97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
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# ResNest: Split-attention networks (arXiv 2020)
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import torch
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from torch import nn
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import torch.nn.functional as F
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def make_divisible(v, divisor=8, min_value=None, round_limit=.9):
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min_value = min_value or divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < round_limit * v:
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new_v += divisor
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return new_v
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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 = x.sigmoid()
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return x
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class SplitAttn(nn.Module):
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"""Split-Attention (aka Splat)
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"""
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def __init__(self, in_channels, out_channels=None, kernel_size=3, stride=1, padding=None,
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dilation=1, groups=1, bias=False, radix=2, rd_ratio=0.25, rd_channels=None, rd_divisor=8,
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act_layer=nn.ReLU, norm_layer=None, drop_block=None, **kwargs):
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super(SplitAttn, self).__init__()
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out_channels = out_channels or in_channels
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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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if rd_channels is None:
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attn_chs = make_divisible(
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in_channels * radix * rd_ratio, min_value=32, divisor=rd_divisor)
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else:
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attn_chs = rd_channels * radix
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padding = kernel_size // 2 if padding is None else padding
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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 else nn.Identity()
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self.act0 = act_layer()
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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 else nn.Identity()
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self.act1 = act_layer()
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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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def forward(self, x):
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x = self.conv(x)
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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 = x_gap.mean(2, keepdims=True).mean(3, keepdims=True)
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x_gap = self.fc1(x_gap)
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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,
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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
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# 输入 N C H W, 输出 N C H W
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if __name__ == '__main__':
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block = SplitAttn(64)
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input = torch.rand(1, 64, 64, 64)
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output = block(input)
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print(input.size(), output.size())
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