def test_train_model_runs_successfully_for_simplified_case(self):
# Note: This test is simply a mock test to ensure that the pipeline
# runs successfully, and is not a test of the quality of the model
# itself.
train_model(
self.trainingPath,
self.modelPath,
self.config_network,
debug_mode=True
)
expectedFiles = [
"checkpoint",
"config_network.json",
"evolution_stats.pkl",
"evolution.pkl",
"model.ckpt.data-00000-of-00001",
"model.ckpt.index",
"model.ckpt.meta",
"report.txt"
]
existingFiles = os.listdir(self.modelPath)
for fileName in expectedFiles:
assert fileName in existingFiles
def compute_training(configfile, path_trainingset, path_model, path_model_init = None, gpu_per = 1.0):
os.chdir(sys.path[0]) # Necessary to fix the directory we are working in
with open(os.path.join(path_model, configfile), 'r') as fd:
config_network = json.loads(fd.read())
#if not os.path.exists(path_model): # Already created before. But can be useful if we want to create models with date of launch.
# os.makedirs(path_model)
#with open(path_model + configname, 'w') as f:
# json.dump(config, f, indent=2)
#with open(path_model + filename, 'r') as fd:
# config_network = json.loads(fd.read())
# Training
from AxonDeepSeg.train_network import train_model
train_model(path_trainingset, path_model, config_network, gpu_per=gpu_per)
def test_train_model_runs_successfully_for_simplified_case(self):
# Note: This test is simply a mock test to ensure that the pipeline
# runs successfully, and is not a test of the quality of the model
# itself.
train_model(str(self.trainingPath),
str(self.modelPath),
self.config_network,
debug_mode=True)
expectedFiles = [
"checkpoint", "config_network.json",
"model.ckpt.data-00000-of-00001", "model.ckpt.index",
"model.ckpt.meta"
]
existingFiles = [f.name for f in self.modelPath.iterdir()]
for fileName in expectedFiles:
assert fileName in existingFiles
# Weighted cost parameters:
"weighted_cost-activate": True,
"weighted_cost-balanced_activate": True,
"weighted_cost-balanced_weights": [1.1, 1, 1.3],
"weighted_cost-boundaries_sigma": 2,
"weighted_cost-boundaries_activate": False,
# Data augmentation parameters:
"da-type": "all",
"da-2-random_rotation-activate": False,
"da-5-noise_addition-activate": False,
"da-3-elastic-activate": True,
"da-0-shifting-activate": True,
"da-4-flipping-activate": True,
"da-1-rescaling-activate": False
}
if os.path.exists(path_model + filename):
with open(path_model + filename, 'r') as fd:
config_network = json.loads(fd.read())
else: # There is no config file for the moment
with open(path_model + filename, 'w') as f:
json.dump(config, f, indent=2)
with open(path_model + filename, 'r') as fd:
config_network = json.loads(fd.read())
tf.reset_default_graph()
train_model(path_training, path_model, config)