def build_model(images, training, override_params=None, arch=None):
"""A helper functiion to creates a ConvNet model and returns predicted logits.
Args:
images: input images tensor.
training: boolean, whether the model is constructed for training.
override_params: A dictionary of params for overriding. Fields must exist in
model_def.GlobalParams.
Returns:
logits: the logits tensor of classes.
endpoints: the endpoints for each layer.
Raises:
When override_params has invalid fields, raises ValueError.
"""
assert isinstance(images, tf.Tensor)
assert os.path.isfile(arch)
with open(arch, 'r') as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
blocks_args, global_params = parse_netarch_string(lines)
if override_params:
global_params = global_params._replace(**override_params)
with tf.variable_scope('single-path'):
model = model_def.MnasNetModel(blocks_args, global_params)
logits, macs = model(images, training=training)
macs /= 1e6 # macs to M
logits = tf.identity(logits, 'logits')
return logits, model.endpoints, macs
def build_model(images,
model_name,
training,
override_params=None,
parse_search_dir=None):
"""A helper functiion to creates a ConvNet 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
model_def.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 == 'single-path':
assert parse_search_dir is not None
blocks_args, global_params = parse_netarch_model(parse_search_dir)
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):
model = model_def.MnasNetModel(blocks_args, global_params)
logits = model(images, training=training)
logits = tf.identity(logits, 'logits')
return logits, model.endpoints