def read_model(model_path, config):
"""Read keras retinanet model from keras.model.save()"""
# Suppress user warning, module does not need to be compiled for prediction
with warnings.catch_warnings():
# warnings.simplefilter('ignore', UserWarning)
model = models.load_model(model_path, backbone_name='resnet50')
return model
def __init__(self, weights=None, saved_model=None):
self.weights = weights
self.saved_model = saved_model
# Read config file - if a config file exists in local dir use it,
# if not use installed.
if os.path.exists("deepforest_config.yml"):
config_path = "deepforest_config.yml"
else:
try:
config_path = get_data("deepforest_config.yml")
except Exception as e:
raise ValueError(
"No deepforest_config.yml found either in local "
"directory or in installed package location. {}".format(e))
print("Reading config file: {}".format(config_path))
self.config = utilities.read_config(config_path)
# Create a label dict, defaults to "Tree"
self.read_classes()
# release version id to flag if release is being used
self.__release_version__ = None
# Load saved model if needed
if self.saved_model:
print("Loading saved model")
# Capture user warning, not relevant here
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
self.model = models.load_model(saved_model)
self.prediction_model = convert_model(self.model)
elif self.weights:
print("Creating model from weights")
backbone = models.backbone(self.config["backbone"])
self.model, self.training_model, self.prediction_model = create_models(
backbone.retinanet, num_classes=1, weights=self.weights)
else:
print(
"A blank deepforest object created. "
"To perform prediction, either train or load an existing model."
)
self.model = None
def main(forest_object,
args=None,
input_type="fit_generator",
list_of_tfrecords=None,
comet_experiment=None):
"""
Main Training Loop
Args:
forest_object: a deepforest class object
args: Keras retinanet argparse
list_of_tfrecords: list of tfrecords to parse
input_type: "fit_generator" or "tfrecord" input type
"""
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# create object that stores backbone information
backbone = models.backbone(args.backbone)
# make sure keras is the minimum required version
check_keras_version()
# optionally choose specific GPU
if args.gpu:
setup_gpu(args.gpu)
# optionally load config parameters
if args.config:
args.config = read_config_file(args.config)
# data input
if input_type == "fit_generator":
# create the generators
train_generator, validation_generator = create_generators(
args, backbone.preprocess_image)
# placeholder target tensor for creating models
targets = None
elif input_type == "tfrecord":
# Create tensorflow iterators
iterator = tfrecords.create_dataset(list_of_tfrecords, args.batch_size)
next_element = iterator.get_next()
# Split into inputs and targets
inputs = next_element[0]
targets = [next_element[1], next_element[2]]
validation_generator = None
else:
raise ValueError(
"{} input type is invalid. Only 'tfrecord' or 'for_generator' "
"input types are accepted for model training".format(input_type))
# create the model
if args.snapshot is not None:
print('Loading model, this may take a second...')
model = models.load_model(args.snapshot, backbone_name=args.backbone)
training_model = model
anchor_params = None
if args.config and 'anchor_parameters' in args.config:
anchor_params = parse_anchor_parameters(args.config)
prediction_model = retinanet_bbox(model=model,
anchor_params=anchor_params)
else:
weights = args.weights
# default to imagenet if nothing else is specified
if weights is None and args.imagenet_weights:
weights = backbone.download_imagenet()
print('Creating model, this may take a second...')
if input_type == "fit_generator":
num_of_classes = train_generator.num_classes()
else:
# Add background class
num_of_classes = len(forest_object.labels.keys())
model, training_model, prediction_model = create_models(
backbone_retinanet=backbone.retinanet,
num_classes=num_of_classes,
weights=weights,
multi_gpu=args.multi_gpu,
freeze_backbone=args.freeze_backbone,
lr=args.lr,
config=args.config,
targets=targets,
freeze_layers=args.freeze_layers)
# print model summary
print(model.summary())
# this lets the generator compute backbone layer shapes using the actual backbone model
if 'vgg' in args.backbone or 'densenet' in args.backbone:
train_generator.compute_shapes = make_shapes_callback(model)
if validation_generator:
validation_generator.compute_shapes = train_generator.compute_shapes
# create the callbacks
callbacks = create_callbacks(model, training_model, prediction_model,
validation_generator, args, comet_experiment)
if not args.compute_val_loss:
validation_generator = None
# start training
if input_type == "fit_generator":
history = training_model.fit_generator(
generator=train_generator,
steps_per_epoch=args.steps,
epochs=args.epochs,
verbose=1,
callbacks=callbacks,
workers=args.workers,
use_multiprocessing=args.multiprocessing,
max_queue_size=args.max_queue_size,
validation_data=validation_generator)
elif input_type == "tfrecord":
# Fit model
history = training_model.fit(x=inputs,
steps_per_epoch=args.steps,
epochs=args.epochs,
callbacks=callbacks)
else:
raise ValueError(
"{} input type is invalid. Only 'tfrecord' or 'for_generator' "
"input types are accepted for model training".format(input_type))
# Assign history to deepforest model class
forest_object.history = history
# return trained model
return model, prediction_model, training_model