MusicGenreClassification/cnnEvaluation.py at main · klemen1999/MusicGenreClassification · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
import tensorflow as tf
import numpy as np
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay
import json
from tqdm import tqdm
import pandas as pd

DATA_FOLDER = "./GTZAN/spectrogram"
LABELS = {"blues":0, "classical":1, "country":2, "disco":3, "hiphop":4, "jazz":5, "metal":6, "pop":7, "reggae":8, "rock":9}

IMAGE_HEIGHT = 224
IMAGE_WIDTH = 224
TEST_RATIO = 0.25
VAL_RATIO = 0.15

MODEL_NAME = "spectrogramSimpleModel-3s-50E"

def load_image(filename, label):
    image = tf.io.read_file(filename)
    image = tf.io.decode_png(image, channels=3)
    image = tf.image.resize(image, [IMAGE_HEIGHT, IMAGE_WIDTH])
    return image, label

def normalize(input_image, label):
    input_image = tf.cast(input_image, tf.float32) / 255.0
    return input_image, label

def filenamesAndLabels(path):
    filenames = []
    labels = []
    for f in os.listdir(path):
        if "png" in f:
            filename = os.path.join(path, f)
            filenames.append(filename)
            label = LABELS[f.split(".")[0]]
            labels.append(label)
    return filenames, labels

def getMetrics(y_pred, y_test, modelName, returnRow=False):
    acc = accuracy_score(y_pred, y_test)
    precision = precision_score(y_test, y_pred, average='macro')
    recall = recall_score(y_test, y_pred, average='macro')
    f1 = f1_score(y_test, y_pred, average='macro')
    if returnRow:
        return [modelName, round(acc,4), round(precision,4), round(recall,4), round(f1,4)]
    else:
        print(f"Model: {modelName}")
        print(f"- CA: {round(acc,4)}")
        print(f"- Precision: {round(precision,4)}")
        print(f"- Recall: {round(recall,4)}")
        print(f"- F1: {round(f1,4)}")

def vizConfusionMat(y_pred, y_test, currentModel, save=False):
    cm = confusion_matrix(y_test, y_pred)
    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=LABELS, )
    disp.plot()
    plt.title(currentModel)
    plt.xticks(rotation = 90)
    plt.tight_layout()
    plt.savefig(f"./GTZAN/results/{currentModel}.jpg") if save else plt.show()

if __name__ == "__main__":
    filenames, labels = filenamesAndLabels(DATA_FOLDER)
    filenames_train, filenames_test, labels_train, labels_test = train_test_split(filenames, labels, test_size=TEST_RATIO,
                                                    random_state=42, shuffle=True, stratify=labels)

    dataset_test = tf.data.Dataset.from_tensor_slices((filenames_test, labels_test))
    test_images = dataset_test.map(load_image, num_parallel_calls=tf.data.AUTOTUNE).map(normalize)


    model = tf.keras.models.load_model(f"./GTZAN/checkpoints/{MODEL_NAME}/model0050.h5")
    # model = tf.keras.models.load_model(f"./GTZAN/checkpoints/{MODEL_NAME}/modelLast.h5")

    labels_pred = []
    for element in tqdm(test_images.as_numpy_iterator()):
        image, label = element
        imageToPredict = image[None, :,:,:]
        currLabel = np.argmax(model.predict(imageToPredict)[0])
        labels_pred.append(currLabel)

    # getMetrics(labels_pred, labels_test, MODEL_NAME)

    results = pd.DataFrame(columns=["modelName","acc","precision","recall","f1"])
    results.loc[len(results)] = getMetrics(labels_pred, labels_test, MODEL_NAME, returnRow=True)
    results.to_csv(f"./GTZAN/results/{MODEL_NAME}.csv", index=False)
    vizConfusionMat(labels_pred, labels_test, MODEL_NAME, save=True)


    # Majority voting

    # filenames, labels = filenamesAndLabels(DATA_FOLDER)
    # filenames_train, filenames_test, labels_train, labels_test = train_test_split(filenames, labels, test_size=TEST_RATIO,
    #                                                 random_state=42, shuffle=True, stratify=labels)
    # model = tf.keras.models.load_model(f"./GTZAN/checkpoints/{MODEL_NAME}/model0050.h5")

    # labels_pred  =[]
    # for filename in tqdm(filenames_test):
    #     currLabelsList = []
    #     filenameList = filename.split("\\")
    #     for i in range(10):
    #         currFilename = "./GTZAN/spectrogram3s/"+filenameList[1][:-4]+f".{i}.png"
    #         try:
    #             image = tf.io.read_file(currFilename)
    #             image = tf.io.decode_png(image, channels=3)
    #             image = tf.image.resize(image, [IMAGE_HEIGHT, IMAGE_WIDTH])
    #             image = tf.cast(image, tf.float32) / 255.0
    #             imageToPredict = image[None,:,:,:]
    #             currLabel = np.argmax(model.predict(imageToPredict)[0])
    #             currLabelsList.append(currLabel)
    #         except:
    #             print(f"File missing: {currFilename}")
    #     currFinalLabel = max(set(currLabelsList), key=currLabelsList.count)
    #     labels_pred.append(currFinalLabel)

    # results = pd.DataFrame(columns=["modelName","acc","precision","recall","f1"])
    # results.loc[len(results)] = getMetrics(labels_pred, labels_test, MODEL_NAME, returnRow=True)
    # results.to_csv(f"./GTZAN/results/{MODEL_NAME}-Majority.csv", index=False)
    # vizConfusionMat(labels_pred, labels_test, MODEL_NAME+"-Majority", save=True)