mmcv.ops.cc_attention 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F

from mmcv.cnn import PLUGIN_LAYERS, Scale

def NEG_INF_DIAG(n, device):
    """Returns a diagonal matrix of size [n, n].

    The diagonal are all "-inf". This is for avoiding calculating the
    overlapped element in the Criss-Cross twice.
    return torch.diag(torch.tensor(float('-inf')).to(device).repeat(n), 0)

[文档]@PLUGIN_LAYERS.register_module() class CrissCrossAttention(nn.Module): """Criss-Cross Attention Module. .. note:: Before v1.3.13, we use a CUDA op. Since v1.3.13, we switch to a pure PyTorch and equivalent implementation. For more details, please refer to Speed comparison for one forward pass - Input size: [2,512,97,97] - Device: 1 NVIDIA GeForce RTX 2080 Ti +-----------------------+---------------+------------+---------------+ | |PyTorch version|CUDA version|Relative speed | +=======================+===============+============+===============+ |with torch.no_grad() |0.00554402 s |0.0299619 s |5.4x | +-----------------------+---------------+------------+---------------+ |no with torch.no_grad()|0.00562803 s |0.0301349 s |5.4x | +-----------------------+---------------+------------+---------------+ Args: in_channels (int): Channels of the input feature map. """ def __init__(self, in_channels): super().__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 = Scale(0.) self.in_channels = in_channels
[文档] def forward(self, x): """forward function of Criss-Cross Attention. Args: x (Tensor): Input feature. \ shape (batch_size, in_channels, height, width) Returns: Tensor: Output of the layer, with shape of \ (batch_size, in_channels, height, width) """ B, C, H, W = x.size() query = self.query_conv(x) key = self.key_conv(x) value = self.value_conv(x) energy_H = torch.einsum('bchw,bciw->bwhi', query, key) + NEG_INF_DIAG( H, query.device) energy_H = energy_H.transpose(1, 2) energy_W = torch.einsum('bchw,bchj->bhwj', query, key) attn = F.softmax([energy_H, energy_W], dim=-1), dim=-1) # [B,H,W,(H+W)] out = torch.einsum('bciw,bhwi->bchw', value, attn[..., :H]) out += torch.einsum('bchj,bhwj->bchw', value, attn[..., H:]) out = self.gamma(out) + x out = out.contiguous() return out
def __repr__(self): s = self.__class__.__name__ s += f'(in_channels={self.in_channels})' return s
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