Rename global_step -> step in contrib/summary API

Since it's more succinct and the API doesn't actually care if the provided step
is the one true global step.

PiperOrigin-RevId: 176063779

4 files changed, 73 insertions, 67 deletions

diff --git a/tensorflow/contrib/summary/summary_ops.py b/tensorflow/contrib/summary/summary_ops.py
index bf810744a1..3e65f83051 100644
--- a/tensorflow/contrib/summary/summary_ops.py
+++ b/tensorflow/contrib/summary/summary_ops.py
@@ -344,10 +344,9 @@ def summary_writer_function(name, tensor, function, family=None):
   return op
 
 
-def generic(name, tensor, metadata=None, family=None, global_step=None):
+def generic(name, tensor, metadata=None, family=None, step=None):
   """Writes a tensor summary if possible."""
-  if global_step is None:
-    global_step = training_util.get_global_step()
+
   def function(tag, scope):
     if metadata is None:
       serialized_metadata = constant_op.constant("")
@@ -358,12 +357,15 @@ def generic(name, tensor, metadata=None, family=None, global_step=None):
     # Note the identity to move the tensor to the CPU.
     return gen_summary_ops.write_summary(
         context.context().summary_writer_resource,
-        global_step, array_ops.identity(tensor),
-        tag, serialized_metadata, name=scope)
+        _choose_step(step),
+        array_ops.identity(tensor),
+        tag,
+        serialized_metadata,
+        name=scope)
   return summary_writer_function(name, tensor, function, family=family)
 
 
-def scalar(name, tensor, family=None, global_step=None):
+def scalar(name, tensor, family=None, step=None):
   """Writes a scalar summary if possible.
 
   Unlike @{tf.contrib.summary.generic} this op may change the dtype
@@ -375,68 +377,68 @@ def scalar(name, tensor, family=None, global_step=None):
       `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`,
       `int8`, `uint16`, `half`, `uint32`, `uint64`.
     family: Optional, the summary's family.
-    global_step: The `int64` monotonic step variable, which defaults
+    step: The `int64` monotonic step variable, which defaults
       to @{tf.train.get_global_step}.
 
   Returns:
     The created @{tf.Operation} or a @{tf.no_op} if summary writing has
     not been enabled for this context.
   """
-  if global_step is None:
-    global_step = training_util.get_global_step()
-  else:
-    global_step = ops.convert_to_tensor(global_step, dtypes.int64)
+
   def function(tag, scope):
     # Note the identity to move the tensor to the CPU.
     return gen_summary_ops.write_scalar_summary(
         context.context().summary_writer_resource,
-        global_step, tag, array_ops.identity(tensor),
+        _choose_step(step),
+        tag,
+        array_ops.identity(tensor),
         name=scope)
+
   return summary_writer_function(name, tensor, function, family=family)
 
 
-def histogram(name, tensor, family=None, global_step=None):
+def histogram(name, tensor, family=None, step=None):
   """Writes a histogram summary if possible."""
-  if global_step is None:
-    global_step = training_util.get_global_step()
+
   def function(tag, scope):
     # Note the identity to move the tensor to the CPU.
     return gen_summary_ops.write_histogram_summary(
         context.context().summary_writer_resource,
-        global_step, tag, array_ops.identity(tensor),
+        _choose_step(step),
+        tag,
+        array_ops.identity(tensor),
         name=scope)
 
   return summary_writer_function(name, tensor, function, family=family)
 
 
-def image(name, tensor, bad_color=None, max_images=3, family=None,
-          global_step=None):
+def image(name, tensor, bad_color=None, max_images=3, family=None, step=None):
   """Writes an image summary if possible."""
-  if global_step is None:
-    global_step = training_util.get_global_step()
+
   def function(tag, scope):
     bad_color_ = (constant_op.constant([255, 0, 0, 255], dtype=dtypes.uint8)
                   if bad_color is None else bad_color)
     # Note the identity to move the tensor to the CPU.
     return gen_summary_ops.write_image_summary(
         context.context().summary_writer_resource,
-        global_step, tag, array_ops.identity(tensor),
+        _choose_step(step),
+        tag,
+        array_ops.identity(tensor),
         bad_color_,
-        max_images, name=scope)
+        max_images,
+        name=scope)
 
   return summary_writer_function(name, tensor, function, family=family)
 
 
-def audio(name, tensor, sample_rate, max_outputs, family=None,
-          global_step=None):
+def audio(name, tensor, sample_rate, max_outputs, family=None, step=None):
   """Writes an audio summary if possible."""
-  if global_step is None:
-    global_step = training_util.get_global_step()
+
   def function(tag, scope):
     # Note the identity to move the tensor to the CPU.
     return gen_summary_ops.write_audio_summary(
         context.context().summary_writer_resource,
-        global_step,
+        _choose_step(step),
         tag,
         array_ops.identity(tensor),
         sample_rate=sample_rate,
@@ -483,15 +485,13 @@ def graph(param, step=None, name=None):
   if writer is None:
     return control_flow_ops.no_op()
   with ops.device("cpu:0"):
-    if step is None:
-      step = training_util.get_global_step()
-    else:
-      step = ops.convert_to_tensor(step, dtypes.int64)
     if isinstance(param, (ops.Graph, graph_pb2.GraphDef)):
       tensor = ops.convert_to_tensor(_serialize_graph(param), dtypes.string)
     else:
       tensor = array_ops.identity(param)
-    return gen_summary_ops.write_graph_summary(writer, step, tensor, name=name)
+    return gen_summary_ops.write_graph_summary(
+        writer, _choose_step(step), tensor, name=name)
+
 
 _graph = graph  # for functions with a graph parameter
 
@@ -527,3 +527,11 @@ def _serialize_graph(arbitrary_graph):
     return arbitrary_graph.as_graph_def(add_shapes=True).SerializeToString()
   else:
     return arbitrary_graph.SerializeToString()
+
+
+def _choose_step(step):
+  if step is None:
+    return training_util.get_global_step()
+  if not isinstance(step, ops.Tensor):
+    return ops.convert_to_tensor(step, dtypes.int64)
+  return step
diff --git a/tensorflow/contrib/summary/summary_ops_test.py b/tensorflow/contrib/summary/summary_ops_test.py
index c5ca054f77..ad89c0c36a 100644
--- a/tensorflow/contrib/summary/summary_ops_test.py
+++ b/tensorflow/contrib/summary/summary_ops_test.py
@@ -97,13 +97,13 @@ class TargetTest(test_util.TensorFlowTestCase):
       self.assertEqual(events[1].summary.value[0].tag, 'scalar')
 
   def testSummaryGlobalStep(self):
-    global_step = training_util.get_or_create_global_step()
+    step = training_util.get_or_create_global_step()
     logdir = tempfile.mkdtemp()
     with summary_ops.create_summary_file_writer(
         logdir, max_queue=0,
         name='t2').as_default(), summary_ops.always_record_summaries():
 
-      summary_ops.scalar('scalar', 2.0, global_step=global_step)
+      summary_ops.scalar('scalar', 2.0, step=step)
 
       events = summary_test_util.events_from_logdir(logdir)
       self.assertEqual(len(events), 2)
diff --git a/tensorflow/core/kernels/summary_kernels.cc b/tensorflow/core/kernels/summary_kernels.cc
index 3706f51cf4..7487e70acc 100644
--- a/tensorflow/core/kernels/summary_kernels.cc
+++ b/tensorflow/core/kernels/summary_kernels.cc
@@ -111,8 +111,8 @@ class WriteSummaryOp : public OpKernel {
     OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &s));
     core::ScopedUnref unref(s);
     const Tensor* tmp;
-    OP_REQUIRES_OK(ctx, ctx->input("global_step", &tmp));
-    const int64 global_step = tmp->scalar<int64>()();
+    OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
+    const int64 step = tmp->scalar<int64>()();
     OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
     const string& tag = tmp->scalar<string>()();
     OP_REQUIRES_OK(ctx, ctx->input("summary_metadata", &tmp));
@@ -121,8 +121,7 @@ class WriteSummaryOp : public OpKernel {
     const Tensor* t;
     OP_REQUIRES_OK(ctx, ctx->input("tensor", &t));
 
-    OP_REQUIRES_OK(ctx,
-                   s->WriteTensor(global_step, *t, tag, serialized_metadata));
+    OP_REQUIRES_OK(ctx, s->WriteTensor(step, *t, tag, serialized_metadata));
   }
 };
 REGISTER_KERNEL_BUILDER(Name("WriteSummary").Device(DEVICE_CPU),
@@ -158,15 +157,15 @@ class WriteScalarSummaryOp : public OpKernel {
     OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &s));
     core::ScopedUnref unref(s);
     const Tensor* tmp;
-    OP_REQUIRES_OK(ctx, ctx->input("global_step", &tmp));
-    const int64 global_step = tmp->scalar<int64>()();
+    OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
+    const int64 step = tmp->scalar<int64>()();
     OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
     const string& tag = tmp->scalar<string>()();
 
     const Tensor* t;
     OP_REQUIRES_OK(ctx, ctx->input("value", &t));
 
-    OP_REQUIRES_OK(ctx, s->WriteScalar(global_step, *t, tag));
+    OP_REQUIRES_OK(ctx, s->WriteScalar(step, *t, tag));
   }
 };
 REGISTER_KERNEL_BUILDER(Name("WriteScalarSummary").Device(DEVICE_CPU),
@@ -181,15 +180,15 @@ class WriteHistogramSummaryOp : public OpKernel {
     OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &s));
     core::ScopedUnref unref(s);
     const Tensor* tmp;
-    OP_REQUIRES_OK(ctx, ctx->input("global_step", &tmp));
-    const int64 global_step = tmp->scalar<int64>()();
+    OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
+    const int64 step = tmp->scalar<int64>()();
     OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
     const string& tag = tmp->scalar<string>()();
 
     const Tensor* t;
     OP_REQUIRES_OK(ctx, ctx->input("values", &t));
 
-    OP_REQUIRES_OK(ctx, s->WriteHistogram(global_step, *t, tag));
+    OP_REQUIRES_OK(ctx, s->WriteHistogram(step, *t, tag));
   }
 };
 REGISTER_KERNEL_BUILDER(Name("WriteHistogramSummary").Device(DEVICE_CPU),
@@ -210,8 +209,8 @@ class WriteImageSummaryOp : public OpKernel {
     OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &s));
     core::ScopedUnref unref(s);
     const Tensor* tmp;
-    OP_REQUIRES_OK(ctx, ctx->input("global_step", &tmp));
-    const int64 global_step = tmp->scalar<int64>()();
+    OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
+    const int64 step = tmp->scalar<int64>()();
     OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
     const string& tag = tmp->scalar<string>()();
     const Tensor* bad_color;
@@ -224,8 +223,7 @@ class WriteImageSummaryOp : public OpKernel {
     const Tensor* t;
     OP_REQUIRES_OK(ctx, ctx->input("tensor", &t));
 
-    OP_REQUIRES_OK(
-        ctx, s->WriteImage(global_step, *t, tag, max_images_, *bad_color));
+    OP_REQUIRES_OK(ctx, s->WriteImage(step, *t, tag, max_images_, *bad_color));
   }
 
  private:
@@ -247,8 +245,8 @@ class WriteAudioSummaryOp : public OpKernel {
     OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &s));
     core::ScopedUnref unref(s);
     const Tensor* tmp;
-    OP_REQUIRES_OK(ctx, ctx->input("global_step", &tmp));
-    const int64 global_step = tmp->scalar<int64>()();
+    OP_REQUIRES_OK(ctx, ctx->input("step", &tmp));
+    const int64 step = tmp->scalar<int64>()();
     OP_REQUIRES_OK(ctx, ctx->input("tag", &tmp));
     const string& tag = tmp->scalar<string>()();
     OP_REQUIRES_OK(ctx, ctx->input("sample_rate", &tmp));
@@ -257,8 +255,8 @@ class WriteAudioSummaryOp : public OpKernel {
     const Tensor* t;
     OP_REQUIRES_OK(ctx, ctx->input("tensor", &t));
 
-    OP_REQUIRES_OK(
-        ctx, s->WriteAudio(global_step, *t, tag, max_outputs_, sample_rate));
+    OP_REQUIRES_OK(ctx,
+                   s->WriteAudio(step, *t, tag, max_outputs_, sample_rate));
   }
 
  private:
@@ -278,8 +276,8 @@ class WriteGraphSummaryOp : public OpKernel {
     OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &s));
     core::ScopedUnref unref(s);
     const Tensor* t;
-    OP_REQUIRES_OK(ctx, ctx->input("global_step", &t));
-    const int64 global_step = t->scalar<int64>()();
+    OP_REQUIRES_OK(ctx, ctx->input("step", &t));
+    const int64 step = t->scalar<int64>()();
     OP_REQUIRES_OK(ctx, ctx->input("tensor", &t));
     std::unique_ptr<GraphDef> graph{new GraphDef};
     if (!ParseProtoUnlimited(graph.get(), t->scalar<string>()())) {
@@ -287,7 +285,7 @@ class WriteGraphSummaryOp : public OpKernel {
           errors::DataLoss("Bad tf.GraphDef binary proto tensor string"));
       return;
     }
-    OP_REQUIRES_OK(ctx, s->WriteGraph(global_step, std::move(graph)));
+    OP_REQUIRES_OK(ctx, s->WriteGraph(step, std::move(graph)));
   }
 };
 REGISTER_KERNEL_BUILDER(Name("WriteGraphSummary").Device(DEVICE_CPU),
diff --git a/tensorflow/core/ops/summary_ops.cc b/tensorflow/core/ops/summary_ops.cc
index 7f6d8b06cd..029ff09906 100644
--- a/tensorflow/core/ops/summary_ops.cc
+++ b/tensorflow/core/ops/summary_ops.cc
@@ -99,7 +99,7 @@ writer: A handle to the summary writer resource.
 
 REGISTER_OP("WriteSummary")
     .Input("writer: resource")
-    .Input("global_step: int64")
+    .Input("step: int64")
     .Input("tensor: T")
     .Input("tag: string")
     .Input("summary_metadata: string")
@@ -109,7 +109,7 @@ REGISTER_OP("WriteSummary")
 Outputs a `Summary` protocol buffer with a tensor.
 
 writer: A handle to a summary writer.
-global_step: The step to write the summary for.
+step: The step to write the summary for.
 tensor: A tensor to serialize.
 tag: The summary's tag.
 summary_metadata: Serialized SummaryMetadata protocol buffer containing
@@ -132,7 +132,7 @@ event: A string containing a binary-encoded tf.Event proto.
 
 REGISTER_OP("WriteScalarSummary")
     .Input("writer: resource")
-    .Input("global_step: int64")
+    .Input("step: int64")
     .Input("tag: string")
     .Input("value: T")
     .Attr("T: realnumbertype")
@@ -143,14 +143,14 @@ Writes a `Summary` protocol buffer with scalar values.
 The input `tag` and `value` must have the scalars.
 
 writer: A handle to a summary writer.
-global_step: The step to write the summary for.
+step: The step to write the summary for.
 tag: Tag for the summary.
 value: Value for the summary.
 )doc");
 
 REGISTER_OP("WriteHistogramSummary")
     .Input("writer: resource")
-    .Input("global_step: int64")
+    .Input("step: int64")
     .Input("tag: string")
     .Input("values: T")
     .Attr("T: realnumbertype = DT_FLOAT")
@@ -165,14 +165,14 @@ has one summary value containing a histogram for `values`.
 This op reports an `InvalidArgument` error if any value is not finite.
 
 writer: A handle to a summary writer.
-global_step: The step to write the summary for.
+step: The step to write the summary for.
 tag: Scalar.  Tag to use for the `Summary.Value`.
 values: Any shape. Values to use to build the histogram.
 )doc");
 
 REGISTER_OP("WriteImageSummary")
     .Input("writer: resource")
-    .Input("global_step: int64")
+    .Input("step: int64")
     .Input("tag: string")
     .Input("tensor: T")
     .Input("bad_color: uint8")
@@ -217,7 +217,7 @@ replaced by this tensor in the output image.  The default value is the color
 red.
 
 writer: A handle to a summary writer.
-global_step: The step to write the summary for.
+step: The step to write the summary for.
 tag: Scalar. Used to build the `tag` attribute of the summary values.
 tensor: 4-D of shape `[batch_size, height, width, channels]` where
   `channels` is 1, 3, or 4.
@@ -227,7 +227,7 @@ bad_color: Color to use for pixels with non-finite values.
 
 REGISTER_OP("WriteAudioSummary")
     .Input("writer: resource")
-    .Input("global_step: int64")
+    .Input("step: int64")
     .Input("tag: string")
     .Input("tensor: float")
     .Input("sample_rate: float")
@@ -249,7 +249,7 @@ build the `tag` of the summary values:
    generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
 
 writer: A handle to a summary writer.
-global_step: The step to write the summary for.
+step: The step to write the summary for.
 tag: Scalar. Used to build the `tag` attribute of the summary values.
 tensor: 2-D of shape `[batch_size, frames]`.
 sample_rate: The sample rate of the signal in hertz.
@@ -258,14 +258,14 @@ max_outputs: Max number of batch elements to generate audio for.
 
 REGISTER_OP("WriteGraphSummary")
     .Input("writer: resource")
-    .Input("global_step: int64")
+    .Input("step: int64")
     .Input("tensor: string")
     .SetShapeFn(shape_inference::NoOutputs)
     .Doc(R"doc(
 Writes a `GraphDef` protocol buffer to a `SummaryWriter`.
 
 writer: Handle of `SummaryWriter`.
-global_step: The step to write the summary for.
+step: The step to write the summary for.
 tensor: A scalar string of the serialized tf.GraphDef proto.
 )doc");