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#include <ATen/ATen.h>
#include <torch/library.h>
namespace vision {
namespace ops {
namespace {
template <class T>
inline void add(T* address, const T& val) {
*address += val;
}
template <typename T>
void ps_roi_pool_forward_kernel_impl(
const T* input,
const T spatial_scale,
int channels,
int height,
int width,
int pooled_height,
int pooled_width,
const T* rois,
int channels_out,
int num_rois,
T* output,
int* channel_mapping) {
for (int n = 0; n < num_rois; ++n) {
const T* offset_rois = rois + n * 5;
int roi_batch_ind = offset_rois[0];
int roi_start_w = round(offset_rois[1] * spatial_scale);
int roi_start_h = round(offset_rois[2] * spatial_scale);
int roi_end_w = round(offset_rois[3] * spatial_scale);
int roi_end_h = round(offset_rois[4] * spatial_scale);
// Force too small ROIs to be 1x1
int roi_width = std::max(roi_end_w - roi_start_w, 1);
int roi_height = std::max(roi_end_h - roi_start_h, 1);
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
int c_in = 0;
for (int c_out = 0; c_out < channels_out; ++c_out) {
for (int ph = 0; ph < pooled_height; ++ph) {
for (int pw = 0; pw < pooled_width; ++pw) {
int hstart = static_cast<int>(floor(static_cast<T>(ph) * bin_size_h));
int wstart = static_cast<int>(floor(static_cast<T>(pw) * bin_size_w));
int hend =
static_cast<int>(ceil(static_cast<T>(ph + 1) * bin_size_h));
int wend =
static_cast<int>(ceil(static_cast<T>(pw + 1) * bin_size_w));
// Add roi offsets and clip to input boundaries
hstart = std::min(std::max(hstart + roi_start_h, 0), height - 1);
hend = std::min(std::max(hend + roi_start_h, 0), height - 1);
wstart = std::min(std::max(wstart + roi_start_w, 0), width - 1);
wend = std::min(std::max(wend + roi_start_w, 0), width - 1);
bool is_empty = (hend <= hstart) || (wend <= wstart);
const T* offset_input =
input + (roi_batch_ind * channels + c_in) * height * width;
T out_sum = 0;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
int input_index = h * width + w;
out_sum += offset_input[input_index];
}
}
int index =
((n * channels_out + c_out) * pooled_height + ph) * pooled_width +
pw;
T bin_area = (hend - hstart) * (wend - wstart);
output[index] = is_empty ? static_cast<T>(0) : out_sum / bin_area;
channel_mapping[index] = c_in;
c_in++;
}
}
}
}
}
template <typename T>
void ps_roi_pool_backward_kernel_impl(
const T* grad_output,
const int* channel_mapping,
int num_rois,
const T spatial_scale,
int channels,
int height,
int width,
int pooled_height,
int pooled_width,
int channels_out,
T* grad_input,
const T* rois) {
for (int n = 0; n < num_rois; ++n) {
const T* offset_rois = rois + n * 5;
int roi_batch_ind = offset_rois[0];
int roi_start_w = roundf(offset_rois[1] * spatial_scale);
int roi_start_h = roundf(offset_rois[2] * spatial_scale);
int roi_end_w = roundf(offset_rois[3] * spatial_scale);
int roi_end_h = roundf(offset_rois[4] * spatial_scale);
// Force too small ROIs to be 1x1
int roi_width = std::max(roi_end_w - roi_start_w, 1);
int roi_height = std::max(roi_end_h - roi_start_h, 1);
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
for (int ph = 0; ph < pooled_height; ++ph) {
for (int pw = 0; pw < pooled_width; ++pw) {
int hstart = static_cast<int>(floor(static_cast<T>(ph) * bin_size_h));
int wstart = static_cast<int>(floor(static_cast<T>(pw) * bin_size_w));
int hend = static_cast<int>(ceil(static_cast<T>(ph + 1) * bin_size_h));
int wend = static_cast<int>(ceil(static_cast<T>(pw + 1) * bin_size_w));
// Add roi offsets and clip to input boundaries
hstart = std::min(std::max(hstart + roi_start_h, 0), height);
hend = std::min(std::max(hend + roi_start_h, 0), height);
wstart = std::min(std::max(wstart + roi_start_w, 0), width);
wend = std::min(std::max(wend + roi_start_w, 0), width);
bool is_empty = (hend <= hstart) || (wend <= wstart);
for (int c_out = 0; c_out < channels_out; ++c_out) {
int index =
((n * channels_out + c_out) * pooled_height + ph) * pooled_width +
pw;
int c_in = channel_mapping[index];
T* grad_input_offset =
grad_input + (roi_batch_ind * channels + c_in) * height * width;
T bin_area = (hend - hstart) * (wend - wstart);
T diff_val =
is_empty ? static_cast<T>(0) : grad_output[index] / bin_area;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
int grad_input_index = h * width + w;
add(grad_input_offset + grad_input_index, diff_val);
}
}
}
}
}
}
}
std::tuple<at::Tensor, at::Tensor> ps_roi_pool_forward_kernel(
const at::Tensor& input,
const at::Tensor& rois,
double spatial_scale,
int64_t pooled_height,
int64_t pooled_width) {
// Check if input tensors are CPU tensors
TORCH_CHECK(input.device().is_cpu(), "input must be a CPU tensor");
TORCH_CHECK(rois.device().is_cpu(), "rois must be a CPU tensor");
TORCH_CHECK(
rois.size(1) == 5, "Tensor rois should have shape as Tensor[K, 5]");
at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2};
at::CheckedFrom c = "ps_roi_pool_forward_kernel";
at::checkAllSameType(c, {input_t, rois_t});
int num_rois = rois.size(0);
int channels = input.size(1);
int height = input.size(2);
int width = input.size(3);
TORCH_CHECK(
channels % (pooled_height * pooled_width) == 0,
"input channels must be a multiple of pooling height * pooling width");
int channels_out = channels / (pooled_height * pooled_width);
auto output = at::zeros(
{num_rois, channels_out, pooled_height, pooled_width}, input.options());
auto channel_mapping =
at::zeros(output.sizes(), input.options().dtype(at::kInt));
auto output_size = output.numel();
if (output_size == 0) {
return std::make_tuple(output, channel_mapping);
}
auto input_ = input.contiguous(), rois_ = rois.contiguous();
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
input.scalar_type(), "ps_roi_pool_forward_kernel", [&] {
ps_roi_pool_forward_kernel_impl<scalar_t>(
input_.data_ptr<scalar_t>(),
spatial_scale,
channels,
height,
width,
pooled_height,
pooled_width,
rois_.data_ptr<scalar_t>(),
channels_out,
num_rois,
output.data_ptr<scalar_t>(),
channel_mapping.data_ptr<int>());
});
return std::make_tuple(output, channel_mapping);
}
at::Tensor ps_roi_pool_backward_kernel(
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 batch_size,
int64_t channels,
int64_t height,
int64_t width) {
// Check if input tensors are CPU tensors
TORCH_CHECK(grad.device().is_cpu(), "grad must be a CPU tensor");
TORCH_CHECK(rois.device().is_cpu(), "rois must be a CPU tensor");
TORCH_CHECK(
channel_mapping.device().is_cpu(),
"channel_mapping must be a CPU tensor");
at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2},
channel_mapping_t{channel_mapping, "channel_mapping", 3};
at::CheckedFrom c = "ps_roi_pool_backward_kernel";
at::checkAllSameType(c, {grad_t, rois_t});
auto num_rois = rois.size(0);
auto grad_input =
at::zeros({batch_size, channels, height, width}, grad.options());
// handle possibly empty gradients
if (grad.numel() == 0) {
return grad_input;
}
int channels_out = channels / (pooled_height * pooled_width);
auto grad_ = grad.contiguous(), rois_ = rois.contiguous();
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
grad.scalar_type(), "ps_roi_pool_backward_kernel", [&] {
ps_roi_pool_backward_kernel_impl<scalar_t>(
grad_.data_ptr<scalar_t>(),
channel_mapping.data_ptr<int>(),
num_rois,
spatial_scale,
channels,
height,
width,
pooled_height,
pooled_width,
channels_out,
grad_input.data_ptr<scalar_t>(),
rois_.data_ptr<scalar_t>());
});
return grad_input;
}
} // namespace
TORCH_LIBRARY_IMPL(torchvision, CPU, m) {
m.impl(
TORCH_SELECTIVE_NAME("torchvision::ps_roi_pool"),
TORCH_FN(ps_roi_pool_forward_kernel));
m.impl(
TORCH_SELECTIVE_NAME("torchvision::_ps_roi_pool_backward"),
TORCH_FN(ps_roi_pool_backward_kernel));
}
} // namespace ops
} // namespace vision
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