Merge pull request #38 from zoyahav/master

Project import generated by Copybara.

tensorflow_transform/analyzers.py

@@ -49,28 +49,36 @@ class Analyzer(object):
 
   Args:
     inputs: The inputs to the analyzer.
-    output_tensors_and_is_asset: List of pairs of `(Tensor, bool)` for each
-      output.  The `Tensor`s are typically placeholders; they will be later
-      be replaced with analysis results.  The boolean value states whether this
-      Tensor represents an asset filename or not.
+    output_dtype_shape_and_is_asset: List of tuples of `(DType, Shape, bool)`
+      for each output.  A tf.placeholder with the given DType and Shape will be
+      constructed to represent the output of the analyzer, and this placeholder
+      will eventually be replaced by the actual value of the analyzer.  The
+      boolean value states whether this Tensor represents an asset filename or
+      not.
     spec: A description of the computation to be done.
+    name: Similar to a TF op name.  Used to define a unique scope for this
+      analyzer, which can be used for debugging info.
 
   Raises:
     ValueError: If the inputs are not all `Tensor`s.
   """
 
-  def __init__(self, inputs, output_tensors_and_is_asset, spec):
+  def __init__(self, inputs, output_dtype_shape_and_is_asset, spec, name):
     for tensor in inputs:
       if not isinstance(tensor, tf.Tensor):
         raise ValueError('Analyzers can only accept `Tensor`s as inputs')
     self._inputs = inputs
-    for output_tensor, is_asset in output_tensors_and_is_asset:
-      if is_asset and output_tensor.dtype != tf.string:
-        raise ValueError(('Tensor {} cannot represent an asset, because it is '
-                          'not a string.').format(output_tensor.name))
-    self._outputs = [output_tensor
-                     for output_tensor, _ in output_tensors_and_is_asset]
-    self._output_is_asset_map = dict(output_tensors_and_is_asset)
+    self._outputs = []
+    self._output_is_asset_map = {}
+    with tf.name_scope(name) as scope:
+      self._name = scope
+      for dtype, shape, is_asset in output_dtype_shape_and_is_asset:
+        output_tensor = tf.placeholder(dtype, shape)
+        if is_asset and output_tensor.dtype != tf.string:
+          raise ValueError(('Tensor {} cannot represent an asset, because it '
+                            'is not a string.').format(output_tensor.name))
+        self._outputs.append(output_tensor)
+        self._output_is_asset_map[output_tensor] = is_asset
     self._spec = spec
     tf.add_to_collection(ANALYZER_COLLECTION, self)
 
@@ -86,6 +94,10 @@ def outputs(self):
   def spec(self):
     return self._spec
 
+  @property
+  def name(self):
+    return self._name
+
   def output_is_asset(self, output_tensor):
     return self._output_is_asset_map[output_tensor]
 
@@ -131,11 +143,9 @@ def _numeric_combine(x, combiner_type, reduce_instance_dims=True):
     # If reducing over batch dimensions, with unknown shape, the result will
     # also have unknown shape.
     shape = None
-  with tf.name_scope(combiner_type):
-    spec = NumericCombineSpec(x.dtype, combiner_type, reduce_instance_dims)
-    return Analyzer([x],
-                    [(tf.placeholder(x.dtype, shape), False)],
-                    spec).outputs[0]
+  spec = NumericCombineSpec(x.dtype, combiner_type, reduce_instance_dims)
+  return Analyzer(
+      [x], [(x.dtype, shape, False)], spec, combiner_type).outputs[0]
 
 
 def min(x, reduce_instance_dims=True):  # pylint: disable=redefined-builtin
@@ -381,9 +391,7 @@ def uniques(x, top_k=None, frequency_threshold=None,
 
     spec = UniquesSpec(tf.string, top_k, frequency_threshold,
                        vocab_filename, store_frequency)
-    return Analyzer([x],
-                    [(tf.placeholder(tf.string, []), True)],
-                    spec).outputs[0]
+    return Analyzer([x], [(tf.string, [], True)], spec, 'uniques').outputs[0]
 
 
 class QuantilesSpec(object):

tensorflow_transform/beam/impl.py

@@ -513,9 +513,8 @@ def __init__(self, analyzers, base_temp_dir):
   def expand(self, analyzer_input_values):
     # For each analyzer output, look up its input values (by tensor name)
     # and run the analyzer on these values.
-    #
     result = {}
-    for idx, analyzer in enumerate(self._analyzers):
+    for analyzer in self._analyzers:
       temp_assets_dir = _make_unique_temp_dir(self._base_temp_dir)
       tf.gfile.MkDir(temp_assets_dir)
       analyzer_impl = analyzer_impls._impl_for_analyzer(
@@ -525,10 +524,10 @@ def expand(self, analyzer_input_values):
       assert len(analyzer.inputs) == 1
       output_pcolls = (
           analyzer_input_values
-          | 'Extract_%d' % idx >> beam.Map(
+          | 'ExtractInput[%s]' % analyzer.name >> beam.Map(
               lambda batch, key: batch[key],
               key=analyzer.inputs[0].name)
-          | 'Analyze_%d' % idx >> analyzer_impl)
+          | 'Analyze[%s]' % analyzer.name >> analyzer_impl)
       assert len(analyzer.outputs) == len(output_pcolls), (
           'Analyzer outputs don\'t match the expected outputs from the '
           'Analyzer definition: %d != %d' %
@@ -537,7 +536,7 @@ def expand(self, analyzer_input_values):
       for collection_idx, (tensor, pcoll) in enumerate(
           zip(analyzer.outputs, output_pcolls)):
         is_asset = analyzer.output_is_asset(tensor)
-        pcoll |= ('WrapAsTensorValue_%d_%d' % (idx, collection_idx)
+        pcoll |= ('WrapAsTensorValue[%s][%d]' % (analyzer.name, collection_idx)
                   >> beam.Map(_TensorValue, is_asset))
         result[tensor.name] = pcoll
     return result
@@ -711,15 +710,15 @@ def expand(self, dataset):
           graph, inputs, analyzer_inputs, unbound_saved_model_dir)
       saved_model_dir = (
           tensor_pcoll_mapping
-          | 'CreateSavedModelForAnaylzerInputs_%d' % level
+          | 'CreateSavedModelForAnaylzerInputs[%d]' % level
           >> _ReplaceTensorsWithConstants(
               unbound_saved_model_dir, base_temp_dir, input_values.pipeline))
 
       # Run this saved model on the input dataset to obtain the inputs to the
       # analyzers.
       analyzer_input_values = (
           input_values
-          | 'ComputeAnalyzerInputs_%d' % level >> beam.ParDo(
+          | 'ComputeAnalyzerInputs[%d]' % level >> beam.ParDo(
               _RunMetaGraphDoFn(
                   input_schema,
                   analyzer_inputs_schema,

tensorflow_transform/coders/example_proto_coder.py

@@ -122,19 +122,21 @@ class _FixedLenFeatureHandler(object):
   def __init__(self, name, feature_spec):
     self._name = name
     self._np_dtype = feature_spec.dtype.as_numpy_dtype
+    self._default_value = feature_spec.default_value
     self._value_fn = _make_feature_value_fn(feature_spec.dtype)
     self._shape = feature_spec.shape
     self._rank = len(feature_spec.shape)
+    if self._rank > 0 and self._default_value:
+      raise ValueError('FixedLenFeature %r got default value for rank > 0, '
+                       'only scalar default values are supported'
+                       % (self._name,))
+    if isinstance(self._default_value, list):
+      raise ValueError('FixedLenFeature %r got non-scalar default value, '
+                       'only scalar default values are supported' %
+                       (self._name,))
     self._size = 1
     for dim in feature_spec.shape:
       self._size *= dim
-    self._default_value = feature_spec.default_value
-    if self._default_value:
-      if list(np.asarray(self._default_value).shape) != self._shape:
-        raise ValueError(
-            'FixedLenFeature %r got default value with incorrect shape' %
-            (self._name,))
-      self._default_value = np.asarray(self._default_value).reshape(-1).tolist()
 
   @property
   def name(self):
@@ -152,7 +154,7 @@ def parse_value(self, feature_map):
       feature = feature_map[self._name]
       values = self._value_fn(feature)
     elif self._default_value is not None:
-      values = self._default_value
+      values = [self._default_value]
     else:
       values = []
 

tensorflow_transform/coders/example_proto_coder_test.py

@@ -168,16 +168,8 @@ def test_example_proto_coder(self):
 
   def test_example_proto_coder_default_value(self):
     input_schema = dataset_schema.from_feature_spec({
-        'scalar_feature_3':
-            tf.FixedLenFeature(shape=[], dtype=tf.float32, default_value=1.0),
-        '1d_vector_feature':
-            tf.FixedLenFeature(
-                shape=[1], dtype=tf.float32, default_value=[2.0]),
-        '2d_vector_feature':
-            tf.FixedLenFeature(
-                shape=[2, 2],
-                dtype=tf.float32,
-                default_value=[[1.0, 2.0], [3.0, 4.0]])
+        'scalar_feature_3': tf.FixedLenFeature(shape=[], dtype=tf.float32,
+                                               default_value=1.0),
     })
     coder = example_proto_coder.ExampleProtoCoder(input_schema)
 
@@ -193,31 +185,25 @@ def test_example_proto_coder_default_value(self):
     # Assert the data is decoded into the expected format.
     expected_decoded = {
         'scalar_feature_3': 1.0,
-        '1d_vector_feature': [2.0],
-        '2d_vector_feature': [[1.0, 2.0], [3.0, 4.0]]
     }
     decoded = coder.decode(data)
     np.testing.assert_equal(expected_decoded, decoded)
 
   def test_example_proto_coder_bad_default_value(self):
     input_schema = dataset_schema.from_feature_spec({
-        '1d_vector_feature':
-            tf.FixedLenFeature(
-                shape=[2], dtype=tf.float32, default_value=[1.0]),
+        'scalar_feature_2': tf.FixedLenFeature(shape=[2], dtype=tf.float32,
+                                               default_value=[1.0, 2.0]),
     })
     with self.assertRaisesRegexp(ValueError,
-                                 'got default value with incorrect shape'):
+                                 'only scalar default values are supported'):
       example_proto_coder.ExampleProtoCoder(input_schema)
 
     input_schema = dataset_schema.from_feature_spec({
-        '2d_vector_feature':
-            tf.FixedLenFeature(
-                shape=[2, 3],
-                dtype=tf.float32,
-                default_value=[[1.0, 1.0], [1.0]]),
+        'scalar_feature_2': tf.FixedLenFeature(shape=[], dtype=tf.float32,
+                                               default_value=[1.0]),
     })
     with self.assertRaisesRegexp(ValueError,
-                                 'got default value with incorrect shape'):
+                                 'only scalar default values are supported'):
       example_proto_coder.ExampleProtoCoder(input_schema)
 
   def test_example_proto_coder_picklable(self):