utils.py

import numpy as np
import torch
import torch.optim as optim
from sklearn.metrics import accuracy_score


def unfold_label(labels, classes):
    new_labels = []

    assert len(np.unique(labels)) == classes
    # minimum value of labels
    mini = np.min(labels)

    for index in range(len(labels)):
        dump = np.full(shape=[classes], fill_value=0).astype(np.int8)
        _class = int(labels[index]) - mini
        dump[_class] = 1
        new_labels.append(dump)

    return np.array(new_labels)


def shuffle_data(samples, labels):
    num = len(labels)
    shuffle_index = np.random.permutation(np.arange(num))
    shuffled_samples = samples[shuffle_index]
    shuffled_labels = labels[shuffle_index]
    return shuffled_samples, shuffled_labels


def shuffle_list(li):
    np.random.shuffle(li)
    return li


def shuffle_list_with_ind(li):
    shuffle_index = np.random.permutation(np.arange(len(li)))
    li = li[shuffle_index]
    return li, shuffle_index


def num_flat_features(x):
    size = x.size()[1:]  # all dimensions except the batch dimension
    num_features = 1
    for s in size:
        num_features *= s
    return num_features


def crossentropyloss():
    loss_fn = torch.nn.CrossEntropyLoss()
    return loss_fn


def mseloss():
    loss_fn = torch.nn.MSELoss()
    return loss_fn


def sgd(parameters, lr, weight_decay=0.00005, momentum=0.9):
    opt = optim.SGD(params=parameters, lr=lr, momentum=momentum, weight_decay=weight_decay)
    return opt


def fix_seed():
    # torch.manual_seed(1108)
    # np.random.seed(1108)
    pass


def write_log(log, log_path):
    f = open(log_path, mode='a')
    f.write(str(log))
    f.write('\n')
    f.close()


def compute_accuracy(predictions, labels):
    accuracy = accuracy_score(y_true=np.argmax(labels, axis=-1), y_pred=np.argmax(predictions, axis=-1))
    return accuracy