transforms.py

#! /usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright 2023 Imperial College London (Pingchuan Ma)
# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

import torch
import torchaudio
import torchvision


class FunctionalModule(torch.nn.Module):
    def __init__(self, functional):
        super().__init__()
        self.functional = functional

    def forward(self, input):
        return self.functional(input)


class VideoTransform:
    def __init__(self, speed_rate):
        self.video_pipeline = torch.nn.Sequential(
            FunctionalModule(lambda x: x.unsqueeze(-1)),
            FunctionalModule(lambda x: x if speed_rate == 1 else torch.index_select(x, dim=0, index=torch.linspace(0, x.shape[0]-1, int(x.shape[0] / speed_rate), dtype=torch.int64))),
            FunctionalModule(lambda x: x.permute(3, 0, 1, 2)),
            FunctionalModule(lambda x: x / 255.),
            torchvision.transforms.CenterCrop(88),
            torchvision.transforms.Normalize(0.421, 0.165),
        )

    def __call__(self, sample):
        return self.video_pipeline(sample)


class AudioTransform:
    def __init__(self):
        self.audio_pipeline = torch.nn.Sequential(
            FunctionalModule(lambda x: torch.nn.functional.layer_norm(x, x.shape, eps=0)),
            FunctionalModule(lambda x: x.transpose(0, 1)),
        )

    def __call__(self, sample):
        return self.audio_pipeline(sample)