Source code for torchvision.ops.roi_pool

import torch
from torch import nn, Tensor

from torch.nn.modules.utils import _pair
from torch.jit.annotations import List, BroadcastingList2

from torchvision.extension import _assert_has_ops
from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape

[docs]def roi_pool( input: Tensor, boxes: Tensor, output_size: BroadcastingList2[int], spatial_scale: float = 1.0, ) -> Tensor: """ Performs Region of Interest (RoI) Pool operator described in Fast R-CNN Arguments: input (Tensor[N, C, H, W]): input tensor 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. If a single Tensor is passed, then the first column should contain the batch index. If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i in a 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 input coordinates to the box coordinates. Default: 1.0 Returns: output (Tensor[K, C, output_size[0], output_size[1]]) """ _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
[docs]class RoIPool(nn.Module): """ See roi_pool """ def __init__(self, output_size: BroadcastingList2[int], spatial_scale: float): super(RoIPool, self).__init__() 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: tmpstr = self.__class__.__name__ + '(' tmpstr += 'output_size=' + str(self.output_size) tmpstr += ', spatial_scale=' + str(self.spatial_scale) tmpstr += ')' return tmpstr


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