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interface.py
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interface.py
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import argparse
import os
import yaml
from utilities.settings import Settings, SettingsOptions, ModelsInfo
from utilities.classification_utils import ClassificationModel, AutoEncoder
from utilities.visualizations import MplVisualization
from utilities.helpers import create_output_dir, save_cell_counts
def run_prediction(arguments: argparse.Namespace) -> None:
"""
Depending on the command, runs the prediction, training or validation.
Args:
arguments (argparse.Namespace): Arguments provided by argparse.
Returns:
None.
Raises:
argparse.ArgumentTypeError: In case of folder/file not existing.
argparse.ArgumentTypeError: If incorrect arguments are provided.
"""
if arguments.path is None or check_path(arguments.path) is None:
raise argparse.ArgumentTypeError("Incorrect path or empty directory")
if os.path.isdir(arguments.path):
files = [
os.path.join(arguments.path, file) for file in os.listdir(arguments.path)
]
else:
files = [arguments.path]
if arguments.command == "predict":
model = ClassificationModel(
settings=settings,
model_info=models_info,
files=files,
model_type="classifier",
name=settings.model,
)
results = model.run_classification()
output_dir = create_output_dir(path=settings.results)
visualizations = MplVisualization(output_path=output_dir)
visualizations.save_predictions_visualizations(
inputs=results, settings=settings
)
save_cell_counts(
path=output_dir,
inputs=results,
gating_type=settings.gating_type,
mse_threshold=settings.mse_threshold,
prob_threshold=settings.softmax_prob_threshold,
)
print("\nClassification is finished")
elif arguments.command == "validate":
model = ClassificationModel(
settings=settings,
model_info=models_info,
files=files,
model_type="classifier",
name=settings.model,
)
model.run_diagnostics()
print("\nValidation is finished")
elif arguments.command == "train":
if arguments.name:
model_name = arguments.name[0]
else:
while True:
model_name = input("Enter model name: ")
if model_name:
model_name = model_name
break
if arguments.model:
training_type = arguments.model[0]
else:
while True:
training_type = input("Enter model type: ")
if training_type in ["autoencoder", "classifier"]:
break
if model_name and training_type:
model_name += ".h5"
if training_type == "classifier":
model = ClassificationModel(
settings=settings,
model_info=models_info,
files=files,
model_type="classifier",
name=model_name,
training_cls=True,
)
else:
model = AutoEncoder(
settings=settings,
model_info=models_info,
files=files,
model_type="ae",
name=model_name,
)
model.run_training()
print("\nTraining is finished")
else:
raise argparse.ArgumentTypeError("No arguments provided")
def run_settings(arguments: argparse.Namespace) -> None:
"""
Shows settings or allows to change them.
Args:
arguments (argparse.Namespace): Arguments provided by argparse.
Returns:
None.
Raises:
argparse.ArgumentTypeError: If incorrect arguments are provided.
"""
if arguments.show:
print(yaml.dump(vars(settings), default_flow_style=False))
elif arguments.change:
try:
available_values = getattr(SettingsOptions, arguments.change[0]).value
while True:
try:
value = arguments.value[0]
except TypeError:
value = input(f"Available options are {available_values}: ")
if value in available_values:
setattr(settings, arguments.change[0], value)
break
else:
print("Invalid option")
except AttributeError:
while True:
try:
value = arguments.value[0]
except TypeError:
value = input(f"Enter new value for {arguments.change[0]}: ")
if value:
try:
setattr(settings, arguments.change[0], int(value))
except ValueError:
setattr(settings, arguments.change[0], str(value))
break
else:
print("Invalid option")
settings.save_settings(command_line=True)
elif arguments.change is None or arguments.show is None:
raise argparse.ArgumentTypeError("No arguments provided")
def check_path(path: str) -> None or str:
"""
Checks if the path is valid.
Args:
path (str): Path to check.
Returns:
None or str: None if path is invalid, path if valid.
"""
if not os.path.exists(path):
return None
else:
return path
def main() -> None:
"""
Main function. Initializes argparse object and sets possible commands.
Returns:
None.
"""
parser = argparse.ArgumentParser(description="CellScanner")
parser.add_argument(
"command",
type=str,
help="Command to run",
choices=["predict", "train", "validate", "settings"],
)
parser.add_argument("-p", "--path", type=str, help="Path to files to process")
parser.add_argument(
"-s", "--show", dest="show", action="store_true", help="Show settings"
)
parser.add_argument(
"-c",
"--change",
type=str,
help="Change settings",
choices=list(vars(settings).keys()),
nargs=1,
)
parser.add_argument(
"-m",
"--model",
type=str,
help="What type of model to train",
choices=["autoencoder", "classifier"],
nargs=1,
)
parser.add_argument("-n", "--name", type=str, help="Name of the model", nargs=1)
parser.add_argument("-v", "--value", type=str, help="Settings value", nargs=1)
arguments = parser.parse_args()
if (
arguments.command == "predict"
or arguments.command == "train"
or arguments.command == "validate"
):
run_prediction(arguments)
elif arguments.command == "settings":
run_settings(arguments)
if __name__ == "__main__":
settings = Settings()
models_info = ModelsInfo()
main()