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models.py
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models.py
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from torch import nn
from lsoftmax import LSoftmaxLinear
class MNISTNet(nn.Module):
def __init__(self, margin, device):
super(MNISTNet, self).__init__()
self.margin = margin
self.device = device
self.conv_0 = nn.Sequential(
nn.BatchNorm2d(1),
nn.Conv2d(1, 64, 3),
nn.PReLU(),
nn.BatchNorm2d(64)
)
self.conv_1 = nn.Sequential(
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(2, 2)
)
self.conv_2 = nn.Sequential(
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(2, 2)
)
self.conv_3 = nn.Sequential(
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 3, padding=1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(2, 2)
)
self.fc = nn.Sequential(
nn.Linear(576, 256),
nn.BatchNorm1d(256)
)
self.lsoftmax_linear = LSoftmaxLinear(
input_features=256, output_features=10, margin=margin, device=self.device)
self.reset_parameters()
def reset_parameters(self):
self.lsoftmax_linear.reset_parameters()
def forward(self, x, target=None):
x = self.conv_0(x)
x = self.conv_1(x)
x = self.conv_2(x)
x = self.conv_3(x)
x = x.view(-1, 576)
x = self.fc(x)
logit = self.lsoftmax_linear(input=x, target=target)
return logit
class MNISTFIG2Net(nn.Module):
def __init__(self, margin, device):
super(MNISTFIG2Net, self).__init__()
self.margin = margin
self.device = device
self.conv_1 = nn.Sequential(
nn.Conv2d(1, 32, 5, padding=2),
nn.PReLU(),
nn.BatchNorm2d(32),
nn.Conv2d(32, 32, 5, padding=2),
nn.PReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(2, 2)
)
self.conv_2 = nn.Sequential(
nn.Conv2d(32, 64, 5, padding=2),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 5, padding=2),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(2, 2)
)
self.conv_3 = nn.Sequential(
nn.Conv2d(64, 128, 5, padding=2),
nn.PReLU(),
nn.BatchNorm2d(128),
nn.Conv2d(128, 128, 5, padding=2),
nn.PReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(2, 2)
)
self.fc = nn.Sequential(
nn.Linear(1152, 2),
nn.BatchNorm1d(2)
)
self.lsoftmax_linear = LSoftmaxLinear(
input_features=2, output_features=10, margin=margin, device=self.device)
self.reset_parameters()
def reset_parameters(self):
self.lsoftmax_linear.reset_parameters()
def forward(self, x, target=None):
x = self.conv_1(x)
x = self.conv_2(x)
x = self.conv_3(x)
x = x.view(-1, 1152)
x = self.fc(x)
logit = self.lsoftmax_linear(input=x, target=target)
return logit, x