def get_model():
global INPUT_SHAPE
if args.net.startswith("resnet50"):
if args.net == "resnet50":
INPUT_SHAPE = (224, 224, 3)
elif args.net == "resnet50_112":
INPUT_SHAPE = (112, 112, 3)
return resnet50_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining)
elif args.net.startswith('senet') or args.net.startswith(
'resnet') or args.net.startswith('vgg'):
INPUT_SHAPE = (112, 112, 3) if args.net.endswith("_112") else (224,
224, 3)
if args.pretraining.startswith('imagenet'):
if args.net.startswith('senet') or args.net.startswith('resnet'):
return senet_model_build(INPUT_SHAPE, NUM_CLASSES,
args.pretraining)
else:
return vgg16_keras_build(INPUT_SHAPE, NUM_CLASSES,
args.pretraining)
else:
return vggface_custom_build(INPUT_SHAPE, NUM_CLASSES,
args.pretraining, args.net,
args.lpf_size)
elif args.net == 'mobilenet96':
INPUT_SHAPE = (96, 96, 3)
return mobilenet_96_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining)
elif args.net == 'mobilenet224':
INPUT_SHAPE = (224, 224, 3)
return mobilenet_224_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining)
elif args.net == 'mobilenet64_bio':
INPUT_SHAPE = (64, 64, 3)
return mobilenet_64_build(INPUT_SHAPE, NUM_CLASSES)
elif args.net.startswith('mobilenetv3large'):
if args.net == 'mobilenetv3large':
INPUT_SHAPE = (224, 224, 3)
elif args.net == 'mobilenetv3large_112':
INPUT_SHAPE = (112, 112, 3)
return mobilenet_v3_large_build(INPUT_SHAPE, NUM_CLASSES,
args.pretraining)
elif args.net == 'mobilenetv3small':
INPUT_SHAPE = (224, 224, 3)
return mobilenet_v3_small_build(INPUT_SHAPE, NUM_CLASSES,
args.pretraining)
elif args.net == 'densenet121bc':
INPUT_SHAPE = (224, 224, 3)
return densenet_121_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining,
args.lpf_size)
elif args.net.startswith('xception'):
INPUT_SHAPE = (71, 71, 3) if args.net == 'xception71' else (299, 299,
3)
return xception_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining,
args.lpf_size)
elif args.net == "shufflenet224":
INPUT_SHAPE = (224, 224, 3)
return shufflenet_224_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining)
elif args.net == "squeezenet":
INPUT_SHAPE = (224, 224, 3)
return squeezenet_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining)
def get_model():
global INPUT_SHAPE
if args.net.startswith('resnet'):
print("RESNET Network")
INPUT_SHAPE = (224, 224, 3)
return resnet_model_build(INPUT_SHAPE, args.pretraining)
elif args.net.startswith('senet'):
print("SENET Network")
INPUT_SHAPE = (224, 224, 3)
return senet_model_build(INPUT_SHAPE, args.pretraining)
elif args.net.startswith('vgg19'):
print("VGG19 Network")
INPUT_SHAPE = (224, 224, 3)
return vgg19_model_build(INPUT_SHAPE, args.pretraining)
else:
print("VGG16 Network")
INPUT_SHAPE = (224, 224, 3)
return vggface_custom_build(INPUT_SHAPE, args.pretraining, args.net)
def get_model():
global INPUT_SHAPE
if args.net.startswith('senet') or args.net.startswith(
'resnet') or args.net.startswith('vgg'):
INPUT_SHAPE = (224, 224, 3)
if args.pretraining.startswith('imagenet'):
if args.net.startswith('senet') or args.net.startswith('resnet'):
return senet_model_build(INPUT_SHAPE, NUM_CLASSES,
args.pretraining)
else:
return vgg16_keras_build(INPUT_SHAPE, NUM_CLASSES,
args.pretraining)
else:
print("VGGFACE Network")
return vggface_custom_build(INPUT_SHAPE, NUM_CLASSES,
args.pretraining, args.net,
args.lpf_size)
elif args.net == 'mobilenet96':
INPUT_SHAPE = (96, 96, 3)
return mobilenet_96_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining)
elif args.net == 'mobilenet224':
INPUT_SHAPE = (224, 224, 3)
return mobilenet_224_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining)
elif args.net == 'mobilenet64_bio':
INPUT_SHAPE = (64, 64, 3)
return mobilenet_64_build(INPUT_SHAPE, NUM_CLASSES)
elif args.net == 'densenet121bc':
INPUT_SHAPE = (224, 224, 3)
return densenet_121_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining,
args.lpf_size)
elif args.net.startswith('xception'):
INPUT_SHAPE = (71, 71, 3) if args.net == 'xception71' else (299, 299,
3)
return xception_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining,
args.lpf_size)
elif args.net == "shufflenet224":
INPUT_SHAPE = (224, 224, 3)
return shufflenet_224_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining)
elif args.net == "squeezenet":
INPUT_SHAPE = (224, 224, 3)
return squeezenet_build(INPUT_SHAPE, NUM_CLASSES, args.pretraining)