arrayfire-py/examples/machine_learning/mnist_common.py at 818fbf39a935d5dbb4597f46cb9a364851d3f766 · arrayfire/arrayfire-py · GitHub

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#!/usr/bin/env python

#######################################################
# Copyright (c) 2024, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################

import os

import arrayfire as af

# sys.path.insert(0, '../common')
from examples.common.idxio import read_idx


def classify(arr: af.Array, k: int, expand_labels: bool) -> str:
    ret_str = ""
    if expand_labels:
        vec = af.cast(arr[:, k], af.f32)
        h_vec = vec.to_list()
        data = []

        for i in range(vec.size):
            data.append((h_vec[i], i))

        data = sorted(data, key=lambda pair: pair[0], reverse=True)  # type: ignore[arg-type,return-value]

        ret_str = str(data[0][1])

    else:
        ret_str = str(int(af.cast(arr[k], af.float32).scalar()))  # type: ignore[arg-type]

    return ret_str


def setup_mnist(frac: float, expand_labels: bool) -> tuple[int, int, int, af.Array, af.Array, af.Array, af.Array]:
    root_path = os.path.dirname(os.path.abspath(__file__))
    file_path = root_path + "/../../assets/examples/data/mnist/"
    idims, idata = read_idx(file_path + "images-subset")
    ldims, ldata = read_idx(file_path + "labels-subset")

    idims.reverse()
    images = af.Array(idata, af.float32, tuple(idims))

    R = af.randu((10000, 1))
    cond = R < min(frac, 0.8)
    train_indices = af.where(cond)
    test_indices = af.where(~cond)

    train_images = af.lookup(images, train_indices, axis=2) / 255
    test_images = af.lookup(images, test_indices, axis=2) / 255

    num_classes = 10
    num_train = train_images.shape[2]
    num_test = test_images.shape[2]

    if expand_labels:
        train_labels = af.constant(0, (num_classes, num_train))
        test_labels = af.constant(0, (num_classes, num_test))

        h_train_idx = train_indices.copy()
        h_test_idx = test_indices.copy()

        ldata = list(map(int, ldata))

        for i in range(num_train):
            ldata_ind = ldata[h_train_idx[i].scalar()]  # type: ignore[index]
            train_labels[ldata_ind, i] = 1  # type: ignore[index]

        for i in range(num_test):
            ldata_ind = ldata[h_test_idx[i].scalar()]  # type: ignore[index]
            test_labels[ldata_ind, i] = 1  # type: ignore[index]

    else:
        labels = af.Array(idata, af.float32, tuple(idims))
        train_labels = labels[train_indices]
        test_labels = labels[test_indices]

    return (num_classes, num_train, num_test, train_images, test_images, train_labels, test_labels)


def display_results(
    test_images: af.Array, test_output: af.Array, test_actual: af.Array, num_display: int, expand_labels: bool
) -> None:  # noqa: E501
    for i in range(num_display):
        print("Predicted: ", classify(test_output, i, expand_labels))
        print("Actual: ", classify(test_actual, i, expand_labels))

        img = af.cast((test_images[:, :, i] > 0.1), af.u8)
        flattened_img = af.moddims(img, (img.size,)).to_list()
        for j in range(28):
            for k in range(28):
                print("\u2588" if flattened_img[j * 28 + k] > 0 else " ", end="")  # type: ignore[operator]
            print()
        input()