text: # Copyright 2017 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.
  # ==============================================================================
  """Tests for 3D convolutions using the XLA JIT."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  from six.moves import xrange  # pylint: disable=redefined-builtin
  
  from tensorflow.compiler.tests.xla_test import XLATestCase
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import nn_ops
  import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
  from tensorflow.python.platform import googletest
  
  
  # Test cloned from
  # tensorflow/python/kernel_tests/conv3d_backprop_filter_v2_grad_test.py
  class Conv3DBackpropFilterV2GradTest(XLATestCase):
  
    def testGradient(self):
      with self.test_session(), self.test_scope():
        for padding in ["SAME", "VALID"]:
          for stride in [1, 2]:
            np.random.seed(1)
            in_shape = [2, 4, 3, 3, 2]
            in_val = constant_op.constant(
                2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32)
            filter_shape = [3, 3, 3, 2, 3]
            strides = [1, stride, stride, stride, 1]
            # Make a convolution op with the current settings, just to easily get
            # the shape of the output.
            conv_out = nn_ops.conv3d(in_val,
                                     array_ops.zeros(filter_shape), strides,
                                     padding)
            out_backprop_shape = conv_out.get_shape().as_list()
            out_backprop_val = constant_op.constant(
                2 * np.random.random_sample(out_backprop_shape) - 1,
                dtype=dtypes.float32)
            output = nn_ops.conv3d_backprop_filter_v2(in_val, filter_shape,
                                                      out_backprop_val, strides,
                                                      padding)
            err = gradient_checker.compute_gradient_error(
                [in_val, out_backprop_val], [in_shape, out_backprop_shape],
                output, filter_shape)
            print("conv3d_backprop_filter gradient err = %g " % err)
            err_tolerance = 1e-3
            self.assertLess(err, err_tolerance)
  
  
  # Test cloned from tensorflow/python/kernel_tests/conv3d_transpose_test.py
  class Conv3DTransposeTest(XLATestCase):
  
    def testConv3DTransposeSingleStride(self):
      with self.test_session(), self.test_scope():
        strides = [1, 1, 1, 1, 1]
  
        # Input, output: [batch, depth, height, width, channel]
        x_shape = [2, 5, 6, 4, 3]
        y_shape = [2, 5, 6, 4, 2]
  
        # Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
        f_shape = [3, 3, 3, 2, 3]
  
        x = constant_op.constant(
            1.0, shape=x_shape, name="x", dtype=dtypes.float32)
        f = constant_op.constant(
            1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
        output = nn_ops.conv3d_transpose(
            x, f, y_shape, strides=strides, padding="SAME")
        value = output.eval()
  
        # We count the number of cells being added at the locations in the output.
        # At the center, #cells = kernel_depth * kernel_height * kernel_width
        # At the corners, #cells = ceil(kernel_depth/2) * ceil(kernel_height/2)
        #                          * ceil(kernel_width/2)
        # At the edges, #cells =
        #   kernel_depth * ceil(kernel_height/2) * ceil(kernel_width/2) or
        #   ceil(kernel_depth/2) * kernel_height * ceil(kernel_width/2) or
        #   ceil(kernel_depth/2) * ceil(kernel_height/2) * kernel_width
        # At the borders, #cells =
        #   ceil(kernel_depth/2) * kernel_height * kernel_width or
        #   kernel_depth * ceil(kernel_height/2) * kernel_width or
        #   kernel_depth * kernel_height * ceil(kernel_width/2)
  
        for n in xrange(x_shape[0]):
          for k in xrange(f_shape[3]):
            for w in xrange(y_shape[3]):
              for h in xrange(y_shape[2]):
                for d in xrange(y_shape[1]):
                  d_in = d > 0 and d < y_shape[1] - 1
                  h_in = h > 0 and h < y_shape[2] - 1
                  w_in = w > 0 and w < y_shape[3] - 1
                  if d_in + h_in + w_in == 3:
                    target = 27 * 3.0
                  elif d_in + h_in + w_in == 2:
                    target = 18 * 3.0
                  elif d_in or h_in or w_in:
                    target = 12 * 3.0
                  else:
                    target = 8 * 3.0
                  self.assertAllClose(target, value[n, d, h, w, k])
  
    def testConv3DTransposeSame(self):
      with self.test_session(), self.test_scope():
        strides = [1, 2, 2, 2, 1]
  
        # Input, output: [batch, depth, height, width, depth]
        x_shape = [2, 5, 6, 4, 3]
        y_shape = [2, 10, 12, 8, 2]
  
        # Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
        f_shape = [3, 3, 3, 2, 3]
  
        x = constant_op.constant(
            1.0, shape=x_shape, name="x", dtype=dtypes.float32)
        f = constant_op.constant(
            1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
        output = nn_ops.conv3d_transpose(
            x, f, y_shape, strides=strides, padding="SAME")
        value = output.eval()
  
        for n in xrange(x_shape[0]):
          for k in xrange(f_shape[3]):
            for w in xrange(y_shape[3]):
              for h in xrange(y_shape[2]):
                for d in xrange(y_shape[1]):
                  # We add a case for locations divisible by the stride.
                  d_in = d % strides[1] == 0 and 0 < d < y_shape[1] - 1
                  h_in = h % strides[2] == 0 and 0 < h < y_shape[2] - 1
                  w_in = w % strides[3] == 0 and 0 < w < y_shape[3] - 1
                  if d_in + h_in + w_in == 3:
                    target = 8 * 3.0
                  elif d_in + h_in + w_in == 2:
                    target = 4 * 3.0
                  elif d_in or h_in or w_in:
                    target = 2 * 3.0
                  else:
                    target = 3.0
                  self.assertAllClose(target, value[n, d, h, w, k])
  
    def testConv3DTransposeValid(self):
      with self.test_session(), self.test_scope():
        strides = [1, 2, 2, 2, 1]
  
        # Input, output: [batch, depth, height, width, depth]
        x_shape = [2, 5, 6, 4, 3]
        y_shape = [2, 11, 13, 9, 2]
  
        # Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
        f_shape = [3, 3, 3, 2, 3]
  
        x = constant_op.constant(
            1.0, shape=x_shape, name="x", dtype=dtypes.float32)
        f = constant_op.constant(
            1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
        output = nn_ops.conv3d_transpose(
            x, f, y_shape, strides=strides, padding="VALID")
        value = output.eval()
  
        cache_values = np.zeros(y_shape, dtype=np.float32)
  
        # The amount of padding added
        pad = 1
  
        for n in xrange(x_shape[0]):
          for k in xrange(f_shape[3]):
            for w in xrange(y_shape[3]):
              for h in xrange(y_shape[2]):
                for d in xrange(y_shape[1]):
                  # We add a case for locations divisible by the stride.
                  d_in = d % strides[1] == 0 and pad < d < y_shape[1] - 1 - pad
                  h_in = h % strides[2] == 0 and pad < h < y_shape[2] - 1 - pad
                  w_in = w % strides[3] == 0 and pad < w < y_shape[3] - 1 - pad
                  if d_in + h_in + w_in == 3:
                    target = 8 * 3.0
                  elif d_in + h_in + w_in == 2:
                    target = 4 * 3.0
                  elif d_in or h_in or w_in:
                    target = 2 * 3.0
                  else:
                    target = 3.0
                  cache_values[n, d, h, w, k] = target
  
            # copy values in the border
            cache_values[n, :, :, 0, k] = cache_values[n, :, :, 1, k]
            cache_values[n, :, :, -1, k] = cache_values[n, :, :, -2, k]
            cache_values[n, :, 0, :, k] = cache_values[n, :, 1, :, k]
            cache_values[n, :, -1, :, k] = cache_values[n, :, -2, :, k]
            cache_values[n, 0, :, :, k] = cache_values[n, 1, :, :, k]
            cache_values[n, -1, :, :, k] = cache_values[n, -2, :, :, k]
  
      self.assertAllClose(cache_values, value)
  
    def testGradient(self):
      x_shape = [2, 3, 4, 3, 2]
      f_shape = [3, 3, 3, 2, 2]
      y_shape = [2, 6, 8, 6, 2]
      strides = [1, 2, 2, 2, 1]
      np.random.seed(1)  # Make it reproducible.
      x_val = np.random.random_sample(x_shape).astype(np.float64)
      f_val = np.random.random_sample(f_shape).astype(np.float64)
      with self.test_session(), self.test_scope():
        x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
        f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
        output = nn_ops.conv3d_transpose(
            x, f, y_shape, strides=strides, padding="SAME")
        err = gradient_checker.compute_gradient_error([x, f], [x_shape, f_shape],
                                                      output, y_shape)
      print("conv3d_transpose gradient err = %g " % err)
      err_tolerance = 0.0005
      self.assertLess(err, err_tolerance)
  
  
  if __name__ == "__main__":
    googletest.main()
  