def extract_features(self, inputs):
im_centered = self.center_inputs(inputs)
net_type = self.cfg['net_type']
if 'resnet' in net_type:
net_fun = net_funcs[net_type]
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = net_fun(im_centered,
global_pool=False,
output_stride=16,
is_training=False)
elif 'mobilenet' in net_type:
net_fun = net_funcs[net_type]
with slim.arg_scope(mobilenet_v2.training_scope()):
net, end_points = net_fun(im_centered)
elif 'efficientnet' in net_type:
if 'use_batch_norm' not in self.cfg.keys():
self.cfg['use_batch_norm'] = False
if 'use_drop_out' not in self.cfg.keys():
self.cfg['use_drop_out'] = False
im_centered /= tf.constant(eff.STDDEV_RGB, shape=[1, 1, 3])
net, end_points = eff.build_model_base(
im_centered,
net_type,
use_batch_norm=self.cfg['use_batch_norm'],
drop_out=self.cfg['use_drop_out'])
else:
raise ValueError(f"Unknown network of type {net_type}")
return net, end_points
def extract_features(self,
inputs,
use_batch_norm=False,
use_drop_out=False):
im_centered = self.center_inputs(inputs)
im_centered /= tf.constant(eff.STDDEV_RGB, shape=[1, 1, 3])
with tf.compat.v1.variable_scope("efficientnet"):
eff_net_type = self.cfg['net_type'].replace('_', '-')
net, end_points = eff.build_model_base(
im_centered,
eff_net_type,
use_batch_norm=use_batch_norm,
drop_out=use_drop_out)
return net, end_points