[ROCm] Skipping subtests that check support for Pooling Ops with 3D tensors

tensorflow/python/eager/backprop_test.py

@@ -1334,6 +1334,8 @@ def grad(dy):
   @test_util.run_in_graph_and_eager_modes
   def testMaxPooling3DGradient(self):
 
+    if test.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     def forward(a):
       r = max_pooling3d(a, pool_size=pool_size, strides=strides, padding='SAME')
       return r

tensorflow/python/keras/backend_test.py

@@ -789,6 +789,8 @@ def test_pool2d(self):
       y = keras.backend.pool2d(x, (2, 2), strides=(2, 2), pool_mode='other')
 
   def test_pool3d(self):
+    if test.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     val = np.random.random((10, 3, 10, 10, 10))
     x = keras.backend.variable(val)
     y = keras.backend.pool3d(x, (2, 2, 2), strides=(1, 1, 1),

tensorflow/python/keras/layers/pooling_test.py

@@ -144,6 +144,8 @@ class Pooling3DTest(test.TestCase):
 
   @tf_test_util.run_in_graph_and_eager_modes
   def test_maxpooling_3d(self):
+    if test.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     pool_size = (3, 3, 3)
     testing_utils.layer_test(
         keras.layers.MaxPooling3D,
@@ -163,6 +165,8 @@ def test_maxpooling_3d(self):
 
   @tf_test_util.run_in_graph_and_eager_modes
   def test_averagepooling_3d(self):
+    if test.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     pool_size = (3, 3, 3)
     testing_utils.layer_test(
         keras.layers.AveragePooling3D,

tensorflow/python/kernel_tests/pool_test.py

@@ -219,6 +219,8 @@ def testPool2D(self):
                     strides=strides)
 
   def testPool3D(self):
+    if test.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     with self.session(use_gpu=test.is_gpu_available()):
       for padding in ["SAME", "VALID"]:
         for pooling_type in ["MAX", "AVG"]:
@@ -274,6 +276,9 @@ def testPoolNC(self):
               strides=[1, 2],
               dilation_rate=[1, 1],
               data_format="NCHW")
+          if test.is_built_with_rocm():
+            # Pooling with 3D tensors is not supported in ROCm
+            continue
           self._test(
               input_shape=[2, 2, 7, 5, 3],
               window_shape=[2, 2, 2],
@@ -358,6 +363,8 @@ def testGradient2D(self):
 
   @test_util.run_deprecated_v1
   def testGradient3D(self):
+    if test.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     with self.session(use_gpu=test.is_gpu_available()):
       for padding in ["SAME", "VALID"]:
         for pooling_type in ["AVG", "MAX"]:

tensorflow/python/ops/nn_test.py

@@ -1291,6 +1291,8 @@ def test2DNumpy(self):
     self.assertAllEqual(self.evaluate(y1), self.evaluate(y2))
 
   def test3DTensor(self):
+    if test_lib.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     x = array_ops.ones([3, 7, 6, 6, 5])
     ksize = 2
     strides = 2
@@ -1301,6 +1303,8 @@ def test3DTensor(self):
     self.assertAllEqual(self.evaluate(y1), self.evaluate(y2))
 
   def test3DNumpy(self):
+    if test_lib.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     x = np.ones([3, 7, 6, 6, 5], dtype=np.float32)
     ksize = 2
     strides = 2
@@ -1355,6 +1359,8 @@ def test2DNumpy(self):
     self.assertAllEqual(self.evaluate(y1), self.evaluate(y2))
 
   def test3DTensor(self):
+    if test_lib.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     x = array_ops.ones([3, 7, 6, 6, 5])
     ksize = 2
     strides = 2
@@ -1365,6 +1371,8 @@ def test3DTensor(self):
     self.assertAllEqual(self.evaluate(y1), self.evaluate(y2))
 
   def test3DNumpy(self):
+    if test_lib.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     x = np.ones([3, 7, 6, 6, 5], dtype=np.float32)
     ksize = 2
     strides = 2

tensorflow/python/ops/parallel_for/control_flow_ops_test.py

@@ -354,6 +354,8 @@ def loop_fn(i):
     self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=[dtypes.float32] * 3)
 
   def test_max_pool3d(self):
+    if test.is_built_with_rocm():
+      self.skipTest("Pooling with 3D tensors is not supported in ROCm")
     with backprop.GradientTape(persistent=True) as g:
       x = random_ops.random_uniform([3, 3, 2, 12, 12, 3])
       g.watch(x)