(
self,
model,
inputs,
seq_lengths=None,
initial_states=None,
outputs_with_grads=None,
)
| 73 | return self.name + '/' + name if self.name is not None else name |
| 74 | |
| 75 | def apply_over_sequence( |
| 76 | self, |
| 77 | model, |
| 78 | inputs, |
| 79 | seq_lengths=None, |
| 80 | initial_states=None, |
| 81 | outputs_with_grads=None, |
| 82 | ): |
| 83 | if initial_states is None: |
| 84 | with scope.NameScope(self.name): |
| 85 | if self.initializer is None: |
| 86 | raise Exception("Either initial states " |
| 87 | "or initializer have to be set") |
| 88 | initial_states = self.initializer.create_states(model) |
| 89 | |
| 90 | preprocessed_inputs = self.prepare_input(model, inputs) |
| 91 | step_model = ModelHelper(name=self.name, param_model=model) |
| 92 | input_t, timestep = step_model.net.AddScopedExternalInputs( |
| 93 | 'input_t', |
| 94 | 'timestep', |
| 95 | ) |
| 96 | utils.raiseIfNotEqual( |
| 97 | len(initial_states), len(self.get_state_names()), |
| 98 | "Number of initial state values provided doesn't match the number " |
| 99 | "of states" |
| 100 | ) |
| 101 | states_prev = step_model.net.AddScopedExternalInputs(*[ |
| 102 | s + '_prev' for s in self.get_state_names() |
| 103 | ]) |
| 104 | states = self._apply( |
| 105 | model=step_model, |
| 106 | input_t=input_t, |
| 107 | seq_lengths=seq_lengths, |
| 108 | states=states_prev, |
| 109 | timestep=timestep, |
| 110 | ) |
| 111 | |
| 112 | external_outputs = set(step_model.net.Proto().external_output) |
| 113 | for state in states: |
| 114 | if state not in external_outputs: |
| 115 | step_model.net.AddExternalOutput(state) |
| 116 | |
| 117 | if outputs_with_grads is None: |
| 118 | outputs_with_grads = [self.get_output_state_index() * 2] |
| 119 | |
| 120 | # states_for_all_steps consists of combination of |
| 121 | # states gather for all steps and final states. It looks like this: |
| 122 | # (state_1_all, state_1_final, state_2_all, state_2_final, ...) |
| 123 | states_for_all_steps = recurrent.recurrent_net( |
| 124 | net=model.net, |
| 125 | cell_net=step_model.net, |
| 126 | inputs=[(input_t, preprocessed_inputs)], |
| 127 | initial_cell_inputs=list(zip(states_prev, initial_states)), |
| 128 | links=dict(zip(states_prev, states)), |
| 129 | timestep=timestep, |
| 130 | scope=self.name, |
| 131 | forward_only=self.forward_only, |
| 132 | outputs_with_grads=outputs_with_grads, |
no test coverage detected