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preactresnet.py
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preactresnet.py
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'''Pre-activation ResNet in PyTorch.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv:1603.05027
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
track_running_stats=True
affine=True
normal_func = nn.BatchNorm2d
# track_running_stats=False
# affine=True
# normal_func = nn.InstanceNorm2d
if not track_running_stats:
print('BN track False')
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride=1, activation='ReLU', softplus_beta=1):
super(PreActBlock, self).__init__()
self.bn1 = normal_func(in_planes, track_running_stats=track_running_stats, affine=affine)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = normal_func(planes, track_running_stats=track_running_stats, affine=affine)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
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)
)
if activation == 'ReLU':
self.relu = nn.ReLU(inplace=True)
print('ReLU')
elif activation == 'Softplus':
self.relu = nn.Softplus(beta=softplus_beta, threshold=20)
print('Softplus')
elif activation == 'GELU':
self.relu = nn.GELU()
print('GELU')
elif activation == 'ELU':
self.relu = nn.ELU(alpha=1.0, inplace=True)
print('ELU')
elif activation == 'LeakyReLU':
self.relu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
print('LeakyReLU')
elif activation == 'SELU':
self.relu = nn.SELU(inplace=True)
print('SELU')
elif activation == 'CELU':
self.relu = nn.CELU(alpha=1.2, inplace=True)
print('CELU')
elif activation == 'Tanh':
self.relu = nn.Tanh()
print('Tanh')
def forward(self, x):
out = self.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
out = self.conv1(out)
out = self.conv2(self.relu(self.bn2(out)))
out += shortcut
return out
class PreActBottleneck(nn.Module):
'''Pre-activation version of the original Bottleneck module.'''
expansion = 4
def __init__(self, in_planes, planes, stride=1, activation='ReLU', softplus_beta=1):
super(PreActBottleneck, self).__init__()
self.bn1 = normal_func(in_planes, track_running_stats=track_running_stats, affine=affine)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn2 = normal_func(planes, track_running_stats=track_running_stats, affine=affine)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn3 = normal_func(planes, track_running_stats=track_running_stats, affine=affine)
self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
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) if hasattr(self, 'shortcut') else x
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 PreActResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10, normalize = False, normalize_only_FN = False, scale = 15, activation='ReLU', softplus_beta=1):
super(PreActResNet, self).__init__()
self.in_planes = 64
self.normalize = normalize
self.normalize_only_FN = normalize_only_FN
self.scale = scale
self.activation = activation
self.softplus_beta = softplus_beta
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
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.bn = normal_func(512 * block.expansion, track_running_stats=track_running_stats, affine=affine)
if self.normalize:
self.linear = nn.Linear(512*block.expansion, num_classes, bias=False)
else:
self.linear = nn.Linear(512*block.expansion, num_classes)
if activation == 'ReLU':
self.relu = nn.ReLU(inplace=True)
print('ReLU')
elif activation == 'Softplus':
self.relu = nn.Softplus(beta=softplus_beta, threshold=20)
print('Softplus')
elif activation == 'GELU':
self.relu = nn.GELU()
print('GELU')
elif activation == 'ELU':
self.relu = nn.ELU(alpha=1.0, inplace=True)
print('ELU')
elif activation == 'LeakyReLU':
self.relu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
print('LeakyReLU')
elif activation == 'SELU':
self.relu = nn.SELU(inplace=True)
print('SELU')
elif activation == 'CELU':
self.relu = nn.CELU(alpha=1.2, inplace=True)
print('CELU')
elif activation == 'Tanh':
self.relu = nn.Tanh()
print('Tanh')
print('Use activation of ' + activation)
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,
activation=self.activation, softplus_beta=self.softplus_beta))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv1(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.relu(self.bn(out))
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
if self.normalize_only_FN:
out = F.normalize(out, p=2, dim=1)
if self.normalize:
out = F.normalize(out, p=2, dim=1) * self.scale
for _, module in self.linear.named_modules():
if isinstance(module, nn.Linear):
module.weight.data = F.normalize(module.weight, p=2, dim=1)
return self.linear(out)
def PreActResNet18(num_classes=10, normalize = False, normalize_only_FN = False, scale = 15, activation='ReLU', softplus_beta=1):
return PreActResNet(PreActBlock, [2,2,2,2], num_classes=num_classes, normalize = normalize
, normalize_only_FN = normalize_only_FN, scale = scale, activation=activation, softplus_beta=softplus_beta)
def PreActResNet34():
return PreActResNet(PreActBlock, [3,4,6,3])
def PreActResNet50():
return PreActResNet(PreActBottleneck, [3,4,6,3])
def PreActResNet101():
return PreActResNet(PreActBottleneck, [3,4,23,3])
def PreActResNet152():
return PreActResNet(PreActBottleneck, [3,8,36,3])
def test():
net = PreActResNet18()
y = net((torch.randn(1,3,32,32)))
print(y.size())
# test()