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import os
import random
import typing as tp
from pathlib import Path
import numpy as np
import pandas as pd
import typer
from datasets.load import load_dataset
from tqdm import tqdm
np.random.seed(42)
random.seed(420)
def main(viz: bool = False):
dataset = load_dataset("mnist")
dataset.set_format("np")
X_train = dataset["train"]["image"]
y_train = dataset["train"]["label"].astype(np.uint8)
X_test = dataset["test"]["image"]
y_test = dataset["test"]["label"].astype(np.uint8)
Xs_train = to_sparse(X_train)
Xs_test = to_sparse(X_test)
max_length = max(x.shape[0] for x in Xs_train + Xs_test)
X_train = shuffle_and_pad(Xs_train, max_length, "train").astype(np.uint8)
X_test = shuffle_and_pad(Xs_test, max_length, "test").astype(np.uint8)
data_path = Path("data")
np.save(data_path / "X_train.npy", X_train)
np.save(data_path / "y_train.npy", y_train)
np.save(data_path / "X_test.npy", X_test)
np.save(data_path / "y_test.npy", y_test)
def shuffle_and_pad(Xs, k, name):
samples = []
for x in tqdm(Xs, desc=f"Padding {name}_{k}"):
N = len(x)
np.random.shuffle(x)
if N < k:
x = np.pad(x, [(0, k - N), (0, 0)])
samples.append(x)
samples = np.stack(samples, axis=0).astype(np.uint8)
return samples
def to_sparse(X: np.ndarray) -> tp.List[np.ndarray]:
N = len(X)
w, h = X.shape[1:]
xx, yy = np.meshgrid(np.arange(w), np.arange(h)[::-1])
xx = xx[None]
xx = np.tile(xx, [N, 1, 1])
yy = yy[None]
yy = np.tile(yy, [N, 1, 1])
X = np.stack([xx, yy, X], axis=-1)
X = X.reshape(N, -1, 3)
return [Xi[Xi[:, 2] > 0] for Xi in X]
if __name__ == "__main__":
typer.run(main)
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