def main():
# Parameters for training
parameters = p.default_parameters()
parameters[p.PREFIX] = 'imitation_bn_false'
parameters[p.TFRECORDS] = 'hm/axial'
parameters[p.LOSS_FN] = Loss.BDICE
parameters[p.LR] = 0.001
parameters[p.NUM_CLASSES] = 5
parameters[p.OPTIMISER] = Optimiser.ADAM
parameters[p.TRAIN_BATCH] = 16
parameters[p.VAL_BATCH] = 16
parameters[p.PATIENCE] = 50
parameters[p.NETWORK] = Network
# parameters[p.NETWORK] = ViewAggregation
p.validate(parameters)
# Create folder for the new model
parameters[p.MODEL_PATH] = misc.new_checkpoint_path(
prefix=parameters[p.PREFIX], tfr=parameters[p.TFRECORDS])
# Load TFRecords
tfrm = TFRecordsManager()
tfrecord_path = misc.get_tfrecords_path() + f"/{parameters[p.TFRECORDS]}/"
dataset = tfrm.load_datasets(tfrecord_path, parameters[p.TRAIN_BATCH],
parameters[p.VAL_BATCH])
network = parameters[p.NETWORK](num_classes=parameters[p.NUM_CLASSES])
solver = Solver(network, parameters)
epoch_metrics = dict()
for epoch in range(1000):
for mode in dataset:
epoch_metrics[mode] = solver.run_epoch(dataset[mode], mode)
best_val_loss = solver.best_val_loss
val_loss = epoch_metrics['val']['imitation_output_loss']
print(
f'ValLoss:[{val_loss}] BestValLoss:[{best_val_loss}] EST:[{solver.early_stopping_tick}]',
flush=True)
for name in ['base', 'imitation', 'label']:
amount = epoch_metrics["val"][f'{name}_output_loss']
print(f'{name}_output_loss:', amount, flush=True)
print(f'imitation_loss:',
epoch_metrics["val"]['imitation_loss'],
flush=True)
print()
if solver.early_stopping_tick > parameters[p.PATIENCE]:
break
def main():
# Parameters for training
parameters = p.default_parameters()
parameters[p.PREFIX] = 'hm_aug_BDICE_50'
parameters[p.TFRECORDS] = 'hm_aug/axial'
parameters[p.LOSS_FN] = Loss.BDICE
parameters[p.LR] = 0.001
parameters[p.NUM_CLASSES] = 5
parameters[p.OPTIMISER] = Optimiser.ADAM
parameters[p.TRAIN_BATCH] = 10
parameters[p.VAL_BATCH] = 30
parameters[p.PATIENCE] = 50
parameters[p.NETWORK] = CDFNet
# parameters[p.NETWORK] = ViewAggregation
p.validate(parameters)
# Create folder for the new model
parameters[p.MODEL_PATH] = misc.new_checkpoint_path(prefix=parameters[p.PREFIX], tfr=parameters[p.TFRECORDS])
# Load TFRecords
tfrm = TFRecordsManager()
tfrecord_path = misc.get_tfrecords_path() + f"/{parameters[p.TFRECORDS]}/"
dataset = tfrm.load_datasets(tfrecord_path, parameters[p.TRAIN_BATCH], parameters[p.VAL_BATCH])
network = parameters[p.NETWORK](num_classes=parameters[p.NUM_CLASSES])
solver = Solver(network, parameters)
epoch_metrics = dict()
for epoch in range(1000):
for mode in dataset:
epoch_metrics[mode] = solver.run_epoch(dataset[mode], mode)
best_val_loss = solver.best_val_loss
val_loss = epoch_metrics['val']['loss']
print(f'ValLoss:[{val_loss}] BestValLoss:[{best_val_loss}] EST:[{solver.early_stopping_tick}]', flush=True)
if solver.early_stopping_tick > parameters[p.PATIENCE]:
break
if type(parameters[p.NETWORK]()) == CDFNet:
# Run predictions for dataset
model_folder = parameters[p.MODEL_PATH].split('/')[-2]
predict(model_folder, parameters[p.TFRECORDS], prefix=parameters[p.PREFIX])