def __init__(self, num_steps):
"""Passes frames through base CNNs and return feature.
Args:
num_steps: int, Number of steps being passed through CNN.
Raises:
ValueError: if invalid network config is passed.
"""
super(BaseModel, self).__init__()
layer = CONFIG.MODEL.BASE_MODEL.LAYER
network = CONFIG.MODEL.BASE_MODEL.NETWORK
local_ckpt = get_pretrained_ckpt(network)
if network in ['Resnet50', 'Resnet50_pretrained']:
base_model = resnet_v2.ResNet50V2(include_top=False,
weights=local_ckpt,
pooling='max',
backend=tf.keras.backend,
layers=tf.keras.layers,
models=tf.keras.models,
utils=tf.keras.utils)
elif CONFIG.model.base_model.network == 'VGGM':
base_model = vggm_net(CONFIG.IMAGE_SIZE)
else:
raise ValueError('%s not supported.' %
CONFIG.MODEL.BASE_MODEL.NETWORK)
self.base_model = Model(inputs=base_model.input,
outputs=base_model.get_layer(layer).output)
self.num_steps = num_steps
def __init__(self):
super(BaseModel, self).__init__()
# define network parameters
layer = CONFIG.MODEL.BASE_MODEL.LAYER
network = CONFIG.MODEL.BASE_MODEL.NETWORK
local_ckpt = get_pretrained_ckpt(network)
# create the different layers of the network
base_model = resnet_v2.ResNet50V2(include_top=False,
weights=local_ckpt,
pooling='max',
backend=tf.keras.backend,
layers=tf.keras.layers,
models=tf.keras.models,
utils=tf.keras.utils)
self.base_model = Model(inputs=base_model.input,
outputs=base_model.get_layer(layer).output)
def ResNet50V2(*args, **kwargs):
return resnet_v2.ResNet50V2(*args, **kwargs)
