model.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class C3D(nn.Module):
    def __init__(self):
        super(C3D,self).__init__()
        self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))

        self.conv2 = nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.pool2 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))

        self.conv3a = nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.conv3b = nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))

        self.conv4a = nn.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.conv4b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))

        self.conv5a = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.conv5b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
        self.pool5 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 1, 1))

        self.fc6 = nn.Linear(8192, 4096)
        self.fc7 = nn.Linear(4096, 4096)
        self.fc8 = nn.Linear(4096, 487)

        self.dropout = nn.Dropout(0.5)

        self.relu = nn.ReLU()
        self.softmax = nn.Softmax()
    
    def forward(self,x):
        h = self.relu(self.conv1(x))
        h = self.pool1(h)

        h = self.relu(self.conv2(h))
        h = self.pool2(h)

        h = self.relu(self.conv3a(h))
        h = self.relu(self.conv3b(h))
        h = self.pool3(h)

        h = self.relu(self.conv4a(h))
        h = self.relu(self.conv4b(h))
        h = self.pool4(h)

        h = self.relu(self.conv5a(h))
        h = self.relu(self.conv5b(h))
        h = self.pool5(h)

        h = h.view(-1, 8192)
        h = self.relu(self.fc6(h))
        h = self.dropout(h)
        a = self.relu(self.fc7(h))
        h = self.dropout(a)

        logits = self.fc8(h)
        probs = self.softmax(logits)

        return a

class fc(nn.Module):
    def __init__(self):
        super(fc,self).__init__()
        self.fc1 = nn.Linear(4096,512)
        self.fc2 = nn.Linear(512,32)
        self.fc3 = nn.Linear(32,1)
        self.dropout = nn.Dropout(0.6)
        self.relu = nn.ReLU()
        self.sig = nn.Sigmoid()

    def forward(self,x):
        x = self.fc1(x)
        x = self.dropout(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.dropout(x)
        x = self.fc3(x)
        x = self.sig(x)

        return x