def predict(self, state, command):
self.language_module.eval()
self.decoder.eval()
token_ids = self.vocab.token_ids(command)
command_variable = torch.LongTensor(token_ids).unsqueeze(0).to(device)
state_variable = dataset.state_to_variable(state).to(device)
encoder_output = self.language_module.forward(command_variable)
decoder_input = encoder_output if FLAGS.continuous_message else discrete_util.discrete_transformation(
encoder_output)
prediction = self.decoder.forward(state_variable, decoder_input)
return dataset.output_from_variable(prediction, state)
def predictedOutputAndDiscreteTransformation(self, state, command):
self.language_module.eval()
self.decoder.eval()
token_ids = self.vocab.token_ids(command)
command_variable = torch.LongTensor(token_ids).unsqueeze(0).to(device)
state_variable = dataset.state_to_variable(state).to(device)
encoder_output = self.language_module.forward(command_variable)
decoder_input, likelihood = discrete_util.discrete_transformation_with_likelihood(
encoder_output)
prediction = self.decoder.forward(state_variable, decoder_input)
return dataset.output_from_variable(prediction,
state), decoder_input, likelihood