generate_lines_data.py

import numpy as np
import pandas as pd
import argparse
import os
import torch
from sklearn.model_selection import train_test_split


parser = argparse.ArgumentParser()
parser.add_argument('--project-dir', metavar='PROJECT_DIR', dest='project_dir', help='path to project directory', \
                    required=False, default='.')
parser.add_argument('--dataset', metavar='DATASET', dest='dataset', help='name of dataset file in data directory', required=False)
parser.add_argument('--data-dir', metavar='DATA_DIR', dest='data_dir', help='path to data directory (used if different from "data")', \
                    required=False, default='data')
parser.add_argument('--img-dim', metavar='IMG_DIM', dest='img_dim', help='height (or width) of output video', required=False, \
                    type=int, default=121)
parser.add_argument('--seq-len', metavar='SEQ_LEN', dest='seq_len', help='sequence length of output video', required=False, \
                    type=int, default=20)
args = parser.parse_args()


# Globals
PROJECT_DIR = args.project_dir
DATA_DIR = args.data_dir
SEQ_LEN = args.seq_len
IMG_DIM = args.img_dim
VELOCITIES = [1, 3]
TEST_SIZE, VAL_SIZE = 0.2, 0.2

def generate_data():
    data = np.array([])
    image = np.zeros((IMG_DIM, IMG_DIM))

    for velocity in VELOCITIES:
        for start_idx in range(IMG_DIM):
            curr_seq = np.array([])
            i = start_idx
            for seq_num in range(SEQ_LEN):
                temp = np.copy(image)
                temp[:, i] = np.ones(IMG_DIM)
                curr_seq = np.append(curr_seq, temp)
                i = (i + velocity) % IMG_DIM
            curr_seq = np.reshape(curr_seq, (1, -1, IMG_DIM, IMG_DIM))
            data = np.vstack((data, curr_seq)) if data.size else curr_seq

    return data

def split_and_dump_data(data):
    num_test = int(np.floor(TEST_SIZE*len(data)))
    num_train_val = len(data) - num_test
    num_val = int(np.floor(num_train_val*VAL_SIZE/(1 - TEST_SIZE)))

    train_data, test_data = train_test_split(data, test_size=num_test, random_state=1337)
    train_data, val_data = train_test_split(train_data, test_size=num_val, random_state=1337)

    data_dict = {
        'train': train_data,
        'val': val_data,
        'test': test_data
    }

    np.save(open(os.path.join(PROJECT_DIR, DATA_DIR, 'moving_bars_{}_{}.npy'.format(SEQ_LEN, IMG_DIM)), 'wb'), data)
    for key in data_dict:
        np.save(open(os.path.join(PROJECT_DIR, DATA_DIR, 'moving_bars_{}_{}_{}.npy'.format(SEQ_LEN, IMG_DIM, key)), 'wb'), \
                data_dict[key])

def main():
    data = generate_data()
    split_and_dump_data(data)

if __name__ == '__main__':
    main()