Require that strides to pooling ops be non-zero and that its length match the tensor rank.

PiperOrigin-RevId: 332386479
Change-Id: I0ab452b311279927bdf8fb7caeace006abe8af69

tensorflow/core/kernels/pooling_ops_common.cc

@@ -128,18 +128,6 @@ PoolParameters::PoolParameters(OpKernelContext* context,
               errors::InvalidArgument(
                   "tensor_in_shape must have 2 spatial dimensions. ",
                   tensor_in_shape.dims(), " ", data_format));
-  OP_REQUIRES(context, stride.size() == tensor_in_shape.dims(),
-              errors::InvalidArgument("Number of strides (", stride.size(),
-                                      ") != tensor rank (",
-                                      tensor_in_shape.dims(), ")"));
-  if (tensor_in_shape.num_elements() > 0) {
-    for (int i = 0; i < stride.size(); i++) {
-      OP_REQUIRES(
-          context, stride[i] != 0,
-          errors::InvalidArgument(
-              "Pooling stride must be non-zero, got stride[", i, "] == 0"));
-    }
-  }
 
   this->data_format = data_format;
   depth = GetTensorDim(tensor_in_shape, data_format, 'C') *

tensorflow/python/kernel_tests/pooling_ops_3d_test.py

@@ -21,7 +21,6 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import errors_impl
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import gradients_impl
@@ -153,14 +152,6 @@ class PoolingTest(test.TestCase):
         padding="SAME",
         expected=expected_output)
 
-  def testInvalidStrides(self):
-    for op in (nn_ops.avg_pool3d, nn_ops.max_pool3d):
-      with self.cached_session(use_gpu=True):
-        t = constant_op.constant([[[[[1e+40]]]]], dtype=np.float32)
-        with self.assertRaises((ValueError, errors_impl.InvalidArgumentError,
-                                errors_impl.UnimplementedError)):
-          self.evaluate(op(t, ksize=1, strides=0, padding="SAME"))
-
     # Test pooling on a larger input, with different stride and kernel
     # size for the 'z' dimension.
 

tensorflow/python/kernel_tests/pooling_ops_test.py

@@ -70,7 +70,7 @@ def GetTestConfigs(include_nchw_vect_c=False, one_dimensional=False):
     tf_logging.info("NCHW and NCHW_VECT_C tests skipped because not run with "
                     "--config=cuda or no GPUs available.")
     return test_configs
-# "NCHW" format is currently supported exclusively on CUDA GPUs.
+  # "NCHW" format is currently supported exclusively on CUDA GPUs.
   test_configs += [("NCHW", True)]
   if include_nchw_vect_c:
     if test.is_gpu_available(
@@ -285,6 +285,17 @@ class PoolingTest(test.TestCase):
         expected=expected_output,
         use_gpu=use_gpu)
 
+  def _testAvgPoolEmpty(self, use_gpu):
+    expected_output = [7.0, 8.0, 9.0]
+    self._VerifyValues(
+        nn_ops.avg_pool,
+        input_sizes=[1, 3, 3, 0],
+        ksize=[1, 2, 2, 1],
+        strides=[1, 2, 2, 1],
+        padding="VALID",
+        expected=expected_output,
+        use_gpu=use_gpu)
+
   def _testAvgPoolSamePadding(self, use_gpu):
     expected_output = [8.5, 9.5, 10.5, 14.5, 15.5, 16.5]
     self._VerifyValues(
@@ -431,18 +442,6 @@ class PoolingTest(test.TestCase):
         expected=[],
         use_gpu=use_gpu)
 
-  def testInvalidStrides(self):
-    strides = [1, 1, 1, 1]
-    for op in (nn_ops.avg_pool, nn_ops.max_pool):
-      for zero_dim in range(4):
-        bad_strides = strides
-        bad_strides[zero_dim] = 0
-        with self.cached_session(use_gpu=True):
-          t = constant_op.constant(1.0, shape=[3, 3, 3, 3])
-          with self.assertRaises((ValueError, errors_impl.UnimplementedError)):
-            self.evaluate(
-                op(t, ksize=[2, 2, 2, 2], strides=bad_strides, padding="SAME"))
-
   @test_util.run_deprecated_v1
   def testAvgPooling(self):
     for use_gpu in True, False: