def main():
# Parse command line args
if len(sys.argv) < 2:
print 'USAGE: train.py project_file.json [output_dir]'
sys.exit(1)
project_file = sys.argv[1]
if len(sys.argv) >= 3:
output_dir = sys.argv[2]
else:
output_dir = '.'
# Load project
project = Project(project_file)
# Generate training data
training_dataset = project.get_training_dataset()
training_genconfig = project.get_training_genconfig()
input_data = training_dataset.generate_input_data(training_genconfig)
output_data = training_dataset.generate_ground_truth_data(training_genconfig)
print str(input_data.shape[0]) + " training samples were generated."
print "Each sample contains " + str(input_data.shape[1]) + " pixels."
# Create output directory if necessary
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Train each model
for i in range(project.get_models_count()):
model = project.get_model(i)
model_name = project.get_model_name(i)
print 'Training model ' + model_name + '...'
model_path = os.path.join(output_dir, model_name + '.model')
model.train(input_data, output_data, open(model_path, 'wb+'))
def main():
# Parse command line args
if len(sys.argv) < 2:
print 'USAGE: validate.py project_file.json [models_dir]'
sys.exit(1)
project_file = sys.argv[1]
if len(sys.argv) >= 3:
models_dir = sys.argv[2]
else:
models_dir = '.'
# Check that the models directory exists
if not os.path.exists(models_dir):
raise IOError('The models directory does not exist.')
# Load project
project = Project(project_file)
# Generate validation data
validation_dataset = project.get_validation_dataset()
validation_genconfig = project.get_validation_genconfig()
input_data = validation_dataset.generate_input_data(validation_genconfig)
output_data = validation_dataset.generate_ground_truth_data(validation_genconfig)
print str(input_data.shape[0]) + ' validation samples were generated.'
print 'Each sample contains ' + str(input_data.shape[1]) + ' pixels.'
# Validate each model
for i in range(project.get_models_count()):
#TODO : implement a validation method that doesn't need training to be finished, so that validation error could be visualized while the app is still running
model = project.get_model(i)
model_name = project.get_model_name(i)
print 'Validating model ' + model_name + '...'
model_filename = os.path.join(models_dir, model_name + '.model')
validate(model, model_filename, model_name, input_data, output_data)
# Show all plots
plt.show()
