def __eq__(self, other):
if isinstance(self, other.__class__):
return all([
util.tensors_equal(self.terminal_actions, other.terminal_actions),
util.tensors_equal(self.checklist_target, other.checklist_target),
util.tensors_equal(self.checklist_mask, other.checklist_mask),
util.tensors_equal(self.checklist, other.checklist),
self.terminal_indices_dict == other.terminal_indices_dict,
])
return NotImplemented
def __eq__(self, other):
if isinstance(self, other.__class__):
return all([
util.tensors_equal(self.hidden_state, other.hidden_state, tolerance=1e-5),
util.tensors_equal(self.memory_cell, other.memory_cell, tolerance=1e-5),
util.tensors_equal(self.previous_action_embedding, other.previous_action_embedding,
tolerance=1e-5),
util.tensors_equal(self.attended_input, other.attended_input, tolerance=1e-5),
])
return NotImplemented
def __eq__(self, other):
if isinstance(self, other.__class__):
return all([
util.tensors_equal(self.terminal_actions, other.terminal_actions),
util.tensors_equal(self.checklist_target, other.checklist_target),
util.tensors_equal(self.checklist_mask, other.checklist_mask),
util.tensors_equal(self.checklist, other.checklist),
self.terminal_indices_dict == other.terminal_indices_dict,
])
return NotImplemented
def __eq__(self, other):
if isinstance(self, other.__class__):
# pylint: disable=protected-access
return all([
self._nonterminal_stack == other._nonterminal_stack,
self._lambda_stacks == other._lambda_stacks,
util.tensors_equal(self._valid_actions, other._valid_actions),
util.tensors_equal(self._context_actions, other._context_actions),
self._is_nonterminal == other._is_nonterminal,
])
return NotImplemented
def __eq__(self, other):
if isinstance(self, other.__class__):
# pylint: disable=protected-access
return all([
self._nonterminal_stack == other._nonterminal_stack,
self._lambda_stacks == other._lambda_stacks,
util.tensors_equal(self._valid_actions, other._valid_actions),
util.tensors_equal(self._context_actions, other._context_actions),
self._is_nonterminal == other._is_nonterminal,
])
return NotImplemented
def __eq__(self, other):
if isinstance(self, other.__class__):
return all([
self.batch_indices == other.batch_indices,
self.action_history == other.action_history,
util.tensors_equal(self.score, other.score, tolerance=1e-3),
util.tensors_equal(self.rnn_state, other.rnn_state, tolerance=1e-4),
self.grammar_state == other.grammar_state,
self.checklist_state == other.checklist_state,
self.possible_actions == other.possible_actions,
self.extras == other.extras,
util.tensors_equal(self.debug_info, other.debug_info, tolerance=1e-6),
])
return NotImplemented
def __eq__(self, other):
if isinstance(self, other.__class__):
return all([
self.batch_indices == other.batch_indices,
self.action_history == other.action_history,
util.tensors_equal(self.score, other.score, tolerance=1e-3),
util.tensors_equal(self.rnn_state, other.rnn_state, tolerance=1e-4),
self.grammar_state == other.grammar_state,
self.checklist_state == other.checklist_state,
self.possible_actions == other.possible_actions,
self.extras == other.extras,
util.tensors_equal(self.debug_info, other.debug_info, tolerance=1e-6),
])
return NotImplemented
def __eq__(self, other):
if isinstance(self, other.__class__):
return all([
self._nonterminal_stack == other._nonterminal_stack,
util.tensors_equal(self._valid_actions, other._valid_actions),
self._is_nonterminal == other._is_nonterminal,
self._reverse_productions == other._reverse_productions,
])
return NotImplemented
def __eq__(self, other):
if isinstance(self, other.__class__):
return all([
util.tensors_equal(self.hidden_state, other.hidden_state, tolerance=1e-5),
util.tensors_equal(self.memory_cell, other.memory_cell, tolerance=1e-5),
util.tensors_equal(self.previous_action_embedding, other.previous_action_embedding,
tolerance=1e-5),
util.tensors_equal(self.attended_input, other.attended_input, tolerance=1e-5),
util.tensors_equal(self.encoder_outputs, other.encoder_outputs, tolerance=1e-5),
util.tensors_equal(self.encoder_output_mask, other.encoder_output_mask, tolerance=1e-5),
])
return NotImplemented
