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wide_resnet.py
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wide_resnet.py
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"""From
https://github.com/RobustBench/robustbench/blob/master/robustbench/model_zoo/architectures/wide_resnet.py"""
import math
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helpers import build_model_with_cfg
from timm.models.registry import register_model
import torch
import torch.nn as nn
import torch.nn.functional as F
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'input_size': (3, 224, 224),
'pool_size': None,
'crop_pct': 0.875,
'interpolation': 'bilinear',
'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN,
'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1',
'classifier': 'fc',
**kwargs
}
default_cfgs = {
'wide_resnet28_10': _cfg(),
'wide_resnet34_10': _cfg(),
'wide_resnet34_20': _cfg(),
'wide_resnet70_16': _cfg(),
'wide_resnet106_16': _cfg(),
}
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, drop_rate=0.0):
super().__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
self.relu2 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
self.drop_rate = drop_rate
self.equalInOut = (in_planes == out_planes)
self.convShortcut = (not self.equalInOut) and nn.Conv2d(
in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) or None
def forward(self, x):
if not self.equalInOut:
x = self.relu1(self.bn1(x))
else:
out = self.relu1(self.bn1(x))
out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
if self.drop_rate > 0:
out = F.dropout(out, p=self.drop_rate, training=self.training)
out = self.conv2(out)
return torch.add(x if self.equalInOut else self.convShortcut(x), out)
class NetworkBlock(nn.Module):
def __init__(self, nb_layers, in_planes, out_planes, block, stride, drop_rate=0.0):
super().__init__()
self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, drop_rate)
def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, drop_rate):
layers = []
for i in range(int(nb_layers)):
layers.append(
block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, drop_rate))
return nn.Sequential(*layers)
def forward(self, x):
return self.layer(x)
class WideResNet(nn.Module):
""" Based on code from https://github.com/yaodongyu/TRADES """
def __init__(self,
depth=28,
num_classes=10,
widen_factor=10,
sub_block1=False,
drop_rate=0.0,
bias_last=True,
in_chans=3,
img_size=224):
super().__init__()
self.num_classes = num_classes
n_channels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
assert ((depth - 4) % 6 == 0)
n = (depth - 4) / 6
block = BasicBlock
# 1st conv before any network block
self.conv1 = nn.Conv2d(in_chans, n_channels[0], kernel_size=3, stride=1, padding=1, bias=False)
# 1st block
self.block1 = NetworkBlock(n, n_channels[0], n_channels[1], block, 1, drop_rate)
if sub_block1:
# 1st sub-block
self.sub_block1 = NetworkBlock(n, n_channels[0], n_channels[1], block, 1, drop_rate)
# 2nd block
self.block2 = NetworkBlock(n, n_channels[1], n_channels[2], block, 2, drop_rate)
# 3rd block
self.block3 = NetworkBlock(n, n_channels[2], n_channels[3], block, 2, drop_rate)
# global average pooling and classifier
self.bn1 = nn.BatchNorm2d(n_channels[3])
self.relu = nn.ReLU(inplace=True)
self.fc = nn.Linear(n_channels[3], num_classes, bias=bias_last)
self.n_channels = n_channels[3]
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear) and not m.bias is None:
m.bias.data.zero_()
def forward(self, x):
out = self.conv1(x)
out = self.block1(out)
out = self.block2(out)
out = self.block3(out)
out = self.relu(self.bn1(out))
out = F.avg_pool2d(out, 8)
out = out.view(-1, self.n_channels)
return self.fc(out)
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.n_channels, num_classes) if num_classes > 0 else nn.Identity()
def _create_wide_resnet(variant, pretrained=False, default_cfg=None, **kwargs):
model = build_model_with_cfg(WideResNet, variant, pretrained, **kwargs)
return model
@register_model
def wide_resnet28_10(pretrained=False, **kwargs):
model_args = dict(depth=28, widen_factor=10, **kwargs)
return _create_wide_resnet('wide_resnet28_10', pretrained, **model_args)
@register_model
def wide_resnet34_10(pretrained=False, **kwargs):
model_args = dict(depth=34, widen_factor=10, **kwargs)
return _create_wide_resnet('wide_resnet34_10', pretrained, **model_args)
@register_model
def wide_resnet34_20(pretrained=False, **kwargs):
model_args = dict(depth=34, widen_factor=20, **kwargs)
return _create_wide_resnet('wide_resnet34_20', pretrained, **model_args)
@register_model
def wide_resnet70_16(pretrained=False, **kwargs):
model_args = dict(depth=70, widen_factor=16, **kwargs)
return _create_wide_resnet('wide_resnet70_16', pretrained, **model_args)
@register_model
def wide_resnet106_16(pretrained=False, **kwargs):
model_args = dict(depth=106, widen_factor=16, **kwargs)
return _create_wide_resnet('wide_resnet106_16', pretrained, **model_args)