def batchnorm_activation_dropout(input_layer, batchnorm, activation_function,
dropout_rate, is_training, name):
layers = [input_layer]
# batchnorm
if batchnorm:
layers.append(
tf.layers.batch_normalization(layers[-1],
name="{}_batchnorm".format(name),
training=is_training))
# activation
if activation_function:
if isinstance(activation_function, str):
activation_function = utils.get_activation_function(
activation_function)
layers.append(
activation_function(layers[-1], name="{}_activation".format(name)))
# dropout
if dropout_rate > 0:
layers.append(
tf.layers.dropout(layers[-1],
dropout_rate,
name="{}_dropout".format(name),
training=is_training))
# remove the input layer from the layers list
del layers[0]
return layers
def batchnorm_activation_dropout(input_layer, batchnorm, activation_function,
dropout_rate, is_training, name):
layers = [input_layer]
# Rationale: passing a bool here will mean that batchnorm and or activation will never activate
assert not isinstance(is_training, bool)
# batchnorm
if batchnorm:
layers.append(
tf.layers.batch_normalization(layers[-1],
name="{}_batchnorm".format(name),
training=is_training))
# activation
if activation_function:
if isinstance(activation_function, str):
activation_function = utils.get_activation_function(
activation_function)
layers.append(
activation_function(layers[-1], name="{}_activation".format(name)))
# dropout
if dropout_rate > 0:
layers.append(
tf.layers.dropout(layers[-1],
dropout_rate,
name="{}_dropout".format(name),
training=is_training))
# remove the input layer from the layers list
del layers[0]
return layers
def get_input_embedder(self, input_name: str, embedder_params: InputEmbedderParameters):
"""
Given an input embedder parameters class, creates the input embedder and returns it
:param input_name: the name of the input to the embedder (used for retrieving the shape). The input should
be a value within the state or the action.
:param embedder_params: the parameters of the class of the embedder
:return: the embedder instance
"""
allowed_inputs = copy.copy(self.spaces.state.sub_spaces)
allowed_inputs["action"] = copy.copy(self.spaces.action)
allowed_inputs["goal"] = copy.copy(self.spaces.goal)
if input_name not in allowed_inputs.keys():
raise ValueError("The key for the input embedder ({}) must match one of the following keys: {}"
.format(input_name, allowed_inputs.keys()))
emb_type = "vector"
if isinstance(allowed_inputs[input_name], TensorObservationSpace):
emb_type = "tensor"
elif isinstance(allowed_inputs[input_name], PlanarMapsObservationSpace):
emb_type = "image"
embedder_path = embedder_params.path(emb_type)
embedder_params_copy = copy.copy(embedder_params)
embedder_params_copy.activation_function = utils.get_activation_function(embedder_params.activation_function)
embedder_params_copy.input_rescaling = embedder_params_copy.input_rescaling[emb_type]
embedder_params_copy.input_offset = embedder_params_copy.input_offset[emb_type]
embedder_params_copy.name = input_name
module = dynamic_import_and_instantiate_module_from_params(embedder_params_copy,
path=embedder_path,
positional_args=[allowed_inputs[input_name].shape])
return module
def get_middleware(self, middleware_params: MiddlewareParameters):
"""
Given a middleware type, creates the middleware and returns it
:param middleware_params: the paramaeters of the middleware class
:return: the middleware instance
"""
mod_name = middleware_params.parameterized_class_name
middleware_path = middleware_params.path
middleware_params_copy = copy.copy(middleware_params)
middleware_params_copy.activation_function = utils.get_activation_function(middleware_params.activation_function)
module = dynamic_import_and_instantiate_module_from_params(middleware_params_copy, path=middleware_path)
return module
def get_output_head(self, head_params: HeadParameters, head_idx: int):
"""
Given a head type, creates the head and returns it
:param head_params: the parameters of the head to create
:param head_idx: the head index
:return: the head
"""
mod_name = head_params.parameterized_class_name
head_path = head_params.path
head_params_copy = copy.copy(head_params)
head_params_copy.activation_function = utils.get_activation_function(head_params_copy.activation_function)
return dynamic_import_and_instantiate_module_from_params(head_params_copy, path=head_path, extra_kwargs={
'agent_parameters': self.ap, 'spaces': self.spaces, 'network_name': self.network_wrapper_name,
'head_idx': head_idx, 'is_local': self.network_is_local})
