code: # 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 operations in eager execution."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.eager import context
  from tensorflow.python.eager import execute
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.layers import core
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import sparse_ops
  
  
  class OpsTest(test_util.TensorFlowTestCase):
  
    def testExecuteBasic(self):
      three = constant_op.constant(3)
      five = constant_op.constant(5)
      product = three * five
      self.assertAllEqual(15, product)
  
    def testMatMulGPU(self):
      if not context.context().num_gpus():
        self.skipTest('No GPUs found')
      three = constant_op.constant([[3.]]).gpu()
      five = constant_op.constant([[5.]]).gpu()
      product = math_ops.matmul(three, five)
      self.assertEqual([[15.0]], product.numpy())
  
    def testExecuteStringAttr(self):
      three = constant_op.constant(3.0)
      checked_three = array_ops.check_numerics(three,
                                               message='just checking')
      self.assertEqual([[3]], checked_three.numpy())
  
    def testExecuteFloatAttr(self):
      three = constant_op.constant(3.0)
      almost_three = constant_op.constant(2.8)
      almost_equal = math_ops.approximate_equal(
          three, almost_three, tolerance=0.3)
      self.assertTrue(almost_equal)
  
    def testExecuteIntAttr(self):
      three = constant_op.constant(3)
      four = constant_op.constant(4)
      total = math_ops.add_n([three, four])
      self.assertAllEqual(7, total)
  
    def testExecuteBoolAttr(self):
      three = constant_op.constant([[3]])
      five = constant_op.constant([[5]])
      product = math_ops.matmul(three, five, transpose_a=True)
      self.assertAllEqual([[15]], product)
  
    def testExecuteOneListOutput(self):
      split_dim = constant_op.constant(1)
      value = constant_op.constant([[0, 1, 2], [3, 4, 5]])
      x1, x2, x3 = array_ops.split(value, 3, axis=split_dim)
      self.assertAllEqual([[0], [3]], x1)
      self.assertAllEqual([[1], [4]], x2)
      self.assertAllEqual([[2], [5]], x3)
  
    def testGraphMode(self):
      graph = ops.Graph()
      with graph.as_default(), context.graph_mode():
        array_ops.placeholder(dtypes.int32)
      self.assertEqual(1, len(graph.get_operations()))
  
    # See comments on handling of int32 tensors on GPU in
    # EagerTensor.__init__.
    def testInt32CPUDefault(self):
      if not context.context().num_gpus():
        self.skipTest('No GPUs found')
      with context.device('/gpu:0'):
        r = constant_op.constant(1) + constant_op.constant(2)
      self.assertAllEqual(r, 3)
  
    def testExecuteListOutputLen1(self):
      split_dim = constant_op.constant(1)
      value = constant_op.constant([[0, 1, 2], [3, 4, 5]])
      result = array_ops.split(value, 1, axis=split_dim)
      self.assertTrue(isinstance(result, list))
      self.assertEqual(1, len(result))
      self.assertAllEqual([[0, 1, 2], [3, 4, 5]], result[0])
  
    def testExecuteListOutputLen0(self):
      empty = constant_op.constant([], dtype=dtypes.int32)
      result = array_ops.unstack(empty, 0)
      self.assertTrue(isinstance(result, list))
      self.assertEqual(0, len(result))
  
    def testExecuteMultipleNonListOutput(self):
      x = constant_op.constant([1, 2, 3, 4, 5, 6])
      y = constant_op.constant([1, 3, 5])
      result = array_ops.listdiff(x, y)
      out, idx = result
      self.assertTrue(out is result.out)
      self.assertTrue(idx is result.idx)
      self.assertAllEqual([2, 4, 6], out)
      self.assertAllEqual([1, 3, 5], idx)
  
    def testExecuteMultipleListOutput(self):
      split_dim = constant_op.constant(1, dtype=dtypes.int64)
      indices = constant_op.constant([[0, 2], [0, 4], [0, 5], [1, 0], [1, 1]],
                                     dtype=dtypes.int64)
      values = constant_op.constant([2, 3, 5, 7, 11])
      shape = constant_op.constant([2, 7], dtype=dtypes.int64)
      result = sparse_ops.gen_sparse_ops.sparse_split(
          split_dim,
          indices,
          values,
          shape,
          num_split=2)
      output_indices, output_values, output_shape = result
      self.assertEqual(2, len(output_indices))
      self.assertEqual(2, len(output_values))
      self.assertEqual(2, len(output_shape))
      self.assertEqual(output_indices, result.output_indices)
      self.assertEqual(output_values, result.output_values)
      self.assertEqual(output_shape, result.output_shape)
      self.assertAllEqual([[0, 2], [1, 0], [1, 1]], output_indices[0])
      self.assertAllEqual([[0, 0], [0, 1]], output_indices[1])
      self.assertAllEqual([2, 7, 11], output_values[0])
      self.assertAllEqual([3, 5], output_values[1])
      self.assertAllEqual([2, 4], output_shape[0])
      self.assertAllEqual([2, 3], output_shape[1])
  
    # TODO(josh11b): Test an op that has multiple outputs, some but not
    # all of which are lists. Examples: barrier_take_many (currently
    # unsupported since it uses a type list) or sdca_optimizer (I don't
    # have an example of legal inputs & outputs).
  
    def testComposition(self):
      x = constant_op.constant(1, dtype=dtypes.int32)
      three_x = x + x + x
      self.assertEquals(dtypes.int32, three_x.dtype)
      self.assertAllEqual(3, three_x)
  
    def testOperatorOverrides(self):
      # TODO(henrytan): test with negative number.
      a = constant_op.constant([1])
      b = constant_op.constant([2])
  
      self.assertAllEqual((-a), [-1])
      self.assertAllEqual(abs(b), [2])
  
      self.assertAllEqual((a + b), [3])
      self.assertAllEqual((a - b), [-1])
      self.assertAllEqual((a * b), [2])
      self.assertAllEqual((a * a), [1])
  
      self.assertAllEqual((a**b), [1])
      self.assertAllEqual((a / b), [1 / 2])
      self.assertAllEqual((a / a), [1])
      self.assertAllEqual((a % b), [1])
  
      self.assertAllEqual((a < b), [True])
      self.assertAllEqual((a <= b), [True])
      self.assertAllEqual((a > b), [False])
      self.assertAllEqual((a >= b), [False])
      self.assertAllEqual((a == b), False)
      self.assertAllEqual((a != b), True)
  
      self.assertAllEqual(1, a[constant_op.constant(0)])
  
    def test_basic_slice(self):
      npt = np.arange(1, 19, dtype=np.float32).reshape(3, 2, 3)
      t = constant_op.constant(npt)
  
      self.assertAllEqual(npt[:, :, :], t[:, :, :])
      self.assertAllEqual(npt[::, ::, ::], t[::, ::, ::])
      self.assertAllEqual(npt[::1, ::1, ::1], t[::1, ::1, ::1])
      self.assertAllEqual(npt[::1, ::5, ::2], t[::1, ::5, ::2])
      self.assertAllEqual(npt[::-1, :, :], t[::-1, :, :])
      self.assertAllEqual(npt[:, ::-1, :], t[:, ::-1, :])
      self.assertAllEqual(npt[:, :, ::-1], t[:, :, ::-1])
      self.assertAllEqual(npt[-2::-1, :, ::1], t[-2::-1, :, ::1])
      self.assertAllEqual(npt[-2::-1, :, ::2], t[-2::-1, :, ::2])
  
    def testDegenerateSlices(self):
      npt = np.arange(1, 19, dtype=np.float32).reshape(3, 2, 3)
      t = constant_op.constant(npt)
      # degenerate by offering a forward interval with a negative stride
      self.assertAllEqual(npt[0:-1:-1, :, :], t[0:-1:-1, :, :])
      # degenerate with a reverse interval with a positive stride
      self.assertAllEqual(npt[-1:0, :, :], t[-1:0, :, :])
      # empty interval in every dimension
      self.assertAllEqual(npt[-1:0, 2:2, 2:3:-1], t[-1:0, 2:2, 2:3:-1])
  
    def testEllipsis(self):
      npt = np.array(
          [[[[[1, 2], [3, 4], [5, 6]]], [[[7, 8], [9, 10], [11, 12]]]]])
      t = constant_op.constant(npt)
  
      self.assertAllEqual(npt[0:], t[0:])
      # implicit ellipsis
      self.assertAllEqual(npt[0:, ...], t[0:, ...])
      # ellipsis alone
      self.assertAllEqual(npt[...], t[...])
      # ellipsis at end
      self.assertAllEqual(npt[0:1, ...], t[0:1, ...])
      # ellipsis at begin
      self.assertAllEqual(npt[..., 0:1], t[..., 0:1])
      # ellipsis at middle
      self.assertAllEqual(npt[0:1, ..., 0:1], t[0:1, ..., 0:1])
  
    def testShrink(self):
      npt = np.array([[[[[1, 2, 4, 5], [5, 6, 7, 8], [9, 10, 11, 12]]],
                       [[[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]]])
      t = constant_op.constant(npt)
      self.assertAllEqual(npt[:, :, :, :, 3], t[:, :, :, :, 3])
      self.assertAllEqual(npt[..., 3], t[..., 3])
      self.assertAllEqual(npt[:, 0], t[:, 0])
      self.assertAllEqual(npt[:, :, 0], t[:, :, 0])
  
    def testOpWithInputsOnDifferentDevices(self):
      if not context.context().num_gpus():
        self.skipTest('No GPUs found')
  
      # The GPU kernel for the Reshape op requires that the
      # shape input be on CPU.
      value = constant_op.constant([1., 2.]).gpu()
      shape = constant_op.constant([2, 1])
      reshaped = array_ops.reshape(value, shape)
      self.assertAllEqual([[1], [2]], reshaped.cpu())
  
    def testInt64(self):
      # Fill requires the first input to be an int32 tensor.
      self.assertAllEqual(
          [1.0, 1.0],
          array_ops.fill(constant_op.constant([2], dtype=dtypes.int64),
                         constant_op.constant(1)))
  
    def testOutputOnHostMemory(self):
      if not context.context().num_gpus():
        self.skipTest('No GPUs found')
      # The Shape op kernel on GPU places the output in host memory.
      value = constant_op.constant([1.]).gpu()
      shape = array_ops.shape(value)
      self.assertEqual([1], shape.numpy())
  
    def testSilentCopy(self):
      if not context.context().num_gpus():
        self.skipTest('No GPUs found')
      # Temporarily replace the context
      # pylint: disable=protected-access
      del context._context
      try:
        context._context = context.Context(
            device_policy=context.DEVICE_PLACEMENT_SILENT)
        cpu_tensor = constant_op.constant(1.0)
        gpu_tensor = cpu_tensor.gpu()
        self.assertAllEqual(cpu_tensor + gpu_tensor, 2.0)
      finally:
        del context._context
        context._context = context.Context()
      # pylint: enable=protected-access
  
    def testSoftPlacement(self):
      if not context.context().num_gpus():
        self.skipTest('No GPUs found')
      # Temporarily replace the context
      # pylint: disable=protected-access
      del context._context
      try:
        context._context = context.Context(
            device_policy=context.DEVICE_PLACEMENT_SILENT,
            config=config_pb2.ConfigProto(allow_soft_placement=True))
        cpu_tensor = constant_op.constant(1.0)
        result = cpu_tensor + cpu_tensor
        self.assertEqual(result.device,
                         '/job:localhost/replica:0/task:0/device:GPU:0')
      finally:
        del context._context
        context._context = context.Context()
      # pylint: enable=protected-access
  
    def testRandomUniform(self):
      scalar_shape = constant_op.constant([], dtype=dtypes.int32)
  
      x = random_ops.random_uniform(scalar_shape)
      self.assertEquals(0, x.shape.ndims)
      self.assertEquals(dtypes.float32, x.dtype)
  
      x = random_ops.random_uniform(
          scalar_shape, minval=constant_op.constant(5.),
          maxval=constant_op.constant(6.))
      self.assertLess(x, 6)
      self.assertGreaterEqual(x, 5)
  
    def testArgsToMatchingEagerDefault(self):
      # Uses default
      ctx = context.context()
      t, r = execute.args_to_matching_eager([[3, 4]], ctx, dtypes.int32)
      self.assertEquals(t, dtypes.int32)
      self.assertEquals(r[0].dtype, dtypes.int32)
      t, r = execute.args_to_matching_eager([[3, 4]], ctx, dtypes.int64)
      self.assertEquals(t, dtypes.int64)
      self.assertEquals(r[0].dtype, dtypes.int64)
      # Doesn't use default
      t, r = execute.args_to_matching_eager(
          [['string', 'arg']], ctx, dtypes.int32)
      self.assertEquals(t, dtypes.string)
      self.assertEquals(r[0].dtype, dtypes.string)
  
    def testFlattenLayer(self):
      flatten_layer = core.Flatten()
      x = constant_op.constant([[[-10, -20], [-30, -40]], [[10, 20], [30, 40]]])
      y = flatten_layer(x)
      self.assertAllEqual([[-10, -20, -30, -40], [10, 20, 30, 40]], y)
  
    def testIdentity(self):
      self.assertAllEqual(2, array_ops.identity(2))
  
    def testIdentityOnVariable(self):
      if not context.context().num_gpus():
        self.skipTest('No GPUs found')
      with context.device('/gpu:0'):
        v = resource_variable_ops.ResourceVariable(True)
      self.assertAllEqual(True, array_ops.identity(v))
  
    def testIncompatibleSetShape(self):
      x = constant_op.constant(1)
      with self.assertRaises(ValueError):
        x.set_shape((1, 2))
  
    def testCompatibleSetShape(self):
      x = constant_op.constant([[1, 2]])
      x.set_shape(tensor_shape.TensorShape([None, 2]))
      self.assertEqual(x.get_shape(), (1, 2))
  
    def testCastScalarToPrimitiveTypes(self):
      x = constant_op.constant(1.3)
      self.assertIsInstance(int(x), int)
      self.assertEqual(int(x), 1)
      self.assertIsInstance(float(x), float)
      self.assertAllClose(float(x), 1.3)
  
    def testCastNonScalarToPrimitiveTypesFails(self):
      x = constant_op.constant([1.3, 2])
      with self.assertRaises(TypeError):
        int(x)
      with self.assertRaises(TypeError):
        float(x)
  
    def testFormatString(self):
      x = constant_op.constant(3.1415)
      self.assertEqual('3.14', '{:.2f}'.format(x))
  
    def testNoOpIsNone(self):
      self.assertTrue(control_flow_ops.no_op() is None)
  
  
  if __name__ == '__main__':
    test.main()
  
apis: tensorflow.python.ops.array_ops.listdiff tensorflow.python.eager.context.Context tensorflow.python.framework.tensor_shape.TensorShape tensorflow.python.framework.constant_op.constant tensorflow.python.ops.array_ops.check_numerics tensorflow.python.framework.ops.Graph tensorflow.python.ops.array_ops.placeholder tensorflow.python.ops.math_ops.matmul tensorflow.python.eager.test.main tensorflow.python.layers.core.Flatten tensorflow.python.ops.math_ops.add_n tensorflow.python.ops.array_ops.unstack tensorflow.python.ops.random_ops.random_uniform tensorflow.python.ops.array_ops.identity tensorflow.python.ops.array_ops.shape tensorflow.python.eager.context.context numpy.arange tensorflow.python.eager.context.device tensorflow.python.eager.context.graph_mode tensorflow.python.ops.math_ops.approximate_equal tensorflow.python.eager.execute.args_to_matching_eager tensorflow.core.protobuf.config_pb2.ConfigProto tensorflow.python.ops.array_ops.split tensorflow.python.ops.sparse_ops.gen_sparse_ops.sparse_split tensorflow.python.ops.resource_variable_ops.ResourceVariable tensorflow.python.ops.array_ops.reshape numpy.array tensorflow.python.ops.control_flow_ops.no_op