Merge pull request #459 from ufal/decoders_refactor

`@tensor` to non-recurrent decoders

Author: jlibovicky

neuralmonkey/decoders/ctc_decoder.py

@@ -32,33 +32,41 @@ def __init__(self,
         self.vocabulary = vocabulary
         self.data_id = data_id
 
-        self._merge_repeated_targets = merge_repeated_targets
-        self._merge_repeated_outputs = merge_repeated_outputs
-        self._beam_width = beam_width
-
-        with self.use_scope():
-            self.train_targets = tf.sparse_placeholder(tf.int32,
-                                                       name="targets")
-
-            self.train_mode = tf.placeholder(tf.bool, name="train_mode")
-
-            # encoder.states_mask is batch-major
-            self._input_lengths = tf.reduce_sum(
-                tf.to_int32(self.encoder.states_mask), 1)
-
-            if beam_width == 1:
-                decoded, _ = tf.nn.ctc_greedy_decoder(
-                    inputs=self._logits, sequence_length=self._input_lengths,
-                    merge_repeated=self._merge_repeated_outputs)
-            else:
-                decoded, _ = tf.nn.ctc_beam_search_decoder(
-                    inputs=self._logits, sequence_length=self._input_lengths,
-                    beam_width=self._beam_width,
-                    merge_repeated=self._merge_repeated_outputs)
-
-            self.decoded = tf.sparse_tensor_to_dense(
-                tf.sparse_transpose(decoded[0]),
-                default_value=self.vocabulary.get_word_index(END_TOKEN))
+        self.merge_repeated_targets = merge_repeated_targets
+        self.merge_repeated_outputs = merge_repeated_outputs
+        self.beam_width = beam_width
+    # pylint: enable=too-many-arguments
+
+    # pylint: disable=no-self-use
+    @tensor
+    def train_targets(self) -> tf.Tensor:
+        return tf.sparse_placeholder(tf.int32, name="targets")
+
+    @tensor
+    def train_mode(self) -> tf.Tensor:
+        return tf.placeholder(tf.bool, name="train_mode")
+    # pylint: disable=no-self-use
+
+    @tensor
+    def input_lengths(self) -> tf.Tensor:
+        # encoder.states_mask is batch-major
+        return tf.reduce_sum(tf.to_int32(self.encoder.states_mask), 1)
+
+    @tensor
+    def decoded(self) -> tf.Tensor:
+        if self.beam_width == 1:
+            decoded, _ = tf.nn.ctc_greedy_decoder(
+                inputs=self.logits, sequence_length=self.input_lengths,
+                merge_repeated=self.merge_repeated_outputs)
+        else:
+            decoded, _ = tf.nn.ctc_beam_search_decoder(
+                inputs=self.logits, sequence_length=self.input_lengths,
+                beam_width=self.beam_width,
+                merge_repeated=self.merge_repeated_outputs)
+
+        return tf.sparse_tensor_to_dense(
+            tf.sparse_transpose(decoded[0]),
+            default_value=self.vocabulary.get_word_index(END_TOKEN))
 
     @property
     def train_loss(self) -> tf.Tensor:
@@ -71,15 +79,15 @@ def runtime_loss(self) -> tf.Tensor:
     @tensor
     def cost(self) -> tf.Tensor:
         loss = tf.nn.ctc_loss(
-            labels=self.train_targets, inputs=self._logits,
-            sequence_length=self._input_lengths,
-            preprocess_collapse_repeated=self._merge_repeated_targets,
-            ctc_merge_repeated=self._merge_repeated_outputs)
+            labels=self.train_targets, inputs=self.logits,
+            sequence_length=self.input_lengths,
+            preprocess_collapse_repeated=self.merge_repeated_targets,
+            ctc_merge_repeated=self.merge_repeated_outputs)
 
         return tf.reduce_sum(loss)
 
     @tensor
-    def _logits(self) -> tf.Tensor:
+    def logits(self) -> tf.Tensor:
         vocabulary_size = len(self.vocabulary)
 
         encoder_states = self.encoder.hidden_states

neuralmonkey/decoders/multi_decoder.py

@@ -6,9 +6,7 @@
 from neuralmonkey.vocabulary import Vocabulary
 from neuralmonkey.model.model_part import ModelPart, FeedDict
 from neuralmonkey.nn.mlp import MultilayerPerceptron
-
-
-# pylint: disable=too-many-instance-attributes
+from neuralmonkey.decorators import tensor
 
 
 class SequenceClassifier(ModelPart):
@@ -53,36 +51,62 @@ def __init__(self,
         self.dropout_keep_prob = dropout_keep_prob
         self.max_output_len = 1
 
-        with self.use_scope():
-            self.train_mode = tf.placeholder(tf.bool, name="train_mode")
-            self.learning_step = tf.get_variable(
-                "learning_step", [], trainable=False,
-                initializer=tf.constant_initializer(0))
-
-            self.gt_inputs = [tf.placeholder(
-                tf.int32, shape=[None], name="targets")]
-            mlp_input = tf.concat([enc.encoded for enc in encoders], 1)
-            mlp = MultilayerPerceptron(
-                mlp_input, layers, self.dropout_keep_prob, len(vocabulary),
-                activation_fn=self.activation_fn, train_mode=self.train_mode)
-
-            self.loss_with_gt_ins = tf.reduce_mean(
-                tf.nn.sparse_softmax_cross_entropy_with_logits(
-                    logits=mlp.logits, labels=self.gt_inputs[0]))
-            self.loss_with_decoded_ins = self.loss_with_gt_ins
-            self.cost = self.loss_with_gt_ins
-
-            self.decoded_seq = [mlp.classification]
-            self.decoded_logits = [mlp.logits]
-            self.runtime_logprobs = [tf.nn.log_softmax(mlp.logits)]
-
-            tf.summary.scalar(
-                'val_optimization_cost', self.cost,
-                collections=["summary_val"])
-            tf.summary.scalar(
-                'train_optimization_cost',
-                self.cost, collections=["summary_train"])
-    # pylint: enable=too-many-arguments
+        tf.summary.scalar(
+            'train_optimization_cost',
+            self.cost, collections=["summary_train"])
+# pylint: enable=too-many-arguments
+
+    # pylint: disable=no-self-use
+    @tensor
+    def train_mode(self) -> tf.Tensor:
+        return tf.placeholder(tf.bool, name="train_mode")
+
+    @tensor
+    def gt_inputs(self) -> List[tf.Tensor]:
+        return [tf.placeholder(tf.int32, shape=[None], name="targets")]
+    # pylint: enable=no-self-use
+
+    @tensor
+    def _mlp(self) -> MultilayerPerceptron:
+        mlp_input = tf.concat([enc.encoded for enc in self.encoders], 1)
+        return MultilayerPerceptron(
+            mlp_input, self.layers,
+            self.dropout_keep_prob, len(self.vocabulary),
+            activation_fn=self.activation_fn, train_mode=self.train_mode)
+
+    @tensor
+    def loss_with_gt_ins(self) -> tf.Tensor:
+        # pylint: disable=no-member,unsubscriptable-object
+        return tf.reduce_mean(
+            tf.nn.sparse_softmax_cross_entropy_with_logits(
+                logits=self._mlp.logits, labels=self.gt_inputs[0]))
+        # pylint: enable=no-member,unsubscriptable-object
+
+    @property
+    def loss_with_decoded_ins(self) -> tf.Tensor:
+        return self.loss_with_gt_ins
+
+    @property
+    def cost(self) -> tf.Tensor:
+        return self.loss_with_gt_ins
+
+    @tensor
+    def decoded_seq(self) -> List[tf.Tensor]:
+        # pylint: disable=no-member
+        return [self._mlp.classification]
+        # pylint: enable=no-member
+
+    @tensor
+    def decoded_logits(self) -> List[tf.Tensor]:
+        # pylint: disable=no-member
+        return [self._mlp.logits]
+        # pylint: enable=no-member
+
+    @tensor
+    def runtime_logprobs(self) -> List[tf.Tensor]:
+        # pylint: disable=no-member
+        return [tf.nn.log_softmax(self._mlp.logits)]
+        # pylint: enable=no-member
 
     @property
     def train_loss(self):
@@ -108,7 +132,9 @@ def feed_dict(self, dataset: Dataset, train: bool = False) -> FeedDict:
             label_tensors, _ = self.vocabulary.sentences_to_tensor(
                 sentences_list, self.max_output_len)
 
+            # pylint: disable=unsubscriptable-object
             fd[self.gt_inputs[0]] = label_tensors[0]
+            # pylint: enable=unsubscriptable-object
 
         fd[self.train_mode] = train
 

neuralmonkey/decoders/sequence_classifier.py

@@ -30,13 +30,21 @@ def __init__(self,
         self.rnn_size = self.encoder.rnn_size * 2
         self.max_output_len = self.encoder.max_input_len
 
-        self.train_targets = tf.placeholder(tf.int32, shape=[None, None],
-                                            name="labeler_targets")
+    # pylint: disable=no-self-use
+    @tensor
+    def train_targets(self) -> tf.Tensor:
+        return tf.placeholder(tf.int32, shape=[None, None],
+                              name="labeler_targets")
 
-        self.train_weights = tf.placeholder(tf.float32, shape=[None, None],
-                                            name="labeler_padding_weights")
+    @tensor
+    def train_weights(self) -> tf.Tensor:
+        return tf.placeholder(tf.float32, shape=[None, None],
+                              name="labeler_padding_weights")
 
-        self.train_mode = tf.placeholder(tf.bool, name="train_mode")
+    @tensor
+    def train_mode(self) -> tf.Tensor:
+        return tf.placeholder(tf.bool, name="train_mode")
+    # pylint: enable=no-self-use
 
     @property
     def train_loss(self) -> tf.Tensor: