def __init__(self, proto_network, batch_size, callback):
proto_network = proto_network.expand_loop_control()
self.proto_network = proto_network.promote(callback)
self.proto_network(batch_size=batch_size)
for k, v in itertools.chain(
self.proto_network.variables.items(), self.proto_network.parameters.items()):
v.variable_instance.name = k
self._inputs = {
i: self.proto_network.variables[i].variable_instance
for i in self.proto_network.inputs
}
self._outputs = {
i: self.proto_network.variables[i].variable_instance
for i in self.proto_network.outputs
}
self._variables = {
k: v.variable_instance
for k, v in itertools.chain(
self.proto_network.variables.items(), self.proto_network.parameters.items())
}
# publish network's parameters to current parameter scope
# like original implementation.
with nn.parameter_scope('', nn.get_current_parameter_scope()):
for k, v in self.proto_network.parameters.items():
nn.parameter.set_parameter(k, v.variable_instance)
def __init__(self,
network_proto,
scope,
batch_size=None,
rng=None,
callback=None):
if batch_size is None:
batch_size = network_proto.batch_size
self.batch_size = batch_size
if rng is None:
rng = np.random.RandomState(1223)
self.rng = rng
if callback is None:
callback = NnpNetworkPass() # No pass
# Variable proto messages as a dictionary with name as a key
variables = {v.name: VariableProto(v) for v in network_proto.variable}
functions = [FunctionProto(f) for f in network_proto.function]
for f in functions:
inputs = [variables[name] for name in f.proto.input]
outputs = [variables[name] for name in f.proto.output]
f.inputs = inputs
f.outputs = outputs
# Apply function passes
for f in self._functions_in_forward_order(variables):
if f.disabled:
continue
callback._apply_function_pass_by_type(f, variables, scope)
callback._apply_function_pass_by_name(f, variables, scope)
# Apply stop-at.
for f in self._functions_in_forward_order(variables):
# callback.verbose2('Applying stop-at for inputs of {}.'.format(f.name))
callback._apply_use_up_to(f.inputs)
# Build computation graph
num_ops = 0
current_scope = nn.get_current_parameter_scope()
with nn.parameter_scope('', scope):
for f in self._functions_in_forward_order(variables):
self._create_function(f, callback, current_scope)
# print(f.name)
num_ops += 1
callback.verbose2('Created {} functions.'.format(num_ops))
variables = self._filter_variables(variables)
inputs = self._get_inputs(variables)
outputs = self._get_outputs(variables)
# Get input variables
self.variables = {v.name: v.variable for v in variables.values()}
self.inputs = {i.name: i.variable for i in inputs}
self.outputs = {o.name: o.variable for o in outputs}