Ross Wightman
5 years ago
parent
a93bae6dc5
commit
58e28dc7e7
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import math
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from collections import OrderedDict
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import torch
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from torch import nn as nn
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from timm.models.registry import register_model
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from timm.models.helpers import load_pretrained
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from timm.models.resnet import ResNet, get_padding, SEModule
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'conv1', 'classifier': 'fc',
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**kwargs
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}
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default_cfgs = {
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'skresnet18': _cfg(url=''),
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'skresnet26d': _cfg()
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}
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class SelectiveKernelAttn(nn.Module):
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def __init__(self, channels, num_paths=2, attn_channels=32,
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
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super(SelectiveKernelAttn, self).__init__()
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self.num_paths = num_paths
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self.pool = nn.AdaptiveAvgPool2d(1)
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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 = torch.sum(x, dim=1)
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#print('attn sum', x.shape)
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x = self.pool(x)
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#print('attn pool', x.shape)
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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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#print('attn sel', x.shape)
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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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#print('attn spl', x.shape)
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x = torch.softmax(x, dim=1)
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return x
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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 SelectiveKernelConv(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=[3, 5], stride=1, dilation=1, groups=1,
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attn_reduction=16, min_attn_channels=32, keep_3x3=True, use_attn=True,
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split_input=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
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super(SelectiveKernelConv, self).__init__()
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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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num_paths = len(kernel_size)
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self.num_paths = num_paths
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self.split_input = split_input
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self.in_channels = in_channels
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self.out_channels = out_channels
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if split_input:
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assert in_channels % num_paths == 0 and out_channels % num_paths == 0
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in_channels = in_channels // num_paths
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out_channels = out_channels // num_paths
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groups = min(out_channels, groups)
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self.paths = nn.ModuleList()
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for k, d in zip(kernel_size, dilation):
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p = get_padding(k, stride, d)
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self.paths.append(nn.Sequential(OrderedDict([
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('conv', nn.Conv2d(
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in_channels, out_channels, kernel_size=k, stride=stride, padding=p, dilation=d, groups=groups)),
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('bn', norm_layer(out_channels)),
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('act', act_layer(inplace=True))
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])))
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if use_attn:
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attn_channels = max(int(out_channels / attn_reduction), min_attn_channels)
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self.attn = SelectiveKernelAttn(out_channels, num_paths, attn_channels)
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else:
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self.attn = None
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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.out_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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if self.attn is not None:
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x = torch.stack(x_paths, dim=1)
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# print('paths', x_paths.shape)
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x_attn = self.attn(x)
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#print('attn', x_attn.shape)
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x = x * x_attn
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#print('amul', x.shape)
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if self.split_input:
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B, N, C, H, W = x.shape
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x = x.reshape(B, N * C, H, W)
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else:
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x = torch.sum(x, dim=1)
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#print('aout', x.shape)
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return x
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class SelectiveKernelBasic(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None,
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cardinality=1, base_width=64, use_se=False, sk_kwargs=None,
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reduce_first=1, dilation=1, previous_dilation=1, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
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super(SelectiveKernelBasic, self).__init__()
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sk_kwargs = sk_kwargs or {}
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assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
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assert base_width == 64, 'BasicBlock doest not support changing base width'
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first_planes = planes // reduce_first
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outplanes = planes * self.expansion
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_selective_first = True # FIXME temporary, for experiments
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if _selective_first:
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self.conv1 = SelectiveKernelConv(
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inplanes, first_planes, stride=stride, dilation=dilation, **sk_kwargs)
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else:
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self.conv1 = nn.Conv2d(
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inplanes, first_planes, kernel_size=3, stride=stride, padding=dilation,
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dilation=dilation, bias=False)
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self.bn1 = norm_layer(first_planes)
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self.act1 = act_layer(inplace=True)
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if _selective_first:
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self.conv2 = nn.Conv2d(
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first_planes, outplanes, kernel_size=3, padding=previous_dilation,
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dilation=previous_dilation, bias=False)
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else:
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self.conv2 = SelectiveKernelConv(
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first_planes, outplanes, dilation=previous_dilation, **sk_kwargs)
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self.bn2 = norm_layer(outplanes)
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self.se = SEModule(outplanes, planes // 4) if use_se else None
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self.act2 = act_layer(inplace=True)
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self.downsample = downsample
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self.stride = stride
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self.dilation = dilation
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.act1(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.se is not None:
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out = self.se(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.act2(out)
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return out
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class SelectiveKernelBottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None,
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cardinality=1, base_width=64, use_se=False, sk_kwargs=None,
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reduce_first=1, dilation=1, previous_dilation=1, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
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super(SelectiveKernelBottleneck, self).__init__()
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sk_kwargs = sk_kwargs or {}
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width = int(math.floor(planes * (base_width / 64)) * cardinality)
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first_planes = width // reduce_first
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outplanes = planes * self.expansion
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self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False)
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self.bn1 = norm_layer(first_planes)
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self.act1 = act_layer(inplace=True)
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self.conv2 = SelectiveKernelConv(
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first_planes, width, stride=stride, dilation=dilation, groups=cardinality, **sk_kwargs)
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self.bn2 = norm_layer(width)
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self.act2 = act_layer(inplace=True)
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self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
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self.bn3 = norm_layer(outplanes)
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self.se = SEModule(outplanes, planes // 4) if use_se else None
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self.act3 = act_layer(inplace=True)
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self.downsample = downsample
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self.stride = stride
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self.dilation = dilation
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.act1(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.act2(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.se is not None:
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out = self.se(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.act3(out)
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return out
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@register_model
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def skresnet26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a ResNet-26 model.
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"""
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default_cfg = default_cfgs['skresnet26d']
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sk_kwargs = dict(
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keep_3x3=False,
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)
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model = ResNet(
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SelectiveKernelBottleneck, [2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True,
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num_classes=num_classes, in_chans=in_chans, block_args=dict(sk_kwargs=sk_kwargs),
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**kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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def skresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a ResNet-18 model.
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"""
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default_cfg = default_cfgs['skresnet18']
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sk_kwargs = dict(
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min_attn_channels=16,
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)
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model = ResNet(
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SelectiveKernelBasic, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
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block_args=dict(sk_kwargs=sk_kwargs), **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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def sksresnet18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a ResNet-18 model.
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"""
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default_cfg = default_cfgs['skresnet18']
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sk_kwargs = dict(
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min_attn_channels=16,
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split_input=True
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)
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model = ResNet(
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SelectiveKernelBasic, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
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block_args=dict(sk_kwargs=sk_kwargs), **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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