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models.py
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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class LogisticRegression(nn.Module):
def __init__(self, input_dim, nr_classes):
super(LogisticRegression, self).__init__()
self.fc = nn.Linear(input_dim, nr_classes)
def forward(self, x):
return self.fc(x)
class FNNet(nn.Module):
def __init__(self, input_dim, interm_dim, output_dim):
super(FNNet, self).__init__()
self.input_dim = input_dim
self.dp1 = torch.nn.Dropout(0.2)
self.dp2 = torch.nn.Dropout(0.2)
self.fc1 = nn.Linear(input_dim, interm_dim)
self.fc2 = nn.Linear(interm_dim, interm_dim)
self.fc3 = nn.Linear(interm_dim, output_dim)
def forward(self, x):
x = self.embed(x)
x = self.fc3(x)
return x
def embed(self, x):
x = self.dp1(F.relu(self.fc1(x.view(-1, self.input_dim))))
x = self.dp2(F.relu(self.fc2(x)))
return x
class ConvNet(nn.Module):
def __init__(self, output_dim):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 5, 1)
self.dp1 = torch.nn.Dropout(0.5)
self.conv2 = nn.Conv2d(32, 64, 5, 1)
self.dp2 = torch.nn.Dropout(0.5)
self.fc1 = nn.Linear(4 * 4 * 64, 128)
self.dp3 = torch.nn.Dropout(0.5)
self.fc2 = nn.Linear(128, output_dim)
def forward(self, x):
x = self.embed(x)
x = self.fc2(x)
return x
def embed(self, x):
x = F.relu(self.dp1(self.conv1(x)))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.dp2(self.conv2(x)))
x = F.max_pool2d(x, 2, 2)
x = x.view(-1, 4 * 4 * 64)
x = F.relu(self.dp3(self.fc1(x)))
return x
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=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.conv1(x))
out = self.conv2(out)
out += self.shortcut(x)
out = F.relu(out)
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.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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.conv1(x))
out = F.relu(self.conv2(out))
out = self.conv3(out)
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 64
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.linear = nn.Linear(512 * block.expansion, num_classes)
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)
def forward(self, x):
out = self.embed(x)
out = self.linear(out)
return out
def embed(self, x):
out = F.relu(self.conv1(x))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
return out
def ResNet18():
return ResNet(BasicBlock, [2, 2, 2, 2])