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Unverified Commit ba8aa764 authored by tudejiang79's avatar tudejiang79 Committed by GitHub
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[Refactor] Repalce the implementation of roi_align_rotated with mlu-ops (#2808)

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mmcv/ops/csrc/common/mlu/roi_align_rotated_utils.hpp deleted 100644 → 0
View file @ d2aecbe4
/*************************************************************************
* Copyright (C) 2022 Cambricon.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
* OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*************************************************************************/
#ifndef ROI_ALIGN_ROTATED_UTILS_HPP_
#define ROI_ALIGN_ROTATED_UTILS_HPP_
struct RoiAlignRotatedParams {
int pooled_height;
int pooled_width;
int sample_ratio;
float spatial_scale;
bool aligned;
bool clockwise;
};
#endif // ROI_ALIGN_ROTATED_UTILS_HPP_
mmcv/ops/csrc/pytorch/mlu/roi_align_rotated_mlu.cpp 100755 → 100644
View file @ ba8aa764
......@@ -9,37 +9,7 @@
* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*************************************************************************/
#include "pytorch_device_registry.hpp"
#include "pytorch_mlu_helper.hpp"
#include "roi_align_rotated_utils.hpp"
namespace {
void policyFunc(int bin_num, cnrtDim3_t *k_dim, cnrtFunctionType_t *k_type) {
unsigned int core_num = torch_mlu::getDeviceAttr(cnrtAttrMcorePerCluster);
unsigned int cluster_num = torch_mlu::getDeviceAttr(cnrtAttrClusterCount);
*k_type = CNRT_FUNC_TYPE_UNION1;
k_dim->x = core_num;
unsigned int use_cluster = (bin_num + core_num - 1) / core_num;
k_dim->y = use_cluster > cluster_num ? cluster_num : use_cluster;
k_dim->z = 1;
}
} // namespace
void KernelRoiAlignRotatedForward(
cnrtDim3_t k_dim, cnrtFunctionType_t k_type, cnrtQueue_t queue,
const cnrtDataType_t d_type, const void *features, const void *rois,
void *output, const int batch, const int height, const int width,
const int channel, const int rois_num,
const RoiAlignRotatedParams roiAlignRotatedParams);
void KernelRoiAlignRotatedBackward(
cnrtDim3_t k_dim, cnrtFunctionType_t k_type, cnrtQueue_t queue,
const cnrtDataType_t d_type, const void *top_grad, const void *rois,
void *bottom_grad, const int batch, const int height, const int width,
const int channel, const int rois_num,
const RoiAlignRotatedParams roiAlignRotatedParams);
#include "mlu_common_helper.h"
void ROIAlignRotatedForwardMLUKernelLauncher(Tensor input, Tensor rois,
Tensor output, int pooled_height,
......@@ -47,153 +17,70 @@ void ROIAlignRotatedForwardMLUKernelLauncher(Tensor input, Tensor rois,
float spatial_scale,
int sampling_ratio, bool aligned,
bool clockwise) {
TORCH_CHECK(((input.scalar_type() == output.scalar_type()) &&
(output.scalar_type() == rois.scalar_type())),
"data types of input, rois and output should be the same, ",
"but now input type is ", input.scalar_type(), ", rois type is ",
rois.scalar_type(), ", output type is ", output.scalar_type(),
".");
TORCH_CHECK(
(input.scalar_type() == at::kFloat || input.scalar_type() == at::kHalf),
"input type should be Float or Half, got ", input.scalar_type(), ".");
TORCH_CHECK(input.dim() == 4, "input should be a 4d tensor, got ",
input.dim(), "D.");
TORCH_CHECK(rois.dim() == 2, "rois should be a 2d tensor, got ", rois.dim(),
"D.");
TORCH_CHECK(output.dim() == 4, "output should be a 4d tensor, got ",
output.dim(), "D.");
TORCH_CHECK((rois.size(0) == output.size(0)),
"the 1st dimensions of rois and output should be the same, ",
"but now the 1st dimension of rois is ", rois.size(0),
", and output is ", output.size(0), ".");
TORCH_CHECK((input.size(1) == output.size(1)),
"the 2nd dimensions of input and output should be the same, ",
"but now the 2nd dimension of input is ", input.size(1),
", and output is ", output.size(1), ".");
int channel = input.size(1);
int width = input.size(3);
int height = input.size(2);
int batch = input.size(0);
int rois_nums = rois.size(0);
cnrtDataType_t d_type = torch_mlu::toCnrtDtype(input.dtype());
// return if zero-elements
if (input.numel() == 0) {
CNLOG(INFO) << "Skip the zero-elements case.";
return;
}
RoiAlignRotatedParams roiAlignRotatedParams{pooled_height, pooled_width,
sampling_ratio, spatial_scale,
aligned, clockwise};
cnrtDim3_t k_dim;
cnrtFunctionType_t k_type;
policyFunc(rois_nums * pooled_height * pooled_width, &k_dim, &k_type);
auto memory_format =
torch_mlu::cnnl::ops::get_channels_last_memory_format(input.dim());
auto input_tensor =
torch_mlu::cnnl::ops::cnnl_contiguous(input, memory_format);
at::Tensor output_tmp =
at::empty({rois_nums, channel, pooled_height, pooled_width},
input.options(), memory_format);
auto input_ = torch_mlu::cnnl::ops::cnnl_contiguous(input, memory_format);
auto rois_contiguous =
torch_mlu::cnnl::ops::cnnl_contiguous(rois, rois.suggest_memory_format());
auto output_contiguous =
torch_mlu::cnnl::ops::cnnl_contiguous(output, memory_format);
// get compute queue
auto queue = torch_mlu::getCurQueue();
MluOpTensorDescriptor input_desc, rois_desc, output_desc;
input_desc.set_with_layout(input_, MLUOP_LAYOUT_NHWC);
rois_desc.set(rois_contiguous);
output_desc.set_with_layout(output_contiguous, MLUOP_LAYOUT_NHWC);
// get ptr of tensors
auto input_impl = torch_mlu::getMluTensorImpl(input_tensor);
auto input_impl = torch_mlu::getMluTensorImpl(input_);
auto input_ptr = input_impl->cnnlMalloc();
auto rois_impl = torch_mlu::getMluTensorImpl(rois);
auto rois_impl = torch_mlu::getMluTensorImpl(rois_contiguous);
auto rois_ptr = rois_impl->cnnlMalloc();
auto output_impl = torch_mlu::getMluTensorImpl(output_tmp);
auto output_impl = torch_mlu::getMluTensorImpl(output_contiguous);
auto output_ptr = output_impl->cnnlMalloc();
KernelRoiAlignRotatedForward(k_dim, k_type, queue, d_type, input_ptr,
rois_ptr, output_ptr, batch, height, width,
channel, rois_nums, roiAlignRotatedParams);
output.copy_(output_tmp);
// get compute handle
auto handle = mluOpGetCurrentHandle();
mluOpRoiAlignRotatedForward(
handle, input_desc.desc(), input_ptr, rois_desc.desc(), rois_ptr,
pooled_height, pooled_width, sampling_ratio, spatial_scale, aligned,
clockwise, output_desc.desc(), output_ptr);
output.copy_(output_contiguous);
}
void ROIAlignRotatedBackwardMLUKernelLauncher(
Tensor top_grad, Tensor rois, Tensor bottom_grad, int pooled_height,
int pooled_width, float spatial_scale, int sampling_ratio, bool aligned,
bool clockwise) {
TORCH_CHECK(((top_grad.scalar_type() == bottom_grad.scalar_type()) &&
(bottom_grad.scalar_type() == rois.scalar_type())),
"data types of top_grad, rois and bottom_grad should be ",
"the same, but now top_grad type is ", top_grad.scalar_type(),
", rois type is ", rois.scalar_type(), ", bottom_grad type is ",
bottom_grad.scalar_type(), ".");
TORCH_CHECK((bottom_grad.scalar_type() == at::kFloat ||
bottom_grad.scalar_type() == at::kHalf),
"Data type of bottom_grad should be Float ro Half, got ",
bottom_grad.scalar_type(), ".");
TORCH_CHECK(bottom_grad.dim() == 4, "bottom_grad should be a 4d tensor, got ",
top_grad.dim(), "D.");
TORCH_CHECK(rois.dim() == 2, "rois should be a 2d tensor, got ", rois.dim(),
"D.");
TORCH_CHECK(top_grad.dim() == 4, "top_grad should be a 4d tensor, got ",
bottom_grad.dim(), "D.");
TORCH_CHECK((rois.size(0) == top_grad.size(0)),
"the 1st dimensions of rois and top_grad should be the same, ",
"but now the 1st dimension of rois is ", rois.size(0),
", and top_grad is ", top_grad.size(0), ".");
TORCH_CHECK((bottom_grad.size(1) == top_grad.size(1)),
"the 2nd dimensions of bottom_grad and top_grad should be ",
"the same, but now the 2nd dimension of bottom_grad is ",
bottom_grad.size(1), ", and top_grad is ", top_grad.size(1), ".");
int channel = bottom_grad.size(1);
int width = bottom_grad.size(3);
int height = bottom_grad.size(2);
int batch = bottom_grad.size(0);
int rois_nums = rois.size(0);
cnrtDataType_t d_type = torch_mlu::toCnrtDtype(bottom_grad.dtype());
// return if zero-elements
if (bottom_grad.numel() == 0) {
CNLOG(INFO) << "Skip the zero-elements case.";
return;
}
RoiAlignRotatedParams roiAlignRotatedParams{pooled_height, pooled_width,
sampling_ratio, spatial_scale,
aligned, clockwise};
cnrtDim3_t k_dim;
cnrtFunctionType_t k_type;
policyFunc(rois_nums * pooled_height * pooled_width, &k_dim, &k_type);
auto memory_format =
torch_mlu::cnnl::ops::get_channels_last_memory_format(top_grad.dim());
auto top_grad_tensor =
auto top_grad_ =
torch_mlu::cnnl::ops::cnnl_contiguous(top_grad, memory_format);
at::Tensor bottom_grad_tmp = at::empty({batch, channel, height, width},
top_grad.options(), memory_format)
.zero_();
// get compute queue
auto queue = torch_mlu::getCurQueue();
auto rois_contiguous =
torch_mlu::cnnl::ops::cnnl_contiguous(rois, rois.suggest_memory_format());
auto bottom_grad_ =
torch_mlu::cnnl::ops::cnnl_contiguous(bottom_grad, memory_format);
// get ptr of tensors
auto bottom_grad_impl = torch_mlu::getMluTensorImpl(bottom_grad_tmp);
auto bottom_grad_ptr = bottom_grad_impl->cnnlMalloc();
auto rois_impl = torch_mlu::getMluTensorImpl(rois);
auto rois_ptr = rois_impl->cnnlMalloc();
auto top_grad_impl = torch_mlu::getMluTensorImpl(top_grad_tensor);
auto top_grad_impl = torch_mlu::getMluTensorImpl(top_grad_);
auto top_grad_ptr = top_grad_impl->cnnlMalloc();
auto rois_impl = torch_mlu::getMluTensorImpl(rois_contiguous);
auto rois_ptr = rois_impl->cnnlMalloc();
auto bottom_grad_impl = torch_mlu::getMluTensorImpl(bottom_grad_);
auto bottom_grad_ptr = bottom_grad_impl->cnnlMalloc();
KernelRoiAlignRotatedBackward(k_dim, k_type, queue, d_type, top_grad_ptr,
rois_ptr, bottom_grad_ptr, batch, height, width,
channel, rois_nums, roiAlignRotatedParams);
bottom_grad.copy_(bottom_grad_tmp);
MluOpTensorDescriptor top_grad_desc, rois_desc, bottom_grad_desc;
top_grad_desc.set_with_layout(top_grad_, MLUOP_LAYOUT_NHWC);
rois_desc.set(rois_contiguous);
bottom_grad_desc.set_with_layout(bottom_grad_, MLUOP_LAYOUT_NHWC);
// get compute handle
auto handle = mluOpGetCurrentHandle();
mluOpRoiAlignRotatedBackward(
handle, top_grad_desc.desc(), top_grad_ptr, rois_desc.desc(), rois_ptr,
pooled_height, pooled_width, sampling_ratio, spatial_scale, aligned,
clockwise, bottom_grad_desc.desc(), bottom_grad_ptr);
bottom_grad.copy_(bottom_grad_);
}
void roi_align_rotated_forward_mlu(Tensor input, Tensor rois, Tensor output,
......
tests/test_ops/test_roi_align_rotated.py
View file @ ba8aa764
......@@ -11,7 +11,6 @@ try:
except ImportError:
from torch.autograd import gradcheck
_USING_PARROTS = False
# yapf:disable
inputs = [([[[[1., 2.], [3., 4.]]]],
[[0., 0.5, 0.5, 1., 1., 0]]),
......
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