mmcv.ops.border_align 源代码

# Copyright (c) OpenMMLab. All rights reserved.
# modified from

import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable

from ..utils import ext_loader

ext_module = ext_loader.load_ext(
    '_ext', ['border_align_forward', 'border_align_backward'])

class BorderAlignFunction(Function):

    def symbolic(g, input, boxes, pool_size):
        return g.op(
            'mmcv::MMCVBorderAlign', input, boxes, pool_size_i=pool_size)

    def forward(ctx, input, boxes, pool_size):
        ctx.pool_size = pool_size
        ctx.input_shape = input.size()

        assert boxes.ndim == 3, 'boxes must be with shape [B, H*W, 4]'
        assert boxes.size(2) == 4, \
            'the last dimension of boxes must be (x1, y1, x2, y2)'
        assert input.size(1) % 4 == 0, \
            'the channel for input feature must be divisible by factor 4'

        # [B, C//4, H*W, 4]
        output_shape = (input.size(0), input.size(1) // 4, boxes.size(1), 4)
        output = input.new_zeros(output_shape)
        # `argmax_idx` only used for backward
        argmax_idx = input.new_zeros(output_shape).to(

            input, boxes, output, argmax_idx, pool_size=ctx.pool_size)

        ctx.save_for_backward(boxes, argmax_idx)
        return output

    def backward(ctx, grad_output):
        boxes, argmax_idx = ctx.saved_tensors
        grad_input = grad_output.new_zeros(ctx.input_shape)
        # complex head architecture may cause grad_output uncontiguous
        grad_output = grad_output.contiguous()
        return grad_input, None, None

border_align = BorderAlignFunction.apply

[文档]class BorderAlign(nn.Module): r"""Border align pooling layer. Applies border_align over the input feature based on predicted bboxes. The details were described in the paper `BorderDet: Border Feature for Dense Object Detection <>`_. For each border line (e.g. top, left, bottom or right) of each box, border_align does the following: 1. uniformly samples `pool_size`+1 positions on this line, involving \ the start and end points. 2. the corresponding features on these points are computed by \ bilinear interpolation. 3. max pooling over all the `pool_size`+1 positions are used for \ computing pooled feature. Args: pool_size (int): number of positions sampled over the boxes' borders (e.g. top, bottom, left, right). """ def __init__(self, pool_size): super(BorderAlign, self).__init__() self.pool_size = pool_size
[文档] def forward(self, input, boxes): """ Args: input: Features with shape [N,4C,H,W]. Channels ranged in [0,C), [C,2C), [2C,3C), [3C,4C) represent the top, left, bottom, right features respectively. boxes: Boxes with shape [N,H*W,4]. Coordinate format (x1,y1,x2,y2). Returns: Tensor: Pooled features with shape [N,C,H*W,4]. The order is (top,left,bottom,right) for the last dimension. """ return border_align(input, boxes, self.pool_size)
def __repr__(self): s = self.__class__.__name__ s += f'(pool_size={self.pool_size})' return s