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#include "../ps_roi_align.h"
#include <torch/autograd.h>
#include <torch/types.h>
namespace vision {
namespace ops {
namespace {
class PSROIAlignFunction
: public torch::autograd::Function<PSROIAlignFunction> {
public:
static torch::autograd::variable_list forward(
torch::autograd::AutogradContext* ctx,
const torch::autograd::Variable& input,
const torch::autograd::Variable& rois,
double spatial_scale,
int64_t pooled_height,
int64_t pooled_width,
int64_t sampling_ratio) {
ctx->saved_data["spatial_scale"] = spatial_scale;
ctx->saved_data["pooled_height"] = pooled_height;
ctx->saved_data["pooled_width"] = pooled_width;
ctx->saved_data["sampling_ratio"] = sampling_ratio;
ctx->saved_data["input_shape"] = input.sizes();
at::AutoDispatchBelowADInplaceOrView g;
auto result = ps_roi_align(
input,
rois,
spatial_scale,
pooled_height,
pooled_width,
sampling_ratio);
auto output = std::get<0>(result);
auto channel_mapping = std::get<1>(result);
ctx->save_for_backward({rois, channel_mapping});
ctx->mark_non_differentiable({channel_mapping});
return {output, channel_mapping};
}
static torch::autograd::variable_list backward(
torch::autograd::AutogradContext* ctx,
const torch::autograd::variable_list& grad_output) {
// Use data saved in forward
auto saved = ctx->get_saved_variables();
auto rois = saved[0];
auto channel_mapping = saved[1];
auto input_shape = ctx->saved_data["input_shape"].toIntList();
auto grad_in = detail::_ps_roi_align_backward(
grad_output[0],
rois,
channel_mapping,
ctx->saved_data["spatial_scale"].toDouble(),
ctx->saved_data["pooled_height"].toInt(),
ctx->saved_data["pooled_width"].toInt(),
ctx->saved_data["sampling_ratio"].toInt(),
input_shape[0],
input_shape[1],
input_shape[2],
input_shape[3]);
return {
grad_in,
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 PSROIAlignBackwardFunction
: public torch::autograd::Function<PSROIAlignBackwardFunction> {
public:
static torch::autograd::variable_list forward(
torch::autograd::AutogradContext* ctx,
const torch::autograd::Variable& grad,
const torch::autograd::Variable& rois,
const torch::autograd::Variable& channel_mapping,
double spatial_scale,
int64_t pooled_height,
int64_t pooled_width,
int64_t sampling_ratio,
int64_t batch_size,
int64_t channels,
int64_t height,
int64_t width) {
at::AutoDispatchBelowADInplaceOrView g;
auto grad_in = detail::_ps_roi_align_backward(
grad,
rois,
channel_mapping,
spatial_scale,
pooled_height,
pooled_width,
sampling_ratio,
batch_size,
channels,
height,
width);
return {grad_in};
}
static torch::autograd::variable_list backward(
torch::autograd::AutogradContext* ctx,
const torch::autograd::variable_list& grad_output) {
TORCH_CHECK(0, "double backwards on ps_roi_align not supported");
}
};
std::tuple<at::Tensor, at::Tensor> ps_roi_align_autograd(
const at::Tensor& input,
const at::Tensor& rois,
double spatial_scale,
int64_t pooled_height,
int64_t pooled_width,
int64_t sampling_ratio) {
auto result = PSROIAlignFunction::apply(
input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
return std::make_tuple(result[0], result[1]);
}
at::Tensor ps_roi_align_backward_autograd(
const at::Tensor& grad,
const at::Tensor& rois,
const at::Tensor& channel_mapping,
double spatial_scale,
int64_t pooled_height,
int64_t pooled_width,
int64_t sampling_ratio,
int64_t batch_size,
int64_t channels,
int64_t height,
int64_t width) {
return PSROIAlignBackwardFunction::apply(
grad,
rois,
channel_mapping,
spatial_scale,
pooled_height,
pooled_width,
sampling_ratio,
batch_size,
channels,
height,
width)[0];
}
} // namespace
TORCH_LIBRARY_IMPL(torchvision, Autograd, m) {
m.impl(
TORCH_SELECTIVE_NAME("torchvision::ps_roi_align"),
TORCH_FN(ps_roi_align_autograd));
m.impl(
TORCH_SELECTIVE_NAME("torchvision::_ps_roi_align_backward"),
TORCH_FN(ps_roi_align_backward_autograd));
}
} // namespace ops
} // namespace vision
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