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