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deep_svdd.py
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import datetime
import random
import sys
from pathlib import Path
import ast
import seaborn as sns
from networks import *
from training import *
def deep_svdd_ssl(AD_name, dataset, nbr_modes, nbr_epochs, batchsize, lr_init, lr_decay, lr_milestones,
weight_decay, nbr_seeds, train_ratio, nbr_pulse_per_scan, nbr_targets, date_string,
supervision="AD", loss="deepsvdd", arch="net0", nbr_modes_SAD=1, SAD_ratio=0.01):
print("\n {}".format(AD_name))
classes = [1, 2, 3, 4]
# Default device to 'cpu' if cuda is not available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
results_dicts_list = []
results_path = './results/' + dataset + '/' + AD_name + '/' + loss + '/{}/'.format(date_string)
for exp_index, seed in enumerate(range(nbr_seeds)):
print("\n seed: {}".format(seed))
# https://pytorch.org/docs/stable/notes/randomness.html
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
g = torch.Generator()
g.manual_seed(seed)
normal_cls = np.random.choice(classes, nbr_modes, replace=False).tolist()
outlier_cls = [cls for cls in classes if cls not in normal_cls]
SAD_cls = np.random.choice(outlier_cls, nbr_modes_SAD, replace=False).tolist()
radar_dataset = simulated_radar_dataset(train_ratio, normal_cls, SAD_cls, SAD_ratio, batchsize, nbr_targets, nbr_pulse_per_scan)
radar_dataset.get_dataloaders(generator=g)
# choose which supervision setup is to be used in terms of training samples availability (WARNING: check the selected loss in training.py to fully understand how supervised an experiment is)
if supervision=="AD":
# vanilla setup, corresponding to Deep SVDD original paper: only normal training samples available
train_loader = radar_dataset.train_loader_sps_norm_2D
elif supervision=="SSL":
train_loader = radar_dataset.train_loader_sps_norm_ssl_2D
elif supervision=="SAD":
train_loader = radar_dataset.train_loader_sps_norm_sad_2D
elif supervision=="SAD+SSL":
train_loader = radar_dataset.train_loader_sps_norm_ssl_sad_2D
else:
raise ValueError("Supervision {} not implemented !".format(supervision))
complete_train_loader = radar_dataset.complete_train_loader_sps_norm_2D
val_loader = radar_dataset.val_loader_sps_norm_2D
test_loader = radar_dataset.test_loader_sps_norm_2D
learning_rate = lr_init
if arch == "net0":
net = SimuSPs_LeNet0(rep_dim=64).to(device)
elif arch == "net1":
net = SimuSPs_LeNet1(rep_dim=64).to(device)
elif arch == "net2":
net = SimuSPs_LeNet2(rep_dim=64).to(device)
elif arch == "net3":
net = SimuSPs_LeNet3(rep_dim=64).to(device)
elif arch == "net4":
net = SimuSPs_LeNet4(rep_dim=64).to(device)
elif arch == "net5":
net = SimuSPs_LeNet5(rep_dim=64).to(device)
elif arch == "net6":
net = SimuSPs_LeNet6(rep_dim=64).to(device)
elif arch == "net7":
net = SimuSPs_LeNet7(rep_dim=64).to(device)
else:
raise ValueError("Architecture {} not implemented !".format(arch))
torch.save(net.state_dict(), results_path + 'untrained_net_seed{}_normal{}.pt'.format(seed, normal_cls))
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=weight_decay, amsgrad=False)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=lr_milestones, gamma=lr_decay)
# Initialize hypersphere center c
c = init_ssl_sad_centers_c(device, train_loader, net)
title = AD_name + " / " + "supervision:{}".format(supervision) + " / " + loss + " / " + "SAD_cls: {}".format(SAD_cls) + " / " + "SAD_ratio: {}".format(SAD_ratio)
get_2D_latent_representations_trainloader(train_loader, net, device, title + " / train data before training", figure_path=results_path, figure_name="latent2Dtrain_untrained_seed{}_normal{}.png".format(seed, normal_cls), plot_centroids=True, centroids=c, supervision=supervision)
get_2D_latent_representations_testloader(test_loader, net, device, title + " / test data before training", figure_path=results_path, figure_name="latent2Dtest_untrained_seed{}_normal{}.png".format(seed, normal_cls), plot_centroids=True, centroids=c, supervision=supervision)
epoch_losses, trainAUCs, valAUCs, testAUCs, test_scores, test_labels, net = training_deepSVDD(train_loader, complete_train_loader, val_loader,
test_loader, normal_cls, net, device, c,
optimizer, scheduler, nbr_epochs, loss_name=loss)
torch.save(net.state_dict(), results_path + 'trained_net_seed{}_normal{}.pt'.format(seed, normal_cls))
###################################################################
best_AUC_epoch = int(np.argmax(valAUCs))
train_AUC = trainAUCs[best_AUC_epoch]
valid_AUC = valAUCs[best_AUC_epoch]
test_AUC = testAUCs[best_AUC_epoch]
###################################################################
fig, ax = plt.subplots(2, 2, figsize=(15, 15))
fig.suptitle(title)
ax[0, 0].plot(np.arange(len(epoch_losses)), epoch_losses)
ax[0, 0].set_xlabel("epoch")
ax[0, 0].title.set_text('Training loss')
ax[0, 1].scatter(np.arange(nbr_epochs + 1), trainAUCs, c='g', label="train")
ax[0, 1].scatter(np.arange(nbr_epochs + 1), valAUCs, c='b', label="valid")
ax[0, 1].scatter(np.arange(nbr_epochs + 1), testAUCs, c='r', label="test")
ax[0, 1].set_xlabel("epoch")
ax[0, 1].legend()
ax[0, 1].title.set_text('AUCs during training')
sns.violinplot(x=test_labels[0], y=test_scores[0], ax=ax[1, 0]).set(title='AD test scores before training')
ax[1, 0].set_xlabel("class idx")
sns.violinplot(x=test_labels[best_AUC_epoch], y=test_scores[best_AUC_epoch], ax=ax[1, 1]).set(title='AD test scores after training')
ax[1, 1].set_xlabel("class idx")
plt.savefig(results_path + 'seed{}_normal{}.png'.format(seed, normal_cls))
get_2D_latent_representations_trainloader(train_loader, net, device, title + " / train data after training", figure_path=results_path, figure_name="latent2Dtrain_trained_seed{}_normal{}.png".format(seed, normal_cls), plot_centroids=True, centroids=c, supervision=supervision)
get_2D_latent_representations_testloader(test_loader, net, device, title + " / test data after training", figure_path=results_path, figure_name="latent2Dtest_trained_seed{}_normal{}.png".format(seed, normal_cls), plot_centroids=True, centroids=c, supervision=supervision)
###################################################################
results_dicts_list = store_comparison_results(results_dicts_list, train_AUC, valid_AUC, test_AUC, dataset,
AD_name, arch, supervision, loss, normal_cls, outlier_cls, SAD_cls,
SAD_ratio, nbr_modes, best_AUC_epoch, nbr_epochs, lr_init,
lr_milestones, batchsize, seed)
results_df = pd.DataFrame(results_dicts_list)
results_df = pd.concat([results_df], ignore_index=True)
results_df_path = results_path + 'experiment_results.csv'
results_df.to_csv(results_df_path)
return results_df
if __name__ == '__main__':
AD_name = str(sys.argv[1])
dataset = str(sys.argv[2])
nbr_modes = int(sys.argv[3])
nbr_epochs = int(sys.argv[4])
batchsize = int(sys.argv[5])
lr_init = float(sys.argv[6])
lr_decay = float(sys.argv[7])
lr_milestones = ast.literal_eval(sys.argv[8])
weight_decay = float(sys.argv[9])
nbr_seeds = int(sys.argv[10])
train_ratio = float(sys.argv[11])
nbr_pulse_per_scan = int(sys.argv[12])
nbr_targets = int(sys.argv[13])
supervision = str(sys.argv[14])
loss = str(sys.argv[15])
arch = str(sys.argv[16])
nbr_modes_SAD = int(sys.argv[17])
SAD_ratio = float(sys.argv[18])
experiment_name = str(sys.argv[19])
date_string = '{date:%Y-%m-%d_%H:%M:%S}'.format(date=datetime.datetime.now())
results_directory = './results/' + dataset + '/' + AD_name + '/' + loss + '/{}/'.format(date_string)
Path(results_directory).mkdir(parents=True, exist_ok=True)
Path('./comparison_graphs/').mkdir(parents=True, exist_ok=True)
results_df = deep_svdd_ssl(AD_name=AD_name, dataset=dataset, nbr_modes=nbr_modes, nbr_epochs=nbr_epochs, batchsize=batchsize,
lr_init=lr_init, lr_decay=lr_decay, lr_milestones=lr_milestones, weight_decay=weight_decay,
nbr_seeds=nbr_seeds, train_ratio=train_ratio, nbr_pulse_per_scan=nbr_pulse_per_scan,
nbr_targets=nbr_targets, date_string=date_string, supervision=supervision, loss=loss, arch=arch,
nbr_modes_SAD=nbr_modes_SAD, SAD_ratio=SAD_ratio)
# add results to last_results_df.csv for comparison graphs generation
global_results_df_file_path = 'comparison_graphs/last_results_df_{}.csv'.format(experiment_name)
global_results_df = pd.read_csv(global_results_df_file_path, index_col=False)
frames = [global_results_df, results_df]
global_results_df = pd.concat(frames, ignore_index=True)
global_results_df.to_csv(global_results_df_file_path, index=False)