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#include "../roi_pool.h"
#include <torch/autograd.h>
#include <torch/types.h>
namespace vision {
namespace ops {
namespace {
class ROIPoolFunction : public torch::autograd::Function<ROIPoolFunction> {
public:
static torch::autograd::variable_list forward(
torch::autograd::AutogradContext* ctx,
const torch::autograd::Variable& input,
const torch::autograd::Variable& rois,
double spatial_scale,
c10::SymInt pooled_height,
c10::SymInt pooled_width) {
ctx->saved_data["spatial_scale"] = spatial_scale;
ctx->saved_data["pooled_height"] = pooled_height;
ctx->saved_data["pooled_width"] = pooled_width;
ctx->saved_data["input_shape"] = input.sym_sizes();
at::AutoDispatchBelowADInplaceOrView g;
auto result = roi_pool_symint(
input, rois, spatial_scale, pooled_height, pooled_width);
auto output = std::get<0>(result);
auto argmax = std::get<1>(result);
ctx->save_for_backward({rois, argmax});
ctx->mark_non_differentiable({argmax});
return {output, argmax};
}
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 argmax = saved[1];
auto input_shape = ctx->saved_data["input_shape"].toList();
auto grad_in = detail::_roi_pool_backward_symint(
grad_output[0],
rois,
argmax,
ctx->saved_data["spatial_scale"].toDouble(),
ctx->saved_data["pooled_height"].toSymInt(),
ctx->saved_data["pooled_width"].toSymInt(),
input_shape[0].get().toSymInt(),
input_shape[1].get().toSymInt(),
input_shape[2].get().toSymInt(),
input_shape[3].get().toSymInt());
return {
grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable(),
torch::autograd::Variable()};
}
};
// TODO: There should be an easier way to do this
class ROIPoolBackwardFunction
: public torch::autograd::Function<ROIPoolBackwardFunction> {
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& argmax,
double spatial_scale,
c10::SymInt pooled_height,
c10::SymInt pooled_width,
c10::SymInt batch_size,
c10::SymInt channels,
c10::SymInt height,
c10::SymInt width) {
at::AutoDispatchBelowADInplaceOrView g;
auto grad_in = detail::_roi_pool_backward_symint(
grad,
rois,
argmax,
spatial_scale,
pooled_height,
pooled_width,
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 roi_pool not supported");
}
};
std::tuple<at::Tensor, at::Tensor> roi_pool_autograd(
const at::Tensor& input,
const at::Tensor& rois,
double spatial_scale,
c10::SymInt pooled_height,
c10::SymInt pooled_width) {
auto result = ROIPoolFunction::apply(
input, rois, spatial_scale, pooled_height, pooled_width);
return std::make_tuple(result[0], result[1]);
}
at::Tensor roi_pool_backward_autograd(
const at::Tensor& grad,
const at::Tensor& rois,
const at::Tensor& argmax,
double spatial_scale,
c10::SymInt pooled_height,
c10::SymInt pooled_width,
c10::SymInt batch_size,
c10::SymInt channels,
c10::SymInt height,
c10::SymInt width) {
return ROIPoolBackwardFunction::apply(
grad,
rois,
argmax,
spatial_scale,
pooled_height,
pooled_width,
batch_size,
channels,
height,
width)[0];
}
} // namespace
TORCH_LIBRARY_IMPL(torchvision, Autograd, m) {
m.impl(
TORCH_SELECTIVE_NAME("torchvision::roi_pool"),
TORCH_FN(roi_pool_autograd));
m.impl(
TORCH_SELECTIVE_NAME("torchvision::_roi_pool_backward"),
TORCH_FN(roi_pool_backward_autograd));
}
} // namespace ops
} // namespace vision
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