#include "op_plugin/AclOpsInterface.h"
#include "op_plugin/utils/OpAdapter.h"
namespace acl_op {
using npu_preparation = at_npu::native::OpPreparation;
namespace {
inline void anchor_response_flags_check(
const at::Tensor& self,
at::IntArrayRef featmap_size)
{
TORCH_CHECK(
featmap_size.size() == 2,
"expected feat_map_size equals to 2, but got size ",
featmap_size.size(), OPS_ERROR(ErrCode::PARAM));
TORCH_CHECK(
self.dim() == 2 && self.size(1) == 4,
"Non-empty 2D gt_bboxes tensor expected but got a tensor with sizes ",
self.sizes(), OPS_ERROR(ErrCode::PARAM));
TORCH_CHECK(
self.scalar_type() == at::kHalf || self.scalar_type() == at::kFloat,
"float16 or float32 tensor expected but got a tensor with dtype: ",
self.scalar_type(), OPS_ERROR(ErrCode::TYPE));
}
}
at::Tensor npu_anchor_response_flags(
const at::Tensor& self,
at::IntArrayRef featmap_size,
at::IntArrayRef stride,
int64_t num_base_anchors)
{
anchor_response_flags_check(self, featmap_size);
auto output_size = op_infer::infersize_npu_anchor_response_flags(featmap_size, num_base_anchors);
auto options = self.options().dtype(at::kByte);
at::Tensor result = npu_preparation::apply_tensor(output_size, options, self);
at::Tensor self_cp = at_npu::native::custom_ops::_npu_dtype_cast(self, at::kFloat);
at_npu::native::OpCommand cmd;
cmd.Name("AnchorResponseFlags")
.Input(self_cp)
.Output(result)
.Attr("featmap_size", featmap_size)
.Attr("strides", stride)
.Attr("num_base_anchors", num_base_anchors)
.Run();
return result;
}
}