model.py

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

class nielsen(nn.Module):
    def __init__(self):
        super(nielsen, self).__init__()
        self.rnn = nn.LSTM(720, 128)
        self.conv1 = nn.Conv2d(3, 10, kernel_size=4, stride=2)
        self.bn1 = nn.BatchNorm2d(10)
        self.max1 = nn.MaxPool2d(kernel_size=2, stride=1, padding=1)
        self.conv2 = nn.Conv2d(10,5, kernel_size=4, stride=2)
        self.bn2 = nn.BatchNorm2d(5)
        self.max2 = nn.MaxPool2d(1,2)
        self.fc = nn.Linear(128,2)

    def forward(self,x):
        x = x / 255.0
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.max1(x)
        x = F.relu(self.bn2(self.conv2(x)))
        x = self.max2(x)
        x = torch.reshape(x,(-1,1,720))
        x, _ = self.rnn(x)
        x = self.fc(x)
        x = F.softmax(x,-1)
        return x

def train(data, t_label, model, optimizer):
    criterion = nn.MSELoss()

    model.zero_grad()
    pred = model(data).squeeze()
    loss = criterion(pred, t_label)
    loss.backward()
    optimizer.step()
    return loss


def eval_label(model,data):
    labels = model(data).squeeze()
    label = torch.argmax(labels,-1)
    return label.tolist()