Spaces:
Build error
Build error
File size: 5,233 Bytes
28c256d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Optional, Tuple, Union
import torch
import torch.nn as nn
from mmengine.utils import is_tuple_of
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['riroi_align_rotated_forward', 'riroi_align_rotated_backward'])
class RiRoIAlignRotatedFunction(Function):
@staticmethod
def forward(ctx: Any,
features: torch.Tensor,
rois: torch.Tensor,
out_size: Union[int, tuple],
spatial_scale: float,
num_samples: int = 0,
num_orientations: int = 8,
clockwise: bool = False) -> torch.Tensor:
if isinstance(out_size, int):
out_h = out_size
out_w = out_size
elif is_tuple_of(out_size, int):
assert len(out_size) == 2
out_h, out_w = out_size
else:
raise TypeError(
f'"out_size" should be an integer or tuple of integers,'
f' but got {out_size}')
ctx.spatial_scale = spatial_scale
ctx.num_samples = num_samples
ctx.num_orientations = num_orientations
ctx.clockwise = clockwise
ctx.save_for_backward(rois)
ctx.feature_size = features.size()
batch_size, num_channels, _, _ = features.size()
num_rois = rois.size(0)
output = features.new_zeros(num_rois, num_channels, out_h, out_w)
ext_module.riroi_align_rotated_forward(
features,
rois,
output,
pooled_height=out_h,
pooled_width=out_w,
spatial_scale=spatial_scale,
num_samples=num_samples,
num_orientations=num_orientations,
clockwise=clockwise)
return output
@staticmethod
def backward(
ctx: Any, grad_output: torch.Tensor
) -> Optional[Tuple[torch.Tensor, None, None, None, None, None, None]]:
feature_size = ctx.feature_size
spatial_scale = ctx.spatial_scale
num_orientations = ctx.num_orientations
clockwise = ctx.clockwise
num_samples = ctx.num_samples
rois = ctx.saved_tensors[0]
assert feature_size is not None
batch_size, num_channels, feature_h, feature_w = feature_size
out_w = grad_output.size(3)
out_h = grad_output.size(2)
grad_input = None
if ctx.needs_input_grad[0]:
grad_input = rois.new_zeros(batch_size, num_channels, feature_h,
feature_w)
ext_module.riroi_align_rotated_backward(
grad_output.contiguous(),
rois,
grad_input,
pooled_height=out_h,
pooled_width=out_w,
spatial_scale=spatial_scale,
num_samples=num_samples,
num_orientations=num_orientations,
clockwise=clockwise)
return grad_input, None, None, None, None, None, None
return None
riroi_align_rotated = RiRoIAlignRotatedFunction.apply
class RiRoIAlignRotated(nn.Module):
"""Rotation-invariant RoI align pooling layer for rotated proposals.
It accepts a feature map of shape (N, C, H, W) and rois with shape
(n, 6) with each roi decoded as (batch_index, center_x, center_y,
w, h, angle). The angle is in radian.
The details are described in the paper `ReDet: A Rotation-equivariant
Detector for Aerial Object Detection <https://arxiv.org/abs/2103.07733>`_.
Args:
out_size (tuple): fixed dimensional RoI output with shape (h, w).
spatial_scale (float): scale the input boxes by this number
num_samples (int): number of inputs samples to take for each
output sample. 0 to take samples densely for current models.
num_orientations (int): number of oriented channels.
clockwise (bool): If True, the angle in each proposal follows a
clockwise fashion in image space, otherwise, the angle is
counterclockwise. Default: False.
"""
def __init__(self,
out_size: tuple,
spatial_scale: float,
num_samples: int = 0,
num_orientations: int = 8,
clockwise: bool = False):
super().__init__()
self.out_size = out_size
self.spatial_scale = float(spatial_scale)
self.num_samples = int(num_samples)
self.num_orientations = int(num_orientations)
self.clockwise = clockwise
def forward(self, features: torch.Tensor,
rois: torch.Tensor) -> torch.Tensor:
return RiRoIAlignRotatedFunction.apply(features, rois, self.out_size,
self.spatial_scale,
self.num_samples,
self.num_orientations,
self.clockwise)
|