def create_models_local(p):
test_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
test_generator = CSVGenerator(p['test_csv'], p['classes_csv'],
test_image_data_generator, 1)
model, training_model, prediction_model = create_models(
num_classes=test_generator.num_classes(), p=p)
return model, training_model, prediction_model, test_generator
if snapshot is not None:
print('Loading model, this may take a second...')
model = models.load_model(snapshot, backbone_name=backbone)
training_model = model
prediction_model = retinanet_bbox(model=model)
else:
weights = weights
# default to imagenet if nothing else is specified
if weights is None and imagenet_weights:
weights = backbone_model.download_imagenet()
print('Creating model, this may take a second...')
modifier = freeze_model if freeze_backbone else None
model = backbone_model.retinanet(train_generator.num_classes(),
modifier=modifier)
if weights is not None:
model.load_weights(weights, by_name=True, skip_mismatch=True)
training_model = model
# make prediction model
prediction_model = retinanet_bbox(model=model)
# compile model
training_model.compile(loss={
'regression': losses.smooth_l1(),
'classification': losses.focal()
},
optimizer=keras.optimizers.adam(lr=1e-5,
clipnorm=0.001))
batch_size=args.batch_size)
if args.val_path:
test_image_data_generator = keras.preprocessing.image.ImageDataGenerator(
)
# create a generator for testing data
test_generator = CSVGenerator(
csv_data_file=args.val_path,
csv_class_file=args.classes,
image_data_generator=test_image_data_generator,
batch_size=args.batch_size)
else:
test_generator = None
num_classes = train_generator.num_classes()
# create the model
print('Creating model, this may take a second...')
model = create_model(num_classes=num_classes, weights=args.weights)
# compile model (note: set loss to None since loss is added inside layer)
model.compile(loss={
'regression': keras_retinanet.losses.smooth_l1(),
'classification': keras_retinanet.losses.focal()
},
optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001))
# print model summary
print(model.summary())