Fix conv_ops_3d and pooling_ops_test scripts

Author: sunway513

tensorflow/python/kernel_tests/conv_ops_3d_test.py

@@ -61,7 +61,7 @@ def _DtypesToTest(self, use_gpu):
         # as we will be using its gradients as reference for fp16 gradients.
         return optional_float64 + [dtypes.float32, dtypes.float16]
     else:
-      return [dtypes.float32, dtypes.float16] + ([dtypes.float64]
+      return [dtypes.float32, dtypes.float16] + ([dtypes.float64] \
       if not test.is_built_with_rocm else [])
 
   def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, stride,

tensorflow/python/kernel_tests/pooling_ops_test.py

@@ -766,7 +766,7 @@ def testDepthwiseMaxPoolInvalidConfigs(self):
   # The following are tests that verify that the CPU and GPU implementations
   # produce the same results.
   def _CompareMaxPoolingFwd(self, input_shape, ksize, strides, padding):
-    for dtype in [np.float32, np.float16] + [np.float64]
+    for dtype in [np.float32, np.float16] + [np.float64] \
     if not test.is_built_with_rocm() else []:
       tensor_input = np.random.rand(*input_shape).astype(dtype)
       with self.cached_session(use_gpu=True):
@@ -781,7 +781,7 @@ def _CompareMaxPoolingFwd(self, input_shape, ksize, strides, padding):
 
   def _CompareMaxPoolingBk(self, input_shape, output_shape, ksize, strides,
                            padding):
-    for dtype in [np.float32, np.float16] + [np.float64]
+    for dtype in [np.float32, np.float16] + [np.float64] \
     if not test.is_built_with_rocm() else []:
       # Generate numbers in a narrow range, so that there are many duplicates
       # in the input.
@@ -812,7 +812,7 @@ def _CompareMaxPoolingBk(self, input_shape, output_shape, ksize, strides,
 
   def _CompareMaxPoolingGradBk(self, input_shape, output_shape, ksize, strides,
                                padding):
-    for dtype in [np.float32, np.float16] + [np.float64]
+    for dtype in [np.float32, np.float16] + [np.float64] \
     if not test.is_built_with_rocm() else []:
       # Generate numbers in a narrow range, so that there are many duplicates
       # in the input.