def create_models(dataset_dir):
"""Initialize the app (available for localhost only)
Parameters:
dataset_dir (:func:`str`): Path to the training set
"""
logger.debug("Creating models...")
if not local_exec:
logger.error("Models can only be generated locally")
exit(1)
# Modify the configuration for local execution
app_config['root'] = os.environ['ROOT']
# Generate inline models and train classifier
denoiser = Denoiser(app_config)
if not exists(dataset_dir) or not isdir(dataset_dir):
logger.error(dataset_dir+" is not a valid directory")
exit(2)
dataset = [join(dataset_dir, f) for f in listdir(dataset_dir)]
denoiser.generate_models(dataset)
logger.info("Inline models generated")
denoiser.train(dataset)
logger.info("Classifier trained")
def create_models(dataset_dir):
"""Initialize the app (available for localhost only)
Parameters:
dataset_dir (:func:`str`): Path to the training set
"""
logger.debug("Creating models...")
if not local_exec:
logger.error("Models can only be generated locally")
exit(1)
# Modify the configuration for local execution
app_config['root'] = os.environ['ROOT']
# Generate inline models and train classifier
denoiser = Denoiser(app_config)
if not exists(dataset_dir) or not isdir(dataset_dir):
logger.error(dataset_dir + " is not a valid directory")
exit(2)
dataset = [join(dataset_dir, f) for f in listdir(dataset_dir)]
denoiser.generate_models(dataset)
logger.info("Inline models generated")
denoiser.train(dataset)
logger.info("Classifier trained")
def mae_albdiv_bn(train_data, test_data):
"""Train the MAE denoiser with albedo divide and batch norm on. Use all
features."""
config.ALBEDO_DIVIDE = True
feature_list = ["normal", "albedo", "depth"]
denoiser = Denoiser(
train_data,
test_data,
adam_lr=1e-4,
num_epochs=
100000, # Stupid number of epochs, early stopping will prevent reaching this
early_stopping=True,
kernel_predict=True,
bn=True,
feature_list=feature_list,
num_layers=7,
batch_size=64,
loss="mae",
model_dir="../experiments/models/mae_albdiv_bn",
log_dir="../experiments/logs/mae_albdiv_bn")
denoiser.buildNetwork()
denoiser.train()
def vgg_albdiv_bn(train_data, test_data):
"""Train the vgg denoiser with albedo divide and batch norm. Use all
features"""
config.ALBEDO_DIVIDE = True
feature_list = ["normal", "albedo", "depth"]
denoiser = Denoiser(
train_data,
test_data,
adam_lr=1e-3,
num_epochs=100000,
early_stopping=True,
kernel_predict=True,
bn=True,
feature_list=feature_list,
num_layers=7,
batch_size=64,
loss="vgg22",
model_dir="../experiments/models/vgg_albdiv_bn",
log_dir="../experiments/logs/vgg_albdiv_bn",
)
denoiser.buildNetwork()
denoiser.train()
