def _testDynamicDecodeRNNWithTrainingHelperMatchesDynamicRNN( # pylint:disable=invalid-name self, use_sequence_length): sequence_length = [3, 4, 3, 1, 0] batch_size = 5 max_time = 8 input_depth = 7 cell_depth = 10 max_out = max(sequence_length) with self.session(use_gpu=True) as sess: inputs = np.random.randn(batch_size, max_time, input_depth).astype(np.float32) cell = tf.nn.rnn_cell.LSTMCell(cell_depth) zero_state = cell.zero_state(dtype=tf.float32, batch_size=batch_size) helper = seq2seq.TrainingHelper(inputs, sequence_length) my_decoder = seq2seq.BasicDecoder(cell=cell, helper=helper, initial_state=zero_state) # Match the variable scope of dynamic_rnn below so we end up # using the same variables with tf.variable_scope("root") as scope: final_decoder_outputs, final_decoder_state, _ = seq2seq.dynamic_decode( my_decoder, # impute_finished=True ensures outputs and final state # match those of dynamic_rnn called with sequence_length not None impute_finished=use_sequence_length, scope=scope) with tf.variable_scope(scope, reuse=True) as scope: final_rnn_outputs, final_rnn_state = tf.nn.dynamic_rnn( cell, inputs, sequence_length=sequence_length if use_sequence_length else None, initial_state=zero_state, scope=scope) sess.run(tf.global_variables_initializer()) sess_results = sess.run({ "final_decoder_outputs": final_decoder_outputs, "final_decoder_state": final_decoder_state, "final_rnn_outputs": final_rnn_outputs, "final_rnn_state": final_rnn_state }) # Decoder only runs out to max_out; ensure values are identical # to dynamic_rnn, which also zeros out outputs and passes along state. self.assertAllClose( sess_results["final_decoder_outputs"].rnn_output, sess_results["final_rnn_outputs"][:, 0:max_out, :]) if use_sequence_length: self.assertAllClose(sess_results["final_decoder_state"], sess_results["final_rnn_state"])
def _get_state(self, inputs, lengths=None, initial_state=None): """Computes the state of the RNN-NADE (NADE bias parameters and RNN state). Args: inputs: A batch of sequences to compute the state from, sized `[batch_size, max(lengths), num_dims]` or `[batch_size, num_dims]`. lengths: The length of each sequence, sized `[batch_size]`. initial_state: An RnnNadeStateTuple, the initial state of the RNN-NADE, or None if the zero state should be used. Returns: final_state: An RnnNadeStateTuple, the final state of the RNN-NADE. """ batch_size = int(inputs.shape[0]) if lengths is None: lengths = tf.tile(tf.shape(inputs)[1:2], [batch_size]) if initial_state is None: initial_rnn_state = self._get_rnn_zero_state(batch_size) else: initial_rnn_state = initial_state.rnn_state helper = contrib_seq2seq.TrainingHelper( inputs=inputs, sequence_length=lengths) decoder = contrib_seq2seq.BasicDecoder( cell=self._rnn_cell, helper=helper, initial_state=initial_rnn_state, output_layer=self._fc_layer) final_outputs, final_rnn_state = contrib_seq2seq.dynamic_decode( decoder)[0:2] # Flatten time dimension. final_outputs_flat = magenta.common.flatten_maybe_padded_sequences( final_outputs.rnn_output, lengths) b_enc, b_dec = tf.split( final_outputs_flat, [self._nade.num_hidden, self._nade.num_dims], axis=1) return RnnNadeStateTuple(b_enc, b_dec, final_rnn_state)
def testStepWithInferenceHelperMultilabel(self): batch_size = 5 vocabulary_size = 7 cell_depth = vocabulary_size start_token = 0 end_token = 6 start_inputs = tf.one_hot( np.ones(batch_size) * start_token, vocabulary_size) # The sample function samples independent bernoullis from the logits. sample_fn = ( lambda x: seq2seq.bernoulli_sample(logits=x, dtype=tf.bool)) # The next inputs are a one-hot encoding of the sampled labels. next_inputs_fn = tf.to_float end_fn = lambda sample_ids: sample_ids[:, end_token] with self.session(use_gpu=True) as sess: with tf.variable_scope("testStepWithInferenceHelper", initializer=tf.constant_initializer(0.01)): cell = tf.nn.rnn_cell.LSTMCell(vocabulary_size) helper = seq2seq.InferenceHelper(sample_fn, sample_shape=[cell_depth], sample_dtype=tf.bool, start_inputs=start_inputs, end_fn=end_fn, next_inputs_fn=next_inputs_fn) my_decoder = seq2seq.BasicDecoder( cell=cell, helper=helper, initial_state=cell.zero_state(dtype=tf.float32, batch_size=batch_size)) output_size = my_decoder.output_size output_dtype = my_decoder.output_dtype self.assertEqual( seq2seq.BasicDecoderOutput(cell_depth, cell_depth), output_size) self.assertEqual( seq2seq.BasicDecoderOutput(tf.float32, tf.bool), output_dtype) (first_finished, first_inputs, first_state) = my_decoder.initialize() (step_outputs, step_state, step_next_inputs, step_finished) = my_decoder.step(tf.constant(0), first_inputs, first_state) batch_size_t = my_decoder.batch_size self.assertIsInstance(first_state, tf.nn.rnn_cell.LSTMStateTuple) self.assertIsInstance(step_state, tf.nn.rnn_cell.LSTMStateTuple) self.assertIsInstance(step_outputs, seq2seq.BasicDecoderOutput) self.assertEqual((batch_size, cell_depth), step_outputs[0].get_shape()) self.assertEqual((batch_size, cell_depth), step_outputs[1].get_shape()) self.assertEqual((batch_size, cell_depth), first_state[0].get_shape()) self.assertEqual((batch_size, cell_depth), first_state[1].get_shape()) self.assertEqual((batch_size, cell_depth), step_state[0].get_shape()) self.assertEqual((batch_size, cell_depth), step_state[1].get_shape()) sess.run(tf.global_variables_initializer()) sess_results = sess.run({ "batch_size": batch_size_t, "first_finished": first_finished, "first_inputs": first_inputs, "first_state": first_state, "step_outputs": step_outputs, "step_state": step_state, "step_next_inputs": step_next_inputs, "step_finished": step_finished }) sample_ids = sess_results["step_outputs"].sample_id self.assertEqual(output_dtype.sample_id, sample_ids.dtype) expected_step_finished = sample_ids[:, end_token] expected_step_next_inputs = sample_ids.astype(np.float32) self.assertAllEqual(expected_step_finished, sess_results["step_finished"]) self.assertAllEqual(expected_step_next_inputs, sess_results["step_next_inputs"])
def testStepWithInferenceHelperCategorical(self): batch_size = 5 vocabulary_size = 7 cell_depth = vocabulary_size start_token = 0 end_token = 6 start_inputs = tf.one_hot( np.ones(batch_size) * start_token, vocabulary_size) # The sample function samples categorically from the logits. sample_fn = lambda x: seq2seq.categorical_sample(logits=x) # The next inputs are a one-hot encoding of the sampled labels. next_inputs_fn = ( lambda x: tf.one_hot(x, vocabulary_size, dtype=tf.float32)) end_fn = lambda sample_ids: tf.equal(sample_ids, end_token) with self.session(use_gpu=True) as sess: with tf.variable_scope("testStepWithInferenceHelper", initializer=tf.constant_initializer(0.01)): cell = tf.nn.rnn_cell.LSTMCell(vocabulary_size) helper = seq2seq.InferenceHelper(sample_fn, sample_shape=(), sample_dtype=tf.int32, start_inputs=start_inputs, end_fn=end_fn, next_inputs_fn=next_inputs_fn) my_decoder = seq2seq.BasicDecoder( cell=cell, helper=helper, initial_state=cell.zero_state(dtype=tf.float32, batch_size=batch_size)) output_size = my_decoder.output_size output_dtype = my_decoder.output_dtype self.assertEqual( seq2seq.BasicDecoderOutput(cell_depth, tf.TensorShape([])), output_size) self.assertEqual( seq2seq.BasicDecoderOutput(tf.float32, tf.int32), output_dtype) (first_finished, first_inputs, first_state) = my_decoder.initialize() (step_outputs, step_state, step_next_inputs, step_finished) = my_decoder.step(tf.constant(0), first_inputs, first_state) batch_size_t = my_decoder.batch_size self.assertIsInstance(first_state, tf.nn.rnn_cell.LSTMStateTuple) self.assertIsInstance(step_state, tf.nn.rnn_cell.LSTMStateTuple) self.assertIsInstance(step_outputs, seq2seq.BasicDecoderOutput) self.assertEqual((batch_size, cell_depth), step_outputs[0].get_shape()) self.assertEqual((batch_size, ), step_outputs[1].get_shape()) self.assertEqual((batch_size, cell_depth), first_state[0].get_shape()) self.assertEqual((batch_size, cell_depth), first_state[1].get_shape()) self.assertEqual((batch_size, cell_depth), step_state[0].get_shape()) self.assertEqual((batch_size, cell_depth), step_state[1].get_shape()) sess.run(tf.global_variables_initializer()) sess_results = sess.run({ "batch_size": batch_size_t, "first_finished": first_finished, "first_inputs": first_inputs, "first_state": first_state, "step_outputs": step_outputs, "step_state": step_state, "step_next_inputs": step_next_inputs, "step_finished": step_finished }) sample_ids = sess_results["step_outputs"].sample_id self.assertEqual(output_dtype.sample_id, sample_ids.dtype) expected_step_finished = (sample_ids == end_token) expected_step_next_inputs = np.zeros( (batch_size, vocabulary_size)) expected_step_next_inputs[np.arange(batch_size), sample_ids] = 1.0 self.assertAllEqual(expected_step_finished, sess_results["step_finished"]) self.assertAllEqual(expected_step_next_inputs, sess_results["step_next_inputs"])
def _testStepWithScheduledOutputTrainingHelper( # pylint:disable=invalid-name self, sampling_probability, use_next_inputs_fn, use_auxiliary_inputs): sequence_length = [3, 4, 3, 1, 0] batch_size = 5 max_time = 8 input_depth = 7 cell_depth = input_depth if use_auxiliary_inputs: auxiliary_input_depth = 4 auxiliary_inputs = np.random.randn( batch_size, max_time, auxiliary_input_depth).astype(np.float32) else: auxiliary_inputs = None with self.session(use_gpu=True) as sess: inputs = np.random.randn(batch_size, max_time, input_depth).astype(np.float32) cell = tf.nn.rnn_cell.LSTMCell(cell_depth) sampling_probability = tf.constant(sampling_probability) if use_next_inputs_fn: def next_inputs_fn(outputs): # Use deterministic function for test. samples = tf.argmax(outputs, axis=1) return tf.one_hot(samples, cell_depth, dtype=tf.float32) else: next_inputs_fn = None helper = seq2seq.ScheduledOutputTrainingHelper( inputs=inputs, sequence_length=sequence_length, sampling_probability=sampling_probability, time_major=False, next_inputs_fn=next_inputs_fn, auxiliary_inputs=auxiliary_inputs) my_decoder = seq2seq.BasicDecoder(cell=cell, helper=helper, initial_state=cell.zero_state( dtype=tf.float32, batch_size=batch_size)) output_size = my_decoder.output_size output_dtype = my_decoder.output_dtype self.assertEqual( seq2seq.BasicDecoderOutput(cell_depth, tf.TensorShape([])), output_size) self.assertEqual(seq2seq.BasicDecoderOutput(tf.float32, tf.int32), output_dtype) (first_finished, first_inputs, first_state) = my_decoder.initialize() (step_outputs, step_state, step_next_inputs, step_finished) = my_decoder.step(tf.constant(0), first_inputs, first_state) if use_next_inputs_fn: output_after_next_inputs_fn = next_inputs_fn( step_outputs.rnn_output) batch_size_t = my_decoder.batch_size self.assertIsInstance(first_state, tf.nn.rnn_cell.LSTMStateTuple) self.assertIsInstance(step_state, tf.nn.rnn_cell.LSTMStateTuple) self.assertIsInstance(step_outputs, seq2seq.BasicDecoderOutput) self.assertEqual((batch_size, cell_depth), step_outputs[0].get_shape()) self.assertEqual((batch_size, ), step_outputs[1].get_shape()) self.assertEqual((batch_size, cell_depth), first_state[0].get_shape()) self.assertEqual((batch_size, cell_depth), first_state[1].get_shape()) self.assertEqual((batch_size, cell_depth), step_state[0].get_shape()) self.assertEqual((batch_size, cell_depth), step_state[1].get_shape()) sess.run(tf.global_variables_initializer()) fetches = { "batch_size": batch_size_t, "first_finished": first_finished, "first_inputs": first_inputs, "first_state": first_state, "step_outputs": step_outputs, "step_state": step_state, "step_next_inputs": step_next_inputs, "step_finished": step_finished } if use_next_inputs_fn: fetches[ "output_after_next_inputs_fn"] = output_after_next_inputs_fn sess_results = sess.run(fetches) self.assertAllEqual([False, False, False, False, True], sess_results["first_finished"]) self.assertAllEqual([False, False, False, True, True], sess_results["step_finished"]) sample_ids = sess_results["step_outputs"].sample_id self.assertEqual(output_dtype.sample_id, sample_ids.dtype) batch_where_not_sampling = np.where(np.logical_not(sample_ids)) batch_where_sampling = np.where(sample_ids) auxiliary_inputs_to_concat = ( auxiliary_inputs[:, 1] if use_auxiliary_inputs else np.array( []).reshape(batch_size, 0).astype(np.float32)) expected_next_sampling_inputs = np.concatenate( (sess_results["output_after_next_inputs_fn"] [batch_where_sampling] if use_next_inputs_fn else sess_results["step_outputs"].rnn_output[batch_where_sampling], auxiliary_inputs_to_concat[batch_where_sampling]), axis=-1) self.assertAllClose( sess_results["step_next_inputs"][batch_where_sampling], expected_next_sampling_inputs) self.assertAllClose( sess_results["step_next_inputs"][batch_where_not_sampling], np.concatenate( (np.squeeze(inputs[batch_where_not_sampling, 1], axis=0), auxiliary_inputs_to_concat[batch_where_not_sampling]), axis=-1))
def _testDynamicDecodeRNN(self, time_major, maximum_iterations=None): # pylint:disable=invalid-name sequence_length = [3, 4, 3, 1, 0] batch_size = 5 max_time = 8 input_depth = 7 cell_depth = 10 max_out = max(sequence_length) with self.session(use_gpu=True) as sess: if time_major: inputs = np.random.randn(max_time, batch_size, input_depth).astype(np.float32) else: inputs = np.random.randn(batch_size, max_time, input_depth).astype(np.float32) cell = tf.nn.rnn_cell.LSTMCell(cell_depth) helper = seq2seq.TrainingHelper(inputs, sequence_length, time_major=time_major) my_decoder = seq2seq.BasicDecoder(cell=cell, helper=helper, initial_state=cell.zero_state( dtype=tf.float32, batch_size=batch_size)) final_outputs, final_state, final_sequence_length = ( seq2seq.dynamic_decode(my_decoder, output_time_major=time_major, maximum_iterations=maximum_iterations)) def _t(shape): if time_major: return (shape[1], shape[0]) + shape[2:] return shape self.assertIsInstance(final_outputs, seq2seq.BasicDecoderOutput) self.assertIsInstance(final_state, tf.nn.rnn_cell.LSTMStateTuple) self.assertEqual( (batch_size, ), tuple(final_sequence_length.get_shape().as_list())) self.assertEqual( _t((batch_size, None, cell_depth)), tuple(final_outputs.rnn_output.get_shape().as_list())) self.assertEqual( _t((batch_size, None)), tuple(final_outputs.sample_id.get_shape().as_list())) sess.run(tf.global_variables_initializer()) sess_results = sess.run({ "final_outputs": final_outputs, "final_state": final_state, "final_sequence_length": final_sequence_length, }) # Mostly a smoke test time_steps = max_out expected_length = sequence_length if maximum_iterations is not None: time_steps = min(max_out, maximum_iterations) expected_length = [ min(x, maximum_iterations) for x in expected_length ] self.assertEqual(_t((batch_size, time_steps, cell_depth)), sess_results["final_outputs"].rnn_output.shape) self.assertEqual(_t((batch_size, time_steps)), sess_results["final_outputs"].sample_id.shape) self.assertCountEqual(expected_length, sess_results["final_sequence_length"])
def _testStepWithTrainingHelper(self, use_output_layer): # pylint:disable=invalid-name sequence_length = [3, 4, 3, 1, 0] batch_size = 5 max_time = 8 input_depth = 7 cell_depth = 10 output_layer_depth = 3 with self.session(use_gpu=True) as sess: inputs = np.random.randn(batch_size, max_time, input_depth).astype(np.float32) cell = tf.nn.rnn_cell.LSTMCell(cell_depth) helper = seq2seq.TrainingHelper(inputs, sequence_length, time_major=False) if use_output_layer: output_layer = tf.layers.Dense(output_layer_depth, use_bias=False) expected_output_depth = output_layer_depth else: output_layer = None expected_output_depth = cell_depth my_decoder = seq2seq.BasicDecoder(cell=cell, helper=helper, initial_state=cell.zero_state( dtype=tf.float32, batch_size=batch_size), output_layer=output_layer) output_size = my_decoder.output_size output_dtype = my_decoder.output_dtype self.assertEqual( seq2seq.BasicDecoderOutput(expected_output_depth, tf.TensorShape([])), output_size) self.assertEqual(seq2seq.BasicDecoderOutput(tf.float32, tf.int32), output_dtype) (first_finished, first_inputs, first_state) = my_decoder.initialize() (step_outputs, step_state, step_next_inputs, step_finished) = my_decoder.step(tf.constant(0), first_inputs, first_state) batch_size_t = my_decoder.batch_size self.assertIsInstance(first_state, tf.nn.rnn_cell.LSTMStateTuple) self.assertIsInstance(step_state, tf.nn.rnn_cell.LSTMStateTuple) self.assertIsInstance(step_outputs, seq2seq.BasicDecoderOutput) self.assertEqual((batch_size, expected_output_depth), step_outputs[0].get_shape()) self.assertEqual((batch_size, ), step_outputs[1].get_shape()) self.assertEqual((batch_size, cell_depth), first_state[0].get_shape()) self.assertEqual((batch_size, cell_depth), first_state[1].get_shape()) self.assertEqual((batch_size, cell_depth), step_state[0].get_shape()) self.assertEqual((batch_size, cell_depth), step_state[1].get_shape()) if use_output_layer: # The output layer was accessed self.assertEqual(len(output_layer.variables), 1) sess.run(tf.global_variables_initializer()) sess_results = sess.run({ "batch_size": batch_size_t, "first_finished": first_finished, "first_inputs": first_inputs, "first_state": first_state, "step_outputs": step_outputs, "step_state": step_state, "step_next_inputs": step_next_inputs, "step_finished": step_finished }) self.assertAllEqual([False, False, False, False, True], sess_results["first_finished"]) self.assertAllEqual([False, False, False, True, True], sess_results["step_finished"]) self.assertEqual(output_dtype.sample_id, sess_results["step_outputs"].sample_id.dtype) self.assertAllEqual( np.argmax(sess_results["step_outputs"].rnn_output, -1), sess_results["step_outputs"].sample_id)