def test_path_from_model_name():
assert path_from_model_name("example_net") == os.path.join(
PROJECT_DIR, "models", "example_net", "latest.h5")
assert path_from_model_name("example_net:latest") == os.path.join(
PROJECT_DIR, "models", "example_net", "latest.h5")
assert path_from_model_name("example_net:epoch1") == os.path.join(
PROJECT_DIR, "models", "example_net", "epoch1.h5")
with pytest.raises(ValueError):
path_from_model_name("A\nB")
def train_wrapper(args: Namespace) -> None:
""" Function for training a network. """
model_name = args.model
if args.cont:
model = load_model(model_name)
history = model.__asf_model_history
weights = model.get_weights()
lr_schedule = ExponentialDecay(9.2e-4,
decay_steps=2000,
decay_rate=0.96,
staircase=True)
# optimizer = model.optimizer
model.compile(loss=jaccard_distance_loss,
optimizer=Adam(learning_rate=lr_schedule),
metrics=['accuracy', MeanIoU(num_classes=2)])
model.set_weights(weights)
# model.compile(
# loss='binary_crossentropy', optimizer='adam', metrics=["accuracy"]
# )
else:
model_path = path_from_model_name(model_name)
if not args.overwrite and os.path.isfile(model_path):
print(f"File {model_name} already exists!")
return
# model = create_model_masked(model_name)
model = create_cdl_model_masked(model_name)
history = {'loss': [], 'accuracy': [], "mean_io_u": []}
train_model(model, history, args.dataset, args.epochs)
def train_wrapper(args: Namespace) -> None:
""" Function for training a network. """
model_name = args.model
if args.cont:
model = load_model(model_name)
history = model.__asf_model_history
else:
model_path = path_from_model_name(model_name)
if not args.overwrite and os.path.isfile(model_path):
print(f"File {model_name} already exists!")
return
model = create_model_masked(model_name)
history = {"loss": [], "acc": [], "val_loss": [], "val_acc": []}
train_model(model, history, args.dataset, args.epochs)
def test_wrapper(args: Namespace) -> None:
model_name = args.model
model = load_model(model_name)
if model_type(model) != dataset_type(args.dataset):
print("ERROR: This dataset is not compatible with your model")
return
if dataset_type(args.dataset) == ModelType.MASKED:
predictions, test_iter = test_model_masked(model, args.dataset)
plot_masked_predictions(predictions, test_iter, args.dataset)
else:
details, confusion_matrix = test_model_binary(model, args.dataset)
model_dir = os.path.dirname(path_from_model_name(model_name))
with open(os.path.join(model_dir, 'results.csv'), 'w') as f:
write_dict_to_csv(details, f)
plot_confusion_chart(confusion_matrix)
plot_predictions(details['Percent'], args.dataset)
def train_wrapper(args: Namespace) -> None:
"""Function for training a network"""
data_type = dataset_type(args.dataset)
model_name = args.model
if args.cont:
model = load_model(model_name)
history = model.__asf_model_history
else:
model_path = path_from_model_name(model_name)
if not args.overwrite and os.path.isfile(model_path):
print(f"File {model_name} already exists!")
return
model = create_model(model_name, data_type)
history = {"loss": [], "acc": [], "val_loss": [], "val_acc": []}
if model_type(model) != data_type:
print("ERROR: This dataset is not compatible with your model")
return
train_model(model, history, args.dataset, args.epochs)
def test_fuzz_path_from_model_name(network_name, tag):
assert path_from_model_name(f"{network_name}:{tag}") == os.path.join(
PROJECT_DIR, "models", network_name, f"{tag}.h5")
def test_fuzz_path_from_model_name(network_name, tag):
assert path_from_model_name(f"{network_name}:{tag}") == os.path.join(
MODEL_DIR, network_name, f"{tag}.h5")