Source code for torchvision.ops.roi_align

from typing import List, Union

import torch
from torch import nn, Tensor
from torch.nn.modules.utils import _pair
from torch.jit.annotations import BroadcastingList2
from torchvision.extension import _assert_has_ops

from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape

[docs]def roi_align( input: Tensor, boxes: Union[Tensor, List[Tensor]], output_size: BroadcastingList2[int], spatial_scale: float = 1.0, sampling_ratio: int = -1, aligned: bool = False, ) -> Tensor: """ Performs Region of Interest (RoI) Align operator with average pooling, as described in Mask 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``. If the tensor is quantized, we expect a batch size of ``N == 1``. 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 (in bins or pixels) after the pooling is performed, as (height, width). spatial_scale (float): a scaling factor that maps the input coordinates to the box coordinates. Default: 1.0 sampling_ratio (int): number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If <= 0, then an adaptive number of grid points are used (computed as ``ceil(roi_width / output_width)``, and likewise for height). Default: -1 aligned (bool): If False, use the legacy implementation. If True, pixel shift the box coordinates it by -0.5 for a better alignment with the two neighboring pixel indices. This version is used in Detectron2 Returns: Tensor[K, C, output_size[0], output_size[1]]: The pooled RoIs. """ _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) return torch.ops.torchvision.roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
[docs]class RoIAlign(nn.Module): """ See :func:`roi_align`. """ def __init__( self, output_size: BroadcastingList2[int], spatial_scale: float, sampling_ratio: int, aligned: bool = False, ): super(RoIAlign, self).__init__() self.output_size = output_size self.spatial_scale = spatial_scale self.sampling_ratio = sampling_ratio self.aligned = aligned def forward(self, input: Tensor, rois: Tensor) -> Tensor: return roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio, self.aligned) def __repr__(self) -> str: tmpstr = self.__class__.__name__ + '(' tmpstr += 'output_size=' + str(self.output_size) tmpstr += ', spatial_scale=' + str(self.spatial_scale) tmpstr += ', sampling_ratio=' + str(self.sampling_ratio) tmpstr += ', aligned=' + str(self.aligned) tmpstr += ')' return tmpstr


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