Python test_util.use_gpu函数代码示例

 def testGain(self):
   shape = (10, 10)
   for dtype in [dtypes.float32, dtypes.float64]:
     init_default = init_ops_v2.Identity()
     init_custom = init_ops_v2.Identity(gain=0.9)
     with test_util.use_gpu():
       self.assertAllClose(self.evaluate(init_default(shape, dtype=dtype)),
                           np.eye(*shape))
     with test_util.use_gpu():
       self.assertAllClose(self.evaluate(init_custom(shape, dtype=dtype)),
                           np.eye(*shape) * 0.9)

  def _VerifyValues(self, image, ksizes, strides, padding, patches):
    """Tests input-output pairs for the ExtractVolumePatches op.

    Args:
      image: Input tensor with shape:
             [batch, in_planes, in_rows, in_cols, depth].
      ksizes: Patch size specified as: [ksize_planes, ksize_rows, ksize_cols].
      strides: Output strides, specified as:
               [stride_planes, stride_rows, stride_cols].
      padding: Padding type.
      patches: Expected output.

    Note:
      rates are not supported as of now.
    """
    ksizes = [1] + ksizes + [1]
    strides = [1] + strides + [1]

    with test_util.use_gpu():
      out_tensor = array_ops.extract_volume_patches(
          constant_op.constant(image),
          ksizes=ksizes,
          strides=strides,
          padding=padding,
          name="im2col_3d")
      self.assertAllClose(patches, self.evaluate(out_tensor))

 def _testGradientsSimple(self, dtype):
   # Test both positive and negative concat axis.
   # -2 and 1 correspond to the same axis for 3-dimensional tensors.
   for axis in [-2, 1]:
     with test_util.use_gpu():
       inp = []
       inp_tensors = []
       for x in [1, 2, 6]:
         shape = [10, x, 2]
         t = np.random.rand(*shape).astype(dtype.as_numpy_dtype)
         if dtype.is_complex:
           t += -1j * t
         inp.append(t)
         inp_tensors.append(
             constant_op.constant(
                 t.flatten(),
                 shape=shape,
                 dtype=dtype))
       c = array_ops.concat(inp_tensors, axis)
       output_shape = [10, 9, 2]
       grad_inp = np.random.rand(*output_shape).astype(dtype.as_numpy_dtype)
       if dtype.is_complex:
         grad_inp += -1j * grad_inp
       grad_tensor = constant_op.constant(
           grad_inp.flatten(), shape=output_shape)
       grad = gradients_impl.gradients([c], inp_tensors, [grad_tensor])
       concated_grad = array_ops.concat(grad, axis)
       result = self.evaluate(concated_grad)
   self.assertAllEqual(result, grad_inp)

 def testNCHWToNHWC2D(self):
   x_val = [[7, 4], [9, 3], [4, 5], [5, 1]]
   x = constant_op.constant(x_val)
   y = nn_ops.data_format_vec_permute(x, src_format="NCHW", dst_format="NHWC")
   with test_util.use_gpu():
     y_val = self.evaluate(y)
     self.assertAllEqual(y_val, [[7, 4], [4, 5], [5, 1], [9, 3]])

 def testTwoOpsIndependent(self):
   with test_util.use_gpu():
     sample_op1, sample_op2 = self._make_ops(32)
     sample1, sample2 = self.evaluate([sample_op1, sample_op2])
     # We expect sample1 and sample2 to be independent.
     # 1 in 2^32 chance of this assertion failing.
     self.assertFalse(np.equal(sample1, sample2).all())

 def testOneOpMultipleStepsIndependent(self):
   with test_util.use_gpu():
     sample_op1, _ = self._make_ops(10)
     # Consecutive runs shouldn't yield identical output.
     sample1a = self.evaluate(sample_op1)
     sample1b = self.evaluate(sample_op1)
     self.assertFalse(np.equal(sample1a, sample1b).all())

 def testZeros(self):
   with test_util.use_gpu():
     for dtype in dtypes.uint8, dtypes.int16, dtypes.int32, dtypes.int64:
       zero = constant_op.constant(0, dtype=dtype)
       one = constant_op.constant(1, dtype=dtype)
       bads = [one // zero]
       if dtype in (dtypes.int32, dtypes.int64):
         bads.append(one % zero)
       for bad in bads:
         try:
           result = self.evaluate(bad)
         except errors_impl.OpError as e:
           # Ideally, we'd get a nice exception.  In theory, this should only
           # happen on CPU, but 32 bit integer GPU division is actually on
           # CPU due to a placer bug.
           # TODO(irving): Make stricter once the placer bug is fixed.
           self.assertIn('Integer division by zero', str(e))
         else:
           # On the GPU, integer division by zero produces all bits set.
           # But apparently on some GPUs "all bits set" for 64 bit division
           # means 32 bits set, so we allow 0xffffffff as well.  This isn't
           # very portable, so we may need to expand this list if other GPUs
           # do different things.
           self.assertTrue(test.is_gpu_available())
           self.assertIn(result, (-1, 0xff, 0xffffffff))

 def testNegativeMinLogits(self):
   random_seed.set_random_seed(78844)
   with test_util.use_gpu():
     logits = constant_op.constant([[np.finfo(np.float32).min] * 1023 + [0]])
     num_samples = 1000
     samples = self.evaluate(random_ops.multinomial(logits, num_samples))
     self.assertAllEqual([[1023] * num_samples], samples)

 def testNHWCToNCHW(self):
   x_val = [7, 4, 9, 3]
   x = constant_op.constant(x_val)
   y = nn_ops.data_format_vec_permute(x)
   with test_util.use_gpu():
     y_val = self.evaluate(y)
     self.assertAllEqual(y_val, [7, 3, 4, 9])

 def testHWNCToNHWC(self):
   x_val = [7, 4, 9, 3]
   x = constant_op.constant(x_val)
   y = nn_ops.data_format_vec_permute(x, src_format="HWNC", dst_format="NHWC")
   with test_util.use_gpu():
     y_val = self.evaluate(y)
     self.assertAllEqual(y_val, [9, 7, 4, 3])

 def _compareScalar(self, func, x, y, dtype):
   with test_util.use_gpu():
     out = func(
         ops.convert_to_tensor(np.array([x]).astype(dtype)),
         ops.convert_to_tensor(np.array([y]).astype(dtype)))
     ret = self.evaluate(out)
   return ret[0]

  def _do_sampling(self, logits, num_samples, sampler):
    """Samples using the supplied sampler and inputs.

    Args:
      logits: Numpy ndarray of shape [batch_size, num_classes].
      num_samples: Int; number of samples to draw.
      sampler: A sampler function that takes (1) a [batch_size, num_classes]
        Tensor, (2) num_samples and returns a [batch_size, num_samples] Tensor.

    Returns:
      Frequencies from sampled classes; shape [batch_size, num_classes].
    """
    with test_util.use_gpu():
      random_seed.set_random_seed(1618)
      op = sampler(constant_op.constant(logits), num_samples)
      d = self.evaluate(op)

    batch_size, num_classes = logits.shape
    freqs_mat = []
    for i in range(batch_size):
      cnts = dict(collections.Counter(d[i, :]))

      # Requires drawn class labels be in range.
      self.assertLess(max(cnts.keys()), num_classes)
      self.assertGreaterEqual(min(cnts.keys()), 0)

      freqs = [(cnts[k] * 1. / num_samples if k in cnts else 0)
               for k in range(num_classes)]
      freqs_mat.append(freqs)

    return freqs_mat

  def Test(self):
    if not use_static_shape_ or a_np_.dtype in (np.int32, np.int64, np.float16):
      self.skipTest("Skipping infeasible gradient test.")

    # Transpose and possibly conjugate a_np_ and b_np_ according to the
    # attributes such that tf.matmul(effective_a_np, effective_b_np, **kwargs)
    # results in a valid matrix multiplication and produces the same result as
    # np.matrix(a_np_) * np.matrix(b_np_)
    effective_a_np = _GetTransposedMatrices(a_np_, "a", kwargs_)
    effective_b_np = _GetTransposedMatrices(b_np_, "b", kwargs_)

    epsilon = np.finfo(a_np_.dtype).eps
    delta = epsilon**(1.0 / 3.0)
    tol = 20 * delta
    with self.session(), test_util.use_gpu():
      theoretical, numerical = gradient_checker_v2.compute_gradient(
          lambda x: math_ops.matmul(x, effective_b_np, **kwargs_),
          [effective_a_np],
          delta=delta)
      self.assertAllClose(theoretical, numerical, rtol=tol, atol=tol)

      theoretical, numerical = gradient_checker_v2.compute_gradient(
          lambda x: math_ops.matmul(effective_a_np, x, **kwargs_),
          [effective_b_np],
          delta=delta)
      self.assertAllClose(theoretical, numerical, rtol=tol, atol=tol)

  def _RunAndVerifyGradientsRandom(self):
    # Random dims of rank 5
    input_shape = np.random.randint(1, 5, size=5)
    # Random number of tensors
    num_tensors = np.random.randint(12, 20)
    # Random dim to concat on
    concat_dim = np.random.randint(5)
    concat_dim_sizes = np.random.randint(1, 5, size=num_tensors)
    with test_util.use_gpu():
      inp = []
      inp_tensors = []
      for x in concat_dim_sizes:
        shape = input_shape
        shape[concat_dim] = x
        t = np.random.rand(*shape).astype("f")
        inp.append(t)
        inp_tensors.append(
            constant_op.constant(t.flatten(), shape=shape,
                                 dtype=dtypes.float32))
      c = array_ops.concat(inp_tensors, concat_dim)
      output_shape = input_shape
      output_shape[concat_dim] = concat_dim_sizes.sum()
      grad_inp = np.random.rand(*output_shape).astype("f")
      grad_tensor = constant_op.constant(grad_inp.flatten(), shape=output_shape)
      grad = gradients_impl.gradients([c], inp_tensors, [grad_tensor])
      concated_grad = array_ops.concat(grad, concat_dim)
      result = self.evaluate(concated_grad)

    self.assertAllEqual(result, grad_inp)

  def testGradientsLastDim(self):
    # Test both positive and negative concat axis.
    # -1 and 2 correspond to the same axis for 3-dimensional tensors.
    for axis in [-1, 2]:
      with test_util.use_gpu():
        inp = []
        inp_tensors = []
        for x in [1, 2, 6]:
          shape = [10, 2, x]
          t = np.random.rand(*shape).astype("f")
          inp.append(t)
          inp_tensors.append(
              constant_op.constant(
                  t.flatten(),
                  shape=shape,
                  dtype=dtypes.float32))
        c = array_ops.concat(inp_tensors, 2)
        output_shape = [10, 2, 9]
        grad_inp = np.random.rand(*output_shape).astype("f")
        grad_tensor = constant_op.constant(
            grad_inp.flatten(), shape=output_shape)
        grad = gradients_impl.gradients([c], inp_tensors, [grad_tensor])
        concated_grad = array_ops.concat(grad, axis)
        result = self.evaluate(concated_grad)

    self.assertAllEqual(result, grad_inp)