self.test_results_dir = "/out"
if __name__ == "__main__":
# Get configuration
# TASK: Fill in parameters of the Config class and specify directory where the data is stored and
# directory where results will go
c = Config()
# Load data
print("Loading data...")
# TASK: LoadHippocampusData is not complete. Go to the implementation and complete it.
data = LoadHippocampusData(c.root_dir,
y_shape=c.patch_size,
z_shape=c.patch_size)
# Create test-train-val split
# In a real world scenario you would probably do multiple splits for
# multi-fold training to improve your model quality
keys = range(len(data))
# Here, random permutation of keys array would be useful in case if we do something like
# a k-fold training and combining the results.
split = dict()
# TASK: create three keys in the dictionary: "train", "val" and "test". In each key, store
# the array with indices of training volumes to be used for training, validation
c.set_model_name(args.modelname)
if args.testonly:
# need to also provide a weights filename if we're only testing
print("Testing mode.")
c.set_test(True)
if not args.weights:
print("Please also provide a weights filename through -w")
sys.exit()
# Load data
print("Loading data...")
# TASK: LoadHippocampusData is not complete. Go to the implementation and complete it.
data = LoadHippocampusData(c.root_dir + "TrainingSet/",
y_shape=c.patch_size,
z_shape=c.patch_size)
# Create test-train-val split
# In a real world scenario you would probably do multiple splits for
# multi-fold training to improve your model quality
data_len = len(data)
keys = range(data_len)
# Here, random permutation of keys array would be useful in case if we do something like
# a k-fold training and combining the results.
# TASK: create three keys in the dictionary: "train", "val" and "test". In each key, store
# the array with indices of training volumes to be used for training, validation
# and testing respectively.
