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