How to use _compare method in autotest

Best Python code snippet using autotest_python

cast_op_test.py

Source:cast_op_test.py

...102    self._testAll(np.random.normal(0, 1e6, 210).reshape([2, 3, 5, 7]))103  # Special values like int32max, int64min, inf, -inf, nan casted to104  # integer values in somewhat unexpected ways. And they behave105  # differently on CPU and GPU.106  def _compare(self, x, dst_dtype, expected, use_gpu=False):107    np.testing.assert_equal(108        self._cast(109            x, dst_dtype, use_gpu=use_gpu), dst_dtype(expected))110  def testIntToFloatBoundary(self):111    i4 = np.iinfo(np.int32)112    i8 = np.iinfo(np.int64)113    self._compare(i4.min, np.float32, i4.min, False)114    self._compare(i4.max, np.float32, i4.max, False)115    self._compare(i8.min, np.float32, i8.min, False)116    self._compare(i8.max, np.float32, i8.max, False)117    self._compare(i4.min, np.float64, i4.min, False)118    self._compare(i4.max, np.float64, i4.max, False)119    self._compare(i8.min, np.float64, i8.min, False)120    self._compare(i8.max, np.float64, i8.max, False)121    # NOTE: GPU does not support int32/int64 for casting.122  def testInfNan(self):123    i4 = np.iinfo(np.int32)124    i8 = np.iinfo(np.int64)125    self._compare(np.inf, np.float32, np.inf, False)126    self._compare(np.inf, np.float64, np.inf, False)127    if sys.byteorder == "big":128      self._compare(np.inf, np.int32, i4.max, False)129      self._compare(np.inf, np.int64, i8.max, False)130    else:131      # np.float64("np.inf").astype(np.int32) is negative on x86 but positive on ppc64le132      # Numpy link to relevant discussion - https://github.com/numpy/numpy/issues/9040133      # Tensorflow link to relevant discussion - https://github.com/tensorflow/tensorflow/issues/9360134      if platform.machine() == "ppc64le":135        self._compare(-np.inf, np.int32, i4.min, False)136        self._compare(-np.inf, np.int64, i8.min, False)137      else:138        self._compare(np.inf, np.int32, i4.min, False)139        self._compare(np.inf, np.int64, i8.min, False)140    self._compare(-np.inf, np.float32, -np.inf, False)141    self._compare(-np.inf, np.float64, -np.inf, False)142    self._compare(-np.inf, np.int32, i4.min, False)143    self._compare(-np.inf, np.int64, i8.min, False)144    self.assertAllEqual(np.isnan(self._cast(np.nan, np.float32, False)), True)145    self.assertAllEqual(np.isnan(self._cast(np.nan, np.float64, False)), True)146    self._compare(np.nan, np.int32, i4.min, False)147    self._compare(np.nan, np.int64, i8.min, False)148    self._compare(np.inf, np.float32, np.inf, True)149    self._compare(np.inf, np.float64, np.inf, True)150    self._compare(-np.inf, np.float32, -np.inf, True)151    self._compare(-np.inf, np.float64, -np.inf, True)152    self.assertAllEqual(np.isnan(self._cast(np.nan, np.float32, True)), True)153    self.assertAllEqual(np.isnan(self._cast(np.nan, np.float64, True)), True)154  def _OpError(self, x, dtype, err):155    with self.test_session():156      with self.assertRaisesOpError(err):157        math_ops.cast(x, dtype).eval()158  def testNotImplemented(self):159    self._OpError(np.arange(0, 10), dtypes.string, "Cast.*int64.*string.*")160  def testCastToTypeOfVariable(self):161    with self.test_session() as sess:162      x = variables.Variable(5, dtype=dtypes.float32)163      y = variables.Variable(True, dtype=dtypes.bool)164      cast = math_ops.cast(y, x.dtype)165      variables.global_variables_initializer().run()...

batch_matmul_op_test.py

Source:batch_matmul_op_test.py

...60    z1 = np.array([(2.+2.j), (-2.+2.j), (2.-2.j), (2.+2.j)]).reshape([1, 2, 2])61    self.assertTrue(np.array_equal(z0, z1))62  # Compares _tfpBatchMatmul(x, y, alpha, adj) and _npBatchMatMul(x, y, alpha,63  # adj)64  def _compare(self, x, y, adj_x, adj_y, use_gpu=False):65    with self.test_session(use_gpu=use_gpu):66      z0 = tf.batch_matmul(x, y, adj_x=adj_x, adj_y=adj_y)67      z0_val = z0.eval()68    z1 = self._npBatchMatmul(x, y, adj_x, adj_y)69    self.assertShapeEqual(z1, z0)70    if z0_val.size != 0:71      err = (np.abs(z0_val - z1) / np.maximum(1, np.abs(z0_val))).max()72      tf.logging.info("error = %f", err)73      self.assertTrue(err < 1e-4)74  # Returns a random float np of "shape".75  def _randFloat(self, shape):76    vals = np.random.normal(0, 1, np.prod(shape)).reshape(shape)77    return np.array(vals, dtype=np.float32)78  def testSimpleFloat(self):79    for use_gpu in [False, True]:80      self._compare(self._randFloat([7, 2, 3]), self._randFloat([7, 3, 5]),81                    False, False, use_gpu)82      self._compare(self._randFloat([7, 2, 3]), self._randFloat([7, 5, 3]),83                    False, True, use_gpu)84      self._compare(self._randFloat([7, 3, 2]), self._randFloat([7, 3, 5]),85                    True, False, use_gpu)86      self._compare(self._randFloat([7, 3, 2]), self._randFloat([7, 5, 3]),87                    True, True, use_gpu)88  def testLargeFloat(self):89    for use_gpu in [False, True]:90      self._compare(self._randFloat([10, 64, 75]),91                    self._randFloat([10, 75, 30]), False, False, use_gpu)92      self._compare(self._randFloat([10, 75, 64]),93                    self._randFloat([10, 75, 30]), True, False, use_gpu)94      self._compare(self._randFloat([10, 64, 75]),95                    self._randFloat([10, 30, 75]), False, True, use_gpu)96      self._compare(self._randFloat([10, 75, 64]),97                    self._randFloat([10, 30, 75]), True, True, use_gpu)98  def testHighNDims(self):99    for use_gpu in [False, True]:100      self._compare(self._randFloat([5, 7, 2, 3]),101                    self._randFloat([5, 7, 3, 5]), False, False, use_gpu)102      self._compare(self._randFloat([5, 7, 3, 2]),103                    self._randFloat([5, 7, 3, 5]), True, False, use_gpu)104      self._compare(self._randFloat([5, 7, 2, 3]),105                    self._randFloat([5, 7, 5, 3]), False, True, use_gpu)106      self._compare(self._randFloat([5, 7, 3, 2]),107                    self._randFloat([5, 7, 5, 3]), True, True, use_gpu)108  # Returns a random complex numpy array of "shape".109  def _randComplex(self, shape):110    real = np.random.normal(0, 1, np.prod(shape))111    imag = np.random.normal(0, 1, np.prod(shape))112    vals = [np.complex(v[0], v[1]) for v in zip(real, imag)]113    return np.array(vals, dtype=np.complex64).reshape(shape)114  def testSimpleComplex(self):115    self._compare(self._randComplex([7, 2, 3]),116                  self._randComplex([7, 3, 5]), False, False)117    self._compare(self._randComplex([7, 2, 3]),118                  self._randComplex([7, 5, 3]), False, True)119    self._compare(self._randComplex([7, 3, 2]),120                  self._randComplex([7, 3, 5]), True, False)121    self._compare(self._randComplex([7, 3, 2]),122                  self._randComplex([7, 5, 3]), True, True)123  def testLargeComplex(self):124    self._compare(self._randComplex([10, 64, 75]),125                  self._randComplex([10, 75, 30]), False,126                  False)127    self._compare(self._randComplex([10, 64, 75]),128                  self._randComplex([10, 30, 75]), False, True)129    self._compare(self._randComplex([10, 75, 64]),130                  self._randComplex([10, 75, 30]), True, False)131    self._compare(self._randComplex([10, 75, 64]),132                  self._randComplex([10, 30, 75]), True, True)133  def testEmpty(self):134    self._compare(np.zeros([0, 3, 2]).astype(np.float32),135                  np.zeros([0, 2, 4]).astype(np.float32), False, False)136    self._compare(np.zeros([3, 2, 0]).astype(np.float32),137                  np.zeros([3, 0, 5]).astype(np.float32), False, False)138    self._compare(np.zeros([3, 0, 2]).astype(np.float32),139                  np.zeros([3, 2, 5]).astype(np.float32), False, False)140    self._compare(np.zeros([3, 3, 2]).astype(np.float32),141                  np.zeros([3, 2, 0]).astype(np.float32), False, False)142class BatchMatmulGradientTest(tf.test.TestCase):143  # loss = sum(batch_matmul(x, y)). Verify dl/dx and dl/dy via the144  # gradient checker.145  def _checkGrad(self, x, y, adj_x, adj_y):146    assert 3 == x.ndim147    assert 3 == y.ndim148    with self.test_session():149      inx = tf.convert_to_tensor(x)150      iny = tf.convert_to_tensor(y)151      z = tf.batch_matmul(inx, iny, adj_x, adj_y)152      loss = tf.reduce_sum(z)153      epsilon = 1e-2154      ((x_jacob_t, x_jacob_n),155       (y_jacob_t, y_jacob_n)) = tf.test.compute_gradient(156           [inx, iny],157           [x.shape, y.shape],158           loss,159           [1],160           x_init_value=[x, y],161           delta=epsilon)162    tf.logging.info("x_jacob_t = %s", x_jacob_t.reshape(x.shape))163    tf.logging.info("x_jacob_n = %s", x_jacob_n.reshape(x.shape))164    self.assertAllClose(x_jacob_t, x_jacob_n, rtol=1e-2, atol=epsilon)165    tf.logging.info("y_jacob_t = %s", y_jacob_t.reshape(y.shape))166    tf.logging.info("y_jacob_n = %s", y_jacob_n.reshape(y.shape))167    self.assertAllClose(y_jacob_t, y_jacob_n, rtol=1e-2, atol=epsilon)168  # Tests a batched matmul of x, and y: x is a 3D tensor of shape [b,169  # n, k] y is a 3D tensor of shape [b, k, m] the batched matmul170  # computes z of shape [b, n, m], where z[i, :, :] = x[i, :, :]171  # matmul y[i, :, :]172  def _compare(self, b, n, k, m):173    x = np.random.normal(0, 1, b * n * k).astype(np.float32).reshape([b, n, k])174    y = np.random.normal(0, 1, b * k * m).astype(np.float32).reshape([b, k, m])175    self._checkGrad(x, y, False, False)176    self._checkGrad(x.reshape([b, k, n]), y, True, False)177    self._checkGrad(x, y.reshape([b, m, k]), False, True)178    self._checkGrad(x.reshape([b, k, n]), y.reshape([b, m, k]), True, True)179  def testSmall(self):180    self._compare(1, 2, 3, 5)181  def testMedium(self):182    self._compare(3, 4, 7, 10)183  # Can't do testLarge using very large inputs because gradient184  # checker will take way too long time.185if __name__ == "__main__":...

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