def load_model_from_run(backbone_name,
load_model_from,
load_weights_from=None,
skip_mismatch=True):
b = backbone(backbone_name)
json_path = get_json_filename(load_model_from)
if not os.path.exists(json_path):
h5_path = get_weights_filename(load_model_from)
if not os.path.exists(h5_path):
raise ValueError("run with name %s doesn't exist" %
load_model_from)
model = load_model(h5_path,
custom_objects=b.custom_objects,
compile=False)
else:
with open(json_path, 'r') as json_file:
json_string = json_file.read()
model = model_from_json(json_string, custom_objects=b.custom_objects)
if load_weights_from:
h5_path = get_weights_filename(load_weights_from)
if os.path.exists(h5_path):
model.load_weights(h5_path,
by_name=True,
skip_mismatch=skip_mismatch)
return model
def save_model_to_run(model, run_name):
json_path = get_json_filename(run_name)
h5_path = get_weights_filename(run_name)
with open(json_path, 'w') as json_file:
json_file.write(model.to_json())
model.save_weights(h5_path)
def load_model_weights_from(model, weights, skip_mismatch):
if weights is None:
return
if os.path.exists(weights):
model.load_weights(weights, by_name=True, skip_mismatch=skip_mismatch)
return
weights_path = get_weights_filename(weights)
if os.path.exists(weights_path):
model.load_weights(weights_path, by_name=True, skip_mismatch=skip_mismatch)
return
raise ValueError('Unknown weights to load from!', weights, weights_path)
def config_seg_callbacks(run_name=None):
callbacks = [
ValidationPrediction(show_confusion_matrix=False),
ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=2,
verbose=1,
mode='auto',
min_lr=1e-7),
]
if run_name:
callbacks.extend([
ModelCheckpoint(get_weights_filename(run_name),
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=True),
CSVLogger(filename=get_csv_filename(run_name))
])
return callbacks
