text: # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Functional tests for 3d convolutional operations."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import math
  
  import numpy as np
  
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import nn_ops
  import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
  from tensorflow.python.platform import test
  
  
  def GetTestConfigs():
    """Get all the valid tests configs to run.
  
    Returns:
      all the valid test configs as tuples of data_format and use_gpu.
    """
    test_configs = [("NDHWC", False), ("NDHWC", True)]
    if test.is_gpu_available(cuda_only=True):
      # "NCDHW" format is only supported on CUDA.
      test_configs += [("NCDHW", True)]
    return test_configs
  
  
  class Conv3DTest(test.TestCase):
  
    def _DtypesToTest(self, use_gpu):
      if use_gpu:
        if not test_util.CudaSupportsHalfMatMulAndConv():
          return [dtypes.float32]
        else:
          # It is important that float32 comes before float16 here,
          # as we will be using its gradients as reference for fp16 gradients.
          return [dtypes.float32, dtypes.float16]
      else:
        return [dtypes.float64, dtypes.float32, dtypes.float16]
  
    def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, stride,
                              padding, data_format, dtype, use_gpu):
      total_size_tensor = 1
      total_size_filter = 1
      for s in tensor_in_sizes:
        total_size_tensor *= s
      for s in filter_in_sizes:
        total_size_filter *= s
  
      # Initializes the input tensor with array containing numbers from 0 to 1.
      # We keep the input tensor values fairly small to avoid overflowing float16
      # during the conv3d.
      x1 = [f * 1.0 / total_size_tensor for f in range(1, total_size_tensor + 1)]
      x2 = [f * 1.0 / total_size_filter for f in range(1, total_size_filter + 1)]
      with self.test_session(use_gpu=use_gpu):
        t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=dtype)
        t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=dtype)
  
        if isinstance(stride, collections.Iterable):
          strides = [1] + list(stride) + [1]
        else:
          strides = [1, stride, stride, stride, 1]
  
        if data_format == "NCDHW":
          t1 = test_util.NHWCToNCHW(t1)
          strides = test_util.NHWCToNCHW(strides)
        conv = nn_ops.conv3d(t1, t2, strides, padding=padding,
                             data_format=data_format)
        if data_format == "NCDHW":
          conv = test_util.NCHWToNHWC(conv)
  
        return conv
  
    def _VerifyValues(self, tensor_in_sizes, filter_in_sizes, stride, padding,
                      expected):
      results = []
      for data_format, use_gpu in GetTestConfigs():
        for dtype in self._DtypesToTest(use_gpu):
          result = self._SetupValuesForDevice(
              tensor_in_sizes,
              filter_in_sizes,
              stride,
              padding,
              data_format,
              dtype,
              use_gpu=use_gpu)
          results.append(result)
  
        with self.test_session() as sess:
          values = sess.run(results)
          for value in values:
            print("expected = ", expected)
            print("actual = ", value)
            tol = 1e-6
            if value.dtype == np.float16:
              tol = 1e-3
  
            self.assertAllClose(expected, value.flatten(), atol=tol, rtol=tol)
  
    def _ComputeReferenceDilatedConv(self, tensor_in_sizes, filter_in_sizes,
                                     stride, dilation, padding, data_format,
                                     use_gpu):
      total_size_tensor = 1
      total_size_filter = 1
      for s in tensor_in_sizes:
        total_size_tensor *= s
      for s in filter_in_sizes:
        total_size_filter *= s
  
      # Initializes the input tensor with array containing incrementing
      # numbers from 1.
      x1 = [f * 1.0 for f in range(1, total_size_tensor + 1)]
      x2 = [f * 1.0 for f in range(1, total_size_filter + 1)]
      with self.test_session(use_gpu=use_gpu):
        t1 = constant_op.constant(x1, shape=tensor_in_sizes)
        t2 = constant_op.constant(x2, shape=filter_in_sizes)
        if isinstance(stride, collections.Iterable):
          strides = list(stride)
        else:
          strides = [stride, stride, stride]
        if data_format == "NCDHW":
          t1 = test_util.NHWCToNCHW(t1)
          full_strides = [1, 1] + strides
          full_dilation = [1, 1] + dilation
        else:
          full_strides = [1] + strides + [1]
          full_dilation = [1] + dilation + [1]
        expected = nn_ops.convolution(
            t1,
            t2,
            padding=padding,
            strides=strides,
            dilation_rate=dilation,
            data_format=data_format)
        computed = nn_ops.conv3d(
            t1,
            t2,
            strides=full_strides,
            dilations=full_dilation,
            padding=padding,
            data_format=data_format)
        if data_format == "NCDHW":
          expected = test_util.NCHWToNHWC(expected)
          computed = test_util.NCHWToNHWC(computed)
      return expected, computed
  
    def _VerifyDilatedConvValues(self, tensor_in_sizes, filter_in_sizes, stride,
                                 padding, dilations):
      expected_results = []
      computed_results = []
      default_dilations = (
          dilations[0] == 1 and dilations[1] == 1 and dilations[2] == 1)
      for data_format, use_gpu in GetTestConfigs():
        # If any dilation rate is larger than 1, only do test on the GPU
        # because we currently do not have a CPU implementation for arbitrary
        # dilation rates.
        if default_dilations or use_gpu:
          expected, computed = self._ComputeReferenceDilatedConv(
              tensor_in_sizes, filter_in_sizes, stride, dilations, padding,
              data_format, use_gpu)
          expected_results.append(expected)
          computed_results.append(computed)
          tolerance = 1e-2 if use_gpu else 1e-5
          with self.test_session() as sess:
            expected_values = sess.run(expected_results)
            computed_values = sess.run(computed_results)
            for e_value, c_value in zip(expected_values, computed_values):
              print("expected = ", e_value)
              print("actual = ", c_value)
              self.assertAllClose(
                  e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-6)
  
    def testConv3D1x1x1Filter(self):
      expected_output = [
          0.18518519, 0.22222222, 0.25925926, 0.40740741, 0.5, 0.59259259,
          0.62962963, 0.77777778, 0.92592593, 0.85185185, 1.05555556, 1.25925926,
          1.07407407, 1.33333333, 1.59259259, 1.2962963, 1.61111111, 1.92592593
      ]
  
      # These are equivalent to the Conv2D1x1 case.
      self._VerifyValues(
          tensor_in_sizes=[1, 2, 3, 1, 3],
          filter_in_sizes=[1, 1, 1, 3, 3],
          stride=1,
          padding="VALID",
          expected=expected_output)
      self._VerifyValues(
          tensor_in_sizes=[1, 2, 1, 3, 3],
          filter_in_sizes=[1, 1, 1, 3, 3],
          stride=1,
          padding="VALID",
          expected=expected_output)
      self._VerifyValues(
          tensor_in_sizes=[1, 1, 2, 3, 3],
          filter_in_sizes=[1, 1, 1, 3, 3],
          stride=1,
          padding="VALID",
          expected=expected_output)
  
    def testConv3D1x1x1Filter2x1x1Dilation(self):
      if test.is_gpu_available(cuda_only=True):
        self._VerifyDilatedConvValues(
            tensor_in_sizes=[1, 3, 6, 1, 1],
            filter_in_sizes=[1, 1, 1, 1, 1],
            stride=1,
            padding="VALID",
            dilations=[2, 1, 1])
  
    # Expected values computed using scipy's correlate function.
    def testConv3D2x2x2Filter(self):
      expected_output = [
          3.77199074, 3.85069444, 3.92939815, 4.2650463, 4.35763889, 4.45023148,
          6.73032407, 6.89236111, 7.05439815, 7.22337963, 7.39930556, 7.57523148,
          9.68865741, 9.93402778, 10.17939815, 10.18171296, 10.44097222,
          10.70023148
      ]
      # expected_shape = [1, 3, 1, 2, 5]
      self._VerifyValues(
          tensor_in_sizes=[1, 4, 2, 3, 3],  # b, z, y, x, fin
          filter_in_sizes=[2, 2, 2, 3, 3],  # z, y, x, fin, fout
          stride=1,
          padding="VALID",
          expected=expected_output)
  
    def testConv3D2x2x2Filter1x2x1Dilation(self):
      if test.is_gpu_available(cuda_only=True):
        self._VerifyDilatedConvValues(
            tensor_in_sizes=[1, 4, 6, 3, 1],
            filter_in_sizes=[2, 2, 2, 1, 1],
            stride=1,
            padding="VALID",
            dilations=[1, 2, 1])
  
    def testConv3DStrides(self):
      expected_output = [
          0.06071429, 0.08988095, 0.10238095, 0.11488095, 0.12738095, 0.13988095,
          0.08452381, 0.26071429, 0.35238095, 0.36488095, 0.37738095, 0.38988095,
          0.40238095, 0.23452381, 0.46071429, 0.61488095, 0.62738095, 0.63988095,
          0.65238095, 0.66488095, 0.38452381, 1.12738095, 1.48988095, 1.50238095,
          1.51488095, 1.52738095, 1.53988095, 0.88452381, 1.32738095, 1.75238095,
          1.76488095, 1.77738095, 1.78988095, 1.80238095, 1.03452381, 1.52738095,
          2.01488095, 2.02738095, 2.03988095, 2.05238095, 2.06488095, 1.18452381,
          2.19404762, 2.88988095, 2.90238095, 2.91488095, 2.92738095, 2.93988095,
          1.68452381, 2.39404762, 3.15238095, 3.16488095, 3.17738095, 3.18988095,
          3.20238095, 1.83452381, 2.59404762, 3.41488095, 3.42738095, 3.43988095,
          3.45238095, 3.46488095, 1.98452381
      ]
      self._VerifyValues(
          tensor_in_sizes=[1, 5, 8, 7, 1],
          filter_in_sizes=[1, 2, 3, 1, 1],
          stride=[2, 3, 1],  # different stride for each spatial dimension
          padding="SAME",
          expected=expected_output)
  
    def testConv3D2x2x2FilterStride2(self):
      expected_output = [
          3.77199074, 3.85069444, 3.92939815, 9.68865741, 9.93402778, 10.17939815
      ]
      self._VerifyValues(
          tensor_in_sizes=[1, 4, 2, 3, 3],
          filter_in_sizes=[2, 2, 2, 3, 3],
          stride=2,
          padding="VALID",
          expected=expected_output)
  
    def testConv3DStride3(self):
      expected_output = [
          1.51140873, 1.57167659, 1.63194444, 1.56349206, 1.62673611, 1.68998016,
          1.6155754, 1.68179563, 1.74801587, 1.9280754, 2.01215278, 2.09623016,
          1.98015873, 2.0672123, 2.15426587, 2.03224206, 2.12227183, 2.21230159,
          4.4280754, 4.65500992, 4.88194444, 4.48015873, 4.71006944, 4.93998016,
          4.53224206, 4.76512897, 4.99801587, 4.84474206, 5.09548611, 5.34623016,
          4.8968254, 5.15054563, 5.40426587, 4.94890873, 5.20560516, 5.46230159
      ]
      self._VerifyValues(
          tensor_in_sizes=[1, 6, 7, 8, 2],
          filter_in_sizes=[3, 2, 1, 2, 3],
          stride=3,
          padding="VALID",
          expected=expected_output)
  
    def testConv3D2x2x2FilterStride2Same(self):
      expected_output = [
          3.77199074, 3.85069444, 3.92939815, 2.0162037, 2.06597222, 2.11574074,
          9.68865741, 9.93402778, 10.17939815, 4.59953704, 4.73263889, 4.86574074
      ]
      self._VerifyValues(
          tensor_in_sizes=[1, 4, 2, 3, 3],
          filter_in_sizes=[2, 2, 2, 3, 3],
          stride=2,
          padding="SAME",
          expected=expected_output)
  
    def testKernelSmallerThanStride(self):
      expected_output = [
          0.03703704, 0.11111111, 0.25925926, 0.33333333, 0.7037037, 0.77777778,
          0.92592593, 1.
      ]
      self._VerifyValues(
          tensor_in_sizes=[1, 3, 3, 3, 1],
          filter_in_sizes=[1, 1, 1, 1, 1],
          stride=2,
          padding="SAME",
          expected=expected_output)
      self._VerifyValues(
          tensor_in_sizes=[1, 3, 3, 3, 1],
          filter_in_sizes=[1, 1, 1, 1, 1],
          stride=2,
          padding="VALID",
          expected=expected_output)
  
      expected_output = [
          0.54081633, 0.58017493, 0.28061224, 0.81632653, 0.85568513, 0.40306122,
          0.41873178, 0.4340379, 0.19642857, 2.46938776, 2.50874636, 1.1377551,
          2.74489796, 2.78425656, 1.26020408, 1.16873178, 1.1840379, 0.51785714,
          1.09511662, 1.10604956, 0.44642857, 1.17164723, 1.18258017, 0.47704082,
          0.3691691, 0.37244898, 0.125
      ]
      self._VerifyValues(
          tensor_in_sizes=[1, 7, 7, 7, 1],
          filter_in_sizes=[2, 2, 2, 1, 1],
          stride=3,
          padding="SAME",
          expected=expected_output)
  
      expected_output = [
          0.540816, 0.580175, 0.816327, 0.855685, 2.469388, 2.508746, 2.744898,
          2.784257
      ]
      self._VerifyValues(
          tensor_in_sizes=[1, 7, 7, 7, 1],
          filter_in_sizes=[2, 2, 2, 1, 1],
          stride=3,
          padding="VALID",
          expected=expected_output)
  
    def testKernelSizeMatchesInputSize(self):
      self._VerifyValues(
          tensor_in_sizes=[1, 2, 1, 2, 1],
          filter_in_sizes=[2, 1, 2, 1, 2],
          stride=1,
          padding="VALID",
          expected=[1.5625, 1.875])
  
    def _ConstructAndTestGradientForConfig(
        self, batch, input_shape, filter_shape, in_depth, out_depth, stride,
        padding, test_input, data_format, use_gpu):
  
      input_planes, input_rows, input_cols = input_shape
      filter_planes, filter_rows, filter_cols = filter_shape
  
      input_shape = [batch, input_planes, input_rows, input_cols, in_depth]
      filter_shape = [
          filter_planes, filter_rows, filter_cols, in_depth, out_depth
      ]
  
      if isinstance(stride, collections.Iterable):
        strides = [1] + list(stride) + [1]
      else:
        strides = [1, stride, stride, stride, 1]
  
      if padding == "VALID":
        output_planes = int(
            math.ceil((input_planes - filter_planes + 1.0) / strides[1]))
        output_rows = int(
            math.ceil((input_rows - filter_rows + 1.0) / strides[2]))
        output_cols = int(
            math.ceil((input_cols - filter_cols + 1.0) / strides[3]))
      else:
        output_planes = int(math.ceil(float(input_planes) / strides[1]))
        output_rows = int(math.ceil(float(input_rows) / strides[2]))
        output_cols = int(math.ceil(float(input_cols) / strides[3]))
      output_shape = [batch, output_planes, output_rows, output_cols, out_depth]
      input_size = 1
      for x in input_shape:
        input_size *= x
      filter_size = 1
      for x in filter_shape:
        filter_size *= x
      input_data = [x * 1.0 / input_size for x in range(0, input_size)]
      filter_data = [x * 1.0 / filter_size for x in range(0, filter_size)]
  
      for data_type in self._DtypesToTest(use_gpu=use_gpu):
        # TODO (mjanusz): Modify gradient_checker to also provide max relative id:3753
        # https://github.com/imdone/tensorflow/issues/3752
        # error and synchronize the tolerance levels between the tests for forward
        # and backward computations.
        if data_type == dtypes.float64:
          tolerance = 1e-8
        elif data_type == dtypes.float32:
          tolerance = 5e-3
        elif data_type == dtypes.float16:
          tolerance = 1e-3
  
        with self.test_session(use_gpu=use_gpu):
          orig_input_tensor = constant_op.constant(
              input_data, shape=input_shape, dtype=data_type, name="input")
          filter_tensor = constant_op.constant(
              filter_data, shape=filter_shape, dtype=data_type, name="filter")
  
          if data_format == "NCDHW":
            input_tensor = test_util.NHWCToNCHW(orig_input_tensor)
            new_strides = test_util.NHWCToNCHW(strides)
          else:
            input_tensor = orig_input_tensor
            new_strides = strides
  
          conv = nn_ops.conv3d(
              input_tensor,
              filter_tensor,
              new_strides,
              padding,
              data_format=data_format,
              name="conv")
  
          if data_format == "NCDHW":
            conv = test_util.NCHWToNHWC(conv)
  
          self.assertEqual(conv.shape, tensor_shape.TensorShape(output_shape))
  
          if test_input:
            jacob_t, jacob_n = gradient_checker.compute_gradient(
                orig_input_tensor, input_shape, conv, output_shape)
          else:
            jacob_t, jacob_n = gradient_checker.compute_gradient(
                filter_tensor, filter_shape, conv, output_shape)
  
          if data_type != dtypes.float16:
            reference_jacob_t = jacob_t
            err = np.fabs(jacob_t - jacob_n).max()
          else:
            # Compare fp16 theoretical gradients to fp32 theoretical gradients,
            # since fp16 numerical gradients are too imprecise.
            err = np.fabs(jacob_t - reference_jacob_t).max()
  
        print("conv3d gradient error = ", err)
        self.assertLess(err, tolerance)
  
    def ConstructAndTestGradient(self, **kwargs):
      for data_format, use_gpu in GetTestConfigs():
        self._ConstructAndTestGradientForConfig(data_format=data_format,
                                                use_gpu=use_gpu, **kwargs)
  
    def testInputGradientValidPaddingStrideOne(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(3, 5, 4),
          filter_shape=(3, 3, 3),
          in_depth=2,
          out_depth=3,
          stride=1,
          padding="VALID",
          test_input=True)
  
    def testFilterGradientValidPaddingStrideOne(self):
      self.ConstructAndTestGradient(
          batch=4,
          input_shape=(4, 6, 5),
          filter_shape=(2, 2, 2),
          in_depth=2,
          out_depth=3,
          stride=1,
          padding="VALID",
          test_input=False)
  
    def testInputGradientValidPaddingStrideTwo(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(6, 3, 5),
          filter_shape=(3, 3, 3),
          in_depth=2,
          out_depth=3,
          stride=2,
          padding="VALID",
          test_input=True)
  
    def testFilterGradientValidPaddingStrideTwo(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(7, 6, 5),
          filter_shape=(2, 2, 2),
          in_depth=2,
          out_depth=3,
          stride=2,
          padding="VALID",
          test_input=False)
  
    def testInputGradientValidPaddingStrideThree(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(3, 7, 6),
          filter_shape=(3, 3, 3),
          in_depth=2,
          out_depth=3,
          stride=3,
          padding="VALID",
          test_input=True)
  
    def testFilterGradientValidPaddingStrideThree(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(4, 4, 7),
          filter_shape=(4, 4, 4),
          in_depth=2,
          out_depth=3,
          stride=3,
          padding="VALID",
          test_input=False)
  
    def testInputGradientSamePaddingStrideOne(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(3, 2, 2),
          filter_shape=(3, 2, 1),
          in_depth=2,
          out_depth=1,
          stride=1,
          padding="SAME",
          test_input=True)
  
    def testFilterGradientSamePaddingStrideOne(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(3, 6, 5),
          filter_shape=(2, 2, 2),
          in_depth=2,
          out_depth=3,
          stride=1,
          padding="SAME",
          test_input=False)
  
    def testInputGradientSamePaddingStrideTwo(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(6, 3, 4),
          filter_shape=(3, 3, 3),
          in_depth=2,
          out_depth=3,
          stride=2,
          padding="SAME",
          test_input=True)
  
    def testFilterGradientSamePaddingStrideTwo(self):
      self.ConstructAndTestGradient(
          batch=4,
          input_shape=(7, 3, 5),
          filter_shape=(2, 2, 2),
          in_depth=2,
          out_depth=3,
          stride=2,
          padding="SAME",
          test_input=False)
  
    def testInputGradientSamePaddingStrideThree(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(9, 3, 6),
          filter_shape=(3, 3, 3),
          in_depth=2,
          out_depth=3,
          stride=3,
          padding="SAME",
          test_input=True)
  
    def testFilterGradientSamePaddingStrideThree(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(9, 4, 7),
          filter_shape=(4, 4, 4),
          in_depth=2,
          out_depth=3,
          stride=3,
          padding="SAME",
          test_input=False)
  
    def testInputGradientSamePaddingDifferentStrides(self):
      self.ConstructAndTestGradient(
          batch=1,
          input_shape=(5, 8, 7),
          filter_shape=(1, 2, 3),
          in_depth=2,
          out_depth=3,
          stride=[2, 3, 1],
          padding="SAME",
          test_input=True)
  
    def testFilterGradientKernelSizeMatchesInputSize(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(5, 4, 3),
          filter_shape=(5, 4, 3),
          in_depth=2,
          out_depth=3,
          stride=1,
          padding="VALID",
          test_input=False)
  
    def testInputGradientKernelSizeMatchesInputSize(self):
      self.ConstructAndTestGradient(
          batch=2,
          input_shape=(5, 4, 3),
          filter_shape=(5, 4, 3),
          in_depth=2,
          out_depth=3,
          stride=1,
          padding="VALID",
          test_input=True)
  
    def disabledtestFilterGradientSamePaddingDifferentStrides(self):
      self.ConstructAndTestGradient(
          batch=1,
          input_shape=(5, 8, 7),
          filter_shape=(1, 2, 3),
          in_depth=2,
          out_depth=3,
          stride=[2, 3, 1],
          padding="SAME",
          test_input=False)
  
    # Testing for backprops
    def _RunAndVerifyBackprop(self, input_sizes, filter_sizes, output_sizes,
                              strides, dilations, padding, data_format, use_gpu,
                              err, mode):
      total_input_size = 1
      total_filter_size = 1
      for s in input_sizes:
        total_input_size *= s
      for s in filter_sizes:
        total_filter_size *= s
      # Initializes the input tensor with array containing incrementing
      # numbers from 1.
      x1 = [f * 1.0 for f in range(1, total_input_size + 1)]
      x2 = [f * 1.0 for f in range(1, total_filter_size + 1)]
      default_dilations = (
          dilations[0] == 1 and dilations[1] == 1 and dilations[2] == 1)
  
      # If any dilation rate is larger than 1, only do test on the GPU
      # because we currently do not have a CPU implementation for arbitrary
      # dilation rates.
      if default_dilations or use_gpu:
        with self.test_session(use_gpu=use_gpu) as sess:
          if data_format == "NCDHW":
            input_sizes = test_util.NHWCToNCHW(input_sizes)
          t1 = constant_op.constant(x1, shape=input_sizes)
          t2 = constant_op.constant(x2, shape=filter_sizes)
          full_strides = [1] + strides + [1]
          full_dilations = [1] + dilations + [1]
          if data_format == "NCDHW":
            full_strides = test_util.NHWCToNCHW(full_strides)
            full_dilations = test_util.NHWCToNCHW(full_dilations)
          actual = nn_ops.conv3d(
              t1,
              t2,
              strides=full_strides,
              dilations=full_dilations,
              padding=padding,
              data_format=data_format)
          expected = nn_ops.convolution(
              t1,
              t2,
              padding=padding,
              strides=strides,
              dilation_rate=dilations,
              data_format=data_format)
          if data_format == "NCDHW":
            actual = test_util.NCHWToNHWC(actual)
            expected = test_util.NCHWToNHWC(expected)
          actual_grad = gradients_impl.gradients(actual, t1
                                                 if mode == "input" else t2)[0]
          expected_grad = gradients_impl.gradients(expected, t1
                                                   if mode == "input" else t2)[0]
          # "values" consists of two tensors for two backprops
          actual_value = sess.run(actual_grad)
          expected_value = sess.run(expected_grad)
          self.assertShapeEqual(actual_value, actual_grad)
          self.assertShapeEqual(expected_value, expected_grad)
        print("expected = ", expected_value)
        print("actual = ", actual_value)
        self.assertArrayNear(expected_value.flatten(), actual_value.flatten(),
                             err)
  
    def testConv3D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1(self):
      if test.is_gpu_available(cuda_only=True):
        for (data_format, use_gpu) in GetTestConfigs():
          self._RunAndVerifyBackprop(
              input_sizes=[1, 3, 6, 1, 1],
              filter_sizes=[2, 2, 1, 1, 1],
              output_sizes=[1, 1, 5, 1, 1],
              strides=[1, 1, 1],
              dilations=[2, 1, 1],
              padding="VALID",
              data_format=data_format,
              use_gpu=use_gpu,
              err=1e-5,
              mode="filter")
  
    def testConv3D2x2Depth3ValidBackpropInputStride1x1Dilation2x1(self):
      if test.is_gpu_available(cuda_only=True):
        for (data_format, use_gpu) in GetTestConfigs():
          self._RunAndVerifyBackprop(
              input_sizes=[1, 3, 6, 1, 1],
              filter_sizes=[2, 2, 1, 1, 1],
              output_sizes=[1, 1, 5, 1, 1],
              strides=[1, 1, 1],
              dilations=[2, 1, 1],
              padding="VALID",
              data_format=data_format,
              use_gpu=use_gpu,
              err=1e-5,
              mode="input")
  
  
  if __name__ == "__main__":
    test.main()
  