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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

23
architectures/tensorflow_components/architecture.py
View File

@@ -15,12 +15,11 @@
#
import time
import numpy as np
import tensorflow as tf
from architectures.architecture import Architecture
from utils import force_list, squeeze_list
from configurations import Preset, MiddlewareTypes
from architectures import architecture
import configurations as conf
import utils
def variable_summaries(var):
"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
@@ -37,14 +36,14 @@ def variable_summaries(var):
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
class TensorFlowArchitecture(Architecture):
class TensorFlowArchitecture(architecture.Architecture):
def __init__(self, tuning_parameters, name="", global_network=None, network_is_local=True):
"""
:param tuning_parameters: The parameters used for running the algorithm
:type tuning_parameters: Preset
:param name: The name of the network
"""
Architecture.__init__(self, tuning_parameters, name)
architecture.Architecture.__init__(self, tuning_parameters, name)
self.middleware_embedder = None
self.network_is_local = network_is_local
assert tuning_parameters.agent.tensorflow_support, 'TensorFlow is not supported for this agent'
@@ -174,7 +173,7 @@ class TensorFlowArchitecture(Architecture):
feed_dict = self._feed_dict(inputs)
# feed targets
targets = force_list(targets)
targets = utils.force_list(targets)
for placeholder_idx, target in enumerate(targets):
feed_dict[self.targets[placeholder_idx]] = target
@@ -186,13 +185,13 @@ class TensorFlowArchitecture(Architecture):
else:
fetches.append(self.tensor_gradients)
fetches += [self.total_loss, self.losses]
if self.tp.agent.middleware_type == MiddlewareTypes.LSTM:
if self.tp.agent.middleware_type == conf.MiddlewareTypes.LSTM:
fetches.append(self.middleware_embedder.state_out)
additional_fetches_start_idx = len(fetches)
fetches += additional_fetches
# feed the lstm state if necessary
if self.tp.agent.middleware_type == MiddlewareTypes.LSTM:
if self.tp.agent.middleware_type == conf.MiddlewareTypes.LSTM:
# we can't always assume that we are starting from scratch here can we?
feed_dict[self.middleware_embedder.c_in] = self.middleware_embedder.c_init
feed_dict[self.middleware_embedder.h_in] = self.middleware_embedder.h_init
@@ -206,7 +205,7 @@ class TensorFlowArchitecture(Architecture):
# extract the fetches
norm_unclipped_grads, grads, total_loss, losses = result[:4]
if self.tp.agent.middleware_type == MiddlewareTypes.LSTM:
if self.tp.agent.middleware_type == conf.MiddlewareTypes.LSTM:
(self.curr_rnn_c_in, self.curr_rnn_h_in) = result[4]
fetched_tensors = []
if len(additional_fetches) > 0:
@@ -308,7 +307,7 @@ class TensorFlowArchitecture(Architecture):
if outputs is None:
outputs = self.outputs
if self.tp.agent.middleware_type == MiddlewareTypes.LSTM:
if self.tp.agent.middleware_type == conf.MiddlewareTypes.LSTM:
feed_dict[self.middleware_embedder.c_in] = self.curr_rnn_c_in
feed_dict[self.middleware_embedder.h_in] = self.curr_rnn_h_in
@@ -317,7 +316,7 @@ class TensorFlowArchitecture(Architecture):
output = self.tp.sess.run(outputs, feed_dict)
if squeeze_output:
output = squeeze_list(output)
output = utils.squeeze_list(output)
return output

4
architectures/tensorflow_components/embedders.py
View File

@@ -1,5 +1,5 @@
#
# Copyright (c) 2017 Intel Corporation
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -13,8 +13,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
import tensorflow as tf
from configurations import EmbedderComplexity

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)

18
architectures/tensorflow_components/heads.py
View File

@@ -13,10 +13,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
import tensorflow as tf
import numpy as np
from utils import force_list
import utils
# Used to initialize weights for policy and value output layers
@@ -36,7 +36,7 @@ class Head(object):
self.loss = []
self.loss_type = []
self.regularizations = []
self.loss_weight = force_list(loss_weight)
self.loss_weight = utils.force_list(loss_weight)
self.target = []
self.input = []
self.is_local = is_local
@@ -50,12 +50,12 @@ class Head(object):
with tf.variable_scope(self.get_name(), initializer=tf.contrib.layers.xavier_initializer()):
self._build_module(input_layer)
self.output = force_list(self.output)
self.target = force_list(self.target)
self.input = force_list(self.input)
self.loss_type = force_list(self.loss_type)
self.loss = force_list(self.loss)
self.regularizations = force_list(self.regularizations)
self.output = utils.force_list(self.output)
self.target = utils.force_list(self.target)
self.input = utils.force_list(self.input)
self.loss_type = utils.force_list(self.loss_type)
self.loss = utils.force_list(self.loss)
self.regularizations = utils.force_list(self.regularizations)
if self.is_local:
self.set_loss()
self._post_build()

1
architectures/tensorflow_components/middleware.py
View File

@@ -13,7 +13,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
import tensorflow as tf
import numpy as np

5
architectures/tensorflow_components/shared_variables.py
View File

@@ -1,5 +1,5 @@
#
# Copyright (c) 2017 Intel Corporation
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -13,7 +13,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
import tensorflow as tf
import numpy as np
@@ -79,4 +78,4 @@ class SharedRunningStats(object):
@property
def shape(self):
return self._shape
return self._shape