Commit 08167e64 authored by Constantin Pape's avatar Constantin Pape

Fix wrong print statements

parent 7e7bf87b
......@@ -48,7 +48,7 @@ def load_training_data(root, image_folder, labels_folder, ext, multichannel):
train_images.sort()
label_pattern = os.path.join(root, labels_folder, f'*{ext}')
print("Looking for labels with the pattern", image_pattern)
print("Looking for labels with the pattern", label_pattern)
train_labels = glob(label_pattern)
assert len(train_labels) > 0, "Did not find any labels"
train_labels.sort()
......@@ -166,7 +166,7 @@ def train_stardist_model(root, model_save_path, image_folder, labels_folder, ext
print("Made train validation split with validation fraction",
validation_fraction, "resulting in")
print(len(x_train), "training images")
print(len(y_train), "validation images")
print(len(x_val), "validation images")
print("Start model training ...")
print("You can connect to the tensorboard by typing 'tensorboaed --logdir=.' in the folder where the training runs")
......
......@@ -36,7 +36,7 @@ def load_training_data(root, image_folder, labels_folder, ext):
train_images.sort()
label_pattern = os.path.join(root, labels_folder, f'*{ext}')
print("Looking for labels with the pattern", image_pattern)
print("Looking for labels with the pattern", label_pattern)
train_labels = glob(label_pattern)
assert len(train_labels) > 0, "Did not find any labels"
train_labels.sort()
......@@ -162,7 +162,7 @@ def train_stardist_model(root, model_save_path, image_folder, labels_folder, ext
print("Made train validation split with validation fraction", validation_fraction, "resulting in")
print(len(x_train), "training images")
print(len(y_train), "validation images")
print(len(x_val), "validation images")
print("Start model training ...")
print("You can connect to the tensorboard by typing 'tensorboaed --logdir=.' in the folder where the training runs")
......@@ -188,7 +188,7 @@ def main():
help="The fraction of available data that is used for validation, default: .1")
parser.add_argument('--patch_size', type=int, nargs=3, default=[128, 128, 128],
help="Size of the image patches used to train the network, default: 128, 128, 128")
aniso_help = """Anisotropy factor, needs to be passed as json encoded list, e.g. \"[.05,0.5,0.5]\".
aniso_help = """Anisotropy factor, needs to be passed as json encoded list, e.g. \"[1.0,1.0,0.5]\".
If not given, will be computed from the dimensions of the input data, default: None"""
parser.add_argument('--anisotropy', type=str, default=None, help=aniso_help)
......
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