def get_predict_args():
parser = OrderedDict()
default_conf = config.CONF
default_conf = OrderedDict([('testing', default_conf['testing'])])
# Add options for modelname
timestamp = default_conf['testing']['timestamp']
timestamp_list = next(os.walk(paths.get_models_dir()))[1]
timestamp_list = sorted(timestamp_list)
if not timestamp_list:
timestamp['value'] = ''
else:
timestamp['value'] = timestamp_list[-1]
timestamp['choices'] = timestamp_list
# Add data and url fields
parser['files'] = fields.Field(
required=False,
missing=None,
type="file",
data_key="data",
location="form",
description="Select the image you want to classify.")
# Use field.String instead of field.Url because I also want to allow uploading of base 64 encoded data strings
parser['urls'] = fields.String(
required=False,
missing=None,
description="Select an URL of the image you want to classify.")
# missing action="append" --> append more than one url
return populate_parser(parser, default_conf)
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(), 'rshare:/models')
except Exception as e:
print(e)
def load_inference_model(timestamp=None, ckpt_name=None):
"""
Load a model for prediction.
Parameters
----------
* timestamp: str
Name of the timestamp to use. The default is the last timestamp in `./models`.
* ckpt_name: str
Name of the checkpoint to use. The default is the last checkpoint in `./models/[timestamp]/ckpts`.
"""
global loaded_ts, loaded_ckpt
global graph, model, conf, class_names, class_info
# Set the timestamp
timestamp_list = next(os.walk(paths.get_models_dir()))[1]
timestamp_list = sorted(timestamp_list)
if not timestamp_list:
raise Exception(
"You have no models in your `./models` folder to be used for inference. "
"Therefore the API can only be used for training.")
elif timestamp is None:
timestamp = timestamp_list[-1]
elif timestamp not in timestamp_list:
raise ValueError(
"Invalid timestamp name: {}. Available timestamp names are: {}".
format(timestamp, timestamp_list))
paths.timestamp = timestamp
print('Using TIMESTAMP={}'.format(timestamp))
# Set the checkpoint model to use to make the prediction
ckpt_list = os.listdir(paths.get_checkpoints_dir())
ckpt_list = sorted([name for name in ckpt_list if name.endswith('.h5')])
if not ckpt_list:
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.")
elif ckpt_name is None:
ckpt_name = ckpt_list[-1]
elif ckpt_name not in ckpt_list:
raise ValueError(
"Invalid checkpoint name: {}. Available checkpoint names are: {}".
format(ckpt_name, ckpt_list))
print('Using CKPT_NAME={}'.format(ckpt_name))
# Clear the previous loaded model
K.clear_session()
# Load the class names and info
splits_dir = paths.get_ts_splits_dir()
class_names = load_class_names(splits_dir=splits_dir)
class_info = None
if 'info.txt' in os.listdir(splits_dir):
class_info = load_class_info(splits_dir=splits_dir)
if len(class_info) != len(class_names):
warnings.warn(
"""The 'classes.txt' file has a different length than the 'info.txt' file.
If a class has no information whatsoever you should leave that classes row empty or put a '-' symbol.
The API will run with no info until this is solved.""")
class_info = None
if class_info is None:
class_info = ['' for _ in range(len(class_names))]
# Load training configuration
conf_path = os.path.join(paths.get_conf_dir(), 'conf.json')
with open(conf_path) as f:
conf = json.load(f)
update_with_saved_conf(conf)
# Load the model
model = load_model(os.path.join(paths.get_checkpoints_dir(), ckpt_name),
custom_objects=utils.get_custom_objects())
graph = tf.get_default_graph()
# Set the model as loaded
loaded_ts = timestamp
loaded_ckpt = ckpt_name
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, graph, model, conf, class_names, class_info
# Set the timestamp
timestamps = next(os.walk(paths.get_models_dir()))[1]
if not timestamps:
raise BadRequest(
"""You have no models in your `./models` folder to be used for inference.
Therefore the API can only be used for training.""")
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 BadRequest(
"""You have no checkpoints in your `./models/{}/ckpts` folder to be used for inference.
Therefore the API can only be used for training.""".format(
TIMESTAMP))
else:
if 'final_model.h5' in ckpts:
MODEL_NAME = 'final_model.h5'
else:
MODEL_NAME = sorted(
[name for name in ckpts if name.endswith('*.h5')])[-1]
print('Using MODEL_NAME={}'.format(MODEL_NAME))
# Clear the previous loaded model
K.clear_session()
# Load the class names and info
splits_dir = paths.get_ts_splits_dir()
class_names = load_class_names(splits_dir=splits_dir)
class_info = None
if 'info.txt' in os.listdir(splits_dir):
class_info = load_class_info(splits_dir=splits_dir)
if len(class_info) != len(class_names):
warnings.warn(
"""The 'classes.txt' file has a different length than the 'info.txt' file.
If a class has no information whatsoever you should leave that classes row empty or put a '-' symbol.
The API will run with no info until this is solved.""")
class_info = None
if class_info is None:
class_info = ['' for _ in range(len(class_names))]
# Load training configuration
conf_path = os.path.join(paths.get_conf_dir(), 'conf.json')
with open(conf_path) as f:
conf = json.load(f)
# Load the model
model = load_model(os.path.join(paths.get_checkpoints_dir(),
MODEL_NAME),
custom_objects=utils.get_custom_objects())
graph = tf.get_default_graph()
# Set the model as loaded
loaded = True
