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PSROIAlign + Dispatcher + Autocast + Code Cleanup (pytorch#2928)
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* Fixing types.

* Dispatcher + Autocast.

* + Autograd.

* Clean up and refactor PSROIAlign implementation:
- Remove primitive const declaration from method names.
- Using references when possible.
- Sync naming of internal methods with other ops.

* Restoring names of internal methods to avoid conflicts.

* Restore include headers.
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@datumbox @vfdev-5
datumbox authored and vfdev-5 committed Dec 4, 2020
1 parent 0d21dd8 commit 858e69d
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Showing 6 changed files with 246 additions and 161 deletions.
203 changes: 139 additions & 64 deletions torchvision/csrc/PSROIAlign.h
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Original file line number Diff line number Diff line change
Expand Up @@ -3,72 +3,75 @@
#include "cpu/vision_cpu.h"

#ifdef WITH_CUDA
#include "autocast.h"
#include "cuda/vision_cuda.h"
#endif
#ifdef WITH_HIP
#include "autocast.h"
#include "hip/vision_cuda.h"
#endif

#include <iostream>

std::tuple<at::Tensor, at::Tensor> PSROIAlign_forward(
// TODO: put this stuff in torchvision namespace

std::tuple<at::Tensor, at::Tensor> ps_roi_align(
const at::Tensor& input,
const at::Tensor& rois,
const float spatial_scale,
const int pooled_height,
const int pooled_width,
const int sampling_ratio) {
if (input.is_cuda()) {
#if defined(WITH_CUDA) || defined(WITH_HIP)
return PSROIAlign_forward_cuda(
input,
rois,
spatial_scale,
pooled_height,
pooled_width,
sampling_ratio);
#else
TORCH_CHECK(false, "Not compiled with GPU support");
#endif
}
return PSROIAlign_forward_cpu(
double spatial_scale,
int64_t pooled_height,
int64_t pooled_width,
int64_t sampling_ratio) {
static auto op = c10::Dispatcher::singleton()
.findSchemaOrThrow("torchvision::ps_roi_align", "")
.typed<decltype(ps_roi_align)>();
return op.call(
input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
}

at::Tensor PSROIAlign_backward(
const at::Tensor& grad,
const at::Tensor& rois,
const at::Tensor& mapping_channel,
const float spatial_scale,
const int pooled_height,
const int pooled_width,
const int sampling_ratio,
const int batch_size,
const int channels,
const int height,
const int width) {
if (grad.is_cuda()) {
#if defined(WITH_CUDA) || defined(WITH_HIP)
return PSROIAlign_backward_cuda(
grad,
rois,
mapping_channel,
spatial_scale,
pooled_height,
pooled_width,
sampling_ratio,
batch_size,
channels,
height,
width);
#else
TORCH_CHECK(false, "Not compiled with GPU support");
std::tuple<at::Tensor, at::Tensor> PSROIAlign_autocast(
const at::Tensor& input,
const at::Tensor& rois,
double spatial_scale,
int64_t pooled_height,
int64_t pooled_width,
int64_t sampling_ratio) {
c10::impl::ExcludeDispatchKeyGuard no_autocast(c10::DispatchKey::Autocast);
auto result = ps_roi_align(
at::autocast::cached_cast(at::kFloat, input),
at::autocast::cached_cast(at::kFloat, rois),
spatial_scale,
pooled_height,
pooled_width,
sampling_ratio);

return std::make_tuple(
std::get<0>(result).to(input.scalar_type()),
std::get<1>(result).to(input.scalar_type()));
}
#endif
}
return PSROIAlign_backward_cpu(

at::Tensor _ps_roi_align_backward(
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) {
static auto op =
c10::Dispatcher::singleton()
.findSchemaOrThrow("torchvision::_ps_roi_align_backward", "")
.typed<decltype(_ps_roi_align_backward)>();
return op.call(
grad,
rois,
mapping_channel,
channel_mapping,
spatial_scale,
pooled_height,
pooled_width,
Expand All @@ -84,40 +87,43 @@ class PSROIAlignFunction
public:
static torch::autograd::variable_list forward(
torch::autograd::AutogradContext* ctx,
torch::autograd::Variable input,
torch::autograd::Variable rois,
const double spatial_scale,
const int64_t pooled_height,
const int64_t pooled_width,
const int64_t sampling_ratio) {
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();
auto result = PSROIAlign_forward(
at::AutoNonVariableTypeMode 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,
torch::autograd::variable_list grad_output) {
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 = PSROIAlign_backward(
auto grad_in = _ps_roi_align_backward(
grad_output[0],
rois,
channel_mapping,
Expand All @@ -129,6 +135,7 @@ class PSROIAlignFunction
input_shape[1],
input_shape[2],
input_shape[3]);

return {grad_in,
torch::autograd::Variable(),
torch::autograd::Variable(),
Expand All @@ -138,14 +145,82 @@ class PSROIAlignFunction
}
};

std::tuple<at::Tensor, at::Tensor> ps_roi_align(
// 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::AutoNonVariableTypeMode g;
auto grad_in = _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> PSROIAlign_autograd(
const at::Tensor& input,
const at::Tensor& rois,
const double spatial_scale,
const int64_t pooled_height,
const int64_t pooled_width,
const int64_t sampling_ratio) {
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::tuple<at::Tensor, at::Tensor>(result[0], result[1]);

return std::make_tuple(result[0], result[1]);
}

at::Tensor PSROIAlign_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];
}
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