|
|
from typing import List, Union |
|
|
|
|
|
import torch |
|
|
import torch.fx |
|
|
from torch import nn, Tensor |
|
|
from torch.jit.annotations import BroadcastingList2 |
|
|
from torch.nn.modules.utils import _pair |
|
|
from torchvision.extension import _assert_has_ops |
|
|
|
|
|
from ..utils import _log_api_usage_once |
|
|
from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format |
|
|
|
|
|
|
|
|
@torch.fx.wrap |
|
|
def roi_pool( |
|
|
input: Tensor, |
|
|
boxes: Union[Tensor, List[Tensor]], |
|
|
output_size: BroadcastingList2[int], |
|
|
spatial_scale: float = 1.0, |
|
|
) -> Tensor: |
|
|
""" |
|
|
Performs Region of Interest (RoI) Pool operator described in Fast R-CNN |
|
|
|
|
|
Args: |
|
|
input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element |
|
|
contains ``C`` feature maps of dimensions ``H x W``. |
|
|
boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2) |
|
|
format where the regions will be taken from. |
|
|
The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. |
|
|
If a single Tensor is passed, then the first column should |
|
|
contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``. |
|
|
If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i |
|
|
in the batch. |
|
|
output_size (int or Tuple[int, int]): the size of the output after the cropping |
|
|
is performed, as (height, width) |
|
|
spatial_scale (float): a scaling factor that maps the box coordinates to |
|
|
the input coordinates. For example, if your boxes are defined on the scale |
|
|
of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of |
|
|
the original image), you'll want to set this to 0.5. Default: 1.0 |
|
|
|
|
|
Returns: |
|
|
Tensor[K, C, output_size[0], output_size[1]]: The pooled RoIs. |
|
|
""" |
|
|
if not torch.jit.is_scripting() and not torch.jit.is_tracing(): |
|
|
_log_api_usage_once(roi_pool) |
|
|
_assert_has_ops() |
|
|
check_roi_boxes_shape(boxes) |
|
|
rois = boxes |
|
|
output_size = _pair(output_size) |
|
|
if not isinstance(rois, torch.Tensor): |
|
|
rois = convert_boxes_to_roi_format(rois) |
|
|
output, _ = torch.ops.torchvision.roi_pool(input, rois, spatial_scale, output_size[0], output_size[1]) |
|
|
return output |
|
|
|
|
|
|
|
|
class RoIPool(nn.Module): |
|
|
""" |
|
|
See :func:`roi_pool`. |
|
|
""" |
|
|
|
|
|
def __init__(self, output_size: BroadcastingList2[int], spatial_scale: float): |
|
|
super().__init__() |
|
|
_log_api_usage_once(self) |
|
|
self.output_size = output_size |
|
|
self.spatial_scale = spatial_scale |
|
|
|
|
|
def forward(self, input: Tensor, rois: Union[Tensor, List[Tensor]]) -> Tensor: |
|
|
return roi_pool(input, rois, self.output_size, self.spatial_scale) |
|
|
|
|
|
def __repr__(self) -> str: |
|
|
s = f"{self.__class__.__name__}(output_size={self.output_size}, spatial_scale={self.spatial_scale})" |
|
|
return s |
|
|
|
