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PreResNet.py
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PreResNet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = conv3x3(in_planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
)
def forward(self, x):
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out)
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out += shortcut
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion*planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class PreActBottleneck(nn.Module):
'''Pre-activation version of the original Bottleneck module.'''
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(PreActBottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
)
def forward(self, x):
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out)
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out = self.conv3(F.relu(self.bn3(out)))
out += shortcut
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 64
self.temperature = 0.1
self.proto_m = 0.99
self.low_dim = 128
self.conv1 = conv3x3(3,64)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512*block.expansion, num_classes)
self.projector = nn.Sequential(
nn.Linear(512 * block.expansion, 512 * block.expansion),
nn.ReLU(), nn.Linear(512 * block.expansion, self.low_dim))
self.register_buffer("prototypes", torch.zeros(num_classes, self.low_dim))
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
@torch.no_grad()
def init_prototypes(self, features, labels):
# features, labels are lists of tensors
assert set(range(len(self.prototypes))) == set(labels.cpu().numpy()), \
f'The missing labels are {set(range(len(self.prototypes))) - set(labels.cpu().numpy())}'
for i in range(self.prototypes.shape[0]):
self.prototypes[i] = features[labels == i].mean(dim=0)
self.prototypes = F.normalize(self.prototypes, p=2, dim=1)
@torch.no_grad()
def update_prototypes(self, features, labels):
avg_features = torch.zeros_like(self.prototypes).to(self.prototypes.device)
for i in range(avg_features.shape[0]):
avg_features[i] = features[labels == i].mean(dim=0)
avg_features = F.normalize(avg_features, p=2, dim=1)
for i in range(avg_features.shape[0]):
if not torch.isnan(avg_features[i]).any():
self.prototypes[i] = self.proto_m * self.prototypes[i] + (1 - self.proto_m) * avg_features[i]
self.prototypes = F.normalize(self.prototypes, p=2, dim=1)
def forward(self, x, forward_pass='default', sharpen=True, lin=0, lout=5):
out = x
if lin < 1 and lout > -1:
out = self.conv1(out)
out = self.bn1(out)
out = F.relu(out)
if lin < 2 and lout > 0:
out = self.layer1(out)
if lin < 3 and lout > 1:
out = self.layer2(out)
if lin < 4 and lout > 2:
out = self.layer3(out)
if lin < 5 and lout > 3:
out = self.layer4(out)
if lout > 4:
out = F.avg_pool2d(out, 4)
features = out.view(out.size(0), -1)
out = self.linear(features)
if forward_pass == 'default':
return out
elif forward_pass == 'backbone':
return features
elif forward_pass == 'cls':
return out
elif forward_pass == 'proj':
q = self.projector(features)
q = F.normalize(q, dim=1)
return q
elif forward_pass == 'cls_proj':
q = self.projector(features)
q = F.normalize(q, dim=1)
return out, q
elif forward_pass == 'all':
q = self.projector(features)
q = F.normalize(q, dim=1)
prototypes = self.prototypes.clone().detach()
if sharpen:
logits_proto = torch.mm(q, prototypes.t()) / self.temperature
else:
logits_proto = torch.mm(q, prototypes.t())
return out, logits_proto, q
else:
raise ValueError('Invalid forward pass {}'.format(forward_pass))
def ResNet18(num_classes=10):
return ResNet(PreActBlock, [2,2,2,2], num_classes=num_classes)
def ResNet34(num_classes=10):
return ResNet(BasicBlock, [3,4,6,3], num_classes=num_classes)
def ResNet50(num_classes=10):
return ResNet(Bottleneck, [3,4,6,3], num_classes=num_classes)
def ResNet101(num_classes=10):
return ResNet(Bottleneck, [3,4,23,3], num_classes=num_classes)
def ResNet152(num_classes=10):
return ResNet(Bottleneck, [3,8,36,3], num_classes=num_classes)
def test():
net = ResNet18()
y = net(Variable(torch.randn(1,3,32,32)))
print(y.size())