model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
        
class ScoringModel(nn.Module):
    def __init__(self, frames_per_clip:int, input_dim: int, hidden_dim: int, num_hidden_layers: int):
        super().__init__()
        self.frames_per_clip = frames_per_clip
        if num_hidden_layers == 0:
            self.model = nn.Linear(input_dim * frames_per_clip, 1)
        else:
            modules = [
                    nn.Linear(input_dim * frames_per_clip, hidden_dim), 
                    nn.ReLU(True),
                    nn.BatchNorm1d(hidden_dim)
                ]
            for _ in range(num_hidden_layers-1):
                modules.extend([
                    nn.Linear(hidden_dim, hidden_dim), 
                    nn.ReLU(True),
                    nn.BatchNorm1d(hidden_dim)
                ])
            modules.append(nn.Linear(hidden_dim, 1))
            self.model = nn.Sequential(*modules)
        
    def forward(self, x):
        return self.model(x)

if __name__ == '__main__':
    batch_size, input_dim, frames_per_clip = 8, 512, 3
    hidden_dim, num_hidden_layers = 0, 0
    x = torch.rand(batch_size, input_dim * frames_per_clip)
    scoring_model = ScoringModel(frames_per_clip, input_dim, hidden_dim, num_hidden_layers)
    print(scoring_model)
    y = scoring_model(x)
    print(y.size()) # should be (batch_size, 1)