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120 lines
5.3 KiB
120 lines
5.3 KiB
""" Selective Kernel Convolution/Attention
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Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import torch
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from torch import nn as nn
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from .conv_bn_act import ConvNormActAa
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from .helpers import make_divisible
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from .trace_utils import _assert
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def _kernel_valid(k):
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if isinstance(k, (list, tuple)):
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for ki in k:
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return _kernel_valid(ki)
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assert k >= 3 and k % 2
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class SelectiveKernelAttn(nn.Module):
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def __init__(self, channels, num_paths=2, attn_channels=32, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
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""" Selective Kernel Attention Module
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Selective Kernel attention mechanism factored out into its own module.
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"""
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super(SelectiveKernelAttn, self).__init__()
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self.num_paths = num_paths
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self.fc_reduce = nn.Conv2d(channels, attn_channels, kernel_size=1, bias=False)
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self.bn = norm_layer(attn_channels)
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self.act = act_layer(inplace=True)
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self.fc_select = nn.Conv2d(attn_channels, channels * num_paths, kernel_size=1, bias=False)
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def forward(self, x):
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_assert(x.shape[1] == self.num_paths, '')
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x = x.sum(1).mean((2, 3), keepdim=True)
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x = self.fc_reduce(x)
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x = self.bn(x)
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x = self.act(x)
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x = self.fc_select(x)
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B, C, H, W = x.shape
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x = x.view(B, self.num_paths, C // self.num_paths, H, W)
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x = torch.softmax(x, dim=1)
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return x
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class SelectiveKernel(nn.Module):
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def __init__(self, in_channels, out_channels=None, kernel_size=None, stride=1, dilation=1, groups=1,
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rd_ratio=1./16, rd_channels=None, rd_divisor=8, keep_3x3=True, split_input=True,
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_layer=None):
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""" Selective Kernel Convolution Module
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As described in Selective Kernel Networks (https://arxiv.org/abs/1903.06586) with some modifications.
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Largest change is the input split, which divides the input channels across each convolution path, this can
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be viewed as a grouping of sorts, but the output channel counts expand to the module level value. This keeps
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the parameter count from ballooning when the convolutions themselves don't have groups, but still provides
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a noteworthy increase in performance over similar param count models without this attention layer. -Ross W
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Args:
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in_channels (int): module input (feature) channel count
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out_channels (int): module output (feature) channel count
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kernel_size (int, list): kernel size for each convolution branch
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stride (int): stride for convolutions
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dilation (int): dilation for module as a whole, impacts dilation of each branch
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groups (int): number of groups for each branch
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rd_ratio (int, float): reduction factor for attention features
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keep_3x3 (bool): keep all branch convolution kernels as 3x3, changing larger kernels for dilations
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split_input (bool): split input channels evenly across each convolution branch, keeps param count lower,
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can be viewed as grouping by path, output expands to module out_channels count
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act_layer (nn.Module): activation layer to use
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norm_layer (nn.Module): batchnorm/norm layer to use
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aa_layer (nn.Module): anti-aliasing module
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drop_layer (nn.Module): spatial drop module in convs (drop block, etc)
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"""
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super(SelectiveKernel, self).__init__()
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out_channels = out_channels or in_channels
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kernel_size = kernel_size or [3, 5] # default to one 3x3 and one 5x5 branch. 5x5 -> 3x3 + dilation
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_kernel_valid(kernel_size)
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if not isinstance(kernel_size, list):
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kernel_size = [kernel_size] * 2
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if keep_3x3:
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dilation = [dilation * (k - 1) // 2 for k in kernel_size]
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kernel_size = [3] * len(kernel_size)
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else:
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dilation = [dilation] * len(kernel_size)
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self.num_paths = len(kernel_size)
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.split_input = split_input
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if self.split_input:
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assert in_channels % self.num_paths == 0
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in_channels = in_channels // self.num_paths
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groups = min(out_channels, groups)
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conv_kwargs = dict(
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stride=stride, groups=groups, act_layer=act_layer, norm_layer=norm_layer,
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aa_layer=aa_layer, drop_layer=drop_layer)
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self.paths = nn.ModuleList([
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ConvNormActAa(in_channels, out_channels, kernel_size=k, dilation=d, **conv_kwargs)
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for k, d in zip(kernel_size, dilation)])
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attn_channels = rd_channels or make_divisible(out_channels * rd_ratio, divisor=rd_divisor)
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self.attn = SelectiveKernelAttn(out_channels, self.num_paths, attn_channels)
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def forward(self, x):
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if self.split_input:
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x_split = torch.split(x, self.in_channels // self.num_paths, 1)
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x_paths = [op(x_split[i]) for i, op in enumerate(self.paths)]
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else:
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x_paths = [op(x) for op in self.paths]
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x = torch.stack(x_paths, dim=1)
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x_attn = self.attn(x)
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x = x * x_attn
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x = torch.sum(x, dim=1)
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return x
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