def single_proc_test():
"""Performs single process evaluation."""
# Setup logging
lu.setup_logging()
# Show the config
logger.info("Config:\n{}".format(cfg))
# Fix the RNG seeds (see RNG comment in core/config.py for discussion)
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Configure the CUDNN backend
torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK
# Evaluate the model
test_model()
def single_proc_train():
"""Performs single process training."""
# Setup logging
lu.setup_logging()
# Show the config
logger.info('Config:\n{}'.format(cfg))
# Setup tensorboard if provided
writer_train = None
writer_eval = None
## If use tensorboard
if cfg.TENSORBOARD and du.is_master_proc(
) and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START:
comment = ''
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
logdir_train = os.path.join(cfg.OUT_DIR, 'runs',
current_time + comment + '_train')
logdir_eval = os.path.join(cfg.OUT_DIR, 'runs',
current_time + comment + '_eval')
if not os.path.exists(logdir_train):
os.makedirs(logdir_train)
if not os.path.exists(logdir_eval):
os.makedirs(logdir_eval)
writer_train = SummaryWriter(logdir_train)
writer_eval = SummaryWriter(logdir_eval)
# Fix the RNG seeds (see RNG comment in core/config.py for discussion)
np.random.seed(cfg.RGRAPH.SEED_TRAIN)
torch.manual_seed(cfg.RGRAPH.SEED_TRAIN)
# Configure the CUDNN backend
torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK
# Train the model
train_model(writer_train, writer_eval, is_master=du.is_master_proc())
if writer_train is not None and writer_eval is not None:
writer_train.close()
writer_eval.close()
def main(cfg):
# Setting up GPU args
use_cuda = (cfg.NUM_GPUS > 0) and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {
'num_workers': cfg.DATA_LOADER.NUM_WORKERS,
'pin_memory': cfg.DATA_LOADER.PIN_MEMORY
} if use_cuda else {}
# Using specific GPU
os.environ['CUDA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
print("Using GPU : {}.\n".format(cfg.GPU_ID))
# Getting the output directory ready (default is "/output")
cfg.OUT_DIR = os.path.join(os.path.abspath('..'), cfg.OUT_DIR)
if not os.path.exists(cfg.OUT_DIR):
os.mkdir(cfg.OUT_DIR)
# Create "DATASET" specific directory
dataset_out_dir = os.path.join(cfg.OUT_DIR, cfg.DATASET.NAME)
if not os.path.exists(dataset_out_dir):
os.mkdir(dataset_out_dir)
# Creating the experiment directory inside the dataset specific directory
# all logs, labeled, unlabeled, validation sets are stroed here
# E.g., output/CIFAR10/{timestamp or cfg.EXP_NAME based on arguments passed}
if cfg.EXP_NAME == 'auto':
now = datetime.now()
exp_dir = f'{now.year}_{now.month}_{now.day}_{now.hour}{now.minute}{now.second}'
else:
exp_dir = cfg.EXP_NAME
exp_dir = os.path.join(dataset_out_dir, exp_dir)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
print("Experiment Directory is {}.\n".format(exp_dir))
else:
print(
"Experiment Directory Already Exists: {}. Reusing it may lead to loss of old logs in the directory.\n"
.format(exp_dir))
cfg.EXP_DIR = exp_dir
# Setup Logger
lu.setup_logging(cfg)
# Dataset preparing steps
print("\n======== PREPARING DATA AND MODEL ========\n")
cfg.DATASET.ROOT_DIR = os.path.join(os.path.abspath('..'),
cfg.DATASET.ROOT_DIR)
data_obj = Data(cfg)
train_data, train_size = data_obj.getDataset(save_dir=cfg.DATASET.ROOT_DIR,
isTrain=True,
isDownload=True)
test_data, test_size = data_obj.getDataset(save_dir=cfg.DATASET.ROOT_DIR,
isTrain=False,
isDownload=True)
print(
"\nDataset {} Loaded Sucessfully.\nTotal Train Size: {} and Total Test Size: {}\n"
.format(cfg.DATASET.NAME, train_size, test_size))
logger.info(
"Dataset {} Loaded Sucessfully. Total Train Size: {} and Total Test Size: {}\n"
.format(cfg.DATASET.NAME, train_size, test_size))
trainSet_path, valSet_path = data_obj.makeTVSets(train_split_ratio=cfg.ACTIVE_LEARNING.INIT_RATIO, \
val_split_ratio=cfg.DATASET.VAL_RATIO, data=train_data, seed_id=cfg.RNG_SEED, save_dir=cfg.EXP_DIR)
trainSet, valSet = data_obj.loadTVPartitions(trainSetPath=trainSet_path,
valSetPath=valSet_path)
print("Data Partitioning Complete. \nTrain Set: {}, Validation Set: {}\n".
format(len(trainSet), len(valSet)))
logger.info("\nTrain Set: {}, Validation Set: {}\n".format(
len(trainSet), len(valSet)))
# Preparing dataloaders for initial training
trainSet_loader = data_obj.getIndexesDataLoader(
indexes=trainSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
valSet_loader = data_obj.getIndexesDataLoader(
indexes=valSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
test_loader = data_obj.getTestLoader(data=test_data,
test_batch_size=cfg.TRAIN.BATCH_SIZE,
seed_id=cfg.RNG_SEED)
# Initialize the models
num_ensembles = cfg.ENSEMBLE.NUM_MODELS
models = []
for i in range(num_ensembles):
models.append(model_builder.build_model(cfg))
print("{} ensemble models of type: {}\n".format(cfg.ENSEMBLE.NUM_MODELS,
cfg.ENSEMBLE.MODEL_TYPE))
logger.info("{} ensemble models of type: {}\n".format(
cfg.ENSEMBLE.NUM_MODELS, cfg.ENSEMBLE.MODEL_TYPE))
# This is to seamlessly use the code originally written for AL episodes
cfg.EPISODE_DIR = cfg.EXP_DIR
# Train models
print("======== ENSEMBLE TRAINING ========")
logger.info("======== ENSEMBLE TRAINING ========")
best_model_paths = []
test_accs = []
for i in range(num_ensembles):
print("=== Training ensemble [{}/{}] ===".format(i + 1, num_ensembles))
# Construct the optimizer
optimizer = optim.construct_optimizer(cfg, models[i])
print("optimizer: {}\n".format(optimizer))
logger.info("optimizer: {}\n".format(optimizer))
# Each ensemble gets its own output directory
cfg.EPISODE_DIR = os.path.join(cfg.EPISODE_DIR,
'model_{} '.format(i + 1))
# Train the model
best_val_acc, best_val_epoch, checkpoint_file = ensemble_train_model(
trainSet_loader, valSet_loader, models[i], optimizer, cfg)
best_model_paths.append(checkpoint_file)
print("Best Validation Accuracy by Model {}: {}\nBest Epoch: {}\n".
format(i + 1, round(best_val_acc, 4), best_val_epoch))
logger.info(
"Best Validation Accuracy by Model {}: {}\tBest Epoch: {}\n".
format(i + 1, round(best_val_acc, 4), best_val_epoch))
# Test the model
print("=== Testing ensemble [{}/{}] ===".format(i + 1, num_ensembles))
test_acc = ensemble_test_model(test_loader,
checkpoint_file,
cfg,
cur_episode=0)
test_accs.append(test_acc)
print("Test Accuracy by Model {}: {}.\n".format(
i + 1, round(test_acc, 4)))
logger.info("Test Accuracy by Model {}: {}.\n".format(i + 1, test_acc))
# Reset EPISODE_DIR
cfg.EPISODE_DIR = cfg.EXP_DIR
# Test each best model checkpoint and report the average
print("======== ENSEMBLE TESTING ========\n")
logger.info("======== ENSEMBLE TESTING ========\n")
mean_test_acc = np.mean(test_accs)
print("Average Ensemble Test Accuracy: {}.\n".format(
round(mean_test_acc, 4)))
logger.info("Average Ensemble Test Accuracy: {}.\n".format(mean_test_acc))
print("================================\n\n")
logger.info("================================\n\n")
def main(cfg):
# Setting up GPU args
use_cuda = (cfg.NUM_GPUS > 0) and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {
'num_workers': cfg.DATA_LOADER.NUM_WORKERS,
'pin_memory': cfg.DATA_LOADER.PIN_MEMORY
} if use_cuda else {}
# Using specific GPU
os.environ['CUDA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
print("Using GPU : {}.\n".format(cfg.GPU_ID))
# Getting the output directory ready (default is "/output")
cfg.OUT_DIR = os.path.join(os.path.abspath('..'), cfg.OUT_DIR)
if not os.path.exists(cfg.OUT_DIR):
os.mkdir(cfg.OUT_DIR)
# Create "DATASET" specific directory
dataset_out_dir = os.path.join(cfg.OUT_DIR, cfg.DATASET.NAME,
cfg.MODEL.TYPE)
if not os.path.exists(dataset_out_dir):
os.makedirs(dataset_out_dir)
# Creating the experiment directory inside the dataset specific directory
# all logs, labeled, unlabeled, validation sets are stroed here
# E.g., output/CIFAR10/resnet18/{timestamp or cfg.EXP_NAME based on arguments passed}
if cfg.EXP_NAME == 'auto':
now = datetime.now()
exp_dir = f'{now.year}_{now.month}_{now.day}_{now.hour}{now.minute}{now.second}'
else:
exp_dir = cfg.EXP_NAME
exp_dir = os.path.join(dataset_out_dir, exp_dir)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
print("Experiment Directory is {}.\n".format(exp_dir))
else:
print(
"Experiment Directory Already Exists: {}. Reusing it may lead to loss of old logs in the directory.\n"
.format(exp_dir))
cfg.EXP_DIR = exp_dir
# Setup Logger
lu.setup_logging(cfg)
# Dataset preparing steps
print("\n======== PREPARING DATA AND MODEL ========\n")
cfg.DATASET.ROOT_DIR = os.path.join(os.path.abspath('..'),
cfg.DATASET.ROOT_DIR)
data_obj = Data(cfg)
train_data, train_size = data_obj.getDataset(save_dir=cfg.DATASET.ROOT_DIR,
isTrain=True,
isDownload=True)
test_data, test_size = data_obj.getDataset(save_dir=cfg.DATASET.ROOT_DIR,
isTrain=False,
isDownload=True)
print(
"\nDataset {} Loaded Sucessfully.\nTotal Train Size: {} and Total Test Size: {}\n"
.format(cfg.DATASET.NAME, train_size, test_size))
logger.info(
"Dataset {} Loaded Sucessfully. Total Train Size: {} and Total Test Size: {}\n"
.format(cfg.DATASET.NAME, train_size, test_size))
print("\nSampling Initial Pool using {}.".format(
str.upper(cfg.INIT_POOL.SAMPLING_FN)))
logger.info("\nSampling Initial Pool using {}.".format(
str.upper(cfg.INIT_POOL.SAMPLING_FN)))
if cfg.INIT_POOL.SAMPLING_FN == 'random':
lSet_path, uSet_path, valSet_path = data_obj.makeLUVSets(train_split_ratio=cfg.INIT_POOL.INIT_RATIO, \
val_split_ratio=cfg.DATASET.VAL_RATIO, data=train_data, seed_id=cfg.RNG_SEED, save_dir=cfg.EXP_DIR)
else:
lSet, uSet = InitialPool(cfg).sample_from_uSet(train_data)
lSet_path = f'{cfg.EXP_DIR}/lSet.npy'
np.save(lSet_path, lSet)
np.save(f'{cfg.EXP_DIR}/lSet_initial.npy', lSet)
uSet_path, valSet_path = data_obj.makeUVSets(
val_split_ratio=cfg.DATASET.VAL_RATIO,
data=uSet,
seed_id=cfg.RNG_SEED,
save_dir=cfg.EXP_DIR)
cfg.ACTIVE_LEARNING.LSET_PATH = lSet_path
cfg.ACTIVE_LEARNING.USET_PATH = uSet_path
cfg.ACTIVE_LEARNING.VALSET_PATH = valSet_path
lSet, uSet, valSet = data_obj.loadPartitions(lSetPath=cfg.ACTIVE_LEARNING.LSET_PATH, \
uSetPath=cfg.ACTIVE_LEARNING.USET_PATH, valSetPath = cfg.ACTIVE_LEARNING.VALSET_PATH)
print(
"Data Partitioning Complete. \nLabeled Set: {}, Unlabeled Set: {}, Validation Set: {}\n"
.format(len(lSet), len(uSet), len(valSet)))
logger.info(
"Labeled Set: {}, Unlabeled Set: {}, Validation Set: {}\n".format(
len(lSet), len(uSet), len(valSet)))
# Preparing dataloaders for initial training
lSet_loader = data_obj.getIndexesDataLoader(
indexes=lSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
valSet_loader = data_obj.getIndexesDataLoader(
indexes=valSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
uSet_loader = data_obj.getIndexesDataLoader(
indexes=uSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
test_loader = data_obj.getTestLoader(data=test_data,
test_batch_size=cfg.TRAIN.BATCH_SIZE,
seed_id=cfg.RNG_SEED)
# Initialize the models
num_ensembles = cfg.ENSEMBLE.NUM_MODELS
models = []
for i in range(num_ensembles):
models.append(model_builder.build_model(cfg))
print("{} ensemble models of type: {}\n".format(cfg.ENSEMBLE.NUM_MODELS,
cfg.ENSEMBLE.MODEL_TYPE))
logger.info("{} ensemble models of type: {}\n".format(
cfg.ENSEMBLE.NUM_MODELS, cfg.ENSEMBLE.MODEL_TYPE))
print("Max AL Episodes: {}\n".format(cfg.ACTIVE_LEARNING.MAX_ITER))
logger.info("Max AL Episodes: {}\n".format(cfg.ACTIVE_LEARNING.MAX_ITER))
for cur_episode in range(0, cfg.ACTIVE_LEARNING.MAX_ITER + 1):
wandb.log({"Episode": cur_episode})
print("======== EPISODE {} BEGINS ========\n".format(cur_episode))
logger.info(
"======== EPISODE {} BEGINS ========\n".format(cur_episode))
# Creating output directory for the episode
episode_dir = os.path.join(cfg.EXP_DIR, f'episode_{cur_episode}')
if not os.path.exists(episode_dir):
os.mkdir(episode_dir)
cfg.EPISODE_DIR = episode_dir
# Train models
print("======== ENSEMBLE TRAINING ========")
logger.info("======== ENSEMBLE TRAINING ========")
best_model_paths = []
test_accs = []
for i in range(num_ensembles):
print("=== Training ensemble [{}/{}] ===".format(
i + 1, num_ensembles))
# Construct the optimizer
optimizer = optim.construct_optimizer(cfg, models[i])
print("optimizer: {}\n".format(optimizer))
logger.info("optimizer: {}\n".format(optimizer))
# Each ensemble gets its own output directory
cfg.EPISODE_DIR = os.path.join(cfg.EPISODE_DIR,
'model_{}'.format(i + 1))
# Train the model
best_val_acc, best_val_epoch, checkpoint_file = ensemble_train_model(
lSet_loader, valSet_loader, models[i], optimizer, cfg)
best_model_paths.append(checkpoint_file)
print("Best Validation Accuracy by Model {}: {}\nBest Epoch: {}\n".
format(i + 1, round(best_val_acc, 4), best_val_epoch))
logger.info(
"EPISODE {} Best Validation Accuracy by Model {}: {}\tBest Epoch: {}\n"
.format(cur_episode, i + 1, round(best_val_acc, 4),
best_val_epoch))
# Test the model
print("=== Testing ensemble [{}/{}] ===".format(
i + 1, num_ensembles))
test_acc = ensemble_test_model(test_loader, checkpoint_file, cfg,
cur_episode)
test_accs.append(test_acc)
print("Test Accuracy by Model {}: {}.\n".format(
i + 1, round(test_acc, 4)))
logger.info("EPISODE {} Test Accuracy by Model {}: {}.\n".format(
cur_episode, i + 1, test_acc))
# Reset EPISODE_DIR
cfg.EPISODE_DIR = episode_dir
# Test each best model checkpoint and report the average
print("======== ENSEMBLE TESTING ========\n")
logger.info("======== ENSEMBLE TESTING ========\n")
mean_test_acc = np.mean(test_accs)
print("Average Ensemble Test Accuracy: {}.\n".format(
round(mean_test_acc, 4)))
logger.info("EPISODE {} Average Ensemble Test Accuracy: {}.\n".format(
cur_episode, mean_test_acc))
wandb.log({"Test Accuracy": mean_test_acc})
global plot_episode_xvalues
global plot_episode_yvalues
global plot_epoch_xvalues
global plot_epoch_yvalues
global plot_it_x_values
global plot_it_y_values
plot_episode_xvalues.append(cur_episode)
plot_episode_yvalues.append(mean_test_acc)
plot_arrays(x_vals=plot_episode_xvalues, y_vals=plot_episode_yvalues, \
x_name="Episodes", y_name="Test Accuracy", dataset_name=cfg.DATASET.NAME, out_dir=cfg.EXP_DIR)
save_plot_values([plot_episode_xvalues, plot_episode_yvalues], \
["plot_episode_xvalues", "plot_episode_yvalues"], out_dir=cfg.EXP_DIR, saveInTextFormat=True)
# No need to perform active sampling in the last episode iteration
if cur_episode == cfg.ACTIVE_LEARNING.MAX_ITER:
break
# Active Sample
print("======== ENSEMBLE ACTIVE SAMPLING ========\n")
logger.info("======== ENSEMBLE ACTIVE SAMPLING ========\n")
al_obj = ActiveLearning(data_obj, cfg)
clf_models = []
for i in range(num_ensembles):
temp = model_builder.build_model(cfg)
clf_models.append(cu.load_checkpoint(best_model_paths[i], temp))
activeSet, new_uSet = al_obj.sample_from_uSet(
None, lSet, uSet, train_data, supportingModels=clf_models)
# Save current lSet, new_uSet and activeSet in the episode directory
data_obj.saveSets(lSet, uSet, activeSet, cfg.EPISODE_DIR)
# Add activeSet to lSet, save new_uSet as uSet and update dataloader for the next episode
lSet = np.append(lSet, activeSet)
uSet = new_uSet
lSet_loader = data_obj.getIndexesDataLoader(
indexes=lSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
valSet_loader = data_obj.getIndexesDataLoader(
indexes=valSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
uSet_loader = data_obj.getSequentialDataLoader(
indexes=uSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
print(
"Ensemble Active Sampling Complete. After Episode {}:\nNew Labeled Set: {}, New Unlabeled Set: {}, Active Set: {}\n"
.format(cur_episode, len(lSet), len(uSet), len(activeSet)))
logger.info(
"Ensemble Active Sampling Complete. After Episode {}:\nNew Labeled Set: {}, New Unlabeled Set: {}, Active Set: {}\n"
.format(cur_episode, len(lSet), len(uSet), len(activeSet)))
print("================================\n\n")
logger.info("================================\n\n")
def main(cfg):
# Setting up GPU args
use_cuda = (cfg.NUM_GPUS > 0) and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {
'num_workers': cfg.DATA_LOADER.NUM_WORKERS,
'pin_memory': cfg.DATA_LOADER.PIN_MEMORY
} if use_cuda else {}
# Using specific GPU
# os.environ['NVIDIA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
# os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# print("Using GPU : {}.\n".format(cfg.GPU_ID))
# Getting the output directory ready (default is "/output")
cfg.OUT_DIR = os.path.join(os.path.abspath('..'), cfg.OUT_DIR)
if not os.path.exists(cfg.OUT_DIR):
os.mkdir(cfg.OUT_DIR)
# Create "DATASET/MODEL TYPE" specific directory
dataset_out_dir = os.path.join(cfg.OUT_DIR, cfg.DATASET.NAME,
cfg.MODEL.TYPE)
if not os.path.exists(dataset_out_dir):
os.makedirs(dataset_out_dir)
# Creating the experiment directory inside the dataset specific directory
# all logs, labeled, unlabeled, validation sets are stroed here
# E.g., output/CIFAR10/resnet18/{timestamp or cfg.EXP_NAME based on arguments passed}
if cfg.EXP_NAME == 'auto':
now = datetime.now()
exp_dir = f'{now.year}_{now.month}_{now.day}_{now.hour}{now.minute}{now.second}'
else:
exp_dir = cfg.EXP_NAME
exp_dir = os.path.join(dataset_out_dir, exp_dir)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
print("Experiment Directory is {}.\n".format(exp_dir))
else:
print(
"Experiment Directory Already Exists: {}. Reusing it may lead to loss of old logs in the directory.\n"
.format(exp_dir))
cfg.EXP_DIR = exp_dir
# Save the config file in EXP_DIR
dump_cfg(cfg)
# Setup Logger
lu.setup_logging(cfg)
# Dataset preparing steps
print("\n======== PREPARING TEST DATA ========\n")
cfg.DATASET.ROOT_DIR = os.path.join(os.path.abspath('..'),
cfg.DATASET.ROOT_DIR)
data_obj = Data(cfg)
test_data, test_size = data_obj.getDataset(save_dir=cfg.DATASET.ROOT_DIR,
isTrain=False,
isDownload=True)
print("\nDataset {} Loaded Sucessfully. Total Test Size: {}\n".format(
cfg.DATASET.NAME, test_size))
logger.info("Dataset {} Loaded Sucessfully. Total Test Size: {}\n".format(
cfg.DATASET.NAME, test_size))
# Preparing dataloaders for testing
test_loader = data_obj.getTestLoader(data=test_data,
test_batch_size=cfg.TRAIN.BATCH_SIZE,
seed_id=cfg.RNG_SEED)
print("======== TESTING ========\n")
logger.info("======== TESTING ========\n")
test_acc = test_model(
test_loader, os.path.join(os.path.abspath('..'), cfg.TEST.MODEL_PATH),
cfg)
print("Test Accuracy: {}.\n".format(round(test_acc, 4)))
logger.info("Test Accuracy {}.\n".format(test_acc))
print('Check the test accuracy inside {}/stdout.log'.format(cfg.EXP_DIR))
print("================================\n\n")
logger.info("================================\n\n")
def main(cfg):
# Setting up GPU args
use_cuda = (cfg.NUM_GPUS > 0) and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {
'num_workers': cfg.DATA_LOADER.NUM_WORKERS,
'pin_memory': cfg.DATA_LOADER.PIN_MEMORY
} if use_cuda else {}
# Auto assign a RNG_SEED when not supplied a value
if cfg.RNG_SEED is None:
cfg.RNG_SEED = np.random.randint(100)
# Using specific GPU
# os.environ['NVIDIA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
# os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# print("Using GPU : {}.\n".format(cfg.GPU_ID))
# Getting the output directory ready (default is "/output")
cfg.OUT_DIR = os.path.join(os.path.abspath('..'), cfg.OUT_DIR)
if not os.path.exists(cfg.OUT_DIR):
os.mkdir(cfg.OUT_DIR)
# Create "DATASET/MODEL TYPE" specific directory
dataset_out_dir = os.path.join(cfg.OUT_DIR, cfg.DATASET.NAME,
cfg.MODEL.TYPE)
if not os.path.exists(dataset_out_dir):
os.makedirs(dataset_out_dir)
# Creating the experiment directory inside the dataset specific directory
# all logs, labeled, unlabeled, validation sets are stroed here
# E.g., output/CIFAR10/resnet18/{timestamp or cfg.EXP_NAME based on arguments passed}
if cfg.EXP_NAME == 'auto':
now = datetime.now()
exp_dir = f'{now.year}_{now.month}_{now.day}_{now.hour}{now.minute}{now.second}'
else:
exp_dir = cfg.EXP_NAME
exp_dir = os.path.join(dataset_out_dir, exp_dir)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
print("Experiment Directory is {}.\n".format(exp_dir))
else:
print(
"Experiment Directory Already Exists: {}. Reusing it may lead to loss of old logs in the directory.\n"
.format(exp_dir))
cfg.EXP_DIR = exp_dir
# Save the config file in EXP_DIR
dump_cfg(cfg)
# Setup Logger
lu.setup_logging(cfg)
# Dataset preparing steps
print("\n======== PREPARING DATA AND MODEL ========\n")
cfg.DATASET.ROOT_DIR = os.path.join(os.path.abspath('..'),
cfg.DATASET.ROOT_DIR)
data_obj = Data(cfg)
train_data, train_size = data_obj.getDataset(save_dir=cfg.DATASET.ROOT_DIR,
isTrain=True,
isDownload=True)
test_data, test_size = data_obj.getDataset(save_dir=cfg.DATASET.ROOT_DIR,
isTrain=False,
isDownload=True)
print(
"\nDataset {} Loaded Sucessfully.\nTotal Train Size: {} and Total Test Size: {}\n"
.format(cfg.DATASET.NAME, train_size, test_size))
logger.info(
"Dataset {} Loaded Sucessfully. Total Train Size: {} and Total Test Size: {}\n"
.format(cfg.DATASET.NAME, train_size, test_size))
lSet_path, uSet_path, valSet_path = data_obj.makeLUVSets(train_split_ratio=cfg.ACTIVE_LEARNING.INIT_L_RATIO, \
val_split_ratio=cfg.DATASET.VAL_RATIO, data=train_data, seed_id=cfg.RNG_SEED, save_dir=cfg.EXP_DIR)
cfg.ACTIVE_LEARNING.LSET_PATH = lSet_path
cfg.ACTIVE_LEARNING.USET_PATH = uSet_path
cfg.ACTIVE_LEARNING.VALSET_PATH = valSet_path
lSet, uSet, valSet = data_obj.loadPartitions(lSetPath=cfg.ACTIVE_LEARNING.LSET_PATH, \
uSetPath=cfg.ACTIVE_LEARNING.USET_PATH, valSetPath = cfg.ACTIVE_LEARNING.VALSET_PATH)
print(
"Data Partitioning Complete. \nLabeled Set: {}, Unlabeled Set: {}, Validation Set: {}\n"
.format(len(lSet), len(uSet), len(valSet)))
logger.info(
"Labeled Set: {}, Unlabeled Set: {}, Validation Set: {}\n".format(
len(lSet), len(uSet), len(valSet)))
# Preparing dataloaders for initial training
lSet_loader = data_obj.getIndexesDataLoader(
indexes=lSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
valSet_loader = data_obj.getIndexesDataLoader(
indexes=valSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
test_loader = data_obj.getTestLoader(data=test_data,
test_batch_size=cfg.TRAIN.BATCH_SIZE,
seed_id=cfg.RNG_SEED)
# Initialize the model.
model = model_builder.build_model(cfg)
print("model: {}\n".format(cfg.MODEL.TYPE))
logger.info("model: {}\n".format(cfg.MODEL.TYPE))
# Construct the optimizer
optimizer = optim.construct_optimizer(cfg, model)
print("optimizer: {}\n".format(optimizer))
logger.info("optimizer: {}\n".format(optimizer))
print("AL Query Method: {}\nMax AL Episodes: {}\n".format(
cfg.ACTIVE_LEARNING.SAMPLING_FN, cfg.ACTIVE_LEARNING.MAX_ITER))
logger.info("AL Query Method: {}\nMax AL Episodes: {}\n".format(
cfg.ACTIVE_LEARNING.SAMPLING_FN, cfg.ACTIVE_LEARNING.MAX_ITER))
for cur_episode in range(0, cfg.ACTIVE_LEARNING.MAX_ITER + 1):
print("======== EPISODE {} BEGINS ========\n".format(cur_episode))
logger.info(
"======== EPISODE {} BEGINS ========\n".format(cur_episode))
# Creating output directory for the episode
episode_dir = os.path.join(cfg.EXP_DIR, f'episode_{cur_episode}')
if not os.path.exists(episode_dir):
os.mkdir(episode_dir)
cfg.EPISODE_DIR = episode_dir
# Train model
print("======== TRAINING ========")
logger.info("======== TRAINING ========")
best_val_acc, best_val_epoch, checkpoint_file = train_model(
lSet_loader, valSet_loader, model, optimizer, cfg)
print("Best Validation Accuracy: {}\nBest Epoch: {}\n".format(
round(best_val_acc, 4), best_val_epoch))
logger.info(
"EPISODE {} Best Validation Accuracy: {}\tBest Epoch: {}\n".format(
cur_episode, round(best_val_acc, 4), best_val_epoch))
# Test best model checkpoint
print("======== TESTING ========\n")
logger.info("======== TESTING ========\n")
test_acc = test_model(test_loader, checkpoint_file, cfg, cur_episode)
print("Test Accuracy: {}.\n".format(round(test_acc, 4)))
logger.info("EPISODE {} Test Accuracy {}.\n".format(
cur_episode, test_acc))
# No need to perform active sampling in the last episode iteration
if cur_episode == cfg.ACTIVE_LEARNING.MAX_ITER:
# Save current lSet, uSet in the final episode directory
data_obj.saveSet(lSet, 'lSet', cfg.EPISODE_DIR)
data_obj.saveSet(uSet, 'uSet', cfg.EPISODE_DIR)
break
# Active Sample
print("======== ACTIVE SAMPLING ========\n")
logger.info("======== ACTIVE SAMPLING ========\n")
al_obj = ActiveLearning(data_obj, cfg)
clf_model = model_builder.build_model(cfg)
clf_model = cu.load_checkpoint(checkpoint_file, clf_model)
activeSet, new_uSet = al_obj.sample_from_uSet(clf_model, lSet, uSet,
train_data)
# Save current lSet, new_uSet and activeSet in the episode directory
data_obj.saveSets(lSet, uSet, activeSet, cfg.EPISODE_DIR)
# Add activeSet to lSet, save new_uSet as uSet and update dataloader for the next episode
lSet = np.append(lSet, activeSet)
uSet = new_uSet
lSet_loader = data_obj.getIndexesDataLoader(
indexes=lSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
valSet_loader = data_obj.getIndexesDataLoader(
indexes=valSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
uSet_loader = data_obj.getSequentialDataLoader(
indexes=uSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
print(
"Active Sampling Complete. After Episode {}:\nNew Labeled Set: {}, New Unlabeled Set: {}, Active Set: {}\n"
.format(cur_episode, len(lSet), len(uSet), len(activeSet)))
logger.info(
"Active Sampling Complete. After Episode {}:\nNew Labeled Set: {}, New Unlabeled Set: {}, Active Set: {}\n"
.format(cur_episode, len(lSet), len(uSet), len(activeSet)))
print("================================\n\n")
logger.info("================================\n\n")
def main(cfg):
# Setting up GPU args
use_cuda = (cfg.NUM_GPUS > 0) and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {
'num_workers': cfg.DATA_LOADER.NUM_WORKERS,
'pin_memory': cfg.DATA_LOADER.PIN_MEMORY
} if use_cuda else {}
# Using specific GPU
os.environ['NVIDIA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
print("Using GPU : {}.\n".format(cfg.GPU_ID))
# Getting the output directory ready (default is "/output")
cfg.OUT_DIR = os.path.join(os.path.abspath('..'), cfg.OUT_DIR)
if not os.path.exists(cfg.OUT_DIR):
os.makedirs(cfg.OUT_DIR)
# Create "DATASET" specific directory
dataset_out_dir = os.path.join(cfg.OUT_DIR, cfg.DATASET.NAME,
cfg.MODEL.TYPE)
if not os.path.exists(dataset_out_dir):
os.makedirs(dataset_out_dir)
# Creating the experiment directory inside the dataset specific directory
# all logs, labeled, unlabeled, validation sets are stroed here
# E.g., output/CIFAR10/resnet18/{timestamp or cfg.EXP_NAME based on arguments passed}
if cfg.EXP_NAME == 'auto':
now = datetime.now()
exp_dir = f'{now.year}_{now.month}_{now.day}_{now.hour}{now.minute}{now.second}'
else:
exp_dir = cfg.EXP_NAME
exp_dir = os.path.join(dataset_out_dir, exp_dir)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
print("Experiment Directory is {}.\n".format(exp_dir))
else:
print(
"Experiment Directory Already Exists: {}. Reusing it may lead to loss of old logs in the directory.\n"
.format(exp_dir))
cfg.EXP_DIR = exp_dir
# Setup Logger
lu.setup_logging(cfg)
# Dataset preparing steps
print("\n======== PREPARING DATA AND MODEL ========\n")
cfg.DATASET.ROOT_DIR = os.path.join(os.path.abspath('..'),
cfg.DATASET.ROOT_DIR)
data_obj = Data(cfg)
train_data, train_size = data_obj.getDataset(save_dir=cfg.DATASET.ROOT_DIR,
isTrain=True,
isDownload=True)
train_data = RotNetDataset(cfg.DATASET.NAME, train_data)
train_size = len(train_data)
print("\n Rotation Dataset {} Loaded Sucessfully.\nTotal Train Size: {}\n".
format(cfg.DATASET.NAME, train_size))
logger.info(
"Rotation Dataset {} Loaded Sucessfully. Total Train Size: {}\n".
format(cfg.DATASET.NAME, train_size))
trainSet_path, valSet_path = data_obj.makeTVSets(val_split_ratio=cfg.DATASET.VAL_RATIO, data=train_data,\
seed_id=cfg.RNG_SEED, save_dir=cfg.EXP_DIR)
cfg.INIT_POOL.TRAINSET_PATH = trainSet_path
cfg.INIT_POOL.VALSET_PATH = valSet_path
trainSet, valSet = data_obj.loadTVPartitions(
trainSetPath=cfg.INIT_POOL.TRAINSET_PATH,
valSetPath=cfg.INIT_POOL.VALSET_PATH)
print("Data Partitioning Complete. \nTrain Set: {}, Validation Set: {}\n".
format(len(trainSet), len(valSet)))
logger.info("Train Set: {}, Validation Set: {}\n".format(
len(trainSet), len(valSet)))
# Preparing dataloaders for initial training
trainSet_loader = data_obj.getSequentialDataLoader(
indexes=trainSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
valSet_loader = data_obj.getSequentialDataLoader(
indexes=valSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
# Initialize the model.
model = model_builder.build_model(cfg)
print("model: {}\n".format(cfg.MODEL.TYPE))
logger.info("model: {}\n".format(cfg.MODEL.TYPE))
# Construct the optimizer
optimizer = optim.construct_optimizer(cfg, model)
print("optimizer: {}\n".format(optimizer))
logger.info("optimizer: {}\n".format(optimizer))
# This is to seamlessly use the code originally written for AL episodes
cfg.EPISODE_DIR = cfg.EXP_DIR
# Train model
print("======== ROTATION TRAINING ========")
logger.info("======== ROTATION TRAINING ========")
best_val_acc, best_val_epoch, checkpoint_file = train_model(
trainSet_loader, valSet_loader, model, optimizer, cfg)
print("Best Validation Accuracy: {}\nBest Epoch: {}\n".format(
round(best_val_acc, 4), best_val_epoch))
logger.info("Best Validation Accuracy: {}\tBest Epoch: {}\n".format(
round(best_val_acc, 4), best_val_epoch))
# Test best model checkpoint
print("======== ROTATION TESTING ========\n")
logger.info("======== ROTATION TESTING ========\n")
test_acc = test_model(trainSet_loader, checkpoint_file, cfg, cur_episode=1)
print("Test Accuracy: {}.\n".format(round(test_acc, 4)))
logger.info("Test Accuracy {}.\n".format(test_acc))
print("================================\n\n")
logger.info("================================\n\n")
def main(cfg):
# Login to wandb
wandb.login()
# Initialize a new wandb run
wandb.init(project="rotation-pred", name=cfg.EXP_NAME)
# Setting up GPU args
use_cuda = (cfg.NUM_GPUS > 0) and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {
'num_workers': cfg.DATA_LOADER.NUM_WORKERS,
'pin_memory': cfg.DATA_LOADER.PIN_MEMORY
} if use_cuda else {}
# Using specific GPU
os.environ['NVIDIA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
print("Using GPU : {}.\n".format(cfg.GPU_ID))
# Getting the output directory ready (default is "/output")
cfg.OUT_DIR = os.path.join(os.path.abspath('..'), cfg.OUT_DIR)
if not os.path.exists(cfg.OUT_DIR):
os.makedirs(cfg.OUT_DIR)
# Create "DATASET" specific directory
dataset_out_dir = os.path.join(cfg.OUT_DIR, cfg.DATASET.NAME,
cfg.MODEL.TYPE)
if not os.path.exists(dataset_out_dir):
os.makedirs(dataset_out_dir)
# Creating the experiment directory inside the dataset specific directory
# all logs, labeled, unlabeled, validation sets are stroed here
# E.g., output/CIFAR10/resnet18/{timestamp or cfg.EXP_NAME based on arguments passed}
if cfg.EXP_NAME == 'auto':
now = datetime.now()
exp_dir = f'{now.year}_{now.month}_{now.day}_{now.hour}{now.minute}{now.second}'
else:
exp_dir = cfg.EXP_NAME
exp_dir = os.path.join(dataset_out_dir, exp_dir)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
print("Experiment Directory is {}.\n".format(exp_dir))
else:
print(
"Experiment Directory Already Exists: {}. Reusing it may lead to loss of old logs in the directory.\n"
.format(exp_dir))
cfg.EXP_DIR = exp_dir
# Setup Logger
lu.setup_logging(cfg)
# Dataset preparing steps
print("\n======== PREPARING DATA AND SSL EVALUATION MODEL ========\n")
cfg.DATASET.ROOT_DIR = os.path.join(os.path.abspath('..'),
cfg.DATASET.ROOT_DIR)
data_obj = Data(cfg)
train_data, train_size = data_obj.getDataset(save_dir=cfg.DATASET.ROOT_DIR,
isTrain=True,
isDownload=True)
trainSet = [i for i in range(train_size)]
print("\n Rotation Dataset {} Loaded Sucessfully.\nTotal Train Size: {}\n".
format(cfg.DATASET.NAME, train_size))
logger.info(
"Rotation Dataset {} Loaded Sucessfully. Total Train Size: {}\n".
format(cfg.DATASET.NAME, train_size))
trainSet_path = data_obj.saveSet(setArray=trainSet,
setName='trainSet',
save_dir=cfg.EXP_DIR)
trainSet = data_obj.loadPartition(setPath=trainSet_path)
# Preparing dataloaders for initial training
trainSet_loader = data_obj.getSequentialDataLoader(
indexes=trainSet, batch_size=cfg.TRAIN.BATCH_SIZE, data=train_data)
# Initialize the evaluation model
if cfg.MODEL.TYPE == 'linear':
model = SSLEvaluator(n_input=cfg.MODEL.NUM_INPUT,
n_classes=cfg.MODEL.NUM_OUTPUT,
n_hidden=None)
else:
model = SSLEvaluator(n_input=cfg.MODEL.NUM_INPUT,
n_classes=cfg.MODEL.NUM_OUTPUT,
n_hidden=cfg.MODEL.NUM_HIDDEN)
print("Evaluation model: {}\n".format(cfg.MODEL.EVAL))
logger.info("Evalution model: {}\n".format(cfg.MODEL.EVAL))
# Initialize the SSL model
ssl_model = model_builder.build_model(cfg)
ssl_checkpoint_file = os.path.join(os.path.abspath('..'),
cfg.TEST.MODEL_PATH)
ssl_model = cu.load_checkpoint(ssl_checkpoint_file, ssl_model)
# Construct the optimizer
optimizer = optim.construct_optimizer(cfg, model)
print("optimizer: {}\n".format(optimizer))
logger.info("optimizer: {}\n".format(optimizer))
# This is to seamlessly use the code originally written for AL episodes
cfg.EPISODE_DIR = cfg.EXP_DIR
# Train model
print("======== EVALUATOR TRAINING ========")
logger.info("======== EVALUATOR TRAINING ========")
_, _, eval_checkpoint_file = train_model(trainSet_loader, None, model,
ssl_model, optimizer, cfg)
# eval_checkpoint_file = os.path.join(os.path.abspath('..'), '')
# Test best model checkpoint
print("======== EVALUATOR TESTING ========\n")
logger.info("======== EVALUATOR TESTING ========\n")
test_acc = test_model(trainSet_loader,
eval_checkpoint_file,
ssl_checkpoint_file,
cfg,
cur_episode=1)
print("Test Accuracy: {}.\n".format(round(test_acc, 4)))
logger.info("Test Accuracy {}.\n".format(test_acc))
print("================================\n\n")
logger.info("================================\n\n")