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model.py
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model.py
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import math
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import torch.optim as optim
def call_bn(bn, x):
return bn(x)
class MLP(nn.Module):
def __init__(self,n_outputs=10):
super(MLP, self).__init__()
self.l1=nn.Linear(784,256)
self.l2=nn.Linear(256,n_outputs)
def forward(self, x):
x=x.view(-1,28*28)
x=F.relu(self.l1(x))
x=self.l2(x)
return x
class CNN(nn.Module):
def __init__(self, n_outputs=10):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, n_outputs)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
class CNN_large(nn.Module):
def __init__(self, input_channel=3, n_outputs=10, dropout_rate=0.25, momentum=0.1):
self.dropout_rate = dropout_rate
self.momentum = momentum
super(CNN_large, self).__init__()
self.c1=nn.Conv2d(input_channel,64,kernel_size=3,stride=1, padding=1)
self.c2=nn.Conv2d(64,64,kernel_size=3,stride=1, padding=1)
self.c3=nn.Conv2d(64,128,kernel_size=3,stride=1, padding=1)
self.c4=nn.Conv2d(128,128,kernel_size=3,stride=1, padding=1)
self.c5=nn.Conv2d(128,196,kernel_size=3,stride=1, padding=1)
self.c6=nn.Conv2d(196,16,kernel_size=3,stride=1, padding=1)
self.l_c1=nn.Linear(256,n_outputs)
self.bn1=nn.BatchNorm2d(64,momentum=self.momentum)
self.bn2=nn.BatchNorm2d(64,momentum=self.momentum)
self.bn3=nn.BatchNorm2d(128,momentum=self.momentum)
self.bn4=nn.BatchNorm2d(128,momentum=self.momentum)
self.bn5=nn.BatchNorm2d(196,momentum=self.momentum)
self.bn6=nn.BatchNorm2d(16,momentum=self.momentum)
def forward(self, x):
h=x
h=self.c1(h)
h=F.relu(call_bn(self.bn1, h))
h=self.c2(h)
h=F.relu(call_bn(self.bn2, h))
h=F.max_pool2d(h, kernel_size=2, stride=2)
h=self.c3(h)
h=F.relu(call_bn(self.bn3, h))
h=self.c4(h)
h=F.relu(call_bn(self.bn4, h))
h=F.max_pool2d(h, kernel_size=2, stride=2)
h=self.c5(h)
h=F.relu(call_bn(self.bn5, h))
h=self.c6(h)
h=F.relu(call_bn(self.bn6, h))
h=F.max_pool2d(h, kernel_size=2, stride=2)
h = h.view(h.size(0), -1)
logit=self.l_c1(h)
return logit
class CNN_co(nn.Module):
def __init__(self, input_channel=3, n_outputs=10, dropout_rate=0.25, top_bn=False):
self.dropout_rate = dropout_rate
self.top_bn = top_bn
super(CNN_co, self).__init__()
self.c1=nn.Conv2d(input_channel,128,kernel_size=3,stride=1, padding=1)
self.c2=nn.Conv2d(128,128,kernel_size=3,stride=1, padding=1)
self.c3=nn.Conv2d(128,128,kernel_size=3,stride=1, padding=1)
self.c4=nn.Conv2d(128,256,kernel_size=3,stride=1, padding=1)
self.c5=nn.Conv2d(256,256,kernel_size=3,stride=1, padding=1)
self.c6=nn.Conv2d(256,256,kernel_size=3,stride=1, padding=1)
self.c7=nn.Conv2d(256,512,kernel_size=3,stride=1, padding=0)
self.c8=nn.Conv2d(512,256,kernel_size=3,stride=1, padding=0)
self.c9=nn.Conv2d(256,128,kernel_size=3,stride=1, padding=0)
self.l_c1=nn.Linear(128,n_outputs)
self.bn1=nn.BatchNorm2d(128)
self.bn2=nn.BatchNorm2d(128)
self.bn3=nn.BatchNorm2d(128)
self.bn4=nn.BatchNorm2d(256)
self.bn5=nn.BatchNorm2d(256)
self.bn6=nn.BatchNorm2d(256)
self.bn7=nn.BatchNorm2d(512)
self.bn8=nn.BatchNorm2d(256)
self.bn9=nn.BatchNorm2d(128)
def forward(self, x,):
h=x
h=self.c1(h)
h=F.leaky_relu(call_bn(self.bn1, h), negative_slope=0.01)
h=self.c2(h)
h=F.leaky_relu(call_bn(self.bn2, h), negative_slope=0.01)
h=self.c3(h)
h=F.leaky_relu(call_bn(self.bn3, h), negative_slope=0.01)
h=F.max_pool2d(h, kernel_size=2, stride=2)
h=F.dropout2d(h, p=self.dropout_rate)
h=self.c4(h)
h=F.leaky_relu(call_bn(self.bn4, h), negative_slope=0.01)
h=self.c5(h)
h=F.leaky_relu(call_bn(self.bn5, h), negative_slope=0.01)
h=self.c6(h)
h=F.leaky_relu(call_bn(self.bn6, h), negative_slope=0.01)
h=F.max_pool2d(h, kernel_size=2, stride=2)
h=F.dropout2d(h, p=self.dropout_rate)
h=self.c7(h)
h=F.leaky_relu(call_bn(self.bn7, h), negative_slope=0.01)
h=self.c8(h)
h=F.leaky_relu(call_bn(self.bn8, h), negative_slope=0.01)
h=self.c9(h)
h=F.leaky_relu(call_bn(self.bn9, h), negative_slope=0.01)
h=F.avg_pool2d(h, kernel_size=h.data.shape[2])
h = h.view(h.size(0), h.size(1))
logit=self.l_c1(h)
if self.top_bn:
logit=call_bn(self.bn_c1, logit)
return logit