models.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from operator import itemgetter

class CNNModel1(nn.Module):
    def __init__(self, fully_layer_1, fully_layer_2, drop_rate):
        super(CNNModel1, self).__init__()

        self.conv1 = nn.Conv2d(3, 32, 2)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, 2)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, 2)
        self.bn3 = nn.BatchNorm2d(128)
        self.conv4 = nn.Conv2d(128, 64, 2)
        self.bn4 = nn.BatchNorm2d(64)
        self.conv5 = nn.Conv2d(64, 32, 2)
        self.bn5 = nn.BatchNorm2d(32)

        self.pool = nn.MaxPool2d(2, 2)
        self.drop_rate = drop_rate
        #self.dropout = nn.Dropout(drop_rate)
        self.fc1 = nn.Linear(#32*5*5,
                             32*8*8, fully_layer_1)
        self.fc2 = nn.Linear(fully_layer_1, fully_layer_2)
        self.fc3 = nn.Linear(fully_layer_2, 2)

    def forward(self, x):
        #print(x.shape)
        x = self.pool(F.relu(self.bn1(self.conv1(x))))
        #print(x.shape)
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        #print(x.shape)
        x = self.pool(F.relu(self.bn3(self.conv3(x))))
        #print(x.shape)
        x = self.pool(F.relu(self.bn4(self.conv4(x))))
        #print(x.shape)
        x = self.pool(F.relu(self.bn5(self.conv5(x))))
        #print(x.shape)

        x = x.view(-1, #32*5*5) # For 200x200 images
                   32*8*8)  # For 300x300 images
        
        """
        x = self.dropout(F.relu(self.fc1(x))
        x = self.dropout(F.relu(self.fc2(x))
        x = self.dropout(x)"""
        x = F.dropout(F.relu(self.fc1(x)), self.drop_rate, self.training)
        x = F.dropout(F.relu(self.fc2(x)), self.drop_rate, self.training)
        x = self.fc3(x)

        return x

# TODO: Create other models