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- from typing import List, Union
- import torch
- 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 convert_boxes_to_roi_format, check_roi_boxes_shape
- 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: 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
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