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models.py
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models.py
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import torch
import torch.nn as nn
class Model1(nn.Module):
"""
Model1 Shallow Network with 3 Convolutions followed by
fully connected layers
"""
def __init__(self):
super(Model1, self).__init__()
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(4, 32, 8, 4)
self.conv2 = nn.Conv2d(32, 64, 4, 2)
self.conv3 = nn.Conv2d(64, 64, 3, 1)
self.flatten = nn.Flatten()
self.fc1 = nn.Linear(3136, 512)
self.fc2 = nn.Linear(512, 3)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.relu(x)
x = self.conv3(x)
x = self.relu(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
class ResBlock(nn.Module):
"""
Residual Block with 2 Convolutions and Batchnorms
followed by a skip-connection
"""
def __init__(self, channels):
super(ResBlock, self).__init__()
self.relu = nn.ReLU()
self.channels = channels
self.conv1 = nn.Conv2d(
channels, channels, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(
channels, channels, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(channels)
self.bn2 = nn.BatchNorm2d(channels)
def forward(self, x):
res = x
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
return x+res
class Model2(nn.Module):
"""
Deeper Network Compared to model1
More Convolutions with skip connections
"""
def __init__(self):
super(Model2, self).__init__()
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(4, 64, kernel_size=3, stride=1, padding=1)
self.res1 = ResBlock(64)
self.res2 = ResBlock(128)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.res3 = ResBlock(256)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.res4 = ResBlock(512)
self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.flatten = nn.Flatten()
self.fc1 = nn.Linear(12800, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 256)
self.fc4 = nn.Linear(256, 64)
self.fc5 = nn.Linear(64, 3)
def forward(self, x):
x = self.conv1(x)
x = self.res1(x)
x = self.pool(x)
x = self.conv2(x)
x = self.res2(x)
x = self.pool(x)
x = self.conv3(x)
x = self.res3(x)
x = self.pool(x)
x = self.conv4(x)
x = self.res4(x)
x = self.pool(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.relu(x)
x = self.fc3(x)
x = self.relu(x)
x = self.fc4(x)
x = self.relu(x)
x = self.fc5(x)
return x
class Model3(nn.Module):
"""
Similar Network to Model2 but has an LSTM unit
"""
def __init__(self):
super(Model3, self).__init__()
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.res1 = ResBlock(64)
self.res2 = ResBlock(128)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.res3 = ResBlock(256)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.flatten = nn.Flatten()
self.fc1 = nn.Linear(1536, 1024)
self.rnn = nn.LSTM(1024, 512, dropout=0.4)
self.fc3 = nn.Linear(512, 64)
self.fc4 = nn.Linear(64, 4)
def forward(self, x, hidden):
x = self.conv1(x)
x = self.pool(x)
x = self.res1(x)
x = self.pool(x)
x = self.conv2(x)
x = self.pool(x)
x = self.res2(x)
x = self.pool(x)
x = self.conv3(x)
x = self.pool(x)
x = self.res3(x)
x = self.pool(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = x.unsqueeze(1)
x, hidden = self.rnn(x, hidden)
x = self.fc3(x)
x = self.relu(x)
x = x.squeeze(1)
x = self.fc4(x)
return x, hidden
def init_hidden(self, device):
h0 = torch.zeros(1, 1, 512).to(device)
c0 = torch.zeros(1, 1, 512).to(device)
return (h0, c0)