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fixup_resnet.py
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fixup_resnet.py
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# From https://github.com/visinf/fast-axiomatic-attribution/blob/main/imagenet/models/fixup_resnet.py
import torch
import torch.nn as nn
import numpy as np
__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101',
'fixup_resnet152', 'xfixup_resnet18', 'xfixup_resnet34', 'xfixup_resnet50', 'xfixup_resnet101', 'xfixup_resnet152']
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class FixupBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(FixupBasicBlock, self).__init__()
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.bias1a = nn.Parameter(torch.zeros(1))
self.conv1 = conv3x3(inplanes, planes, stride)
self.bias1b = nn.Parameter(torch.zeros(1))
self.relu = nn.ReLU(inplace=True)
self.bias2a = nn.Parameter(torch.zeros(1))
self.conv2 = conv3x3(planes, planes)
self.scale = nn.Parameter(torch.ones(1))
self.bias2b = nn.Parameter(torch.zeros(1))
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x + self.bias1a)
out = self.relu(out + self.bias1b)
out = self.conv2(out + self.bias2a)
out = out * self.scale + self.bias2b
if self.downsample is not None:
identity = self.downsample(x + self.bias1a)
out += identity
out = self.relu(out)
return out
class FixupBottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(FixupBottleneck, self).__init__()
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.bias1a = nn.Parameter(torch.zeros(1))
self.conv1 = conv1x1(inplanes, planes)
self.bias1b = nn.Parameter(torch.zeros(1))
self.bias2a = nn.Parameter(torch.zeros(1))
self.conv2 = conv3x3(planes, planes, stride)
self.bias2b = nn.Parameter(torch.zeros(1))
self.bias3a = nn.Parameter(torch.zeros(1))
self.conv3 = conv1x1(planes, planes * self.expansion)
self.scale = nn.Parameter(torch.ones(1))
self.bias3b = nn.Parameter(torch.zeros(1))
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x + self.bias1a)
out = self.relu(out + self.bias1b)
out = self.conv2(out + self.bias2a)
out = self.relu(out + self.bias2b)
out = self.conv3(out + self.bias3a)
out = out * self.scale + self.bias3b
if self.downsample is not None:
identity = self.downsample(x + self.bias1a)
out += identity
out = self.relu(out)
return out
class FixupResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
super(FixupResNet, self).__init__()
self.num_layers = sum(layers)
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bias1 = nn.Parameter(torch.zeros(1))
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.bias2 = nn.Parameter(torch.zeros(1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, FixupBasicBlock):
nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(
2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.5))
nn.init.constant_(m.conv2.weight, 0)
if m.downsample is not None:
nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(
2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
elif isinstance(m, FixupBottleneck):
nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(
2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.25))
nn.init.normal_(m.conv2.weight, mean=0, std=np.sqrt(
2 / (m.conv2.weight.shape[0] * np.prod(m.conv2.weight.shape[2:]))) * self.num_layers ** (-0.25))
nn.init.constant_(m.conv3.weight, 0)
if m.downsample is not None:
nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(
2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
elif isinstance(m, nn.Linear):
nn.init.constant_(m.weight, 0)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = conv1x1(self.inplanes, planes *
block.expansion, stride)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x + self.bias1)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x + self.bias2)
return x
def fixup_resnet18(**kwargs):
"""Constructs a Fixup-ResNet-18 model.
"""
model = FixupResNet(FixupBasicBlock, [2, 2, 2, 2], **kwargs)
return model
def fixup_resnet34(**kwargs):
"""Constructs a Fixup-ResNet-34 model.
"""
model = FixupResNet(FixupBasicBlock, [3, 4, 6, 3], **kwargs)
return model
def fixup_resnet50(**kwargs):
"""Constructs a Fixup-ResNet-50 model.
"""
model = FixupResNet(FixupBottleneck, [3, 4, 6, 3], **kwargs)
return model
def fixup_resnet101(**kwargs):
"""Constructs a Fixup-ResNet-101 model.
"""
model = FixupResNet(FixupBottleneck, [3, 4, 23, 3], **kwargs)
return model
def fixup_resnet152(**kwargs):
"""Constructs a Fixup-ResNet-152 model.
"""
model = FixupResNet(FixupBottleneck, [3, 8, 36, 3], **kwargs)
return model
class XFixupBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(XFixupBasicBlock, self).__init__()
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.relu(out)
out = self.conv2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class XFixupBottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(XFixupBottleneck, self).__init__()
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, planes)
self.conv2 = conv3x3(planes, planes, stride)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.relu(out)
out = self.conv2(out)
out = self.relu(out)
out = self.conv3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class XFixupResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
super(XFixupResNet, self).__init__()
self.num_layers = sum(layers)
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes, bias=False)
for m in self.modules():
if isinstance(m, XFixupBasicBlock):
nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(
2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.5))
nn.init.constant_(m.conv2.weight, 0)
if m.downsample is not None:
nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(
2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
elif isinstance(m, XFixupBottleneck):
nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(
2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.25))
nn.init.normal_(m.conv2.weight, mean=0, std=np.sqrt(
2 / (m.conv2.weight.shape[0] * np.prod(m.conv2.weight.shape[2:]))) * self.num_layers ** (-0.25))
nn.init.constant_(m.conv3.weight, 0)
if m.downsample is not None:
nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(
2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
elif isinstance(m, nn.Linear):
nn.init.constant_(m.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = conv1x1(self.inplanes, planes *
block.expansion, stride)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def xfixup_resnet18(**kwargs):
"""Constructs a Fixup-ResNet-18 model.
"""
model = XFixupResNet(XFixupBasicBlock, [2, 2, 2, 2], **kwargs)
return model
def xfixup_resnet34(**kwargs):
"""Constructs a Fixup-ResNet-34 model.
"""
model = XFixupResNet(XFixupBasicBlock, [3, 4, 6, 3], **kwargs)
return model
def xfixup_resnet50(**kwargs):
"""Constructs a Fixup-ResNet-50 model.
"""
model = XFixupResNet(XFixupBottleneck, [3, 4, 6, 3], **kwargs)
return model
def xfixup_resnet101(**kwargs):
"""Constructs a Fixup-ResNet-101 model.
"""
model = XFixupResNet(XFixupBottleneck, [3, 4, 23, 3], **kwargs)
return model
def xfixup_resnet152(**kwargs):
"""Constructs a Fixup-ResNet-152 model.
"""
model = XFixupResNet(XFixupBottleneck, [3, 8, 36, 3], **kwargs)
return model