.oh = dst.layout.shape[2];
} else { ret.ow = dst.layout.shape[3];
megdnn_assert(format == Format::NHWCD4); } else {
ret.c = src.layout.shape[2] * 4; megdnn_assert(format == Format::NHWCD4);
ret.ih = src.layout.shape[1]; ret.c = src.layout.shape[2] * 4;
ret.iw = src.layout.shape[3]; ret.ih = src.layout.shape[1];
ret.oh = dst.layout.shape[1]; ret.iw = src.layout.shape[3];
ret.ow = dst.layout.shape[3]; ret.oh = dst.layout.shape[1];
} ret.ow = dst.layout.shape[3];
if (src.layout.dtype.enumv() == DTypeEnum::Float32 || }
MEGDNN_FLOAT16_SELECT( if (src.layout.dtype.enumv() == DTypeEnum::Float32 ||
(src.layout.dtype.enumv() == DTypeEnum::Float16 || MEGDNN_FLOAT16_SELECT(
src.layout.dtype.enumv() == DTypeEnum::BFloat16), (src.layout.dtype.enumv() == DTypeEnum::Float16 ||
false) || src.layout.dtype.enumv() == DTypeEnum::BFloat16),
src.layout.dtype.enumv() == DTypeEnum::Int8 || false) ||
src.layout.dtype.enumv() == DTypeEnum::Uint8 || src.layout.dtype.enumv() == DTypeEnum::Int8 ||
src.layout.dtype.enumv() == DTypeEnum::QuantizedS8 || src.layout.dtype.enumv() == DTypeEnum::Uint8 ||
src.layout.dtype.enumv() == DTypeEnum::Quantized8Asymm) { src.layout.dtype.enumv() == DTypeEnum::QuantizedS8 ||
ret.sptr = src.compatible_ptr<ctype>(); src.layout.dtype.enumv() == DTypeEnum::Quantized8Asymm) {
ret.mptr = mat.ptr<mtype>(); ret.sptr = src.compatible_ptr<ctype>();
ret.dptr = dst.compatible_ptr<ctype>(); ret.mptr = mat.ptr<mtype>();
} else if (src.layout.dtype.enumv() == DTypeEnum::QuantizedS8) { ret.dptr = dst.compatible_ptr<ctype>();
ret.sptr = src.compatible_ptr<ctype>(); } else if (src.layout.dtype.enumv() == DTypeEnum::QuantizedS8) {
ret.mptr = mat.ptr<mtype>(); ret.sptr = src.compatible_ptr<ctype>();
ret.dptr = dst.compatible_ptr<ctype>(); ret.mptr = mat.ptr<mtype>();
} else { ret.dptr = dst.compatible_ptr<ctype>();
ret.sptr = nullptr; } else {
ret.mptr = nullptr; ret.sptr = nullptr;
ret.dptr = nullptr; ret.mptr = nullptr;
} ret.dptr = nullptr;
ret.workspace = workspace;
return ret;
} }
}; ret.workspace = workspace;
return ret;
// ctype: C type of input data type.
// mtype: C type of transformation matrix data type.
template <typename ctype, typename mtype>
void kern_naive(const KernParam<ctype, mtype>& kern_param,
size_t task_id);
public:
using WarpPerspectiveForward::WarpPerspectiveForward;
void exec(_megdnn_tensor_in src, _megdnn_tensor_in mat,
_megdnn_tensor_in mat_idx, _megdnn_tensor_out dst,
_megdnn_workspace workspace) override;
size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
const TensorLayout&,
const TensorLayout&) override {
return 0;
} }
};
private: // ctype: C type of input data type.
template <typename ctype, typename mtype> // mtype: C type of transformation matrix data type.
void kern_naive_nhwcd4(const KernParam<ctype, mtype>& kern_param, template <typename ctype, typename mtype>
size_t task_id); void kern_naive(const KernParam<ctype, mtype>& kern_param, size_t task_id);
public:
using WarpPerspectiveForward::WarpPerspectiveForward;
void exec(_megdnn_tensor_in src, _megdnn_tensor_in mat,
_megdnn_tensor_in mat_idx, _megdnn_tensor_out dst,
_megdnn_workspace workspace) override;
size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
const TensorLayout&,
const TensorLayout&) override {
return 0;
}
private:
template <typename ctype, typename mtype>
void kern_naive_nhwcd4(const KernParam<ctype, mtype>& kern_param,
size_t task_id);
}; };
class WarpPerspectiveBackwardDataImpl : public WarpPerspectiveBackwardData { class WarpPerspectiveBackwardDataImpl : public WarpPerspectiveBackwardData {
protected: protected:
template <typename ctype, typename mtype> template <typename ctype, typename mtype>
struct KernParam { struct KernParam {
size_t n, c, ih, iw, oh, ow; size_t n_src, n_mat, c, ih, iw, oh, ow;
ctype *sptr, *hptr; ctype *sptr, *hptr;
mtype* mptr; mtype* mptr;
int* midx_ptr; //!< can be null
static KernParam from_tensors(_megdnn_tensor_in mat, static KernParam from_tensors(_megdnn_tensor_in mat,
_megdnn_tensor_in mat_idx,
_megdnn_tensor_in diff, _megdnn_tensor_in diff,
_megdnn_tensor_out grad) { _megdnn_tensor_out grad) {
KernParam ret; KernParam ret;
ret.n = grad.layout.shape[0], ret.c = grad.layout.shape[1], ret.n_src = grad.layout.shape[0], ret.c = grad.layout.shape[1];
ret.ih = grad.layout.shape[2], ret.iw = grad.layout.shape[3]; ret.ih = grad.layout.shape[2], ret.iw = grad.layout.shape[3];
ret.oh = diff.layout.shape[2], ret.ow = diff.layout.shape[3]; ret.oh = diff.layout.shape[2], ret.ow = diff.layout.shape[3];
ret.hptr = diff.ptr<ctype>(); ret.hptr = diff.ptr<ctype>();
ret.mptr = mat.ptr<mtype>(); ret.mptr = mat.ptr<mtype>();
ret.sptr = grad.ptr<ctype>(); ret.sptr = grad.ptr<ctype>();
if (mat_idx.raw_ptr) {
megdnn_assert(mat_idx.layout.ndim == 1);
ret.n_mat = mat_idx.layout.shape[0];
ret.midx_ptr = mat_idx.ptr<int>();
} else {
megdnn_assert(mat_idx.layout.ndim == 0);
ret.n_mat = ret.n_src;
ret.midx_ptr = nullptr;
}
return ret; return ret;
} }
}; };
public: public:
using WarpPerspectiveBackwardData::WarpPerspectiveBackwardData; using WarpPerspectiveBackwardData::WarpPerspectiveBackwardData;
void exec(_megdnn_tensor_in mat, _megdnn_tensor_in diff, void exec(_megdnn_tensor_in mat, _megdnn_tensor_in mat_idx,
_megdnn_tensor_out grad, _megdnn_workspace workspace) override; _megdnn_tensor_in diff, _megdnn_tensor_out grad,
_megdnn_workspace workspace) override;
size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&, size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
const TensorLayout&,
const TensorLayout&) override { const TensorLayout&) override {
return 0; return 0;
} }
private: private:
template <typename ctype, typename mtype> template <typename ctype, typename mtype>
void kern_naive(const KernParam<ctype, mtype>& kern_param); void kern_naive(const KernParam<ctype, mtype>& kern_param);
...@@ -162,23 +177,35 @@ class WarpPerspectiveBackwardMatImpl : public WarpPerspectiveBackwardMat { ...@@ -162,23 +177,35 @@ class WarpPerspectiveBackwardMatImpl : public WarpPerspectiveBackwardMat {
protected: protected:
template <typename ctype, typename mtype> template <typename ctype, typename mtype>
struct KernParam { struct KernParam {
size_t n, c, ih, iw, oh, ow; size_t n_src, n_mat, c, ih, iw, oh, ow;
ctype *sptr, *hptr; ctype *sptr, *hptr;
mtype* mptr, *res; mtype *mptr, *res;
int* midx_ptr; //!< can be null
float border_val; float border_val;
static KernParam from_tensors(float border_val_, _megdnn_tensor_in src, static KernParam from_tensors(float border_val_, _megdnn_tensor_in src,
_megdnn_tensor_in mat, _megdnn_tensor_in mat,
_megdnn_tensor_in mat_idx,
_megdnn_tensor_in diff, _megdnn_tensor_in diff,
_megdnn_tensor_out grad) { _megdnn_tensor_out grad) {
KernParam ret; KernParam ret;
ret.border_val = border_val_; ret.border_val = border_val_;
ret.n = src.layout.shape[0], ret.c = src.layout.shape[1], ret.n_src = src.layout.shape[0], ret.c = src.layout.shape[1];
ret.ih = src.layout.shape[2], ret.iw = src.layout.shape[3]; ret.ih = src.layout.shape[2], ret.iw = src.layout.shape[3];
ret.oh = diff.layout.shape[2], ret.ow = diff.layout.shape[3]; ret.oh = diff.layout.shape[2], ret.ow = diff.layout.shape[3];
ret.hptr = diff.ptr<ctype>(); ret.hptr = diff.ptr<ctype>();
ret.mptr = mat.ptr<mtype>(); ret.mptr = mat.ptr<mtype>();
ret.sptr = src.ptr<ctype>(); ret.sptr = src.ptr<ctype>();
ret.res = grad.ptr<mtype>(); ret.res = grad.ptr<mtype>();
if (mat_idx.raw_ptr) {
megdnn_assert(mat_idx.layout.ndim == 1);
ret.n_mat = mat_idx.layout.shape[0];
ret.midx_ptr = mat_idx.ptr<int>();
} else {
megdnn_assert(mat_idx.layout.ndim == 0);
ret.n_mat = ret.n_src;
ret.midx_ptr = nullptr;
}
return ret; return ret;
} }
}; };
...@@ -186,10 +213,10 @@ protected: ...@@ -186,10 +213,10 @@ protected:
public: public:
using WarpPerspectiveBackwardMat::WarpPerspectiveBackwardMat; using WarpPerspectiveBackwardMat::WarpPerspectiveBackwardMat;
void exec(_megdnn_tensor_in src, _megdnn_tensor_in mat, void exec(_megdnn_tensor_in src, _megdnn_tensor_in mat,
_megdnn_tensor_in diff, _megdnn_tensor_out grad, _megdnn_tensor_in mat_idx, _megdnn_tensor_in diff,
_megdnn_workspace workspace) override; _megdnn_tensor_out grad, _megdnn_workspace workspace) override;
size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&, size_t get_workspace_in_bytes(const TensorLayout&, const TensorLayout&,
const TensorLayout&, const TensorLayout&, const TensorLayout&,
const TensorLayout&) override { const TensorLayout&) override {
return 0; return 0;
} }
......
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#include "test/common/warp_perspective.h" #include "test/common/warp_perspective.h"
...@@ -19,6 +20,10 @@ using namespace warp_perspective; ...@@ -19,6 +20,10 @@ using namespace warp_perspective;
void WarpPerspectiveMatIdxProxy::deduce_layout(WarpPerspective*, void WarpPerspectiveMatIdxProxy::deduce_layout(WarpPerspective*,
TensorLayoutArray&) {} TensorLayoutArray&) {}
void WarpPerspectiveMatIdxProxy::deduce_layout(WarpPerspectiveBackwardData*,
TensorLayoutArray&) {}
void WarpPerspectiveMatIdxProxy::deduce_layout(WarpPerspectiveBackwardMat*,
TensorLayoutArray&) {}
void WarpPerspectiveMatIdxProxy::exec(WarpPerspective* opr, void WarpPerspectiveMatIdxProxy::exec(WarpPerspective* opr,
const TensorNDArray& tensors) { const TensorNDArray& tensors) {
...@@ -31,6 +36,30 @@ void WarpPerspectiveMatIdxProxy::exec(WarpPerspective* opr, ...@@ -31,6 +36,30 @@ void WarpPerspectiveMatIdxProxy::exec(WarpPerspective* opr,
opr->exec(tensors[0], tensors[1], tensors[2], tensors[3], W.workspace()); opr->exec(tensors[0], tensors[1], tensors[2], tensors[3], W.workspace());
} }
void WarpPerspectiveMatIdxProxy::exec(WarpPerspectiveBackwardData* opr,
const TensorNDArray& tensors) {
if (!W.valid()) {
W = WorkspaceWrapper(opr->handle(), 0);
}
megdnn_assert(tensors.size() == 4);
W.update(opr->get_workspace_in_bytes(tensors[0].layout, tensors[1].layout,
tensors[2].layout, tensors[3].layout));
opr->exec(tensors[0], tensors[1], tensors[2], tensors[3], W.workspace());
}
void WarpPerspectiveMatIdxProxy::exec(WarpPerspectiveBackwardMat* opr,
const TensorNDArray& tensors) {
if (!W.valid()) {
W = WorkspaceWrapper(opr->handle(), 0);
}
megdnn_assert(tensors.size() == 5);
W.update(opr->get_workspace_in_bytes(tensors[0].layout, tensors[1].layout,
tensors[2].layout, tensors[3].layout,
tensors[4].layout));
opr->exec(tensors[0], tensors[1], tensors[2], tensors[3], tensors[4],
W.workspace());
}
std::vector<TestArg> warp_perspective::get_cv_args() { std::vector<TestArg> warp_perspective::get_cv_args() {
std::vector<TestArg> args; std::vector<TestArg> args;
...@@ -101,10 +130,10 @@ void warp_perspective::run_mat_idx_test(Handle* handle) { ...@@ -101,10 +130,10 @@ void warp_perspective::run_mat_idx_test(Handle* handle) {
// test NHWC // test NHWC
param.format = WarpPerspective::Param::Format::NHWC; param.format = WarpPerspective::Param::Format::NHWC;
checker.set_param(param) checker.set_param(param)
.set_rng(2, &mat_idx_rng) .set_rng(2, &mat_idx_rng)
.set_epsilon(1e-1) .set_epsilon(1e-1)
.set_dtype(2, dtype::Int32()); .set_dtype(2, dtype::Int32());
checker.execs({{N_SRC, 10, 11, 3}, {2, 3, 3}, {2}, {2, 11, 12, 3}}); checker.execs({{N_SRC, 10, 11, 3}, {2, 3, 3}, {2}, {2, 11, 12, 3}});
} }
......
...@@ -22,7 +22,11 @@ namespace test { ...@@ -22,7 +22,11 @@ namespace test {
struct WarpPerspectiveMatIdxProxy { struct WarpPerspectiveMatIdxProxy {
WorkspaceWrapper W; WorkspaceWrapper W;
static void deduce_layout(WarpPerspective*, TensorLayoutArray&); static void deduce_layout(WarpPerspective*, TensorLayoutArray&);
static void deduce_layout(WarpPerspectiveBackwardData*, TensorLayoutArray&);
static void deduce_layout(WarpPerspectiveBackwardMat*, TensorLayoutArray&);
void exec(WarpPerspective* opr, const TensorNDArray& tensors); void exec(WarpPerspective* opr, const TensorNDArray& tensors);
void exec(WarpPerspectiveBackwardData* opr, const TensorNDArray& tensors);
void exec(WarpPerspectiveBackwardMat* opr, const TensorNDArray& tensors);
}; };
class WarpPerspectiveMatRNG final : public IIDRNG { class WarpPerspectiveMatRNG final : public IIDRNG {
......
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#include "test/cuda/fixture.h" #include "test/cuda/fixture.h"
...@@ -21,10 +22,10 @@ namespace { ...@@ -21,10 +22,10 @@ namespace {
using namespace megdnn; using namespace megdnn;
using namespace test; using namespace test;
class NanMatRNG: public RNG { class NanMatRNG : public RNG {
void gen(const TensorND &tensor_) override void gen(const TensorND& tensor_) override
{ {
auto &gen = RandomState::generator(); auto& gen = RandomState::generator();
std::uniform_real_distribution<dt_float32> pdist3(1.9f, 2.1f); std::uniform_real_distribution<dt_float32> pdist3(1.9f, 2.1f);
std::uniform_real_distribution<dt_float32> pdist(0.9f, 1.1f); std::uniform_real_distribution<dt_float32> pdist(0.9f, 1.1f);
std::uniform_real_distribution<dt_float32> pdisth(0.4f, 0.6f); std::uniform_real_distribution<dt_float32> pdisth(0.4f, 0.6f);
...@@ -32,7 +33,7 @@ class NanMatRNG: public RNG { ...@@ -32,7 +33,7 @@ class NanMatRNG: public RNG {
std::uniform_real_distribution<dt_float32> ndist3(-2.1f, -1.9f); std::uniform_real_distribution<dt_float32> ndist3(-2.1f, -1.9f);
std::uniform_real_distribution<dt_float32> ndisth(-0.6f, -0.4f); std::uniform_real_distribution<dt_float32> ndisth(-0.6f, -0.4f);
std::uniform_int_distribution<int> dice(0, 5); std::uniform_int_distribution<int> dice(0, 5);
float *ptr = tensor_.ptr<dt_float32>(); float* ptr = tensor_.ptr<dt_float32>();
auto N = tensor_.layout.shape[0]; auto N = tensor_.layout.shape[0];
for (size_t n = 0; n < N; ++n) { for (size_t n = 0; n < N; ++n) {
for (size_t i = 0; i < 9; ++i) { for (size_t i = 0; i < 9; ++i) {
...@@ -65,7 +66,7 @@ class NanMatRNG: public RNG { ...@@ -65,7 +66,7 @@ class NanMatRNG: public RNG {
} }
}; };
} // anonymous namespace } // anonymous namespace
namespace megdnn { namespace megdnn {
namespace test { namespace test {
...@@ -171,17 +172,15 @@ TEST_F(CUDA, WARP_PERSPECTIVE_CV) { ...@@ -171,17 +172,15 @@ TEST_F(CUDA, WARP_PERSPECTIVE_CV) {
} }
#endif #endif
TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD) TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD) {
{
using Param = WarpPerspective::Param; using Param = WarpPerspective::Param;
Checker<WarpPerspectiveForward> checker(handle_cuda()); Checker<WarpPerspectiveForward> checker(handle_cuda());
WarpPerspectiveMatRNG rng; WarpPerspectiveMatRNG rng;
checker.set_rng(1, &rng); checker.set_rng(1, &rng);
for (auto bmode: {WarpPerspective::BorderMode::WRAP, for (auto bmode : {WarpPerspective::BorderMode::WRAP,
WarpPerspective::BorderMode::REFLECT, WarpPerspective::BorderMode::REFLECT,
WarpPerspective::BorderMode::REPLICATE, WarpPerspective::BorderMode::REPLICATE,
WarpPerspective::BorderMode::CONSTANT}) WarpPerspective::BorderMode::CONSTANT}) {
{
WarpPerspective::Param param; WarpPerspective::Param param;
param.border_val = 0.3f; param.border_val = 0.3f;
param.bmode = bmode; param.bmode = bmode;
...@@ -204,8 +203,7 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD) ...@@ -204,8 +203,7 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD)
// nan case // nan case
NanMatRNG rng_nan; NanMatRNG rng_nan;
UniformFloatRNG rng_zero(0, 0); UniformFloatRNG rng_zero(0, 0);
for (auto rng: std::vector<RNG *>{&rng_nan, &rng_zero}) for (auto rng : std::vector<RNG*>{&rng_nan, &rng_zero}) {
{
param::WarpPerspective param; param::WarpPerspective param;
param.bmode = param::WarpPerspective::BorderMode::CONSTANT; param.bmode = param::WarpPerspective::BorderMode::CONSTANT;
param.imode = param::WarpPerspective::InterpolationMode::LINEAR; param.imode = param::WarpPerspective::InterpolationMode::LINEAR;
...@@ -213,20 +211,18 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD) ...@@ -213,20 +211,18 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD)
param.border_val = 1.737; param.border_val = 1.737;
checker.set_param(param); checker.set_param(param);
// no invalid mem access is enough; no need to check value // no invalid mem access is enough; no need to check value
checker.set_expect_exec_fail([](){}); checker.set_expect_exec_fail([]() {});
checker.exec({{1000, 2, 10, 11}, {1000, 3, 3}, {1000, 2, 12, 13}}); checker.exec({{1000, 2, 10, 11}, {1000, 3, 3}, {1000, 2, 12, 13}});
} }
} }
TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_INTMAX) TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_INTMAX) {
{
require_compute_capability(6, 0); require_compute_capability(6, 0);
using Param = WarpPerspective::Param; using Param = WarpPerspective::Param;
Checker<WarpPerspectiveForward> checker(handle_cuda()); Checker<WarpPerspectiveForward> checker(handle_cuda());
WarpPerspectiveMatRNG rng; WarpPerspectiveMatRNG rng;
checker.set_rng(1, &rng); checker.set_rng(1, &rng);
for (auto bmode: {WarpPerspective::BorderMode::REPLICATE}) for (auto bmode : {WarpPerspective::BorderMode::REPLICATE}) {
{
WarpPerspective::Param param; WarpPerspective::Param param;
param.border_val = 0.3f; param.border_val = 0.3f;
param.bmode = bmode; param.bmode = bmode;
...@@ -235,27 +231,24 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_INTMAX) ...@@ -235,27 +231,24 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_INTMAX)
param.format = Param::Format::NHWC; param.format = Param::Format::NHWC;
checker.set_param(param); checker.set_param(param);
checker.set_epsilon(0.15).set_max_avg_error(4e-2); checker.set_epsilon(0.15).set_max_avg_error(4e-2);
size_t n = (INT_MAX) / (512 * 512 * 3); size_t n = (INT_MAX) / (512 * 512 * 3);
checker.execs( checker.execs(
{{n + 1, 512, 512, 3}, {n + 1, 3, 3}, {n + 1, 25, 25, 3}}); {{n + 1, 512, 512, 3}, {n + 1, 3, 3}, {n + 1, 25, 25, 3}});
} }
} }
TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_FP16) {
TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_FP16)
{
using Param = WarpPerspective::Param; using Param = WarpPerspective::Param;
Checker<WarpPerspectiveForward> checker(handle_cuda()); Checker<WarpPerspectiveForward> checker(handle_cuda());
WarpPerspectiveMatRNG rng; WarpPerspectiveMatRNG rng;
checker.set_rng(1, &rng); checker.set_rng(1, &rng);
checker.set_dtype(0, dtype::Float16()) checker.set_dtype(0, dtype::Float16())
.set_dtype(1, dtype::Float32()) .set_dtype(1, dtype::Float32())
.set_dtype(2, dtype::Float16()); .set_dtype(2, dtype::Float16());
for (auto bmode: {WarpPerspective::BorderMode::WRAP, for (auto bmode : {WarpPerspective::BorderMode::WRAP,
WarpPerspective::BorderMode::REFLECT, WarpPerspective::BorderMode::REFLECT,
WarpPerspective::BorderMode::REPLICATE, WarpPerspective::BorderMode::REPLICATE,
WarpPerspective::BorderMode::CONSTANT}) WarpPerspective::BorderMode::CONSTANT}) {
{
WarpPerspective::Param param; WarpPerspective::Param param;
param.border_val = 0.3f; param.border_val = 0.3f;
param.bmode = bmode; param.bmode = bmode;
...@@ -278,8 +271,7 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_FP16) ...@@ -278,8 +271,7 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_FP16)
// nan case // nan case
NanMatRNG rng_nan; NanMatRNG rng_nan;
UniformFloatRNG rng_zero(0, 0); UniformFloatRNG rng_zero(0, 0);
for (auto rng: std::vector<RNG *>{&rng_nan, &rng_zero}) for (auto rng : std::vector<RNG*>{&rng_nan, &rng_zero}) {
{
param::WarpPerspective param; param::WarpPerspective param;
param.bmode = param::WarpPerspective::BorderMode::CONSTANT; param.bmode = param::WarpPerspective::BorderMode::CONSTANT;
param.imode = param::WarpPerspective::InterpolationMode::LINEAR; param.imode = param::WarpPerspective::InterpolationMode::LINEAR;
...@@ -287,13 +279,12 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_FP16) ...@@ -287,13 +279,12 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_FP16)
param.border_val = 1.737; param.border_val = 1.737;
checker.set_param(param); checker.set_param(param);
// no invalid mem access is enough; no need to check value // no invalid mem access is enough; no need to check value
checker.set_expect_exec_fail([](){}); checker.set_expect_exec_fail([]() {});
checker.exec({{1000, 2, 10, 11}, {1000, 3, 3}, {1000, 2, 12, 13}}); checker.exec({{1000, 2, 10, 11}, {1000, 3, 3}, {1000, 2, 12, 13}});
} }
} }
TEST_F(CUDA, WARP_PERSPECTIVE_NCHW4) TEST_F(CUDA, WARP_PERSPECTIVE_NCHW4) {
{
using Param = WarpPerspective::Param; using Param = WarpPerspective::Param;
WarpPerspective::Param param; WarpPerspective::Param param;
Checker<WarpPerspectiveForward> checker(handle_cuda()); Checker<WarpPerspectiveForward> checker(handle_cuda());
...@@ -348,31 +339,29 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_NCHW_INT8) { ...@@ -348,31 +339,29 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_NCHW_INT8) {
warp_perspective::run_int8_test(handle_cuda()); warp_perspective::run_int8_test(handle_cuda());
} }
TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_DATA) TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_DATA) {
{
Checker<WarpPerspectiveBackwardData> checker(handle_cuda()); Checker<WarpPerspectiveBackwardData> checker(handle_cuda());
WarpPerspectiveMatRNG rng; WarpPerspectiveMatRNG rng;
checker.set_rng(0, &rng); checker.set_rng(0, &rng);
for (int i = 0; i < 1; ++i) { for (int i = 0; i < 1; ++i) {
for (auto bmode: {WarpPerspective::BorderMode::WRAP, for (auto bmode : {WarpPerspective::BorderMode::WRAP,
WarpPerspective::BorderMode::REFLECT, WarpPerspective::BorderMode::REFLECT,
WarpPerspective::BorderMode::REPLICATE, WarpPerspective::BorderMode::REPLICATE,
WarpPerspective::BorderMode::CONSTANT}) WarpPerspective::BorderMode::CONSTANT}) {
{
WarpPerspective::Param param; WarpPerspective::Param param;
param.border_val = 0.3f; param.border_val = 0.3f;
param.bmode = bmode; param.bmode = bmode;
param.imode = param::WarpPerspective::InterpolationMode::LINEAR; param.imode = param::WarpPerspective::InterpolationMode::LINEAR;
checker.set_param(param); checker.set_param(param);
checker.execs({{2, 3, 3}, {2, 3, 11, 12}, {2, 3, 10, 11}}); checker.execs({{2, 3, 3}, {2, 3, 11, 12}, {2, 3, 10, 11}});
checker.execs({{22000, 3, 3}, {22000, 3, 11, 12}, {22000, 3, 10, 11}}); checker.execs(
{{22000, 3, 3}, {22000, 3, 11, 12}, {22000, 3, 10, 11}});
} }
} }
// nan case // nan case
NanMatRNG rng_nan; NanMatRNG rng_nan;
UniformFloatRNG rng_zero(0, 0); UniformFloatRNG rng_zero(0, 0);
for (auto rng: std::vector<RNG *>{&rng_nan, &rng_zero}) for (auto rng : std::vector<RNG*>{&rng_nan, &rng_zero}) {
{
param::WarpPerspective param; param::WarpPerspective param;
param.bmode = param::WarpPerspective::BorderMode::CONSTANT; param.bmode = param::WarpPerspective::BorderMode::CONSTANT;
param.imode = param::WarpPerspective::InterpolationMode::LINEAR; param.imode = param::WarpPerspective::InterpolationMode::LINEAR;
...@@ -380,39 +369,54 @@ TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_DATA) ...@@ -380,39 +369,54 @@ TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_DATA)
param.border_val = 1.737; param.border_val = 1.737;
checker.set_param(param); checker.set_param(param);
// no invalid mem access is enough; no need to check value // no invalid mem access is enough; no need to check value
checker.set_expect_exec_fail([](){}); checker.set_expect_exec_fail([]() {});
checker.exec({{1000, 3, 3}, {1000, 2, 10, 11}, {1000, 2, 12, 13}}); checker.exec({{1000, 3, 3}, {1000, 2, 10, 11}, {1000, 2, 12, 13}});
} }
{
Checker<WarpPerspectiveBackwardData, WarpPerspectiveMatIdxProxy>
checker(handle_cuda());
constexpr int N_SRC = 5;
UniformIntRNG mat_idx_rng{0, N_SRC - 1};
checker.set_rng(0, &rng);
checker.set_dtype(1, dtype::Int32());
checker.set_rng(1, &mat_idx_rng);
param::WarpPerspective param;
param.bmode = param::WarpPerspective::BorderMode::REFLECT;
param.imode = param::WarpPerspective::InterpolationMode::LINEAR;
checker.set_param(param);
checker.set_epsilon(1 + 1e-3);
checker.execs({{2, 3, 3}, {2}, {2, 12, 11, 12}, {N_SRC, 12, 10, 11}});
checker.execs(
{{123, 3, 3}, {123}, {123, 56, 16, 15}, {N_SRC, 56, 17, 13}});
}
} }
TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_MAT) TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_MAT) {
{
Checker<WarpPerspectiveBackwardMat> checker(handle_cuda()); Checker<WarpPerspectiveBackwardMat> checker(handle_cuda());
WarpPerspectiveMatRNG rng; WarpPerspectiveMatRNG rng;
checker.set_rng(1, &rng); checker.set_rng(1, &rng);
for (int i = 0; i < 1; ++i) { for (int i = 0; i < 1; ++i) {
for (auto bmode: {WarpPerspective::BorderMode::WRAP, for (auto bmode : {WarpPerspective::BorderMode::WRAP,
WarpPerspective::BorderMode::REFLECT, WarpPerspective::BorderMode::REFLECT,
WarpPerspective::BorderMode::REPLICATE, WarpPerspective::BorderMode::REPLICATE,
WarpPerspective::BorderMode::CONSTANT}) WarpPerspective::BorderMode::CONSTANT}) {
{
WarpPerspective::Param param; WarpPerspective::Param param;
param.border_val = 0.3f; param.border_val = 0.3f;
param.imode = param::WarpPerspective::InterpolationMode::LINEAR; param.imode = param::WarpPerspective::InterpolationMode::LINEAR;
param.bmode = bmode; param.bmode = bmode;
checker.set_param(param); checker.set_param(param);
checker.set_epsilon(1e-2); checker.set_epsilon(1e-2);
checker.execs({ checker.execs({{1000, 3, 11, 12},
{1000, 3, 11, 12}, {1000, 3, 3}, {1000, 3, 3},
{1000, 3, 10, 11}, {1000, 3, 3} {1000, 3, 10, 11},
}); {1000, 3, 3}});
} }
} }
// nan case // nan case
NanMatRNG rng_nan; NanMatRNG rng_nan;
UniformFloatRNG rng_zero(0, 0); UniformFloatRNG rng_zero(0, 0);
for (auto rng: std::vector<RNG *>{&rng_nan, &rng_zero}) for (auto rng : std::vector<RNG*>{&rng_nan, &rng_zero}) {
{
param::WarpPerspective param; param::WarpPerspective param;
param.bmode = param::WarpPerspective::BorderMode::CONSTANT; param.bmode = param::WarpPerspective::BorderMode::CONSTANT;
param.imode = param::WarpPerspective::InterpolationMode::LINEAR; param.imode = param::WarpPerspective::InterpolationMode::LINEAR;
...@@ -420,26 +424,50 @@ TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_MAT) ...@@ -420,26 +424,50 @@ TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_MAT)
param.border_val = 1.737; param.border_val = 1.737;
checker.set_param(param); checker.set_param(param);
// no invalid mem access is enough; no need to check value // no invalid mem access is enough; no need to check value
checker.set_expect_exec_fail([](){}); checker.set_expect_exec_fail([]() {});
checker.exec({{1000, 2, 10, 11}, {1000, 3, 3}, checker.exec({{1000, 2, 10, 11},
{1000, 2, 12, 13}, {1000, 3, 3}}); {1000, 3, 3},
{1000, 2, 12, 13},
{1000, 3, 3}});
}
{
Checker<WarpPerspectiveBackwardMat, WarpPerspectiveMatIdxProxy> checker(
handle_cuda());
constexpr int N_SRC = 5;
UniformIntRNG mat_idx_rng{0, N_SRC - 1};
checker.set_rng(1, &rng);
checker.set_dtype(2, dtype::Int32());
checker.set_rng(2, &mat_idx_rng);
param::WarpPerspective param;
param.bmode = param::WarpPerspective::BorderMode::REFLECT;
param.imode = param::WarpPerspective::InterpolationMode::LINEAR;
checker.set_param(param);
checker.set_epsilon(1 + 1e-3);
checker.execs({{N_SRC, 12, 10, 11},
{2, 3, 3},
{2},
{2, 12, 11, 12},
{2, 3, 3}});
checker.execs({{N_SRC, 56, 17, 13},
{123, 3, 3},
{123},
{123, 56, 16, 15},
{123, 3, 3}});
} }
} }
TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_BFLOAT16) TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_BFLOAT16) {
{
using Param = WarpPerspective::Param; using Param = WarpPerspective::Param;
Checker<WarpPerspectiveForward> checker(handle_cuda()); Checker<WarpPerspectiveForward> checker(handle_cuda());
WarpPerspectiveMatRNG rng; WarpPerspectiveMatRNG rng;
checker.set_rng(1, &rng); checker.set_rng(1, &rng);
checker.set_dtype(0, dtype::BFloat16()) checker.set_dtype(0, dtype::BFloat16())
.set_dtype(1, dtype::Float32()) .set_dtype(1, dtype::Float32())
.set_dtype(2, dtype::BFloat16()); .set_dtype(2, dtype::BFloat16());
for (auto bmode: {WarpPerspective::BorderMode::WRAP, for (auto bmode : {WarpPerspective::BorderMode::WRAP,
WarpPerspective::BorderMode::REFLECT, WarpPerspective::BorderMode::REFLECT,
WarpPerspective::BorderMode::REPLICATE, WarpPerspective::BorderMode::REPLICATE,
WarpPerspective::BorderMode::CONSTANT}) WarpPerspective::BorderMode::CONSTANT}) {
{
WarpPerspective::Param param; WarpPerspective::Param param;
param.border_val = 0.3f; param.border_val = 0.3f;
param.bmode = bmode; param.bmode = bmode;
...@@ -457,21 +485,19 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_BFLOAT16) ...@@ -457,21 +485,19 @@ TEST_F(CUDA, WARP_PERSPECTIVE_FORWARD_BFLOAT16)
} }
} }
TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_DATA_BFLOAT16) TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_DATA_BFLOAT16) {
{
Checker<WarpPerspectiveBackwardData> checker(handle_cuda()); Checker<WarpPerspectiveBackwardData> checker(handle_cuda());
WarpPerspectiveMatRNG rng; WarpPerspectiveMatRNG rng;
checker.set_rng(0, &rng) checker.set_rng(0, &rng)
.set_epsilon(1e-1) .set_epsilon(1e-1)
.set_dtype(0, dtype::Float32()) .set_dtype(0, dtype::Float32())
.set_dtype(1, dtype::BFloat16()) .set_dtype(1, dtype::BFloat16())
.set_dtype(2, dtype::BFloat16()); .set_dtype(2, dtype::BFloat16());
for (int i = 0; i < 1; ++i) { for (int i = 0; i < 1; ++i) {
for (auto bmode: {WarpPerspective::BorderMode::WRAP, for (auto bmode : {WarpPerspective::BorderMode::WRAP,
WarpPerspective::BorderMode::REFLECT, WarpPerspective::BorderMode::REFLECT,
WarpPerspective::BorderMode::REPLICATE, WarpPerspective::BorderMode::REPLICATE,
WarpPerspective::BorderMode::CONSTANT}) WarpPerspective::BorderMode::CONSTANT}) {
{
WarpPerspective::Param param; WarpPerspective::Param param;
param.border_val = 0.3f; param.border_val = 0.3f;
param.bmode = bmode; param.bmode = bmode;
...@@ -482,31 +508,29 @@ TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_DATA_BFLOAT16) ...@@ -482,31 +508,29 @@ TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_DATA_BFLOAT16)
} }
} }
TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_MAT_BFLOAT16) TEST_F(CUDA, WARP_PERSPECTIVE_BACKWARD_MAT_BFLOAT16) {
{
Checker<WarpPerspectiveBackwardMat> checker(handle_cuda()); Checker<WarpPerspectiveBackwardMat> checker(handle_cuda());
WarpPerspectiveMatRNG rng; WarpPerspectiveMatRNG rng;
checker.set_rng(1, &rng) checker.set_rng(1, &rng)
.set_epsilon(1e-2) .set_epsilon(1e-2)
.set_dtype(0, dtype::BFloat16()) .set_dtype(0, dtype::BFloat16())
.set_dtype(1, dtype::Float32()) .set_dtype(1, dtype::Float32())
.set_dtype(2, dtype::BFloat16()) .set_dtype(2, dtype::BFloat16())
.set_dtype(3, dtype::Float32()); .set_dtype(3, dtype::Float32());
for (int i = 0; i < 1; ++i) { for (int i = 0; i < 1; ++i) {
for (auto bmode: {WarpPerspective::BorderMode::WRAP, for (auto bmode : {WarpPerspective::BorderMode::WRAP,
WarpPerspective::BorderMode::REFLECT, WarpPerspective::BorderMode::REFLECT,
WarpPerspective::BorderMode::REPLICATE, WarpPerspective::BorderMode::REPLICATE,
WarpPerspective::BorderMode::CONSTANT}) WarpPerspective::BorderMode::CONSTANT}) {
{
WarpPerspective::Param param; WarpPerspective::Param param;
param.border_val = 0.3f; param.border_val = 0.3f;
param.imode = param::WarpPerspective::InterpolationMode::LINEAR; param.imode = param::WarpPerspective::InterpolationMode::LINEAR;
param.bmode = bmode; param.bmode = bmode;
checker.set_param(param); checker.set_param(param);
checker.execs({ checker.execs({{1000, 3, 11, 12},
{1000, 3, 11, 12}, {1000, 3, 3}, {1000, 3, 3},
{1000, 3, 10, 11}, {1000, 3, 3} {1000, 3, 10, 11},
}); {1000, 3, 3}});
} }
} }
} }
...@@ -549,14 +573,14 @@ TEST_F(CUDA, BENCHMARK_WARP_PERSPECTIVE_NCHW4) { ...@@ -549,14 +573,14 @@ TEST_F(CUDA, BENCHMARK_WARP_PERSPECTIVE_NCHW4) {
benchmarker.set_dtype(0, dtype::QuantizedS8(1.0f)); benchmarker.set_dtype(0, dtype::QuantizedS8(1.0f));
benchmarker.set_dtype(2, dtype::QuantizedS8(1.0f)); benchmarker.set_dtype(2, dtype::QuantizedS8(1.0f));
run({TensorShape{1, 25, 256, 256, 4}, {1, 3, 3}, {1, 25, 256, 5120, 4}}); run({TensorShape{1, 25, 256, 256, 4}, {1, 3, 3}, {1, 25, 256, 5120, 4}});
run({TensorShape{1, 25, 256, 5120, 4}, {1, 3, 3}, {1,25, 256, 256, 4}}); run({TensorShape{1, 25, 256, 5120, 4}, {1, 3, 3}, {1, 25, 256, 256, 4}});
run({TensorShape{1, 25, 256, 256, 4}, {1, 3, 3}, {1, 25, 512, 512, 4}}); run({TensorShape{1, 25, 256, 256, 4}, {1, 3, 3}, {1, 25, 512, 512, 4}});
run({TensorShape{1, 25, 512, 512, 4}, {1, 3, 3}, {1, 25, 256, 256, 4}}); run({TensorShape{1, 25, 512, 512, 4}, {1, 3, 3}, {1, 25, 256, 256, 4}});
} }
#endif #endif
} // namespace test } // namespace test
} // namespace megdnn } // namespace megdnn
// vim: syntax=cpp.doxygen // vim: syntax=cpp.doxygen
...@@ -6,18 +6,18 @@ ...@@ -6,18 +6,18 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#include "./internal/megdnn_opr_wrapper.inl"
#include "megbrain/opr/imgproc.h" #include "megbrain/opr/imgproc.h"
#include "megbrain/opr/utility.h" #include "./internal/megdnn_opr_wrapper.inl"
#include "megbrain/graph/grad_impl.h" #include "megbrain/graph/grad_impl.h"
#include "megbrain/opr/utility.h"
using namespace mgb; using namespace mgb;
using namespace opr; using namespace opr;
/* ======================= WarpPerspectiveForward ======================= */ /* ======================= WarpPerspectiveForward ======================= */
MGB_DYN_TYPE_OBJ_FINAL_IMPL(WarpPerspectiveForward); MGB_DYN_TYPE_OBJ_FINAL_IMPL(WarpPerspectiveForward);
...@@ -54,8 +54,7 @@ void WarpPerspectiveForward::add_input_layout_constraint() { ...@@ -54,8 +54,7 @@ void WarpPerspectiveForward::add_input_layout_constraint() {
} }
void WarpPerspectiveForward::outshape_by_symvar_do_get_output_shape( void WarpPerspectiveForward::outshape_by_symvar_do_get_output_shape(
TensorShape &dest, const ShapeInferInfo &shpinfo) { TensorShape& dest, const ShapeInferInfo& shpinfo) {
TensorShape oshp2d; TensorShape oshp2d;
cg::copy_tensor_value_to_shape(oshp2d, *shpinfo.shpval_inp_val.at(0)); cg::copy_tensor_value_to_shape(oshp2d, *shpinfo.shpval_inp_val.at(0));
auto imgshp = shpinfo.shape_inp_shp.at(0), auto imgshp = shpinfo.shape_inp_shp.at(0),
...@@ -112,8 +111,8 @@ void WarpPerspectiveForward::scn_do_execute() { ...@@ -112,8 +111,8 @@ void WarpPerspectiveForward::scn_do_execute() {
} }
size_t WarpPerspectiveForward::get_workspace_size_bytes( size_t WarpPerspectiveForward::get_workspace_size_bytes(
const TensorShapeArray &input_shapes, const TensorShapeArray& input_shapes,
const TensorShapeArray &output_shapes) const { const TensorShapeArray& output_shapes) const {
if (input().size() == 3) { if (input().size() == 3) {
return intl::_MegDNNOprMethInvoker<2, 1>::get_workspace_in_bytes( return intl::_MegDNNOprMethInvoker<2, 1>::get_workspace_in_bytes(
megdnn_opr(), this, input_shapes, output_shapes); megdnn_opr(), this, input_shapes, output_shapes);
...@@ -129,19 +128,34 @@ void WarpPerspectiveForward::record_execute_deps(ExecDependencyArray& deps) { ...@@ -129,19 +128,34 @@ void WarpPerspectiveForward::record_execute_deps(ExecDependencyArray& deps) {
#ifdef MGB_ENABLE_GRAD #ifdef MGB_ENABLE_GRAD
MGB_IMPL_OPR_GRAD(WarpPerspectiveForward) { MGB_IMPL_OPR_GRAD(WarpPerspectiveForward) {
mgb_assert(opr.input().size() == 3, if (opr.input().size() == 4) {
"backward with mat_idx is currently unsupported"); if (wrt_idx == 0) {
// wrt data
SymbolVar grad = WarpPerspectiveBackwardData::make(
opr.input(1), opr.input(2), out_grad[0], opr.input(0),
opr.param());
return grad.node();
} else if (wrt_idx == 1) {
// wrt mat
SymbolVar grad = WarpPerspectiveBackwardMat::make(
opr.input(0), opr.input(1), opr.input(2), out_grad[0],
opr.param());
return grad.node();
} else {
return InvalidGrad::make(opr, wrt_idx);
}
}
mgb_assert(opr.input().size() == 3);
if (wrt_idx == 0) { if (wrt_idx == 0) {
// wrt data // wrt data
SymbolVar grad = WarpPerspectiveBackwardData::make( SymbolVar grad = WarpPerspectiveBackwardData::make(
opr.input(1), out_grad[0], opr.input(0), opr.input(1), out_grad[0], opr.input(0), opr.param());
opr.param());
return grad.node(); return grad.node();
} else if (wrt_idx == 1){ } else if (wrt_idx == 1) {
// wrt mat // wrt mat
SymbolVar grad = WarpPerspectiveBackwardMat::make( SymbolVar grad = WarpPerspectiveBackwardMat::make(
opr.input(0), opr.input(1), out_grad[0], opr.input(0), opr.input(1), out_grad[0], opr.param());
opr.param());
return grad.node(); return grad.node();
} else } else
return InvalidGrad::make(opr, wrt_idx); return InvalidGrad::make(opr, wrt_idx);
...@@ -151,14 +165,116 @@ MGB_IMPL_OPR_GRAD(WarpPerspectiveForward) { ...@@ -151,14 +165,116 @@ MGB_IMPL_OPR_GRAD(WarpPerspectiveForward) {
/* ====================== WarpPerspectiveBackwardData ====================== */ /* ====================== WarpPerspectiveBackwardData ====================== */
MGB_DYN_TYPE_OBJ_FINAL_IMPL(WarpPerspectiveBackwardData); MGB_DYN_TYPE_OBJ_FINAL_IMPL(WarpPerspectiveBackwardData);
MEGDNN_OPR_INIT3(WarpPerspectiveBackwardData, "warp_perspective_bwd_data",
2, false); WarpPerspectiveBackwardData::WarpPerspectiveBackwardData(
VarNode* mat, VarNode* out_diff, VarNode* in_for_shape,
const Param& param, const OperatorNodeConfig& config)
: Super(OperatorNodeBaseCtorParam{mat->owner_graph(),
config,
"warp_perspective_bwd_data",
{mat}},
2, false) {
init_megdnn_opr(*this, param);
add_input({mat, out_diff, in_for_shape});
intl::MegDNNOprInitPostCtor<WarpPerspectiveBackwardData>::apply(*this);
}
WarpPerspectiveBackwardData::WarpPerspectiveBackwardData(
VarNode* mat, VarNode* mat_idx, VarNode* out_diff,
VarNode* in_for_shape, const Param& param,
const OperatorNodeConfig& config)
: Super(OperatorNodeBaseCtorParam{mat->owner_graph(),
config,
"warp_perspective_bwd_data",
{mat, mat_idx}},
3, false) {
init_megdnn_opr(*this, param);
add_input({mat, mat_idx, out_diff, in_for_shape});
intl::MegDNNOprInitPostCtor<WarpPerspectiveBackwardData>::apply(*this);
}
SymbolVar WarpPerspectiveBackwardData::make(SymbolVar i0, SymbolVar i1,
SymbolVar i2, const Param& param,
const OperatorNodeConfig& config) {
intl::MegDNNOprInitInputsModifier<WarpPerspectiveBackwardData>::apply(
param, {&i0, &i1, &i2});
return i0.insert_single_output_opr<WarpPerspectiveBackwardData>(
i0.node(), i1.node(), i2.node(), param, config);
}
SymbolVar WarpPerspectiveBackwardData::make(SymbolVar i0, SymbolVar i1,
SymbolVar i2, SymbolVar i3,
const Param& param,
const OperatorNodeConfig& config) {
intl::MegDNNOprInitInputsModifier<WarpPerspectiveBackwardData>::apply(
param, {&i0, &i1, &i2, &i3});
return i0.insert_single_output_opr<WarpPerspectiveBackwardData>(
i0.node(), i1.node(), i2.node(), i3.node(), param, config);
}
void WarpPerspectiveBackwardData::scn_do_execute() {
if (input().size() == 3) {
megdnn_opr()->exec(input(0)->dev_tensor().as_megdnn(),
input(1)->dev_tensor().as_megdnn(),
output(0)->dev_tensor().as_megdnn(),
intl::get_megdnn_workspace_from_var(output(1)));
} else {
mgb_assert(input().size() == 4);
megdnn_opr()->exec(input(0)->dev_tensor().as_megdnn(),
input(1)->dev_tensor().as_megdnn(),
input(2)->dev_tensor().as_megdnn(),
output(0)->dev_tensor().as_megdnn(),
intl::get_megdnn_workspace_from_var(output(1)));
}
}
/* ====================== WarpPerspectiveBackwardMat ====================== */ /* ====================== WarpPerspectiveBackwardMat ====================== */
MGB_DYN_TYPE_OBJ_FINAL_IMPL(WarpPerspectiveBackwardMat); MGB_DYN_TYPE_OBJ_FINAL_IMPL(WarpPerspectiveBackwardMat);
MEGDNN_OPR_INIT3(WarpPerspectiveBackwardMat, "warp_perspective_bwd_mat",
1, true); WarpPerspectiveBackwardMat::WarpPerspectiveBackwardMat(
VarNode* src, VarNode* mat, VarNode* mat_idx, VarNode* out_diff,
const Param& param, const OperatorNodeConfig& config)
: Super(OperatorNodeBaseCtorParam{src->owner_graph(),
config,
"warp_perspective_bwd_mat",
{src, mat, mat_idx}},
1, true) {
init_megdnn_opr(*this, param);
if (mat_idx) {
add_input({src, mat, mat_idx, out_diff});
} else {
add_input({src, mat, out_diff});
}
intl::MegDNNOprInitPostCtor<WarpPerspectiveBackwardMat>::apply(*this);
}
void WarpPerspectiveBackwardMat::scn_do_execute() {
if (input().size() == 3) {
megdnn_opr()->exec(input(0)->dev_tensor().as_megdnn(),
input(1)->dev_tensor().as_megdnn(),
input(2)->dev_tensor().as_megdnn(),
output(0)->dev_tensor().as_megdnn(),
intl::get_megdnn_workspace_from_var(output(1)));
} else {
mgb_assert(input().size() == 4);
megdnn_opr()->exec(input(0)->dev_tensor().as_megdnn(),
input(1)->dev_tensor().as_megdnn(),
input(2)->dev_tensor().as_megdnn(),
input(3)->dev_tensor().as_megdnn(),
output(0)->dev_tensor().as_megdnn(),
intl::get_megdnn_workspace_from_var(output(1)));
}
}
SymbolVar WarpPerspectiveBackwardMat::make(
SymbolVar i0, SymbolVar i1, SymbolVar i2, SymbolVar i3,
const Param& param, const OperatorNodeConfig& config) {
intl::MegDNNOprInitInputsModifier<WarpPerspectiveBackwardMat>::apply(
param, {&i0, &i1, &i2, &i3});
return i0.insert_single_output_opr<WarpPerspectiveBackwardMat>(
i0.node(), i1.node(), i2.node(), i3.node(), param, config);
}
/* ====================== Cv operator ====================== */ /* ====================== Cv operator ====================== */
...@@ -188,8 +304,7 @@ void ResizeForward::add_input_layout_constraint() { ...@@ -188,8 +304,7 @@ void ResizeForward::add_input_layout_constraint() {
} }
void ResizeForward::outshape_by_symvar_do_get_output_shape( void ResizeForward::outshape_by_symvar_do_get_output_shape(
TensorShape &dest, const ShapeInferInfo &shpinfo) { TensorShape& dest, const ShapeInferInfo& shpinfo) {
TensorShape oshp2d; TensorShape oshp2d;
cg::copy_tensor_value_to_shape(oshp2d, *shpinfo.shpval_inp_val.at(0)); cg::copy_tensor_value_to_shape(oshp2d, *shpinfo.shpval_inp_val.at(0));
auto imgshp = shpinfo.shape_inp_shp.at(0); auto imgshp = shpinfo.shape_inp_shp.at(0);
...@@ -232,7 +347,7 @@ size_t ResizeForward::get_workspace_size_bytes( ...@@ -232,7 +347,7 @@ size_t ResizeForward::get_workspace_size_bytes(
megdnn_opr(), this, input_shapes, output_shapes); megdnn_opr(), this, input_shapes, output_shapes);
} }
void ResizeForward::record_execute_deps(ExecDependencyArray &deps) { void ResizeForward::record_execute_deps(ExecDependencyArray& deps) {
record_megdnn_opr(deps); record_megdnn_opr(deps);
} }
...@@ -268,19 +383,17 @@ void WarpAffineForward::add_input_layout_constraint() { ...@@ -268,19 +383,17 @@ void WarpAffineForward::add_input_layout_constraint() {
} }
void WarpAffineForward::outshape_by_symvar_do_get_output_shape( void WarpAffineForward::outshape_by_symvar_do_get_output_shape(
TensorShape &dest, const ShapeInferInfo &shpinfo) { TensorShape& dest, const ShapeInferInfo& shpinfo) {
TensorShape oshp2d; TensorShape oshp2d;
cg::copy_tensor_value_to_shape(oshp2d, *shpinfo.shpval_inp_val.at(0)); cg::copy_tensor_value_to_shape(oshp2d, *shpinfo.shpval_inp_val.at(0));
auto imgshp = shpinfo.shape_inp_shp.at(0), auto imgshp = shpinfo.shape_inp_shp.at(0),
matshp = shpinfo.shape_inp_shp.at(1); matshp = shpinfo.shape_inp_shp.at(1);
mgb_assert( mgb_assert((imgshp.ndim == 4 || imgshp.ndim == 5) && matshp.ndim == 3 &&
(imgshp.ndim == 4 || imgshp.ndim == 5) && matshp.ndim == 3 && oshp2d.ndim == 2 && oshp2d.ndim == 2 && matshp.shape[0] == imgshp.shape[0] &&
matshp.shape[0] == imgshp.shape[0] && matshp.shape[1] == 2 && matshp.shape[2] == 3,
matshp.shape[1] == 2 && matshp.shape[2] == 3, "shape mismatch for WarpAffineForward: img=%s mat=%s out2d=%s",
"shape mismatch for WarpAffineForward: img=%s mat=%s out2d=%s", imgshp.to_string().c_str(), matshp.to_string().c_str(),
imgshp.to_string().c_str(), matshp.to_string().c_str(), oshp2d.to_string().c_str());
oshp2d.to_string().c_str());
size_t height_idx = 0; size_t height_idx = 0;
if (param().format == Param::Format::NCHW) { if (param().format == Param::Format::NCHW) {
...@@ -305,18 +418,19 @@ void WarpAffineForward::init_output_static_infer_desc() { ...@@ -305,18 +418,19 @@ void WarpAffineForward::init_output_static_infer_desc() {
} }
void WarpAffineForward::scn_do_execute() { void WarpAffineForward::scn_do_execute() {
intl::MegDNNOprMethInvoker<megdnn::WarpAffine>:: intl::MegDNNOprMethInvoker<megdnn::WarpAffine>::exec(megdnn_opr(), this);
exec(megdnn_opr(), this);
} }
size_t WarpAffineForward::get_workspace_size_bytes( size_t WarpAffineForward::get_workspace_size_bytes(
const TensorShapeArray &input_shapes, const TensorShapeArray& input_shapes,
const TensorShapeArray &output_shapes) const { const TensorShapeArray& output_shapes) const {
return intl::MegDNNOprMethInvoker<megdnn::WarpAffine>:: return intl::MegDNNOprMethInvoker<
get_workspace_in_bytes(megdnn_opr(), this, input_shapes, output_shapes); megdnn::WarpAffine>::get_workspace_in_bytes(megdnn_opr(), this,
input_shapes,
output_shapes);
} }
void WarpAffineForward::record_execute_deps(ExecDependencyArray &deps) { void WarpAffineForward::record_execute_deps(ExecDependencyArray& deps) {
record_megdnn_opr(deps); record_megdnn_opr(deps);
} }
...@@ -325,7 +439,7 @@ void WarpAffineForward::record_execute_deps(ExecDependencyArray &deps) { ...@@ -325,7 +439,7 @@ void WarpAffineForward::record_execute_deps(ExecDependencyArray &deps) {
MGB_DYN_TYPE_OBJ_FINAL_IMPL(RemapForward); MGB_DYN_TYPE_OBJ_FINAL_IMPL(RemapForward);
MEGDNN_OPR_INIT2(RemapForward, "remap") MEGDNN_OPR_INIT2(RemapForward, "remap")
void RemapForward::init_output_dtype(){ void RemapForward::init_output_dtype() {
output(0)->dtype(input(0)->dtype()); output(0)->dtype(input(0)->dtype());
} }
......
...@@ -37,13 +37,59 @@ namespace serialization { ...@@ -37,13 +37,59 @@ namespace serialization {
} }
} }
}; };
template<>
struct OprMaker<opr::WarpPerspectiveBackwardData, 0> {
using Opr = opr::WarpPerspectiveBackwardData;
using Param = Opr::Param;
static cg::OperatorNodeBase* make(const Param& param,
const cg::VarNodeArray& inputs,
ComputingGraph& graph,
const OperatorNodeConfig& config) {
MGB_MARK_USED_VAR(graph);
if (inputs.size() == 3) {
return Opr::make(inputs[0], inputs[1], inputs[2], param, config)
.node()
->owner_opr();
} else {
mgb_assert(inputs.size() == 4);
return Opr::make(inputs[0], inputs[1], inputs[2], inputs[3],
param, config)
.node()
->owner_opr();
}
}
};
template<>
struct OprMaker<opr::WarpPerspectiveBackwardMat, 0> {
using Opr = opr::WarpPerspectiveBackwardMat;
using Param = Opr::Param;
static cg::OperatorNodeBase* make(const Param& param,
const cg::VarNodeArray& inputs,
ComputingGraph& graph,
const OperatorNodeConfig& config) {
MGB_MARK_USED_VAR(graph);
if (inputs.size() == 3) {
return Opr::make(inputs[0], inputs[1], inputs[2], param, config)
.node()
->owner_opr();
} else {
mgb_assert(inputs.size() == 4);
return Opr::make(inputs[0], inputs[1], inputs[2], inputs[3],
param, config)
.node()
->owner_opr();
}
}
};
} // namespace serialization } // namespace serialization
namespace opr { namespace opr {
MGB_SEREG_OPR(WarpPerspective, 0); MGB_SEREG_OPR(WarpPerspective, 0);
MGB_SEREG_OPR(WarpPerspectiveBackwardData, 3); MGB_SEREG_OPR(WarpPerspectiveBackwardData, 0);
MGB_SEREG_OPR(WarpPerspectiveBackwardMat, 3); MGB_SEREG_OPR(WarpPerspectiveBackwardMat, 0);
MGB_SEREG_OPR(Rotate, 1); MGB_SEREG_OPR(Rotate, 1);
MGB_SEREG_OPR(CvtColor, 1); MGB_SEREG_OPR(CvtColor, 1);
......
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
* *
* Unless required by applicable law or agreed to in writing, * Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
* implied.
*/ */
#pragma once #pragma once
...@@ -33,77 +34,93 @@ namespace opr { ...@@ -33,77 +34,93 @@ namespace opr {
* Impl note: this operator might have 3 or 4 inputs depending on whether * Impl note: this operator might have 3 or 4 inputs depending on whether
* \p mat_idx is given * \p mat_idx is given
*/ */
MGB_DEFINE_OPR_CLASS(WarpPerspectiveForward, MGB_DEFINE_OPR_CLASS(
WarpPerspectiveForward,
intl::WorkspaceSizeInfer< intl::WorkspaceSizeInfer<
intl::OutshapeBySymvarSCNOpr<mixin::MegDNNOprHolderImpl< intl::OutshapeBySymvarSCNOpr<mixin::MegDNNOprHolderImpl<
megdnn::WarpPerspectiveForward>>>) // { megdnn::WarpPerspectiveForward>>>) // {
public: public:
WarpPerspectiveForward( WarpPerspectiveForward(VarNode* in_tensor, VarNode* mat, VarNode* mat_idx,
VarNode *in_tensor, VarNode *mat, VarNode *mat_idx, VarNode* out_shape, const Param& param,
VarNode *out_shape, const OperatorNodeConfig& config);
const Param &param,
const OperatorNodeConfig &config); static SymbolVar make(SymbolVar in_tensor, SymbolVar mat, SymbolVar mat_idx,
SymbolVar out_shape, const Param& param = {},
static SymbolVar make(SymbolVar in_tensor, const OperatorNodeConfig& config = {});
SymbolVar mat, SymbolVar mat_idx, SymbolVar out_shape,
const Param &param = {}, static SymbolVar make(SymbolVar in_tensor, SymbolVar mat, SymbolVar out_shape,
const OperatorNodeConfig &config = {}); const Param& param = {},
const OperatorNodeConfig& config = {}) {
static SymbolVar make(SymbolVar in_tensor, SymbolVar mat, return make(in_tensor, mat, SymbolVar{}, out_shape, param, config);
SymbolVar out_shape, }
const Param &param = {},
const OperatorNodeConfig &config = {}) { static SymbolVar make(SymbolVar in_tensor, SymbolVar mat,
return make(in_tensor, mat, SymbolVar{}, out_shape, param, config); const TensorShape& out_shape, const Param& param = {},
} const OperatorNodeConfig& config = {}) {
return make(in_tensor, mat, cg::var_from_tensor_shape(in_tensor, out_shape),
static SymbolVar make(SymbolVar in_tensor, SymbolVar mat, param, config);
const TensorShape &out_shape, }
const Param &param = {},
const OperatorNodeConfig &config = {}) private:
{ void init_output_dtype() override;
return make(in_tensor, mat, void add_input_layout_constraint() override;
cg::var_from_tensor_shape( void init_output_static_infer_desc() override;
in_tensor, out_shape), param, config); void outshape_by_symvar_do_get_output_shape(
} TensorShape& dest, const ShapeInferInfo& shpinfo) override;
private: void scn_do_execute() override;
void init_output_dtype() override; size_t get_workspace_size_bytes(
void add_input_layout_constraint() override; const TensorShapeArray& input_shapes,
void init_output_static_infer_desc() override; const TensorShapeArray& output_shapes) const override;
void outshape_by_symvar_do_get_output_shape(
TensorShape &dest, const ShapeInferInfo &shpinfo) override; void record_execute_deps(ExecDependencyArray& deps) override;
}; // namespace opr
void scn_do_execute() override;
size_t get_workspace_size_bytes(
const TensorShapeArray &input_shapes,
const TensorShapeArray &output_shapes) const override;
void record_execute_deps(ExecDependencyArray& deps) override;
};
using WarpPerspective = WarpPerspectiveForward; using WarpPerspective = WarpPerspectiveForward;
MGB_DEFINE_OPR_CLASS(WarpPerspectiveBackwardData, MGB_DEFINE_OPR_CLASS(
intl::MegDNNOprWrapperBwd<megdnn::WarpPerspectiveBackwardData>) // { WarpPerspectiveBackwardData,
public: intl::MegDNNOprWrapperBwd<megdnn::WarpPerspectiveBackwardData>) // {
WarpPerspectiveBackwardData(VarNode *mat, VarNode *out_diff, public:
VarNode *in_for_shape, const Param &param, WarpPerspectiveBackwardData(VarNode* mat, VarNode* out_diff,
const OperatorNodeConfig &config); VarNode* in_for_shape, const Param& param,
const OperatorNodeConfig& config);
static SymbolVar make(SymbolVar mat, SymbolVar out_diff,
SymbolVar in_for_shape, const Param &param = {}, WarpPerspectiveBackwardData(VarNode* mat, VarNode* mat_idx, VarNode* out_diff,
const OperatorNodeConfig &config = {}); VarNode* in_for_shape, const Param& param,
}; const OperatorNodeConfig& config);
MGB_DEFINE_OPR_CLASS(WarpPerspectiveBackwardMat, static SymbolVar make(SymbolVar mat, SymbolVar out_diff, SymbolVar in_for_shape,
intl::MegDNNOprWrapperBwd<megdnn::WarpPerspectiveBackwardMat>) // { const Param& param = {},
public: const OperatorNodeConfig& config = {});
WarpPerspectiveBackwardMat(
VarNode *src, VarNode *mat, VarNode *out_diff, static SymbolVar make(SymbolVar mat, SymbolVar mat_idx, SymbolVar out_diff,
const Param &param, const OperatorNodeConfig &config); SymbolVar in_for_shape, const Param& param = {},
static SymbolVar make( const OperatorNodeConfig& config = {});
SymbolVar src, SymbolVar mat, SymbolVar out_diff,
const Param &param = {}, const OperatorNodeConfig &config = {}); void scn_do_execute() override;
}; }; // namespace mgb
MGB_DEFINE_OPR_CLASS(
WarpPerspectiveBackwardMat,
intl::MegDNNOprWrapperBwd<megdnn::WarpPerspectiveBackwardMat>) // {
public:
WarpPerspectiveBackwardMat(VarNode* src, VarNode* mat, VarNode* mat_idx,
VarNode* out_diff, const Param& param,
const OperatorNodeConfig& config);
static SymbolVar make(SymbolVar src, SymbolVar mat, SymbolVar out_diff,
const Param& param = {},
const OperatorNodeConfig& config = {}) {
return make(src, mat, {}, out_diff, param, config);
}
static SymbolVar make(SymbolVar src, SymbolVar mat, SymbolVar mat_idx,
SymbolVar out_diff, const Param& param = {},
const OperatorNodeConfig& config = {});
void scn_do_execute() override;
}
;
/* ============================= shape infer ============================== */ /* ============================= shape infer ============================== */
//! param: src, dst //! param: src, dst
...@@ -116,68 +133,67 @@ using CvtColor = CvtColorForward; ...@@ -116,68 +133,67 @@ using CvtColor = CvtColorForward;
using GaussianBlur = GaussianBlurForward; using GaussianBlur = GaussianBlurForward;
/* ============================= user set shape =========================== */ /* ============================= user set shape =========================== */
MGB_DEFINE_OPR_CLASS(ResizeForward, MGB_DEFINE_OPR_CLASS(
intl::WorkspaceSizeInfer< ResizeForward,
intl::OutshapeBySymvarSCNOpr<mixin::MegDNNOprHolderImpl< intl::WorkspaceSizeInfer<intl::OutshapeBySymvarSCNOpr<
megdnn::ResizeForward>>>) // { mixin::MegDNNOprHolderImpl<megdnn::ResizeForward>>>) // {
public: public:
ResizeForward( ResizeForward(VarNode* in_tensor, VarNode* out_shape, const Param& param,
VarNode *in_tensor, VarNode *out_shape, const Param &param, const OperatorNodeConfig& config);
const OperatorNodeConfig &config);
static SymbolVar make(SymbolVar in_tensor, SymbolVar out_shape,
static SymbolVar make(SymbolVar in_tensor, SymbolVar out_shape, const Param& param = {},
const Param &param = {}, const OperatorNodeConfig& config = {});
const OperatorNodeConfig &config = {});
static SymbolVar make(SymbolVar in_tensor, const TensorShape& out_shape,
static SymbolVar make(SymbolVar in_tensor, const TensorShape &out_shape, const Param& param = {},
const Param &param = {}, const OperatorNodeConfig& config = {}) {
const OperatorNodeConfig &config = {}) return make(in_tensor, cg::var_from_tensor_shape(in_tensor, out_shape),
{ param, config);
return make(in_tensor, }
cg::var_from_tensor_shape(
in_tensor, out_shape), param, config); private:
} void init_output_dtype() override;
void add_input_layout_constraint() override;
private: void init_output_static_infer_desc() override;
void init_output_dtype() override; void outshape_by_symvar_do_get_output_shape(
void add_input_layout_constraint() override; TensorShape& dest, const ShapeInferInfo& shpinfo) override;
void init_output_static_infer_desc() override;
void outshape_by_symvar_do_get_output_shape( void scn_do_execute() override;
TensorShape &dest, const ShapeInferInfo &shpinfo) override; size_t get_workspace_size_bytes(
const TensorShapeArray& input_shapes,
void scn_do_execute() override; const TensorShapeArray& output_shapes) const override;
size_t get_workspace_size_bytes( void record_execute_deps(ExecDependencyArray& deps) override;
const TensorShapeArray &input_shapes, }
const TensorShapeArray &output_shapes) const override; ;
void record_execute_deps(ExecDependencyArray &deps) override;
};
using Resize = ResizeForward; using Resize = ResizeForward;
MGB_DEFINE_OPR_CLASS(ResizeBackward, MGB_DEFINE_OPR_CLASS(ResizeBackward,
intl::MegDNNOprWrapperBwd<megdnn::ResizeBackward>) // { intl::MegDNNOprWrapperBwd<megdnn::ResizeBackward>) // {
public: public:
ResizeBackward(VarNode *out_diff, ResizeBackward(VarNode* out_diff, VarNode* in_for_shape, const Param& param,
VarNode *in_for_shape, const Param &param, const OperatorNodeConfig& config);
const OperatorNodeConfig &config);
static SymbolVar make(SymbolVar out_diff, static SymbolVar make(SymbolVar out_diff, SymbolVar in_for_shape,
SymbolVar in_for_shape, const Param &param = {}, const Param& param = {},
const OperatorNodeConfig &config = {}); const OperatorNodeConfig& config = {});
}; }
;
MGB_DEFINE_OPR_CLASS(RemapForward, MGB_DEFINE_OPR_CLASS(RemapForward,
intl::MegDNNOprWrapperFwd<megdnn::RemapForward>) // { intl::MegDNNOprWrapperFwd<megdnn::RemapForward>) // {
public: public:
RemapForward( RemapForward(VarNode* in_tensor, VarNode* map, const Param& param,
VarNode *in_tensor, VarNode* map, const OperatorNodeConfig& config);
const Param &param, const OperatorNodeConfig &config);
static SymbolVar make(SymbolVar in_tensor, SymbolVar map,
static SymbolVar make(SymbolVar in_tensor, SymbolVar map, const Param &param = {}, const Param& param = {},
const OperatorNodeConfig &config = {}); const OperatorNodeConfig& config = {});
private: private:
void init_output_dtype() override; void init_output_dtype() override;
}; }
;
using Remap = RemapForward; using Remap = RemapForward;
/*! /*!
...@@ -191,47 +207,42 @@ using Remap = RemapForward; ...@@ -191,47 +207,42 @@ using Remap = RemapForward;
* Input mat shape: batch, 2, 2; note that the mat is used to translate output * Input mat shape: batch, 2, 2; note that the mat is used to translate output
* coordinate onto input coordinate, so it is not inversed. * coordinate onto input coordinate, so it is not inversed.
*/ */
MGB_DEFINE_OPR_CLASS(WarpAffineForward, MGB_DEFINE_OPR_CLASS(
intl::WorkspaceSizeInfer< WarpAffineForward,
intl::OutshapeBySymvarSCNOpr<mixin::MegDNNOprHolderImpl< intl::WorkspaceSizeInfer<intl::OutshapeBySymvarSCNOpr<
megdnn::WarpAffineForward>>>) // { mixin::MegDNNOprHolderImpl<megdnn::WarpAffineForward>>>) // {
public: public:
WarpAffineForward( WarpAffineForward(VarNode* in_tensor, VarNode* mat, VarNode* out_shape,
VarNode *in_tensor, VarNode *mat, VarNode *out_shape, const Param& param, const OperatorNodeConfig& config);
const Param &param,
const OperatorNodeConfig &config); static SymbolVar make(SymbolVar in_tensor, SymbolVar mat, SymbolVar out_shape,
const Param& param = {},
static SymbolVar make(SymbolVar in_tensor, SymbolVar mat, const OperatorNodeConfig& config = {});
SymbolVar out_shape,
const Param &param = {}, static SymbolVar make(SymbolVar in_tensor, SymbolVar mat,
const OperatorNodeConfig &config = {}); const TensorShape& out_shape, const Param& param = {},
const OperatorNodeConfig& config = {}) {
static SymbolVar make(SymbolVar in_tensor, SymbolVar mat, return make(in_tensor, mat, cg::var_from_tensor_shape(in_tensor, out_shape),
const TensorShape &out_shape, param, config);
const Param &param = {}, }
const OperatorNodeConfig &config = {})
{ private:
return make(in_tensor, mat, void init_output_dtype() override;
cg::var_from_tensor_shape( void add_input_layout_constraint() override;
in_tensor, out_shape), param, config); void init_output_static_infer_desc() override;
} void outshape_by_symvar_do_get_output_shape(
TensorShape& dest, const ShapeInferInfo& shpinfo) override;
private:
void init_output_dtype() override; void scn_do_execute() override;
void add_input_layout_constraint() override; size_t get_workspace_size_bytes(
void init_output_static_infer_desc() override; const TensorShapeArray& input_shapes,
void outshape_by_symvar_do_get_output_shape( const TensorShapeArray& output_shapes) const override;
TensorShape &dest, const ShapeInferInfo &shpinfo) override; void record_execute_deps(ExecDependencyArray& deps) override;
}
void scn_do_execute() override; ;
size_t get_workspace_size_bytes(
const TensorShapeArray &input_shapes,
const TensorShapeArray &output_shapes) const override;
void record_execute_deps(ExecDependencyArray &deps) override;
};
using WarpAffine = WarpAffineForward; using WarpAffine = WarpAffineForward;
} // opr } // opr
} // mgb } // mgb
// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}} // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}
...@@ -216,7 +216,10 @@ TEST(TestOprImgproc, WarpPerspectiveWithMatIdx) { ...@@ -216,7 +216,10 @@ TEST(TestOprImgproc, WarpPerspectiveWithMatIdx) {
.set_input_generator(1, gen_mat) .set_input_generator(1, gen_mat)
.set_input_generator(2, gen_mat_idx) .set_input_generator(2, gen_mat_idx)
.set_input_dtype(2, dtype::Int32{}) .set_input_dtype(2, dtype::Int32{})
/*! it's hard to make the grad check success,
the cuda implementation is grad sum */
.disable_grad_check() .disable_grad_check()
.set_input_allow_grad(2,false)
.run({TensorShape{N_SRC, C, 4, 5}, {N_MAT, 3, 3}, {N_MAT}}) .run({TensorShape{N_SRC, C, 4, 5}, {N_MAT, 3, 3}, {N_MAT}})
.run({TensorShape{N_SRC, C, 6, 5}, {N_MAT, 3, 3}, {N_MAT}}) .run({TensorShape{N_SRC, C, 6, 5}, {N_MAT, 3, 3}, {N_MAT}})
.run({TensorShape{N_SRC, C, 22, 19}, {N_MAT, 3, 3}, {N_MAT}}); .run({TensorShape{N_SRC, C, 22, 19}, {N_MAT, 3, 3}, {N_MAT}});
......
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