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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from annotator.uniformer.mmcv.cnn import PLUGIN_LAYERS, Scale
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def NEG_INF_DIAG(n, device):
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"""Returns a diagonal matrix of size [n, n].
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The diagonal are all "-inf". This is for avoiding calculating the
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overlapped element in the Criss-Cross twice.
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"""
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return torch.diag(torch.tensor(float('-inf')).to(device).repeat(n), 0)
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@PLUGIN_LAYERS.register_module()
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class CrissCrossAttention(nn.Module):
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"""Criss-Cross Attention Module.
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.. note::
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Before v1.3.13, we use a CUDA op. Since v1.3.13, we switch
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to a pure PyTorch and equivalent implementation. For more
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details, please refer to https://github.com/open-mmlab/mmcv/pull/1201.
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Speed comparison for one forward pass
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- Input size: [2,512,97,97]
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- Device: 1 NVIDIA GeForce RTX 2080 Ti
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+-----------------------+---------------+------------+---------------+
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| |PyTorch version|CUDA version|Relative speed |
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+=======================+===============+============+===============+
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|with torch.no_grad() |0.00554402 s |0.0299619 s |5.4x |
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+-----------------------+---------------+------------+---------------+
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|no with torch.no_grad()|0.00562803 s |0.0301349 s |5.4x |
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+-----------------------+---------------+------------+---------------+
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Args:
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in_channels (int): Channels of the input feature map.
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"""
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def __init__(self, in_channels):
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super().__init__()
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self.query_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
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self.key_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
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self.value_conv = nn.Conv2d(in_channels, in_channels, 1)
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self.gamma = Scale(0.)
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self.in_channels = in_channels
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def forward(self, x):
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"""forward function of Criss-Cross Attention.
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Args:
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x (Tensor): Input feature. \
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shape (batch_size, in_channels, height, width)
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Returns:
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Tensor: Output of the layer, with shape of \
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(batch_size, in_channels, height, width)
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"""
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B, C, H, W = x.size()
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query = self.query_conv(x)
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key = self.key_conv(x)
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value = self.value_conv(x)
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energy_H = torch.einsum('bchw,bciw->bwhi', query, key) + NEG_INF_DIAG(
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H, query.device)
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energy_H = energy_H.transpose(1, 2)
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energy_W = torch.einsum('bchw,bchj->bhwj', query, key)
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attn = F.softmax(
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torch.cat([energy_H, energy_W], dim=-1), dim=-1)
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out = torch.einsum('bciw,bhwi->bchw', value, attn[..., :H])
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out += torch.einsum('bchj,bhwj->bchw', value, attn[..., H:])
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out = self.gamma(out) + x
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out = out.contiguous()
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return out
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def __repr__(self):
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s = self.__class__.__name__
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s += f'(in_channels={self.in_channels})'
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return s
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