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backbone.py
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backbone.py
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''' Implementation for ResNet '''
import torch
import torch.nn as nn
from head import Normalized_Softmax_Loss, Normalized_BCE_Loss, Unified_Cross_Entropy_Loss
def conv3x3(in_planes, out_planes, stride=1, groups=1):
"""3x3 convolution with padding"""
if stride==2:
return nn.Conv2d(in_planes, out_planes, kernel_size=2, stride=stride, bias=False)
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
class IRBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(IRBlock, self).__init__()
self.downsample = downsample
self.bn0 = nn.BatchNorm2d(inplanes)
self.conv1 = conv3x3(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.prelu = nn.PReLU(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
def forward(self, x):
out = self.bn0(x)
out = self.conv1(out)
out = self.bn1(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
else:
identity = x
return out + identity
class LResNetXEIR(nn.Module):
def __init__(self, block, layers, zero_init_residual=True, num_classes=10):
super(LResNetXEIR, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.inplanes)
self.prelu = nn.PReLU(self.inplanes)
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
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.bn4 = nn.BatchNorm2d(512)
self.dropout = nn.Dropout(p=0.4)
self.fc5 = nn.Conv2d(512, 512, (7, 7), bias=False)
self.bn5 = nn.BatchNorm1d(512, affine=True)
self.bn5.weight.requires_grad = False
# self.loss = Normalized_Softmax_Loss(512, num_classes)
# self.loss = Normalized_BCE_Loss(512, num_classes)
self.loss = Unified_Cross_Entropy_Loss(512, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, a=1, mode='fan_in', nonlinearity='leaky_relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.weight, 1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.PReLU):
nn.init.constant_(m.weight, 0.25)
elif isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d):
if m.weight is not None:
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
if zero_init_residual:
for m in self.modules():
if isinstance(m, IRBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1):
if stride != 1:
downsample = nn.Sequential(
conv3x3(self.inplanes, planes, stride),
nn.BatchNorm2d(planes)
)
else:
downsample = None
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def restrict_weights(self):
for m in self.modules():
if isinstance(m, nn.PReLU):
m.weight.data.clamp_(min=0.01)
def forward(self, x, t=None, partial_index=None):
if not self.training:
x = torch.cat((x, x.flip(3)), 0)
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn4(x)
x = self.dropout(x)
x = self.fc5(x)
x = torch.flatten(x, 1)
feat = self.bn5(x)
if not self.training:
a, b = feat.chunk(2)
return a + b
else:
return self.loss(feat, t, partial_index)
def LResNet50EIR(**kwargs):
model = LResNetXEIR(IRBlock, [3, 4, 14, 3], **kwargs)
return model
def LResNet100EIR(**kwargs):
model = LResNetXEIR(IRBlock, [3, 13, 30, 3], **kwargs)
return model
def LResNet200EIR(**kwargs):
model = LResNetXEIR(IRBlock, [3, 33, 60, 3], **kwargs)
return model
if __name__ == '__main__':
model = LResNet50EIR()
print(model)