def forward_fn(inputs, is_train):
"""Forward pass function.
Args:
* inputs: inputs to the network's forward pass
* is_train: whether to use the forward pass with training operations inserted
Returns:
* outputs: outputs from the network's forward pass
"""
nb_classes = FLAGS.nb_classes
depth_mult = FLAGS.mobilenet_depth_mult
if FLAGS.mobilenet_version == 1:
scope_fn = MobileNetV1.mobilenet_v1_arg_scope
with slim.arg_scope(scope_fn(is_training=is_train)): # pylint: disable=not-context-manager
outputs, __ = MobileNetV1.mobilenet_v1(inputs,
is_training=is_train,
num_classes=nb_classes,
depth_multiplier=depth_mult)
elif FLAGS.mobilenet_version == 2:
scope_fn = MobileNetV2.training_scope
with slim.arg_scope(scope_fn(is_training=is_train)): # pylint: disable=not-context-manager
outputs, __ = MobileNetV2.mobilenet(inputs,
num_classes=nb_classes,
depth_multiplier=depth_mult)
else:
raise ValueError('invalid MobileNet version: {}'.format(
FLAGS.mobilenet_version))
return outputs
def mobilenetv2_head(inputs, is_training=True):
with slim.arg_scope(mobilenetv2_scope(is_training=is_training, trainable=True)):
net, _ = mobilenet_v2.mobilenet(input_tensor=inputs,
num_classes=None,
is_training=False,
depth_multiplier=1.0,
scope='MobilenetV2',
conv_defs=V2_HEAD_DEF,
finegrain_classification_mode=False)
net = tf.squeeze(net, [1, 2])
return net