DGMMC_ESC.py

import os
import numpy as np
import pandas as pd
import math
import torch
import torch.optim as optim
from torch.utils.data import random_split
from src.Datasets import ESC50Dataset
from utils_DGMMC import DGMMClassifier, train_from_features_PCA, test_from_features_PCA, get_means_bandwidth_from_features, CrossEntropy
from utils import get_trained_PCA

if __name__ == "__main__":

    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    print('Code running on :', device)

    P = range(5, 105, 5)
    G = [1]
    folders = [1,2,3,4,5]

    classes = 50
    batch_size = 64
    nb_epochs = 30

    EXPERIMENT_PATH = os.path.join('experiments_no_whitening', 'ESC')
    FEATURES_ABOSLUTE_PATH = os.path.join('/home/jeremy/Documents/Datasets/ESC50', 'features')

    for i in folders:

        folder_path = os.path.join(EXPERIMENT_PATH, 'fold_{}'.format(i))

        SDGM_folder_path = os.path.join(folder_path, 'DGMMC')
        if os.path.isdir(SDGM_folder_path) is False:
            os.mkdir(SDGM_folder_path)

        results_path = os.path.join(SDGM_folder_path, 'results')
        if os.path.isdir(results_path) is False:
            os.mkdir(results_path)

        models_path = os.path.join(SDGM_folder_path, 'models')
        if os.path.isdir(models_path) is False:
            os.mkdir(models_path)

        trainset = ESC50Dataset(FEATURES_ABOSLUTE_PATH, os.path.join(folder_path, 'train_data.csv'))
        train_ds, val_ds = random_split(trainset, [math.floor(0.90*len(trainset)), len(trainset) - math.floor(0.90*len(trainset))])

        trainloader = torch.utils.data.DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory = True)
        valloader = torch.utils.data.DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory = True)

        testset = ESC50Dataset(FEATURES_ABOSLUTE_PATH, os.path.join(folder_path, 'test_data.csv'))
        testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory = True)

        pca = get_trained_PCA(trainloader, 1024, whiten=False)

        for p in P:
            for g in G:

                cumsum = np.cumsum(pca.explained_variance_ratio_)

                if p != 100:
                    d = np.argmax(cumsum >= p/100) + 1
                else:
                    d = 1024

                print(d)

                init_means, init_stds = get_means_bandwidth_from_features(classes, trainloader, pca, d)

                model = DGMMClassifier(d,classes,g, init_means)
                model.to(device)

                criterion = CrossEntropy()

                optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, nesterov=True)

                scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=nb_epochs, eta_min=1e-4)
                best_loss = math.inf
                
                model_path = os.path.join(models_path, 'model_P_{}_G_{}.pt'.format(p, g))

                tr = []
                val = []
                for epoch in range(nb_epochs):

                    model, train_loss, train_acc = train_from_features_PCA(classes, device, model, trainloader, criterion, optimizer, pca, d)
                    tr.append(np.hstack((train_loss, train_acc)))

                    val_loss, val_acc = test_from_features_PCA(classes, device, model, valloader, criterion, pca, d)
                    val.append(np.hstack((val_loss, val_acc)))

                    print("[Epoch {}/{}] tr_loss: {:.4f} -- tr_acc: {:.3f} -- val_loss: {:.4f} -- val_acc: {:.3f}".format(epoch, nb_epochs, train_loss, train_acc, val_loss, val_acc))

                    if val_loss < best_loss:
                        torch.save(model, model_path)
                        best_loss = val_loss

                    scheduler.step()

                best_model = torch.load(model_path)
                best_model.eval()
                best_model.to(device)

                test_loss, test_acc = test_from_features_PCA(classes, device, best_model, testloader, criterion, pca, d)
                print("Test: test_loss: {:.5f} -- test_acc: {:.3f}".format(test_loss, test_acc))

                # Save results
                tr = np.stack(tr, axis=0)
                df_tr = pd.DataFrame(tr, columns=['loss', 'acc'])
                fpath = os.path.join(results_path, 'train_P_{}_G_{}.csv'.format(p, g))
                df_tr.to_csv(fpath, sep=';')
                
                val = np.stack(val, axis=0)
                df_val = pd.DataFrame(val, columns=['loss', 'acc'])
                fpath = os.path.join(results_path, 'val_P_{}_G_{}.csv'.format(p, g))
                df_val.to_csv(fpath, sep=';')

                te = np.vstack((test_loss, test_acc)).transpose()
                df_test = pd.DataFrame(te, columns=['loss', 'acc'])
                fpath = os.path.join(results_path, 'test_P_{}_G_{}.csv'.format(p, g))
                df_test.to_csv(fpath, sep=';')

                feat_infos = np.vstack((p/100, d)).transpose()
                df_feat_info = pd.DataFrame(feat_infos, columns=['P', 'Features_kept'])
                fpath = os.path.join(results_path, 'Features_P_{}_G_{}.csv'.format(p, g))
                df_feat_info.to_csv(fpath, sep=';')