DGMMC_ImageNet_no_projection.py

import os
import numpy as np
import pandas as pd
import math
import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import random_split

from src.Datasets import ImageNetFeaturesDataset
from utils_DGMMC import DGMMClassifier, train_from_features_PCA, test_from_features_PCA, CrossEntropy, get_means_bandwidth_from_features
from utils import get_trained_PCA
from torch.utils.data import Dataset, DataLoader


if __name__ == "__main__":

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

    G = [1]
    runs = [0]
    embeddings = ['IMAGEBIND', 'CLIP']

    classes = 1000
    batch_size = 128
    nb_epochs = 10

    EXPERIMENT_PATH = os.path.join('experiments_no_projection', 'ImageNet')

    IMAGENET_PATH = os.path.join('/home/jeremy/Documents/Datasets/ImageNet')
    FEATURES_ABOSLUTE_PATH = '/home/jeremy/Documents/Datasets/ImageNet/Features'

    for embedding in embeddings:

        embeding_folder = os.path.join(EXPERIMENT_PATH, embedding)
        if os.path.isdir(embeding_folder) is False:
            os.mkdir(embeding_folder)

        SDGM_folder_path = os.path.join(embeding_folder, '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 = ImageNetFeaturesDataset(IMAGENET_PATH, os.path.join(FEATURES_ABOSLUTE_PATH, embedding, 'train'), split='train')
        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=16, pin_memory = True)
        valloader = torch.utils.data.DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=16, pin_memory = True)

        testset = ImageNetFeaturesDataset(IMAGENET_PATH, os.path.join(FEATURES_ABOSLUTE_PATH, embedding, 'val'), split='val')
        testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=16, pin_memory = True)

        if embedding == 'CLIP':
            d = 768
        else:
            d = 1024

        for g in G:
            
            init_means, init_stds = get_means_bandwidth_from_features(classes, trainloader)

            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_D_{}_G_{}.pt'.format(d, g))

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

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

                val_loss, val_acc = test_from_features_PCA(classes, device, model, valloader, criterion)
                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)
            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_D_{}_G_{}.csv'.format(d, 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_D_{}_G_{}.csv'.format(d, 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_D_{}_G_{}.csv'.format(d, g))