Update outdated references to functions imported by standard_ops.py t…

…o their actual locations.

PiperOrigin-RevId: 578583336
Change-Id: I91f4970eb185d31a24d69ce1b45371454ff9ca2a

tf_slim/layers/layers.py

@@ -45,7 +45,6 @@
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import sparse_ops
-from tensorflow.python.ops import standard_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.training import moving_averages
@@ -2585,8 +2584,8 @@ def one_hot_encoding(labels,
   with ops.name_scope(scope, 'OneHotEncoding', [labels, num_classes]) as sc:
     labels = ops.convert_to_tensor(labels)
     if labels.dtype == dtypes.int32:
-      labels = standard_ops.to_int64(labels)
-    outputs = standard_ops.one_hot(
+      labels = math_ops.to_int64(labels)
+    outputs = array_ops.one_hot(
         labels, num_classes, on_value=on_value, off_value=off_value)
     return utils.collect_named_outputs(outputs_collections, sc, outputs)
 
@@ -3301,7 +3300,7 @@ def legacy_fully_connected(x,
         trainable=trainable)
     x_2_dim = x if len(dims) <= 2 else array_ops.reshape(
         x, [-1, num_input_units])
-    y = standard_ops.matmul(x_2_dim, w)
+    y = math_ops.matmul(x_2_dim, w)
 
     if bias_init is not None:
       bias_collections = set(

tf_slim/layers/regularizers.py

@@ -26,7 +26,6 @@
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
-from tensorflow.python.ops import standard_ops
 
 __all__ = ['l1_regularizer',
            'l2_regularizer',
@@ -66,9 +65,9 @@ def l1(weights, name=None):
       my_scale = ops.convert_to_tensor(scale,
                                        dtype=weights.dtype.base_dtype,
                                        name='scale')
-      return standard_ops.multiply(
+      return math_ops.multiply(
           my_scale,
-          standard_ops.reduce_sum(standard_ops.abs(weights)),
+          math_ops.reduce_sum(math_ops.abs(weights)),
           name=name)
 
   return l1
@@ -105,7 +104,7 @@ def l2(weights):
       my_scale = ops.convert_to_tensor(scale,
                                        dtype=weights.dtype.base_dtype,
                                        name='scale')
-      return standard_ops.multiply(my_scale, nn.l2_loss(weights), name=name)
+      return math_ops.multiply(my_scale, nn.l2_loss(weights), name=name)
 
   return l2
 

tf_slim/layers/summaries.py

@@ -24,7 +24,7 @@
 # pylint: disable=g-direct-tensorflow-import
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.ops import standard_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.summary import summary
 
 __all__ = [
@@ -88,17 +88,17 @@ def summarize_activation(op):
   if op.op.type in ('Relu', 'Softplus', 'Relu6'):
     # Using inputs to avoid floating point equality and/or epsilons.
     _add_scalar_summary(
-        standard_ops.reduce_mean(
-            standard_ops.to_float(
-                standard_ops.less(op.op.inputs[
-                    0], standard_ops.cast(0.0, op.op.inputs[0].dtype)))),
+        math_ops.reduce_mean(
+            math_ops.to_float(
+                math_ops.less(op.op.inputs[
+                    0], math_ops.cast(0.0, op.op.inputs[0].dtype)))),
         '%s/zeros' % op.op.name)
   if op.op.type == 'Relu6':
     _add_scalar_summary(
-        standard_ops.reduce_mean(
-            standard_ops.to_float(
-                standard_ops.greater(op.op.inputs[
-                    0], standard_ops.cast(6.0, op.op.inputs[0].dtype)))),
+        math_ops.reduce_mean(
+            math_ops.to_float(
+                math_ops.greater(op.op.inputs[
+                    0], math_ops.cast(6.0, op.op.inputs[0].dtype)))),
         '%s/sixes' % op.op.name)
   return _add_histogram_summary(op, '%s/activation' % op.op.name)