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Cleanup imports.

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Till now, most of the modules were importing all of the module objects
(variables, classes, functions, other imports) into module namespace,
which potentially could (and was) cause of unintentional use of class or
methods, which was indirect imported.

With this patch, all the star imports were substituted with top-level
module, which provides desired class or function.

Besides, all imports where sorted (where possible) in a way pep8[1]
suggests - first are imports from standard library, than goes third
party imports (like numpy, tensorflow etc) and finally coach modules.
All of those sections are separated by one empty line.

[1] https://www.python.org/dev/peps/pep-0008/#imports
This commit is contained in:
Roman Dobosz
2018-04-12 19:46:32 +02:00
parent cafa152382
commit 1b095aeeca
75 changed files with 1169 additions and 1139 deletions
Download Patch File Download Diff File Expand all files Collapse all files

33
architectures/network_wrapper.py
View File

@@ -13,20 +13,21 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import collections
from collections import OrderedDict
from configurations import Preset, Frameworks
from logger import *
import configurations as conf
import logger
try:
import tensorflow as tf
from architectures.tensorflow_components.general_network import GeneralTensorFlowNetwork
from architectures.tensorflow_components import general_network as tf_net #import GeneralTensorFlowNetwork
except ImportError:
failed_imports.append("TensorFlow")
logger.failed_imports.append("TensorFlow")
try:
from architectures.neon_components.general_network import GeneralNeonNetwork
from architectures.neon_components import general_network as neon_net
except ImportError:
failed_imports.append("Neon")
logger.failed_imports.append("Neon")
class NetworkWrapper(object):
@@ -50,12 +51,12 @@ class NetworkWrapper(object):
self.name = name
self.sess = tuning_parameters.sess
if self.tp.framework == Frameworks.TensorFlow:
general_network = GeneralTensorFlowNetwork
elif self.tp.framework == Frameworks.Neon:
general_network = GeneralNeonNetwork
if self.tp.framework == conf.Frameworks.TensorFlow:
general_network = tf_net.GeneralTensorFlowNetwork
elif self.tp.framework == conf.Frameworks.Neon:
general_network = neon_net.GeneralNeonNetwork
else:
raise Exception("{} Framework is not supported".format(Frameworks().to_string(self.tp.framework)))
raise Exception("{} Framework is not supported".format(conf.Frameworks().to_string(self.tp.framework)))
# Global network - the main network shared between threads
self.global_network = None
@@ -77,13 +78,13 @@ class NetworkWrapper(object):
self.target_network = general_network(tuning_parameters, '{}/target'.format(name),
network_is_local=True)
if not self.tp.distributed and self.tp.framework == Frameworks.TensorFlow:
if not self.tp.distributed and self.tp.framework == conf.Frameworks.TensorFlow:
variables_to_restore = tf.global_variables()
variables_to_restore = [v for v in variables_to_restore if '/online' in v.name]
self.model_saver = tf.train.Saver(variables_to_restore)
if self.tp.sess and self.tp.checkpoint_restore_dir:
checkpoint = tf.train.latest_checkpoint(self.tp.checkpoint_restore_dir)
screen.log_title("Loading checkpoint: {}".format(checkpoint))
logger.screen.log_title("Loading checkpoint: {}".format(checkpoint))
self.model_saver.restore(self.tp.sess, checkpoint)
self.update_target_network()
@@ -178,8 +179,8 @@ class NetworkWrapper(object):
def save_model(self, model_id):
saved_model_path = self.model_saver.save(self.tp.sess, os.path.join(self.tp.save_model_dir,
str(model_id) + '.ckpt'))
screen.log_dict(
OrderedDict([
logger.screen.log_dict(
collections.OrderedDict([
("Saving model", saved_model_path),
]),
prefix="Checkpoint"