kafka_yolov5/segutils/core/nn/ca_block.py

import torch
import torch.nn as nn
import torch.nn.functional as F

from torch.autograd.function import once_differentiable
#from core.nn import _C

__all__ = ['CrissCrossAttention', 'ca_weight', 'ca_map']


class _CAWeight(torch.autograd.Function):
    @staticmethod
    def forward(ctx, t, f):
        weight = _C.ca_forward(t, f)

        ctx.save_for_backward(t, f)

        return weight

    @staticmethod
    @once_differentiable
    def backward(ctx, dw):
        t, f = ctx.saved_tensors

        dt, df = _C.ca_backward(dw, t, f)
        return dt, df


class _CAMap(torch.autograd.Function):
    @staticmethod
    def forward(ctx, weight, g):
        out = _C.ca_map_forward(weight, g)

        ctx.save_for_backward(weight, g)

        return out

    @staticmethod
    @once_differentiable
    def backward(ctx, dout):
        weight, g = ctx.saved_tensors

        dw, dg = _C.ca_map_backward(dout, weight, g)

        return dw, dg


ca_weight = _CAWeight.apply
ca_map = _CAMap.apply


class CrissCrossAttention(nn.Module):
    """Criss-Cross Attention Module"""

    def __init__(self, in_channels):
        super(CrissCrossAttention, self).__init__()
        self.query_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
        self.key_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
        self.value_conv = nn.Conv2d(in_channels, in_channels, 1)
        self.gamma = nn.Parameter(torch.zeros(1))

    def forward(self, x):
        proj_query = self.query_conv(x)
        proj_key = self.key_conv(x)
        proj_value = self.value_conv(x)

        energy = ca_weight(proj_query, proj_key)
        attention = F.softmax(energy, 1)
        out = ca_map(attention, proj_value)
        out = self.gamma * out + x

        return out