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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.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)
@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 https://github.com/open-mmlab/mmcv/pull/1201.
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
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(
torch.cat([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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