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#define MAX_POINT 32
void ms_deformable_im2col_cuda_c2345(
const float* feat_c2,
const float* feat_c3,
const float* feat_c4,
const float* feat_c5,
const int h_c2, const int w_c2,
const int h_c3, const int w_c3,
const int h_c4, const int w_c4,
const int h_c5, const int w_c5,
const float* data_sampling_loc,
const float* data_attn_weight,
const int batch_size,
const int channels,
const int num_views,
const int num_query,
const int num_point,
float* data_col
);
void ms_deformable_im2col_cuda_c23456(
const float* feat_c2,
const float* feat_c3,
const float* feat_c4,
const float* feat_c5,
const float* feat_c6,
const int h_c2, const int w_c2,
const int h_c3, const int w_c3,
const int h_c4, const int w_c4,
const int h_c5, const int w_c5,
const int h_c6, const int w_c6,
const float* data_sampling_loc,
const float* data_attn_weight,
const int batch_size,
const int channels,
const int num_views,
const int num_query,
const int num_point,
float* data_col
);
void ms_deformable_col2im_cuda_c2345(
const float* grad_col,
const float* feat_c2,
const float* feat_c3,
const float* feat_c4,
const float* feat_c5,
const int h_c2, const int w_c2,
const int h_c3, const int w_c3,
const int h_c4, const int w_c4,
const int h_c5, const int w_c5,
const float* data_sampling_loc,
const float* data_attn_weight,
const int batch_size,
const int channels,
const int num_views,
const int num_query,
const int num_point,
float* grad_value_c2,
float* grad_value_c3,
float* grad_value_c4,
float* grad_value_c5,
float* grad_sampling_loc,
float* grad_attn_weight
);
void ms_deformable_col2im_cuda_c23456(
const float *grad_col,
const float *feat_c2,
const float *feat_c3,
const float *feat_c4,
const float *feat_c5,
const float *feat_c6,
const int h_c2, const int w_c2,
const int h_c3, const int w_c3,
const int h_c4, const int w_c4,
const int h_c5, const int w_c5,
const int h_c6, const int w_c6,
const float *data_sampling_loc,
const float *data_attn_weight,
const int batch_size,
const int channels,
const int num_views,
const int num_query,
const int num_point,
float *grad_value_c2,
float *grad_value_c3,
float *grad_value_c4,
float *grad_value_c5,
float *grad_value_c6,
float *grad_sampling_loc,
float *grad_attn_weight
);
at::Tensor ms_deform_attn_cuda_c2345_forward(
const at::Tensor& feat_c2, // [B, N, H, W, C]
const at::Tensor& feat_c3, // [B, N, H, W, C]
const at::Tensor& feat_c4, // [B, N, H, W, C]
const at::Tensor& feat_c5, // [B, N, H, W, C]
const at::Tensor& sampling_loc, // [B, Q, P, 3]
const at::Tensor& attn_weight // [B, Q, P, 4]
) {
AT_ASSERTM(feat_c2.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c3.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c4.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c5.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(feat_c2.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c3.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c4.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c5.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(sampling_loc.is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.is_cuda(), "attn_weight must be a CUDA tensor");
const int batch_size = feat_c2.size(0);
const int num_views = feat_c2.size(1);
const int channels = feat_c2.size(4);
const int num_query = sampling_loc.size(1);
const int num_point = sampling_loc.size(2);
AT_ASSERTM(num_point <= MAX_POINT, "num_point exceed limits");
const int h_c2 = feat_c2.size(2);
const int w_c2 = feat_c2.size(3);
const int h_c3 = feat_c3.size(2);
const int w_c3 = feat_c3.size(3);
const int h_c4 = feat_c4.size(2);
const int w_c4 = feat_c4.size(3);
const int h_c5 = feat_c5.size(2);
const int w_c5 = feat_c5.size(3);
auto output = at::zeros({ batch_size, num_query, channels, num_point }, feat_c2.options());
ms_deformable_im2col_cuda_c2345(
feat_c2.data_ptr<float>(),
feat_c3.data_ptr<float>(),
feat_c4.data_ptr<float>(),
feat_c5.data_ptr<float>(),
h_c2, w_c2, h_c3, w_c3, h_c4, w_c4, h_c5, w_c5,
sampling_loc.data_ptr<float>(),
attn_weight.data_ptr<float>(),
batch_size, channels, num_views, num_query, num_point,
output.data_ptr<float>()
);
return output;
}
at::Tensor ms_deform_attn_cuda_c23456_forward(
const at::Tensor& feat_c2, // [B, N, H, W, C]
const at::Tensor& feat_c3, // [B, N, H, W, C]
const at::Tensor& feat_c4, // [B, N, H, W, C]
const at::Tensor& feat_c5, // [B, N, H, W, C]
const at::Tensor& feat_c6, // [B, N, H, W, C]
const at::Tensor& sampling_loc, // [B, Q, P, 3]
const at::Tensor& attn_weight // [B, Q, P, 4]
) {
AT_ASSERTM(feat_c2.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c3.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c4.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c5.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c6.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(feat_c2.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c3.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c4.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c5.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c6.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(sampling_loc.is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.is_cuda(), "attn_weight must be a CUDA tensor");
const int batch_size = feat_c2.size(0);
const int num_views = feat_c2.size(1);
const int channels = feat_c2.size(4);
const int num_query = sampling_loc.size(1);
const int num_point = sampling_loc.size(2);
AT_ASSERTM(num_point <= MAX_POINT, "num_point exceed limits");
const int h_c2 = feat_c2.size(2);
const int w_c2 = feat_c2.size(3);
const int h_c3 = feat_c3.size(2);
const int w_c3 = feat_c3.size(3);
const int h_c4 = feat_c4.size(2);
const int w_c4 = feat_c4.size(3);
const int h_c5 = feat_c5.size(2);
const int w_c5 = feat_c5.size(3);
const int h_c6 = feat_c6.size(2);
const int w_c6 = feat_c6.size(3);
auto output = at::zeros({ batch_size, num_query, channels, num_point }, feat_c2.options());
ms_deformable_im2col_cuda_c23456(
feat_c2.data_ptr<float>(),
feat_c3.data_ptr<float>(),
feat_c4.data_ptr<float>(),
feat_c5.data_ptr<float>(),
feat_c6.data_ptr<float>(),
h_c2, w_c2, h_c3, w_c3, h_c4, w_c4, h_c5, w_c5, h_c6, w_c6,
sampling_loc.data_ptr<float>(),
attn_weight.data_ptr<float>(),
batch_size, channels, num_views, num_query, num_point,
output.data_ptr<float>()
);
return output;
}
std::vector<at::Tensor> ms_deform_attn_cuda_c2345_backward(
const at::Tensor& grad_output,
const at::Tensor& feat_c2, // [B, N, H, W, C]
const at::Tensor& feat_c3, // [B, N, H, W, C]
const at::Tensor& feat_c4, // [B, N, H, W, C]
const at::Tensor& feat_c5, // [B, N, H, W, C]
const at::Tensor& sampling_loc, // [B, Q, P, 3]
const at::Tensor& attn_weight // [B, Q, P, 4]
) {
AT_ASSERTM(feat_c2.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c3.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c4.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c5.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
AT_ASSERTM(feat_c2.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c3.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c4.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c5.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(sampling_loc.is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.is_cuda(), "attn_weight must be a CUDA tensor");
AT_ASSERTM(grad_output.is_cuda(), "grad_output must be a CUDA tensor");
const int batch_size = feat_c2.size(0);
const int num_views = feat_c2.size(1);
const int channels = feat_c2.size(4);
const int num_query = sampling_loc.size(1);
const int num_point = sampling_loc.size(2);
AT_ASSERTM(num_point <= MAX_POINT, "num_point exceed limits");
auto grad_value_c2 = at::zeros_like(feat_c2);
auto grad_value_c3 = at::zeros_like(feat_c3);
auto grad_value_c4 = at::zeros_like(feat_c4);
auto grad_value_c5 = at::zeros_like(feat_c5);
auto grad_sampling_loc = at::zeros_like(sampling_loc);
auto grad_attn_weight = at::zeros_like(attn_weight);
const int h_c2 = feat_c2.size(2);
const int w_c2 = feat_c2.size(3);
const int h_c3 = feat_c3.size(2);
const int w_c3 = feat_c3.size(3);
const int h_c4 = feat_c4.size(2);
const int w_c4 = feat_c4.size(3);
const int h_c5 = feat_c5.size(2);
const int w_c5 = feat_c5.size(3);
ms_deformable_col2im_cuda_c2345(
grad_output.data_ptr<float>(),
feat_c2.data_ptr<float>(),
feat_c3.data_ptr<float>(),
feat_c4.data_ptr<float>(),
feat_c5.data_ptr<float>(),
h_c2, w_c2, h_c3, w_c3, h_c4, w_c4, h_c5, w_c5,
sampling_loc.data_ptr<float>(),
attn_weight.data_ptr<float>(),
batch_size, channels, num_views, num_query, num_point,
grad_value_c2.data_ptr<float>(),
grad_value_c3.data_ptr<float>(),
grad_value_c4.data_ptr<float>(),
grad_value_c5.data_ptr<float>(),
grad_sampling_loc.data_ptr<float>(),
grad_attn_weight.data_ptr<float>()
);
return {
grad_value_c2, grad_value_c3, grad_value_c4, grad_value_c5, grad_sampling_loc, grad_attn_weight
};
}
std::vector<at::Tensor> ms_deform_attn_cuda_c23456_backward(
const at::Tensor& grad_output,
const at::Tensor& feat_c2, // [B, N, H, W, C]
const at::Tensor& feat_c3, // [B, N, H, W, C]
const at::Tensor& feat_c4, // [B, N, H, W, C]
const at::Tensor& feat_c5, // [B, N, H, W, C]
const at::Tensor& feat_c6, // [B, N, H, W, C]
const at::Tensor& sampling_loc, // [B, Q, P, 3]
const at::Tensor& attn_weight // [B, Q, P, 4]
) {
AT_ASSERTM(feat_c2.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c3.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c4.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c5.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(feat_c6.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
AT_ASSERTM(feat_c2.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c3.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c4.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c5.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(feat_c6.is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(sampling_loc.is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.is_cuda(), "attn_weight must be a CUDA tensor");
AT_ASSERTM(grad_output.is_cuda(), "grad_output must be a CUDA tensor");
const int batch_size = feat_c2.size(0);
const int num_views = feat_c2.size(1);
const int channels = feat_c2.size(4);
const int num_query = sampling_loc.size(1);
const int num_point = sampling_loc.size(2);
AT_ASSERTM(num_point <= MAX_POINT, "num_point exceed limits");
auto grad_value_c2 = at::zeros_like(feat_c2);
auto grad_value_c3 = at::zeros_like(feat_c3);
auto grad_value_c4 = at::zeros_like(feat_c4);
auto grad_value_c5 = at::zeros_like(feat_c5);
auto grad_value_c6 = at::zeros_like(feat_c6);
auto grad_sampling_loc = at::zeros_like(sampling_loc);
auto grad_attn_weight = at::zeros_like(attn_weight);
const int h_c2 = feat_c2.size(2);
const int w_c2 = feat_c2.size(3);
const int h_c3 = feat_c3.size(2);
const int w_c3 = feat_c3.size(3);
const int h_c4 = feat_c4.size(2);
const int w_c4 = feat_c4.size(3);
const int h_c5 = feat_c5.size(2);
const int w_c5 = feat_c5.size(3);
const int h_c6 = feat_c6.size(2);
const int w_c6 = feat_c6.size(3);
ms_deformable_col2im_cuda_c23456(
grad_output.data_ptr<float>(),
feat_c2.data_ptr<float>(),
feat_c3.data_ptr<float>(),
feat_c4.data_ptr<float>(),
feat_c5.data_ptr<float>(),
feat_c6.data_ptr<float>(),
h_c2, w_c2, h_c3, w_c3, h_c4, w_c4, h_c5, w_c5, h_c6, w_c6,
sampling_loc.data_ptr<float>(),
attn_weight.data_ptr<float>(),
batch_size, channels, num_views, num_query, num_point,
grad_value_c2.data_ptr<float>(),
grad_value_c3.data_ptr<float>(),
grad_value_c4.data_ptr<float>(),
grad_value_c5.data_ptr<float>(),
grad_value_c6.data_ptr<float>(),
grad_sampling_loc.data_ptr<float>(),
grad_attn_weight.data_ptr<float>()
);
return {
grad_value_c2, grad_value_c3, grad_value_c4, grad_value_c5, grad_value_c6, grad_sampling_loc, grad_attn_weight
};
}
#ifdef TORCH_EXTENSION_NAME
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("_ms_deform_attn_cuda_c2345_forward", &ms_deform_attn_cuda_c2345_forward, "pass");
m.def("_ms_deform_attn_cuda_c2345_backward", &ms_deform_attn_cuda_c2345_backward, "pass");
m.def("_ms_deform_attn_cuda_c23456_forward", &ms_deform_attn_cuda_c23456_forward, "pass");
m.def("_ms_deform_attn_cuda_c23456_backward", &ms_deform_attn_cuda_c23456_backward, "pass");
}
#endif |