def train(user_conf):
"""
Parameters
----------
user_conf : dict
Json dict (created with json.dumps) with the user's configuration parameters that will replace the defaults.
Must be loaded with json.loads()
For example:
user_conf={'num_classes': 'null', 'lr_step_decay': '0.1', 'lr_step_schedule': '[0.7, 0.9]', 'use_early_stopping': 'false'}
"""
CONF = config.CONF
# Update the conf with the user input
for group, val in sorted(CONF.items()):
for g_key, g_val in sorted(val.items()):
g_val['value'] = json.loads(user_conf[g_key])
# Check the configuration
try:
config.check_conf(conf=CONF)
except Exception as e:
raise BadRequest(e)
CONF = config.conf_dict(conf=CONF)
timestamp = datetime.now().strftime('%Y-%m-%d_%H%M%S')
config.print_conf_table(CONF)
K.clear_session() # remove the model loaded for prediction
train_fn(TIMESTAMP=timestamp, CONF=CONF)
# Sync with NextCloud folders (if NextCloud is available)
try:
mount_nextcloud(paths.get_models_dir(), 'ncplants:/models')
except Exception as e:
print(e)
def train(**args):
"""
Train an image classifier
"""
update_with_query_conf(user_args=args)
CONF = config.conf_dict
timestamp = datetime.now().strftime('%Y-%m-%d_%H%M%S')
config.print_conf_table(CONF)
K.clear_session() # remove the model loaded for prediction
train_fn(TIMESTAMP=timestamp, CONF=CONF)
# Sync with NextCloud folders (if NextCloud is available)
try:
mount_nextcloud(paths.get_models_dir(), 'ncplants:/models')
except Exception as e:
print(e)
def load_inference_model():
"""
Load a model for prediction.
If several timestamps are available in `./models` it will load `.models/api` or the last timestamp if `api` is not
available.
If several checkpoints are available in `./models/[timestamp]/ckpts` it will load
`.models/[timestamp]/ckpts/final_model.h5` or the last checkpoint if `final_model.h5` is not available.
"""
global loaded, conf, MODEL_NAME, LABELS_FILE
# Set the timestamp
timestamps = next(os.walk(paths.get_models_dir()))[1]
if not timestamps:
raise Exception(
"You have no models in your `./models` folder to be used for inference. "
"This module does not come with a pretrained model so you have to train a model to use it for prediction."
)
else:
if 'api' in timestamps:
TIMESTAMP = 'api'
else:
TIMESTAMP = sorted(timestamps)[-1]
paths.timestamp = TIMESTAMP
print('Using TIMESTAMP={}'.format(TIMESTAMP))
# Set the checkpoint model to use to make the prediction
ckpts = os.listdir(paths.get_checkpoints_dir())
if not ckpts:
raise Exception(
"You have no checkpoints in your `./models/{}/ckpts` folder to be used for inference. "
.format(TIMESTAMP) +
"Therefore the API can only be used for training.")
else:
if 'model.pb' in ckpts:
MODEL_NAME = 'model.pb'
else:
MODEL_NAME = sorted(
[name for name in ckpts if name.endswith('*.pb')])[-1]
print('Using MODEL_NAME={}'.format(MODEL_NAME))
if 'conv_labels.txt' in ckpts:
LABELS_FILE = 'conv_labels.txt'
else:
LABELS_FILE = sorted(
[name for name in ckpts if name.endswith('*.txt')])[-1]
print('Using LABELS_FILE={}'.format(LABELS_FILE))
# Clear the previous loaded model
K.clear_session()
# Load the class names and info
ckpts_dir = paths.get_checkpoints_dir()
MODEL_NAME = os.path.join(ckpts_dir, MODEL_NAME)
LABELS_FILE = os.path.join(ckpts_dir, LABELS_FILE)
# Load training configuration
conf_path = os.path.join(paths.get_conf_dir(), 'conf.json')
with open(conf_path) as f:
conf = json.load(f)
# Set the model as loaded
loaded = True