dataset_test.py

import options as opt
import matplotlib.pyplot as plt
import torch.optim as optim
import numpy as np
import time

from dataset import GridDataset
from torch.utils.data import DataLoader


def dataset2dataloader(
    dataset, num_workers=opt.num_workers, shuffle=True
):
    return DataLoader(
        dataset,
        batch_size=opt.batch_size,
        shuffle=shuffle,
        num_workers=num_workers,
        drop_last=False
    )


dataset = GridDataset(
    video_path=opt.video_path,
    alignments_dir=opt.alignments_dir,
    file_list=opt.train_list,
    vid_pad=opt.vid_padding,
    image_dir=opt.images_dir,
    txt_pad=opt.txt_padding,
    phase='train'
)

loader = dataset2dataloader(dataset)


def fetch_samples(num_samples=10):
    samples = []
    sample_no = 0

    for sample in loader:
        sample_no += 1
        samples.append(sample)

        if sample_no >= num_samples:
            break

    return samples


samples = fetch_samples()
print(samples[0])
print('END')