def build_mnasnet_base(images, model_name, training, override_params=None):
"""A helper functiion to create a MnasNet base model and return global_pool.
Args:
images: input images tensor.
model_name: string, the model name of a pre-defined MnasNet.
training: boolean, whether the model is constructed for training.
override_params: A dictionary of params for overriding. Fields must exist in
mnasnet_model.GlobalParams.
Returns:
features: global pool features.
endpoints: the endpoints for each layer.
Raises:
When model_name specified an undefined model, raises NotImplementedError.
When override_params has invalid fields, raises ValueError.
"""
assert isinstance(images, tf.Tensor)
blocks_args, global_params = get_model_params(model_name, override_params)
with tf.variable_scope(model_name):
model = mnasnet_model.MnasNetModel(blocks_args, global_params)
features = model(images, training=training, features_only=True)
features = tf.identity(features, 'global_pool')
return features, model.endpoints
def build_mnasnet_model(images, model_name, training, override_params=None):
"""A helper functiion to create a MnasNet model and return predicted logits.
Args:
images: input images tensor.
model_name: string, the model name of a pre-defined MnasNet.
training: boolean, whether the model is constructed for training.
override_params: A dictionary of params for overriding. Fields must exist in
mnasnet_model.GlobalParams.
Returns:
logits: the logits tensor of classes.
endpoints: the endpoints for each layer.
Raises:
When model_name specified an undefined model, raises NotImplementedError.
When override_params has invalid fields, raises ValueError.
"""
assert isinstance(images, tf.Tensor)
blocks_args, global_params = get_model_params(model_name, override_params)
with tf.variable_scope(model_name):
model = mnasnet_model.MnasNetModel(blocks_args, global_params)
logits = model(images, training=training)
logits = tf.squeeze(tf.expand_dims(logits, 0), 0)
logits = tf.identity(logits, 'logits')
return logits, model.endpoints
def build_mnasnet_model(images, model_name, training, override_params=None):
"""A helper functiion to creates a MnasNet model and returns predicted logits.
Args:
images: input images tensor.
model_name: string, the model name of a pre-defined MnasNet.
training: boolean, whether the model is constructed for training.
override_params: A dictionary of params for overriding. Fields must exist in
mnasnet_model.GlobalParams.
Returns:
logits: the logits tensor of classes.
endpoints: the endpoints for each layer.
Raises:
When model_name specified an undefined model, raises NotImplementedError.
When override_params has invalid fields, raises ValueError.
"""
assert isinstance(images, tf.Tensor)
if model_name == 'mnasnet-a1':
blocks_args, global_params = mnasnet_a1()
elif model_name == 'mnasnet-b1':
blocks_args, global_params = mnasnet_b1()
elif 'legrnet' in model_name:
blocks_args, global_params = eval(model_name)()
else:
raise NotImplementedError('model name is not pre-defined: %s' %
model_name)
if override_params:
# ValueError will be raised here if override_params has fields not included
# in global_params.
global_params = global_params._replace(**override_params)
with tf.variable_scope(model_name):
if 'legrnet' in model_name:
model = legrnet_model.LeGRNetModel(blocks_args, global_params)
else:
model = mnasnet_model.MnasNetModel(blocks_args, global_params)
logits = model(images, training=training)
logits = tf.identity(logits, 'logits')
return logits, model.endpoints
