def adversarial_discovery(full_train_path, full_test_path, model_0, save_path):
"""
Collect the adversarial - false positives based on model_0
for the train and validation set.
"""
print('++================================================++')
print("++ Beginning False Positive Adversarial Discovery ++")
print('++================================================++')
# Do not include boundary uncertainty in full train loader. We only need the model predictions, we do not
# calculate the loss! Use the HIERARCH_SHIFT flag along to decide if the Heirarchical model will use
# randomly shifted windows. Note, we flag that this is the full dataset to make sure that during
# adversarial discovery we alwas sample the midlle of oversized windows
full_train_loader = get_loader_fuzzy(
full_train_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
include_boundaries=False,
shift_windows=parameters.HIERARCHICAL_SHIFT_WINDOWS,
is_full_dataset=True,
full_window_predict=True)
full_test_loader = get_loader_fuzzy(
full_test_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
include_boundaries=False,
full_window_predict=True)
# For now let us try including all of the false negatives!
train_adversarial_file = "model_0-False_Pos_Train.txt"
if parameters.HIERARCHICAL_SHIFT_WINDOWS:
train_adversarial_file = "model_0-False_Pos_Train_Shift.txt"
adversarial_train_files = adversarial_discovery_helper(
full_train_loader, model_0)
adversarial_train_save_path = os.path.join(save_path,
train_adversarial_file)
with open(adversarial_train_save_path, 'w') as f:
for file in adversarial_train_files:
f.write('{}\n'.format(file))
adversarial_test_files = adversarial_discovery_helper(
full_test_loader, model_0)
adversarial_test_save_path = os.path.join(save_path,
"model_0-False_Pos_Test.txt")
with open(adversarial_test_save_path, 'w') as f:
for file in adversarial_test_files:
f.write('{}\n'.format(file))
return adversarial_train_files, adversarial_test_files
def main(args):
"""
Example runs:
"""
# Load Model
model, model_id = loadModel(args.model)
# Put in eval mode!
print(model_id)
# For the window classification setting we literally just want to test on the full_spect chunks
if args.local_files:
test_data_path = parameters.LOCAL_FULL_TEST
else:
test_data_path = parameters.REMOTE_FULL_TEST
eval_loader = get_loader_fuzzy(test_data_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
full_window_predict=True)
eval_model(eval_loader, model, args.model)
def main():
args = parser.parse_args()
# Use the 24hr datasets!
if args.local_files:
if parameters.LOSS.upper() == "FOCAL":
train_data_path = parameters.LOCAL_FULL_TRAIN
test_data_path = parameters.LOCAL_FULL_TEST
else:
train_data_path = parameters.LOCAL_TRAIN_FILES
test_data_path = parameters.LOCAL_TEST_FILES
else:
if parameters.LOSS.upper() == "FOCAL":
train_data_path = parameters.REMOTE_FULL_TRAIN
test_data_path = parameters.REMOTE_FULL_TEST
else:
train_data_path = parameters.REMOTE_TRAIN_FILES
test_data_path = parameters.REMOTE_TEST_FILES
if parameters.LOSS.upper() != "FOCAL":
train_data_path, _ = create_dataset_path(
train_data_path,
neg_samples=parameters.NEG_SAMPLES,
call_repeats=parameters.CALL_REPEATS,
shift_windows=parameters.SHIFT_WINDOWS)
test_data_path, _ = create_dataset_path(
test_data_path,
neg_samples=parameters.TEST_NEG_SAMPLES,
call_repeats=1)
else:
if parameters.SHIFT_WINDOWS:
train_data_path += '_OversizeCalls'
train_loader = get_loader_fuzzy(train_data_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
shift_windows=parameters.SHIFT_WINDOWS,
full_window_predict=True)
test_loader = get_loader_fuzzy(test_data_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
full_window_predict=True)
# For now model 18 signifies this!!!
save_path = create_save_path(
time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime()), args.save_local)
dloaders = {'train': train_loader, 'valid': test_loader}
## Training
model = get_model(parameters.MODEL_ID)
model.to(parameters.device)
print(model)
writer = SummaryWriter(save_path)
writer.add_scalar('batch_size', parameters.BATCH_SIZE)
writer.add_scalar(
'weight_decay',
parameters.HYPERPARAMETERS[parameters.MODEL_ID]['l2_reg'])
loss_func, include_boundaries = get_loss()
# Honestly probably do not need to have hyper-parameters per model, but leave it for now.
optimizer = torch.optim.Adam(
model.parameters(),
lr=parameters.HYPERPARAMETERS[parameters.MODEL_ID]['lr'],
weight_decay=parameters.HYPERPARAMETERS[parameters.MODEL_ID]['l2_reg'])
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
parameters.HYPERPARAMETERS[parameters.MODEL_ID]['lr_decay_step'],
gamma=parameters.HYPERPARAMETERS[parameters.MODEL_ID]['lr_decay'])
start_time = time.time()
model_wts = None
model_wts = train(dloaders,
model,
loss_func,
optimizer,
scheduler,
writer,
parameters.NUM_EPOCHS,
include_boundaries=include_boundaries)
if model_wts:
model.load_state_dict(model_wts)
if not os.path.exists(save_path):
os.makedirs(save_path)
save_path = save_path + "/" + "model.pt"
torch.save(model, save_path)
print('Saved best model based on {} to path {}'.format(
parameters.TRAIN_MODEL_SAVE_CRITERIA.upper(), save_path))
else:
print('For some reason I don\'t have a model to save')
print('Training time: {:10f} minutes'.format(
(time.time() - start_time) / 60))
writer.close()
def main():
args = parser.parse_args()
# What do we need to do across all of the settings!
# Get the data loaders!
args = parser.parse_args()
if args.local_files:
train_data_path = parameters.LOCAL_TRAIN_FILES
test_data_path = parameters.LOCAL_TEST_FILES
full_train_path = parameters.LOCAL_FULL_TRAIN
full_test_path = parameters.LOCAL_FULL_TEST
else:
train_data_path = parameters.REMOTE_TRAIN_FILES
test_data_path = parameters.REMOTE_TEST_FILES
full_train_path = parameters.REMOTE_FULL_TRAIN
full_test_path = parameters.REMOTE_FULL_TEST
if parameters.HIERARCHICAL_SHIFT_WINDOWS:
full_train_path += '_OversizeCalls'
model_0_train_data_path, include_boundaries = create_dataset_path(
train_data_path,
neg_samples=parameters.NEG_SAMPLES,
call_repeats=parameters.CALL_REPEATS,
shift_windows=parameters.SHIFT_WINDOWS)
model_0_test_data_path, _ = create_dataset_path(
test_data_path,
neg_samples=parameters.TEST_NEG_SAMPLES,
call_repeats=1)
# Check if a different dataset is being used for Model_1
model_1_train_data_path = model_0_train_data_path
model_1_test_data_path = model_0_test_data_path
if str(parameters.HIERARCHICAL_REPEATS).lower() != "same":
# SHould prob just have neg samples x1 since doesnt matter!!
model_1_train_data_path, _ = create_dataset_path(
train_data_path,
neg_samples=parameters.NEG_SAMPLES,
call_repeats=parameters.HIERARCHICAL_REPEATS,
shift_windows=parameters.HIERARCHICAL_SHIFT_WINDOWS)
# Model 0 Loaders
model_0_train_loader = get_loader_fuzzy(
model_0_train_data_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
include_boundaries=include_boundaries,
shift_windows=parameters.SHIFT_WINDOWS,
full_window_predict=True)
model_0_test_loader = get_loader_fuzzy(
model_0_test_data_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
include_boundaries=include_boundaries,
full_window_predict=True)
# Model 1 Loaders
model_1_train_loader = get_loader_fuzzy(
model_1_train_data_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
include_boundaries=include_boundaries,
shift_windows=parameters.HIERARCHICAL_SHIFT_WINDOWS,
full_window_predict=True)
model_1_test_loader = get_loader_fuzzy(
model_1_test_data_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
include_boundaries=include_boundaries,
full_window_predict=True)
if args.models_path is None:
save_path = create_save_path(time.strftime("%Y-%m-%d_%H:%M:%S",
time.localtime()),
args.save_local,
save_prefix='Hierarchical_')
else:
save_path = args.models_path
# Case 1) Do the entire pipeline! Can break now the pipeline into 3 helper functions!
if args.full_pipeline:
# Train and save model_0
#model_0 = train_model_0(dloaders, save_path)
model_0 = train_model_0(model_0_train_loader, model_0_test_loader,
save_path)
# Do the adversarial discovery
adversarial_train_files, adversarial_test_files = adversarial_discovery(
full_train_path, full_test_path, model_0, save_path)
# Train and save model 1
train_model_1(adversarial_train_files, adversarial_test_files,
model_1_train_loader, model_1_test_loader, save_path)
# Just generate new adversarial examples
elif args.adversarial:
# Load model_0
model_0_path = os.path.join(save_path, "Model_0/model.pt")
model_0 = torch.load(model_0_path, map_location=parameters.device)
adversarial_discovery(full_train_path, full_test_path, model_0,
save_path)
# Train just model_1
elif args.model1:
# Read in the adversarial files
train_adversarial_file = "model_0-False_Pos_Train.txt"
if parameters.HIERARCHICAL_SHIFT_WINDOWS:
train_adversarial_file = "model_0-False_Pos_Train_Shift.txt"
adversarial_train_save_path = os.path.join(save_path,
train_adversarial_file)
adversarial_train_files = []
with open(adversarial_train_save_path, 'r') as f:
files = f.readlines()
for file in files:
adversarial_train_files.append(file.strip())
adversarial_test_save_path = os.path.join(
save_path, "model_0-False_Pos_Test.txt")
adversarial_test_files = []
with open(adversarial_test_save_path, 'r') as f:
files = f.readlines()
for file in files:
adversarial_test_files.append(file.strip())
train_model_1(adversarial_train_files, adversarial_test_files,
model_1_train_loader, model_1_test_loader, save_path)
else:
print("Invalid running mode!")
def main():
# What do we need to do across all of the settings!
# Get the data loaders!
args = parser.parse_args()
if args.local_files:
train_data_path = parameters.LOCAL_TRAIN_FILES
test_data_path = parameters.LOCAL_TEST_FILES
full_train_path = parameters.LOCAL_FULL_TRAIN
full_test_path = parameters.LOCAL_FULL_TEST
else:
if parameters.DATASET.lower() == "noab":
train_data_path = parameters.REMOTE_TRAIN_FILES
test_data_path = parameters.REMOTE_TEST_FILES
full_train_path = parameters.REMOTE_FULL_TRAIN
full_test_path = parameters.REMOTE_FULL_TEST
else:
train_data_path = parameters.REMOTE_BAI_TRAIN_FILES
test_data_path = parameters.REMOTE_BAI_TEST_FILES
full_train_path = parameters.REMOTE_FULL_TRAIN_BAI
full_test_path = parameters.REMOTE_FULL_TEST_BAI
# Get oversized calls if shifting windows or repeating for model 2
# We should try to remove both of these This is an issue too!
if parameters.HIERARCHICAL_SHIFT_WINDOWS: # or parameters.HIERARCHICAL_REPEATS > 1:
full_train_path += '_OversizeCalls'
# For model 2 we need to have oversized calls to generate the randomly located repeats
if parameters.HIERARCHICAL_REPEATS_POS > 1 or parameters.HIERARCHICAL_REPEATS_NEG > 1:
full_train_path += '_OversizeCalls'
model_0_train_data_path, include_boundaries = create_dataset_path(train_data_path, neg_samples=parameters.NEG_SAMPLES,
call_repeats=parameters.CALL_REPEATS,
shift_windows=parameters.SHIFT_WINDOWS)
model_0_test_data_path, _ = create_dataset_path(test_data_path, neg_samples=parameters.TEST_NEG_SAMPLES,
call_repeats=1)
# Check if a different dataset is being used for Model_1
model_1_train_data_path = model_0_train_data_path
model_1_test_data_path = model_0_test_data_path
# Remove this same thing!
#if str(parameters.HIERARCHICAL_REPEATS).lower() != "same" or parameters.HIERARCHICAL_REPEATS_POS > 1 or parameters.HIERARCHICAL_REPEATS_NEG > 1:
if parameters.HIERARCHICAL_REPEATS_POS > 1 or parameters.HIERARCHICAL_REPEATS_NEG > 1:
# SHould prob just have neg samples x1 since doesnt matter!!
# For now set call repeats to 1, but get shifting windows so we later can do call repeats!
#shift_windows = parameters.HIERARCHICAL_REPEATS > 1 or parameters.HIERARCHICAL_SHIFT_WINDOWS
# For now should make shift windows just be true! Because it does not make a lot of sense to do
# repeats without shifting windows since we can only repeat the pos examples
shift_windows = True
# Set this to 1 because we take care of this later!!!!
call_repeats = 1
model_1_train_data_path, _ = create_dataset_path(train_data_path, neg_samples=parameters.NEG_SAMPLES,
call_repeats=call_repeats,
shift_windows=shift_windows)
# Model 0 Loaders
model_0_train_loader = get_loader_fuzzy(model_0_train_data_path, parameters.BATCH_SIZE, random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM, scale=parameters.SCALE,
include_boundaries=include_boundaries, shift_windows=parameters.SHIFT_WINDOWS)
model_0_test_loader = get_loader_fuzzy(model_0_test_data_path, parameters.BATCH_SIZE, random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM, scale=parameters.SCALE, include_boundaries=include_boundaries)
# Model 1 Loaders
model_1_train_loader = get_loader_fuzzy(model_1_train_data_path, parameters.BATCH_SIZE, random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM, scale=parameters.SCALE,
include_boundaries=include_boundaries, shift_windows=parameters.HIERARCHICAL_SHIFT_WINDOWS)
model_1_test_loader = get_loader_fuzzy(model_1_test_data_path, parameters.BATCH_SIZE, random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM, scale=parameters.SCALE, include_boundaries=include_boundaries)
if args.path is None:
save_path = create_save_path(time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime()), args.save_local, save_prefix='Hierarchical_')
else:
save_path = args.path
# Case 1) Do the entire pipeline! Can break now the pipeline into 3 helper functions!
if args.full_pipeline:
# Train and save model_0
if args.model_0 == None:
model_0 = train_model_0(model_0_train_loader, model_0_test_loader , save_path, args.pre_train_0)
else: # Load and save model_0
model_0 = torch.load(args.model_0, map_location=parameters.device)
first_model_save_path = os.path.join(save_path, "Model_0")
if not os.path.exists(first_model_save_path):
os.makedirs(first_model_save_path)
model_save_path = os.path.join(first_model_save_path, "model.pt")
torch.save(model_0, model_save_path)
# Do the adversarial discovery
adversarial_train_files, adversarial_test_files = adversarial_discovery(full_train_path, full_test_path,
model_0_train_loader, model_0_test_loader, model_0, save_path)
# Train and save model 1
train_model_1(adversarial_train_files, adversarial_test_files, model_1_train_loader, model_1_test_loader,
save_path, args.pre_train_1)
# Just generate new adversarial examples
elif args.adversarial:
# Load model_0
model_0_path = os.path.join(save_path, "Model_0/model.pt")
model_0 = torch.load(model_0_path, map_location=parameters.device)
adversarial_discovery(full_train_path, full_test_path, model_0_train_loader, model_0_test_loader, model_0, save_path)
# Train just model_1
elif args.model1:
# Read in the adversarial files
train_adversarial_file = "model_0-False_Pos_Train.txt"
#if parameters.HIERARCHICAL_SHIFT_WINDOWS or parameters.HIERARCHICAL_REPEATS > 1:
if parameters.HIERARCHICAL_REPEATS_POS > 1 or parameters.HIERARCHICAL_REPEATS_NEG > 1:
train_adversarial_file = "model_0-False_Pos_Train_Shift.txt"
adversarial_train_save_path = os.path.join(save_path, train_adversarial_file)
adversarial_train_files = []
with open(adversarial_train_save_path, 'r') as f:
files = f.readlines()
for file in files:
adversarial_train_files.append(file.strip())
adversarial_test_save_path = os.path.join(save_path, "model_0-False_Pos_Test.txt")
adversarial_test_files = []
with open(adversarial_test_save_path, 'r') as f:
files = f.readlines()
for file in files:
adversarial_test_files.append(file.strip())
train_model_1(adversarial_train_files, adversarial_test_files, model_1_train_loader,
model_1_test_loader, save_path, args.pre_train_1)
elif args.visualize:
model_0_path = os.path.join(save_path, "Model_0/model.pt")
model_0 = torch.load(model_0_path, map_location=parameters.device)
model_1_name = hierarchical_model_1_path()
model_1_path = os.path.join(save_path, model_1_name+'/model.pt')
model_1 = torch.load(model_1_path, map_location=parameters.device)
# Read in the adversarial files
train_adversarial_file = "model_0-False_Pos_Train.txt"
#if parameters.HIERARCHICAL_SHIFT_WINDOWS or parameters.HIERARCHICAL_REPEATS > 1:
if parameters.HIERARCHICAL_REPEATS_POS > 1 or parameters.HIERARCHICAL_REPEATS_NEG > 1:
train_adversarial_file = "model_0-False_Pos_Train_Shift.txt"
adversarial_train_save_path = os.path.join(save_path, train_adversarial_file)
adversarial_train_files = []
with open(adversarial_train_save_path, 'r') as f:
files = f.readlines()
for file in files:
adversarial_train_files.append(file.strip())
visualize_adversarial(adversarial_train_files, model_1_train_loader, model_0, model_1)
else:
print ("Invalid running mode!")
def adversarial_discovery(full_train_path, full_test_path, model_1_train_loader, model_1_test_loader, model_0, save_path):
"""
Collect the adversarial - false positives based on model_0
for the train and validation set.
"""
# Test our little function
print ('++================================================++')
print ("++ Beginning False Positive Adversarial Discovery ++")
print ('++================================================++')
# Do not include boundary uncertainty in full train loader. We only need the model predictions, we do not
# calculate the loss! Use the HIERARCH_SHIFT flag along to decide if the Heirarchical model will use
# randomly shifted windows. Note, we flag that this is the full dataset to make sure that during
# adversarial discovery we alwas sample the midlle of oversized windows
#shift_windows = parameters.HIERARCHICAL_SHIFT_WINDOWS # or parameters.HIERARCHICAL_REPEATS > 1
shift_windows = parameters.HIERARCHICAL_REPEATS_POS > 1 or parameters.HIERARCHICAL_REPEATS_NEG > 1
full_train_loader = get_loader_fuzzy(full_train_path, parameters.BATCH_SIZE, random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM, scale=parameters.SCALE,
include_boundaries=False, shift_windows=shift_windows,
is_full_dataset=True)
full_test_loader = get_loader_fuzzy(full_test_path, parameters.BATCH_SIZE, random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM, scale=parameters.SCALE, include_boundaries=False)
# For now let us try including all of the false negatives!
train_adversarial_file = "model_0-False_Pos_Train.txt"
if shift_windows:
train_adversarial_file = "model_0-False_Pos_Train_Shift.txt"
adversarial_train_files, model_0_fp_train_logits, model_0_fp_train_preds, model_0_fp_binary_train_preds = adversarial_discovery_helper(full_train_loader,
model_0, min_length=parameters.FALSE_POSITIVE_THRESHOLD)
# Save the adversarial feature file paths
adversarial_train_save_path = os.path.join(save_path, train_adversarial_file)
with open(adversarial_train_save_path, 'w') as f:
for file in adversarial_train_files:
f.write('{}\n'.format(file))
# Save model_0 FP train predictions both raw and transformed with additional label
print ("Saving model_0 FP predictions on the train data")
save_model_0_predictions(save_path, adversarial_train_files, model_0_fp_train_logits, "model_0_fp_train_logits")
save_model_0_predictions(save_path, adversarial_train_files, model_0_fp_train_preds, "model_0_fp_train_preds")
save_model_0_predictions(save_path, adversarial_train_files, model_0_fp_binary_train_preds, "model_0_fp_binary_train_preds")
transformed_model_0_fp_binary_train_preds = transform_model_0_predictions(model_0_fp_binary_train_preds)
save_model_0_predictions(save_path, adversarial_train_files, transformed_model_0_fp_binary_train_preds, "transformed_model_0_fp_binary_train_preds")
# Save model_0 TP train predictions both raw and transformed with additional label
print ("Saving model_0 TP predictions on the train data")
elephant_train_files, model_0_tp_train_logits, model_0_tp_train_preds, model_0_tp_binary_train_preds, gt_train_labels = model_0_Elephant_Predictions(model_1_train_loader, model_0)
save_model_0_predictions(save_path, elephant_train_files, model_0_tp_train_logits, "model_0_tp_train_logits")
save_model_0_predictions(save_path, elephant_train_files, model_0_tp_train_preds, "model_0_tp_train_preds")
save_model_0_predictions(save_path, elephant_train_files, model_0_tp_binary_train_preds, "model_0_tp_binary_train_preds")
transformed_model_0_tp_binary_train_preds = transform_model_0_predictions(model_0_tp_binary_train_preds, gt_train_labels)
save_model_0_predictions(save_path, elephant_train_files, transformed_model_0_tp_binary_train_preds, "transformed_model_0_tp_binary_train_preds")
test_adversarial_files = "model_0-False_Pos_Test.txt"
adversarial_test_files, model_0_fp_test_logits, model_0_fp_test_preds, model_0_fp_binary_test_preds = adversarial_discovery_helper(full_test_loader,
model_0, min_length=parameters.FALSE_POSITIVE_THRESHOLD)
adversarial_test_save_path = os.path.join(save_path, test_adversarial_files)
with open(adversarial_test_save_path, 'w') as f:
for file in adversarial_test_files:
f.write('{}\n'.format(file))
# Save model_0 FP test predictions both raw and transformed with additional label
print ("Saving model_0 FP predictions on the test data")
save_model_0_predictions(save_path, adversarial_test_files, model_0_fp_test_logits, "model_0_fp_test_logits")
save_model_0_predictions(save_path, adversarial_test_files, model_0_fp_test_preds, "model_0_fp_test_preds")
save_model_0_predictions(save_path, adversarial_test_files, model_0_fp_binary_test_preds, "model_0_fp_binary_test_preds")
transformed_model_0_fp_binary_test_preds = transform_model_0_predictions(model_0_fp_binary_test_preds)
save_model_0_predictions(save_path, adversarial_test_files, transformed_model_0_fp_binary_test_preds, "transformed_model_0_fp_binary_test_preds")
# Save model_0 TP train predictions both raw and transformed with additional label
print ("Saving model_0 TP predictions on the test data")
elephant_test_files, model_0_tp_test_logits, model_0_tp_test_preds, model_0_tp_binary_test_preds, gt_test_labels = model_0_Elephant_Predictions(model_1_test_loader, model_0)
save_model_0_predictions(save_path, elephant_test_files, model_0_tp_test_logits, "model_0_tp_test_logits")
save_model_0_predictions(save_path, elephant_test_files, model_0_tp_test_preds, "model_0_tp_test_preds")
save_model_0_predictions(save_path, elephant_test_files, model_0_tp_binary_test_preds, "model_0_tp_binary_test_preds")
transformed_model_0_tp_binary_test_preds = transform_model_0_predictions(model_0_tp_binary_test_preds, gt_test_labels)
save_model_0_predictions(save_path, elephant_test_files, transformed_model_0_tp_binary_test_preds, "transformed_model_0_tp_binary_test_preds")
return adversarial_train_files, adversarial_test_files
def main():
args = parser.parse_args()
if args.local_files:
train_data_path = parameters.LOCAL_TRAIN_FILES
test_data_path = parameters.LOCAL_TEST_FILES
full_train_path = parameters.LOCAL_FULL_TRAIN
full_test_path = parameters.LOCAL_FULL_TEST
else:
if parameters.DATASET.lower() == "noab":
train_data_path = parameters.REMOTE_TRAIN_FILES
test_data_path = parameters.REMOTE_TEST_FILES
full_train_path = parameters.REMOTE_FULL_TRAIN
full_test_path = parameters.REMOTE_FULL_TEST
else:
train_data_path = parameters.REMOTE_BAI_TRAIN_FILES
test_data_path = parameters.REMOTE_BAI_TEST_FILES
full_train_path = parameters.REMOTE_FULL_TRAIN_BAI
full_test_path = parameters.REMOTE_FULL_TEST_BAI
train_data_path, include_boundaries = create_dataset_path(train_data_path, neg_samples=parameters.NEG_SAMPLES,
call_repeats=parameters.CALL_REPEATS,
shift_windows=parameters.SHIFT_WINDOWS)
test_data_path, _ = create_dataset_path(test_data_path, neg_samples=parameters.TEST_NEG_SAMPLES,
call_repeats=1)
train_loader = get_loader_fuzzy(train_data_path, parameters.BATCH_SIZE, random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM, scale=parameters.SCALE,
include_boundaries=include_boundaries, shift_windows=parameters.SHIFT_WINDOWS)
test_loader = get_loader_fuzzy(test_data_path, parameters.BATCH_SIZE, random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM, scale=parameters.SCALE, include_boundaries=include_boundaries)
# For now we don't need to save the model
save_path = create_save_path(time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime()), args.save_local)
train_dataloaders = {'train':train_loader, 'valid':test_loader}
# Load the full data sets - SET SHUFFLE = False
full_train_loader = get_loader_fuzzy(full_train_path, parameters.BATCH_SIZE, shuffle=False,
norm=parameters.NORM, scale=parameters.SCALE,
include_boundaries=False, shift_windows=False,
is_full_dataset=True)
full_test_loader = get_loader_fuzzy(full_test_path, parameters.BATCH_SIZE, shuffle=False,
norm=parameters.NORM, scale=parameters.SCALE, include_boundaries=False)
full_dataloaders = {'train':full_train_loader, 'valid': full_test_loader}
model = get_model(parameters.MODEL_ID)
model.to(parameters.device)
print(model)
writer = SummaryWriter(save_path)
writer.add_scalar('batch_size', parameters.BATCH_SIZE)
writer.add_scalar('weight_decay', parameters.HYPERPARAMETERS[parameters.MODEL_ID]['l2_reg'])
# Want to use focal loss! Next thing to check on!
loss_func, include_boundaries = get_loss()
# Honestly probably do not need to have hyper-parameters per model, but leave it for now.
optimizer = torch.optim.Adam(model.parameters(), lr=parameters.HYPERPARAMETERS[parameters.MODEL_ID]['lr'],
weight_decay=parameters.HYPERPARAMETERS[parameters.MODEL_ID]['l2_reg'])
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, parameters.HYPERPARAMETERS[parameters.MODEL_ID]['lr_decay_step'],
gamma=parameters.HYPERPARAMETERS[parameters.MODEL_ID]['lr_decay'])
start_time = time.time()
curriculum_profiling(model, train_dataloaders, full_dataloaders, loss_func, optimizer, scheduler, writer)
print('Training time: {:10f} minutes'.format((time.time()-start_time)/60))
writer.close()
def main():
args = parser.parse_args()
use_focal = False
if parameters.LOSS.lower() == "focal" or parameters.LOSS.lower(
) == "focal_chunk":
use_focal = True
if args.local_files:
if use_focal:
train_data_path = parameters.LOCAL_FULL_TRAIN
test_data_path = parameters.LOCAL_FULL_TEST
else:
train_data_path = parameters.LOCAL_TRAIN_FILES
test_data_path = parameters.LOCAL_TEST_FILES
else:
if parameters.DATASET.lower() == "noab":
if use_focal:
train_data_path = parameters.REMOTE_FULL_TRAIN
test_data_path = parameters.REMOTE_FULL_TEST
else:
train_data_path = parameters.REMOTE_TRAIN_FILES
test_data_path = parameters.REMOTE_TEST_FILES
else:
if use_focal:
train_data_path = parameters.REMOTE_FULL_TRAIN_BAI
test_data_path = parameters.REMOTE_FULL_TEST_BAI
else:
train_data_path = parameters.REMOTE_BAI_TRAIN_FILES
test_data_path = parameters.REMOTE_BAI_TEST_FILES
if use_focal:
include_boundaries = False
else:
train_data_path, include_boundaries = create_dataset_path(
train_data_path,
neg_samples=parameters.NEG_SAMPLES,
call_repeats=parameters.CALL_REPEATS,
shift_windows=parameters.SHIFT_WINDOWS)
test_data_path, _ = create_dataset_path(
test_data_path,
neg_samples=parameters.TEST_NEG_SAMPLES,
call_repeats=1)
train_loader = get_loader_fuzzy(train_data_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
include_boundaries=include_boundaries,
shift_windows=parameters.SHIFT_WINDOWS)
test_loader = get_loader_fuzzy(test_data_path,
parameters.BATCH_SIZE,
random_seed=parameters.DATA_LOADER_SEED,
norm=parameters.NORM,
scale=parameters.SCALE,
include_boundaries=include_boundaries)
save_path = create_save_path(
time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime()), args.save_local)
dloaders = {'train': train_loader, 'valid': test_loader}
## Training
# Load a pre-trained model
if parameters.PRE_TRAIN:
model = torch.load(args.pre_train, map_location=parameters.device)
else:
model = get_model(parameters.MODEL_ID)
model.to(parameters.device)
print(model)
writer = SummaryWriter(save_path)
writer.add_scalar('batch_size', parameters.BATCH_SIZE)
writer.add_scalar(
'weight_decay',
parameters.HYPERPARAMETERS[parameters.MODEL_ID]['l2_reg'])
# Want to use focal loss! Next thing to check on!
loss_func, include_boundaries = get_loss()
# Honestly probably do not need to have hyper-parameters per model, but leave it for now.
optimizer = torch.optim.Adam(
model.parameters(),
lr=parameters.HYPERPARAMETERS[parameters.MODEL_ID]['lr'],
weight_decay=parameters.HYPERPARAMETERS[parameters.MODEL_ID]['l2_reg'])
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
parameters.HYPERPARAMETERS[parameters.MODEL_ID]['lr_decay_step'],
gamma=parameters.HYPERPARAMETERS[parameters.MODEL_ID]['lr_decay'])
start_time = time.time()
model_wts = None
model_wts = train(dloaders,
model,
loss_func,
optimizer,
scheduler,
writer,
parameters.NUM_EPOCHS,
include_boundaries=include_boundaries)
if model_wts:
model.load_state_dict(model_wts)
if not os.path.exists(save_path):
os.makedirs(save_path)
save_path = save_path + "/" + "model.pt"
torch.save(model, save_path)
print('Saved best model based on {} to path {}'.format(
parameters.TRAIN_MODEL_SAVE_CRITERIA.upper(), save_path))
else:
print('For some reason I don\'t have a model to save')
print('Training time: {:10f} minutes'.format(
(time.time() - start_time) / 60))
writer.close()
