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carla_classification.py
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import argparse
import os
import torch
import pandas
import numpy as np
from utils.mypath import MyPath
from termcolor import colored
from utils.config import create_config
from utils.common_config import get_train_transformations, get_val_transformations,\
get_val_transformations1, \
get_train_dataset, get_train_dataloader, get_aug_train_dataset,\
get_val_dataset, get_val_dataloader,\
get_optimizer, get_model, get_criterion,\
adjust_learning_rate, inject_sub_anomaly
from utils.evaluate_utils import get_predictions, classification_evaluate, pr_evaluate
from utils.train_utils import self_sup_classification_train
from statsmodels.tsa.stattools import adfuller
import random
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
set_seed(4)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
FLAGS = argparse.ArgumentParser(description='classification Loss')
FLAGS.add_argument('--config_env', help='Location of path config file')
FLAGS.add_argument('--config_exp', help='Location of experiments config file')
FLAGS.add_argument('--fname', help='Config the file name of Dataset')
def main():
global best_f1
args = FLAGS.parse_args()
p = create_config(args.config_env, args.config_exp, args.fname)
print(colored('CARLA Self-supervised Classification stage --> ', 'yellow'))
# CUDNN
# torch.backends.cudnn.benchmark = True
# Data
print(colored('\n- Get dataset and dataloaders for ' + p['train_db_name'] + ' dataset - timeseries ' + p['fname'], 'green'))
train_transformations = get_train_transformations(p)
sanomaly = inject_sub_anomaly(p)
val_transformations = get_val_transformations1(p)
train_dataset = get_aug_train_dataset(p, train_transformations, to_neighbors_dataset = True)
train_dataloader = get_train_dataloader(p, train_dataset)
if p['train_db_name'] == 'MSL' or p['train_db_name'] == 'SMAP':
if p['fname'] == 'All':
with open(os.path.join(MyPath.db_root_dir('msl'), 'labeled_anomalies.csv'), 'r') as file:
csv_reader = pandas.read_csv(file, delimiter=',')
data_info = csv_reader[csv_reader['spacecraft'] == p['train_db_name']]
ii = 0
for file_name in data_info['chan_id']:
p['fname'] = file_name
if ii == 0 :
base_dataset = get_train_dataset(p, train_transformations, sanomaly,
to_neighbors_dataset=True)
val_dataset = get_val_dataset(p, val_transformations, sanomaly, True, base_dataset.mean,
base_dataset.std)
else:
new_base_dataset = get_train_dataset(p, train_transformations, sanomaly,
to_neighbors_dataset=True)
new_val_dataset = get_val_dataset(p, val_transformations, sanomaly, True, new_base_dataset.mean,
new_base_dataset.std)
val_dataset.concat_ds(new_val_dataset)
base_dataset.concat_ds(new_base_dataset)
ii+=1
else:
#base_dataset = get_aug_train_dataset(p, train_transformations, to_neighbors_dataset = True)
info_ds = get_train_dataset(p, train_transformations, sanomaly, to_neighbors_dataset=False)
val_dataset = get_val_dataset(p, val_transformations, sanomaly, False, info_ds.mean, info_ds.std)
elif p['train_db_name'] == 'yahoo':
filename = os.path.join('/home/zahraz/hz18_scratch/zahraz/datasets/', 'Yahoo/', p['fname'])
dataset = []
# print(filename)
df = pandas.read_csv(filename)
dataset.append({
'value': df['value'].tolist(),
'label': df['is_anomaly'].tolist()
})
ts = dataset[0]
data = np.array(ts['value'])
labels = np.array(ts['label'])
l = len(data) // 2
n = 0
while adfuller(data[:l], 1)[1] > 0.05 or adfuller(data[:l])[1] > 0.05:
data = np.diff(data)
labels = labels[1:]
n += 1
l -= n
all_train_data = data[:l]
all_test_data = data[l:]
all_train_labels = labels[:l]
all_test_labels= labels[l:]
mean, std = all_train_data.mean(), all_train_data.std()
all_test_data = (all_test_data - mean) / std
TRAIN_TS = all_train_data
train_label = all_train_labels
TEST_TS = all_test_data
test_label = all_test_labels
base_dataset = get_train_dataset(p, train_transformations, sanomaly,
to_augmented_dataset=True, data=TRAIN_TS, label=train_label)
val_dataset = get_val_dataset(p, val_transformations, sanomaly, False, base_dataset.mean, base_dataset.std,
TEST_TS, test_label)
elif p['train_db_name'] == 'smd' or p['train_db_name'] == 'kpi' or p['train_db_name'] == 'swat' \
or p['train_db_name'] == 'swan' or p['train_db_name'] == 'gecco' or p['train_db_name'] == 'wadi' or p['train_db_name'] == 'ucr':
base_dataset = get_train_dataset(p, train_transformations, sanomaly, to_augmented_dataset=True)
val_dataset = get_val_dataset(p, val_transformations, sanomaly, False, base_dataset.mean,
base_dataset.std)
val_dataloader = get_val_dataloader(p, val_dataset)
print(colored('-- Train samples size: %d - Test samples size: %d' %(len(train_dataset), len(val_dataset)), 'green'))
# Model
model = get_model(p, p['pretext_model'])
model = torch.nn.DataParallel(model)
model = model.to(device)
# Optimizer
optimizer = get_optimizer(p, model, p['update_cluster_head_only'])
# Warning
if p['update_cluster_head_only']:
print(colored('WARNING: classification will only update the cluster head', 'red'))
# Loss function
criterion = get_criterion(p)
criterion.to(device)
print(colored('\n- Model initialisation', 'green'))
# Checkpoint
if os.path.exists(p['classification_checkpoint']):
print(colored('-- Model initialised from last checkpoint: {}'.format(p['classification_checkpoint']), 'green'))
checkpoint = torch.load(p['classification_checkpoint'], map_location='cpu')
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch']
best_loss = checkpoint['best_loss']
best_loss_head = checkpoint['best_loss_head']
normal_label = checkpoint['normal_label']
else:
print(colored('-- No checkpoint file at {} -- new model initialised'.format(p['classification_checkpoint']), 'green'))
start_epoch = 0
best_loss = 1e4
best_loss_head = None
normal_label = 0
best_f1 = -1 * np.inf
# best_loss = np.inf
print(colored('\n- Training:', 'blue'))
for epoch in range(start_epoch, p['epochs']):
print(colored('-- Epoch %d/%d' %(epoch+1, p['epochs']), 'blue'))
lr = adjust_learning_rate(p, optimizer, epoch)
self_sup_classification_train(train_dataloader, model, criterion, optimizer, epoch,
p['update_cluster_head_only'])
if (epoch == p['epochs']-1):
tst_dl = get_val_dataloader(p, train_dataset)
predictions, _ = get_predictions(p, tst_dl, model, True, True)
else:
tst_dl = get_val_dataloader(p, train_dataset)
predictions = get_predictions(p, tst_dl, model, False, False)
label_counts = torch.bincount(predictions[0]['predictions'])
majority_label = label_counts.argmax()
classification_stats = classification_evaluate(predictions)
lowest_loss_head = classification_stats['lowest_loss_head']
lowest_loss = classification_stats['lowest_loss']
predictions = get_predictions(p, val_dataloader, model, False, False)
rep_f1 = pr_evaluate(predictions, compute_confusion_matrix=False, majority_label=majority_label)
if rep_f1 > best_f1:
best_f1 = rep_f1
nomral_label = majority_label
# print('New Checkpoint ...')
torch.save({'model': model.module.state_dict(), 'head': best_loss_head, 'normal_label': normal_label}, p['classification_model'])
torch.save({'optimizer': optimizer.state_dict(), 'model': model.state_dict(),
'epoch': epoch + 1, 'best_loss': best_loss, 'best_loss_head': best_loss_head, 'normal_label': normal_label},
p['classification_checkpoint'])
model_checkpoint = torch.load(p['classification_model'], map_location='cpu')
model.module.load_state_dict(model_checkpoint['model'])
torch.save({'optimizer': optimizer.state_dict(), 'model': model.state_dict(),
'epoch': p['epochs'], 'best_loss': best_loss, 'best_loss_head': best_loss_head, 'normal_label': normal_label},
p['classification_checkpoint'])
normal_label = model_checkpoint['normal_label']
tst_dl = get_val_dataloader(p, val_dataset)
predictions, _ = get_predictions(p, tst_dl, model, True)
if __name__ == "__main__":
main()