Merge pull request #179 from TomNong/connector-update

Fix *StochasticConnector when `transform=False`

CHANGELOG.md

@@ -21,6 +21,7 @@
 * Fix [GPT-2](https://github.com/asyml/texar/tree/master/examples/gpt-2) tokenization loading path. ([#165](https://github.com/asyml/texar/pull/165))
 * Fix [examples/vae_text](https://github.com/asyml/texar/tree/master/examples/vae_text) EOS bug. ([#168](https://github.com/asyml/texar/pull/168)) 
 * Fix transformer [bleu_tool.py](https://github.com/asyml/texar/blob/master/examples/transformer/bleu_tool.py) when `translation_length` is 0. ([#176](https://github.com/asyml/texar/pull/176))
+* Fix `StochasticConnector` and `ReparameterizedStochasticConnector` when `transform=False`. ([#179](https://github.com/asyml/texar/pull/179))
 
 ## [v0.2.0](https://github.com/asyml/texar/releases/tag/v0.2.0) (2019-04-09)
 

texar/modules/connectors/__init__.py

@@ -23,4 +23,3 @@
 
 from texar.modules.connectors.connector_base import *
 from texar.modules.connectors.connectors import *
-

texar/modules/connectors/connectors.py

@@ -1,4 +1,4 @@
-# Copyright 2018 The Texar Authors. All Rights Reserved.
+# Copyright 2019 The Texar Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -54,7 +54,10 @@ def _assert_same_size(outputs, output_size):
     flat_output = nest.flatten(outputs)
 
     for (output, size) in zip(flat_output, flat_output_size):
-        if output[0].shape != tf.TensorShape(size):
+        if isinstance(size, tf.TensorShape):
+            if output.shape == size:
+                pass
+        elif output[0].shape != tf.TensorShape(size):
             raise ValueError(
                 "The output size does not match the the required output_size")
 
@@ -518,7 +521,8 @@ class instance.
             - output: A Tensor or a (nested) tuple of Tensors with the same \
             structure and size of :attr:`output_size`. The batch dimension \
             equals :attr:`num_samples` if specified, or is determined by the \
-            distribution dimensionality.
+            distribution dimensionality. If :attr:`transform` is `False`, \
+            :attr:`output` will be equal to :attr:`sample`.
             - sample: The sample from the distribution, prior to transformation.
 
         Raises:
@@ -549,9 +553,10 @@ class instance.
             fn_modules = ['tensorflow', 'tensorflow.nn', 'texar.custom']
             activation_fn = get_function(self.hparams.activation_fn, fn_modules)
             output = _mlp_transform(sample, self._output_size, activation_fn)
+        else:
+            output = sample
 
         _assert_same_size(output, self._output_size)
-
         if not self._built:
             self._add_internal_trainable_variables()
             self._built = True
@@ -616,7 +621,7 @@ def default_hparams():
     def _build(self,
                distribution='MultivariateNormalDiag',
                distribution_kwargs=None,
-               transform=False,
+               transform=True,
                num_samples=None):
         """Samples from a distribution and optionally performs transformation
         with an MLP layer.
@@ -649,7 +654,8 @@ class instance.
             - output: A Tensor or a (nested) tuple of Tensors with the same \
             structure and size of :attr:`output_size`. The batch dimension \
             equals :attr:`num_samples` if specified, or is determined by the \
-            distribution dimensionality.
+            distribution dimensionality. If :attr:`transform` is `False`, \
+            :attr:`output` will be equal to :attr:`sample`.
             - sample: The sample from the distribution, prior to transformation.
 
         Raises:
@@ -661,31 +667,32 @@ class instance.
              "tensorflow.contrib.distributions", "texar.custom"])
 
         if num_samples:
-            output = dstr.sample(num_samples)
+            sample = dstr.sample(num_samples)
         else:
-            output = dstr.sample()
+            sample = dstr.sample()
 
         if dstr.event_shape == []:
-            output = tf.reshape(output,
-                                output.shape.concatenate(tf.TensorShape(1)))
+            sample = tf.reshape(sample,
+                                sample.shape.concatenate(tf.TensorShape(1)))
 
         # Disable gradients through samples
-        output = tf.stop_gradient(output)
+        sample = tf.stop_gradient(sample)
 
-        output = tf.cast(output, tf.float32)
+        sample = tf.cast(sample, tf.float32)
 
         if transform:
             fn_modules = ['tensorflow', 'tensorflow.nn', 'texar.custom']
             activation_fn = get_function(self.hparams.activation_fn, fn_modules)
-            output = _mlp_transform(output, self._output_size, activation_fn)
+            output = _mlp_transform(sample, self._output_size, activation_fn)
+        else:
+            output = sample
 
         _assert_same_size(output, self._output_size)
-
         if not self._built:
             self._add_internal_trainable_variables()
             self._built = True
 
-        return output
+        return output, sample
 
 
 #class ConcatConnector(ConnectorBase):

texar/modules/connectors/connectors_test.py

@@ -15,7 +15,8 @@
 from texar.core import layers
 from texar.modules import ConstantConnector
 from texar.modules import MLPTransformConnector
-from texar.modules import ReparameterizedStochasticConnector
+from texar.modules import (ReparameterizedStochasticConnector,
+                           StochasticConnector)
 from texar.modules.connectors.connectors import _assert_same_size
 
 # pylint: disable=too-many-locals, invalid-name
@@ -132,6 +133,36 @@ def test_reparameterized_stochastic_connector(self):
                # self.assertAlmostEqual(0, sample_mu[i], delta=0.2)
                # self.assertAlmostEqual(1, sample_var[i], delta=0.2)
 
+    def test_stochastic_connector(self):
+        """Tests the logic of
+        :class:`~texar.modules.StochasticConnector`.
+        """
+        state_size = (10, 10)
+        variable_size = 100
+        state_size_ts = tf.TensorShape([self._batch_size, variable_size])
+        gauss_connector = StochasticConnector(state_size)
+        mu = tf.zeros([self._batch_size, variable_size])
+        var = tf.ones([self._batch_size, variable_size])
+        gauss_ds = tfpd.MultivariateNormalDiag(loc=mu, scale_diag=var)
+        output_1, _ = gauss_connector(gauss_ds)
+
+        gauss_connector_2 = StochasticConnector(state_size_ts)
+        output_2, sample2 = gauss_connector_2(
+            distribution="MultivariateNormalDiag",
+            distribution_kwargs={"loc": mu, "scale_diag": var}, transform=False)
+        test_list = [output_1, output_2, sample2]
+
+        with self.test_session() as sess:
+            sess.run(tf.global_variables_initializer())
+            out_list = sess.run(test_list)
+            out1 = out_list[0]
+            out2 = out_list[1]
+            sample2 = out_list[2]
+            self.assertEqual(out1[0].shape,
+                             tf.TensorShape([self._batch_size, state_size[0]]))
+            self.assertEqual(out2.shape, state_size_ts)
+            self.assertEqual(out2.shape, sample2.shape)
+
     #def test_concat_connector(self): # pylint: disable=too-many-locals
     #    """Tests the logic of
     #    :class:`~texar.modules.connectors.ConcatConnector`.