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#include "../deform_conv2d.h"
#include <torch/autograd.h>
#include <torch/types.h>
namespace vision {
namespace ops {
namespace {
class DeformConv2dFunction
: public torch::autograd::Function<DeformConv2dFunction> {
public:
static torch::autograd::variable_list forward(
torch::autograd::AutogradContext* ctx,
const torch::autograd::Variable& input,
const torch::autograd::Variable& weight,
const torch::autograd::Variable& offset,
const torch::autograd::Variable& mask,
const torch::autograd::Variable& bias,
c10::SymInt stride_h,
c10::SymInt stride_w,
c10::SymInt pad_h,
c10::SymInt pad_w,
c10::SymInt dilation_h,
c10::SymInt dilation_w,
c10::SymInt groups,
c10::SymInt offset_groups,
bool use_mask) {
at::AutoDispatchBelowADInplaceOrView g;
auto output = deform_conv2d_symint(
input,
weight,
offset,
mask,
bias,
stride_h,
stride_w,
pad_h,
pad_w,
dilation_h,
dilation_w,
groups,
offset_groups,
use_mask);
ctx->save_for_backward({input, weight, offset, mask, bias});
ctx->saved_data["stride_h"] = stride_h;
ctx->saved_data["stride_w"] = stride_w;
ctx->saved_data["pad_h"] = pad_h;
ctx->saved_data["pad_w"] = pad_w;
ctx->saved_data["dilation_h"] = dilation_h;
ctx->saved_data["dilation_w"] = dilation_w;
ctx->saved_data["groups"] = groups;
ctx->saved_data["offset_groups"] = offset_groups;
ctx->saved_data["use_mask"] = use_mask;
return {
output,
};
}
static torch::autograd::variable_list backward(
torch::autograd::AutogradContext* ctx,
const torch::autograd::variable_list& grad_output) {
auto saved = ctx->get_saved_variables();
auto input = saved[0];
auto weight = saved[1];
auto offset = saved[2];
auto mask = saved[3];
auto bias = saved[4];
auto stride_h = ctx->saved_data["stride_h"].toSymInt();
auto stride_w = ctx->saved_data["stride_w"].toSymInt();
auto pad_h = ctx->saved_data["pad_h"].toSymInt();
auto pad_w = ctx->saved_data["pad_w"].toSymInt();
auto dilation_h = ctx->saved_data["dilation_h"].toSymInt();
auto dilation_w = ctx->saved_data["dilation_w"].toSymInt();
auto groups = ctx->saved_data["groups"].toSymInt();
auto offset_groups = ctx->saved_data["offset_groups"].toSymInt();
auto use_mask = ctx->saved_data["use_mask"].toBool();
auto grads = detail::_deform_conv2d_backward_symint(
grad_output[0],
input,
weight,
offset,
mask,
bias,
stride_h,
stride_w,
pad_h,
pad_w,
dilation_h,
dilation_w,
groups,
offset_groups,
use_mask);
auto grad_input = std::get<0>(grads);
auto grad_weight = std::get<1>(grads);
auto grad_offset = std::get<2>(grads);
auto grad_mask = std::get<3>(grads);
auto grad_bias = std::get<4>(grads);
return {
grad_input,
grad_weight,
grad_offset,
grad_mask,
grad_bias,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
};
}
};
// TODO: There should be an easier way to do this
class DeformConv2dBackwardFunction
: public torch::autograd::Function<DeformConv2dBackwardFunction> {
public:
static torch::autograd::variable_list forward(
torch::autograd::AutogradContext* ctx,
const torch::autograd::Variable& grad,
const torch::autograd::Variable& input,
const torch::autograd::Variable& weight,
const torch::autograd::Variable& offset,
const torch::autograd::Variable& mask,
const torch::autograd::Variable& bias,
c10::SymInt stride_h,
c10::SymInt stride_w,
c10::SymInt pad_h,
c10::SymInt pad_w,
c10::SymInt dilation_h,
c10::SymInt dilation_w,
c10::SymInt groups,
c10::SymInt offset_groups,
bool use_mask) {
at::AutoDispatchBelowADInplaceOrView g;
auto result = detail::_deform_conv2d_backward_symint(
grad,
input,
weight,
offset,
mask,
bias,
stride_h,
stride_w,
pad_h,
pad_w,
dilation_h,
dilation_w,
groups,
offset_groups,
use_mask);
auto grad_input = std::get<0>(result);
auto grad_weight = std::get<1>(result);
auto grad_offset = std::get<2>(result);
auto grad_mask = std::get<3>(result);
auto grad_bias = std::get<4>(result);
return {
grad_input,
grad_weight,
grad_offset,
grad_mask,
grad_bias,
};
}
static torch::autograd::variable_list backward(
torch::autograd::AutogradContext* ctx,
const torch::autograd::variable_list& grad_output) {
TORCH_CHECK(0, "double backwards on deform_conv2d not supported");
}
};
at::Tensor deform_conv2d_autograd(
const at::Tensor& input,
const at::Tensor& weight,
const at::Tensor& offset,
const at::Tensor& mask,
const at::Tensor& bias,
c10::SymInt stride_h,
c10::SymInt stride_w,
c10::SymInt pad_h,
c10::SymInt pad_w,
c10::SymInt dilation_h,
c10::SymInt dilation_w,
c10::SymInt groups,
c10::SymInt offset_groups,
bool use_mask) {
return DeformConv2dFunction::apply(
input,
weight,
offset,
mask,
bias,
stride_h,
stride_w,
pad_h,
pad_w,
dilation_h,
dilation_w,
groups,
offset_groups,
use_mask)[0];
}
std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>
deform_conv2d_backward_autograd(
const at::Tensor& grad,
const at::Tensor& input,
const at::Tensor& weight,
const at::Tensor& offset,
const at::Tensor& mask,
const at::Tensor& bias,
c10::SymInt stride_h,
c10::SymInt stride_w,
c10::SymInt pad_h,
c10::SymInt pad_w,
c10::SymInt dilation_h,
c10::SymInt dilation_w,
c10::SymInt groups,
c10::SymInt offset_groups,
bool use_mask) {
auto result = DeformConv2dBackwardFunction::apply(
grad,
input,
weight,
offset,
mask,
bias,
stride_h,
stride_w,
pad_h,
pad_w,
dilation_h,
dilation_w,
groups,
offset_groups,
use_mask);
return std::make_tuple(result[0], result[1], result[2], result[3], result[4]);
}
} // namespace
TORCH_LIBRARY_IMPL(torchvision, Autograd, m) {
m.impl(
TORCH_SELECTIVE_NAME("torchvision::deform_conv2d"),
TORCH_FN(deform_conv2d_autograd));
m.impl(
TORCH_SELECTIVE_NAME("torchvision::_deform_conv2d_backward"),
TORCH_FN(deform_conv2d_backward_autograd));
}
} // namespace ops
} // namespace vision
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