How to use _compare method in autotest

Best Python code snippet using autotest_python Github


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...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 - # Tensorflow link to relevant discussion - 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()...

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...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"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, 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, imag = np.random.normal(0, 1, 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"x_jacob_t = %s", x_jacob_t.reshape(x.shape))163"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"y_jacob_t = %s", y_jacob_t.reshape(y.shape))166"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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