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Cleanup imports.
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
@@ -13,15 +13,18 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import ngraph as ng
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from ngraph.frontends import neon
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from ngraph.util import names as ngraph_names
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from architectures.neon_components.embedders import *
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from architectures.neon_components.heads import *
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from architectures.neon_components.middleware import *
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from architectures.neon_components.architecture import *
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from configurations import InputTypes, OutputTypes, MiddlewareTypes
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from architectures.neon_components import architecture
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from architectures.neon_components import embedders
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from architectures.neon_components import middleware
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from architectures.neon_components import heads
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import configurations as conf
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class GeneralNeonNetwork(NeonArchitecture):
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class GeneralNeonNetwork(architecture.NeonArchitecture):
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def __init__(self, tuning_parameters, name="", global_network=None, network_is_local=True):
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self.global_network = global_network
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self.network_is_local = network_is_local
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@@ -34,7 +37,7 @@ class GeneralNeonNetwork(NeonArchitecture):
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self.activation_function = self.get_activation_function(
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tuning_parameters.agent.hidden_layers_activation_function)
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NeonArchitecture.__init__(self, tuning_parameters, name, global_network, network_is_local)
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architecture.NeonArchitecture.__init__(self, tuning_parameters, name, global_network, network_is_local)
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def get_activation_function(self, activation_function_string):
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activation_functions = {
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@@ -53,36 +56,36 @@ class GeneralNeonNetwork(NeonArchitecture):
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# the observation can be either an image or a vector
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def get_observation_embedding(with_timestep=False):
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if self.input_height > 1:
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return ImageEmbedder((self.input_depth, self.input_height, self.input_width), self.batch_size,
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name="observation")
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return embedders.ImageEmbedder((self.input_depth, self.input_height, self.input_width), self.batch_size,
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name="observation")
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else:
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return VectorEmbedder((self.input_depth, self.input_width + int(with_timestep)), self.batch_size,
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name="observation")
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return embedders.VectorEmbedder((self.input_depth, self.input_width + int(with_timestep)), self.batch_size,
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name="observation")
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input_mapping = {
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InputTypes.Observation: get_observation_embedding(),
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InputTypes.Measurements: VectorEmbedder(self.measurements_size, self.batch_size, name="measurements"),
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InputTypes.GoalVector: VectorEmbedder(self.measurements_size, self.batch_size, name="goal_vector"),
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InputTypes.Action: VectorEmbedder((self.num_actions,), self.batch_size, name="action"),
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InputTypes.TimedObservation: get_observation_embedding(with_timestep=True),
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conf.InputTypes.Observation: get_observation_embedding(),
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conf.InputTypes.Measurements: embedders.VectorEmbedder(self.measurements_size, self.batch_size, name="measurements"),
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conf.InputTypes.GoalVector: embedders.VectorEmbedder(self.measurements_size, self.batch_size, name="goal_vector"),
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conf.InputTypes.Action: embedders.VectorEmbedder((self.num_actions,), self.batch_size, name="action"),
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conf.InputTypes.TimedObservation: get_observation_embedding(with_timestep=True),
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}
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return input_mapping[embedder_type]
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def get_middleware_embedder(self, middleware_type):
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return {MiddlewareTypes.LSTM: None, # LSTM over Neon is currently not supported in Coach
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MiddlewareTypes.FC: FC_Embedder}.get(middleware_type)(self.activation_function)
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return {conf.MiddlewareTypes.LSTM: None, # LSTM over Neon is currently not supported in Coach
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conf.MiddlewareTypes.FC: middleware.FC_Embedder}.get(middleware_type)(self.activation_function)
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def get_output_head(self, head_type, head_idx, loss_weight=1.):
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output_mapping = {
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OutputTypes.Q: QHead,
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OutputTypes.DuelingQ: DuelingQHead,
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OutputTypes.V: None, # Policy Optimization algorithms over Neon are currently not supported in Coach
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OutputTypes.Pi: None, # Policy Optimization algorithms over Neon are currently not supported in Coach
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OutputTypes.MeasurementsPrediction: None, # DFP over Neon is currently not supported in Coach
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OutputTypes.DNDQ: None, # NEC over Neon is currently not supported in Coach
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OutputTypes.NAF: None, # NAF over Neon is currently not supported in Coach
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OutputTypes.PPO: None, # PPO over Neon is currently not supported in Coach
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OutputTypes.PPO_V: None # PPO over Neon is currently not supported in Coach
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conf.OutputTypes.Q: heads.QHead,
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conf.OutputTypes.DuelingQ: heads.DuelingQHead,
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conf.OutputTypes.V: None, # Policy Optimization algorithms over Neon are currently not supported in Coach
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conf.OutputTypes.Pi: None, # Policy Optimization algorithms over Neon are currently not supported in Coach
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conf.OutputTypes.MeasurementsPrediction: None, # DFP over Neon is currently not supported in Coach
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conf.OutputTypes.DNDQ: None, # NEC over Neon is currently not supported in Coach
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conf.OutputTypes.NAF: None, # NAF over Neon is currently not supported in Coach
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conf.OutputTypes.PPO: None, # PPO over Neon is currently not supported in Coach
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conf.OutputTypes.PPO_V: None # PPO over Neon is currently not supported in Coach
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}
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return output_mapping[head_type](self.tp, head_idx, loss_weight, self.network_is_local)
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@@ -104,7 +107,7 @@ class GeneralNeonNetwork(NeonArchitecture):
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done_creating_input_placeholders = False
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for network_idx in range(self.num_networks):
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with name_scope('network_{}'.format(network_idx)):
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with ngraph_names.name_scope('network_{}'.format(network_idx)):
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####################
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# Input Embeddings #
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####################
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