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89 lines
2.9 KiB
89 lines
2.9 KiB
""" Split Attention Conv2d (for ResNeSt Models)
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Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
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Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
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Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
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"""
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import torch
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import torch.nn.functional as F
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from torch import nn
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class RadixSoftmax(nn.Module):
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def __init__(self, radix, cardinality):
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super(RadixSoftmax, self).__init__()
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self.radix = radix
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self.cardinality = cardinality
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def forward(self, x):
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batch = x.size(0)
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if self.radix > 1:
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x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
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x = F.softmax(x, dim=1)
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x = x.reshape(batch, -1)
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else:
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x = torch.sigmoid(x)
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return x
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class SplitAttnConv2d(nn.Module):
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"""Split-Attention Conv2d
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"""
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0,
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dilation=1, groups=1, bias=False, radix=2, reduction_factor=4,
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act_layer=nn.ReLU, norm_layer=None, drop_block=None, **kwargs):
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super(SplitAttnConv2d, self).__init__()
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self.radix = radix
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self.drop_block = drop_block
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mid_chs = out_channels * radix
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attn_chs = max(in_channels * radix // reduction_factor, 32)
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self.conv = nn.Conv2d(
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in_channels, mid_chs, kernel_size, stride, padding, dilation,
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groups=groups * radix, bias=bias, **kwargs)
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self.bn0 = norm_layer(mid_chs) if norm_layer is not None else None
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self.act0 = act_layer(inplace=True)
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self.fc1 = nn.Conv2d(out_channels, attn_chs, 1, groups=groups)
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self.bn1 = norm_layer(attn_chs) if norm_layer is not None else None
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self.act1 = act_layer(inplace=True)
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self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=groups)
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self.rsoftmax = RadixSoftmax(radix, groups)
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@property
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def in_channels(self):
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return self.conv.in_channels
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@property
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def out_channels(self):
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return self.fc1.out_channels
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def forward(self, x):
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x = self.conv(x)
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if self.bn0 is not None:
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x = self.bn0(x)
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if self.drop_block is not None:
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x = self.drop_block(x)
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x = self.act0(x)
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B, RC, H, W = x.shape
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if self.radix > 1:
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x = x.reshape((B, self.radix, RC // self.radix, H, W))
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x_gap = x.sum(dim=1)
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else:
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x_gap = x
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x_gap = F.adaptive_avg_pool2d(x_gap, 1)
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x_gap = self.fc1(x_gap)
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if self.bn1 is not None:
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x_gap = self.bn1(x_gap)
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x_gap = self.act1(x_gap)
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x_attn = self.fc2(x_gap)
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x_attn = self.rsoftmax(x_attn).view(B, -1, 1, 1)
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if self.radix > 1:
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out = (x * x_attn.reshape((B, self.radix, RC // self.radix, 1, 1))).sum(dim=1)
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else:
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out = x * x_attn
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return out.contiguous()
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