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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

57
architectures/tensorflow_components/general_network.py
View File

@@ -13,15 +13,16 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
import tensorflow as tf
from architectures.tensorflow_components.embedders import *
from architectures.tensorflow_components.heads import *
from architectures.tensorflow_components.middleware import *
from architectures.tensorflow_components.architecture import *
from configurations import InputTypes, OutputTypes, MiddlewareTypes
from architectures.tensorflow_components import architecture
from architectures.tensorflow_components import embedders
from architectures.tensorflow_components import middleware
from architectures.tensorflow_components import heads
import configurations as conf
class GeneralTensorFlowNetwork(TensorFlowArchitecture):
class GeneralTensorFlowNetwork(architecture.TensorFlowArchitecture):
"""
A generalized version of all possible networks implemented using tensorflow.
"""
@@ -37,7 +38,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
self.activation_function = self.get_activation_function(
tuning_parameters.agent.hidden_layers_activation_function)
TensorFlowArchitecture.__init__(self, tuning_parameters, name, global_network, network_is_local)
architecture.TensorFlowArchitecture.__init__(self, tuning_parameters, name, global_network, network_is_local)
def get_activation_function(self, activation_function_string):
activation_functions = {
@@ -56,37 +57,37 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
# the observation can be either an image or a vector
def get_observation_embedding(with_timestep=False):
if self.input_height > 1:
return ImageEmbedder((self.input_height, self.input_width, self.input_depth), name="observation",
input_rescaler=self.tp.agent.input_rescaler)
return embedders.ImageEmbedder((self.input_height, self.input_width, self.input_depth), name="observation",
input_rescaler=self.tp.agent.input_rescaler)
else:
return VectorEmbedder((self.input_width + int(with_timestep), self.input_depth), name="observation")
return embedders.VectorEmbedder((self.input_width + int(with_timestep), self.input_depth), name="observation")
input_mapping = {
InputTypes.Observation: get_observation_embedding(),
InputTypes.Measurements: VectorEmbedder(self.measurements_size, name="measurements"),
InputTypes.GoalVector: VectorEmbedder(self.measurements_size, name="goal_vector"),
InputTypes.Action: VectorEmbedder((self.num_actions,), name="action"),
InputTypes.TimedObservation: get_observation_embedding(with_timestep=True),
conf.InputTypes.Observation: get_observation_embedding(),
conf.InputTypes.Measurements: embedders.VectorEmbedder(self.measurements_size, name="measurements"),
conf.InputTypes.GoalVector: embedders.VectorEmbedder(self.measurements_size, name="goal_vector"),
conf.InputTypes.Action: embedders.VectorEmbedder((self.num_actions,), name="action"),
conf.InputTypes.TimedObservation: get_observation_embedding(with_timestep=True),
}
return input_mapping[embedder_type]
def get_middleware_embedder(self, middleware_type):
return {MiddlewareTypes.LSTM: LSTM_Embedder,
MiddlewareTypes.FC: FC_Embedder}.get(middleware_type)(self.activation_function)
return {conf.MiddlewareTypes.LSTM: middleware.LSTM_Embedder,
conf.MiddlewareTypes.FC: middleware.FC_Embedder}.get(middleware_type)(self.activation_function)
def get_output_head(self, head_type, head_idx, loss_weight=1.):
output_mapping = {
OutputTypes.Q: QHead,
OutputTypes.DuelingQ: DuelingQHead,
OutputTypes.V: VHead,
OutputTypes.Pi: PolicyHead,
OutputTypes.MeasurementsPrediction: MeasurementsPredictionHead,
OutputTypes.DNDQ: DNDQHead,
OutputTypes.NAF: NAFHead,
OutputTypes.PPO: PPOHead,
OutputTypes.PPO_V: PPOVHead,
OutputTypes.CategoricalQ: CategoricalQHead,
OutputTypes.QuantileRegressionQ: QuantileRegressionQHead
conf.OutputTypes.Q: heads.QHead,
conf.OutputTypes.DuelingQ: heads.DuelingQHead,
conf.OutputTypes.V: heads.VHead,
conf.OutputTypes.Pi: heads.PolicyHead,
conf.OutputTypes.MeasurementsPrediction: heads.MeasurementsPredictionHead,
conf.OutputTypes.DNDQ: heads.DNDQHead,
conf.OutputTypes.NAF: heads.NAFHead,
conf.OutputTypes.PPO: heads.PPOHead,
conf.OutputTypes.PPO_V: heads.PPOVHead,
conf.OutputTypes.CategoricalQ: heads.CategoricalQHead,
conf.OutputTypes.QuantileRegressionQ: heads.QuantileRegressionQHead
}
return output_mapping[head_type](self.tp, head_idx, loss_weight, self.network_is_local)