| |
| 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) |
| 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 |
|
|
