diff options
Diffstat (limited to 'tensorflow/contrib/layers/python/layers/layers_test.py')
| -rw-r--r-- | tensorflow/contrib/layers/python/layers/layers_test.py | 73 |
1 files changed, 68 insertions, 5 deletions
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index 2837a3172d..7ccd9d8868 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -1766,10 +1766,12 @@ class BatchNormTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'undefined'): _layers.batch_norm(inputs, data_format='NCHW') - def _testCreateOp(self, fused): + def _testCreateOp(self, fused, dtype=None): + if dtype is None: + dtype = dtypes.float32 height, width = 3, 3 with self.test_session(): - images = np.random.uniform(size=(5, height, width, 3)).astype('f') + images = np.random.uniform(size=(5, height, width, 3)).astype(dtype.as_numpy_dtype) output = _layers.batch_norm(images, fused=fused) expected_name = ('BatchNorm/FusedBatchNorm' if fused else 'BatchNorm/batchnorm') @@ -1784,6 +1786,9 @@ class BatchNormTest(test.TestCase): def testCreateOpFused(self): self._testCreateOp(True) + def testCreateOpFusedFloat16(self): + self._testCreateOp(True, dtypes.float16) + def _testCreateOpBetaRegularizer(self, fused=True): height, width = 3, 3 with self.test_session(): @@ -2651,10 +2656,68 @@ class BatchNormTest(test.TestCase): def testBatchNormBeta(self): # Test case for 11673 with self.test_session() as sess: - a = array_ops.placeholder(dtypes.float32, shape=(10, 10, 10, 10)) - b = _layers.batch_norm(a, center=False, data_format='NCHW', - zero_debias_moving_mean=True) + a_32 = array_ops.placeholder(dtypes.float32, shape=(10, 10, 10, 10)) + b_32 = _layers.batch_norm(a_32, center=False, data_format='NCHW', + zero_debias_moving_mean=True) + a_16 = array_ops.placeholder(dtypes.float16, shape=(10, 10, 10, 10)) + b_16 = _layers.batch_norm(a_16, center=False, data_format='NCHW', + zero_debias_moving_mean=True) + sess.run(variables_lib.global_variables_initializer()) + + def testVariablesAreFloat32(self): + height, width = 3, 3 + with self.test_session(): + images = random_ops.random_uniform((5, height, width, 3), + seed=1, dtype=dtypes.float16) + _layers.batch_norm(images, scale=True) + beta = variables.get_variables_by_name('beta')[0] + gamma = variables.get_variables_by_name('gamma')[0] + self.assertEqual(beta.dtype, dtypes.float32_ref) + self.assertEqual(gamma.dtype, dtypes.float32_ref) + moving_mean = variables.get_variables_by_name('moving_mean')[0] + moving_variance = variables.get_variables_by_name('moving_variance')[0] + self.assertEqual(moving_mean.dtype, dtypes.float32_ref) + self.assertEqual(moving_variance.dtype, dtypes.float32_ref) + + def _runFusedBatchNorm(self, shape, dtype): + channels = shape[1] + images = np.arange(np.product(shape), dtype=dtype).reshape(shape) + beta = init_ops.constant_initializer( + np.arange( + 2, channels + 2, dtype=np.float32)) + gamma = init_ops.constant_initializer( + np.arange( + 10, channels + 10, dtype=np.float32) * 2.0) + mean = init_ops.constant_initializer( + np.arange( + 3, channels + 3, dtype=np.float32) * 5.0) + variance = init_ops.constant_initializer( + np.arange( + 1, channels + 1, dtype=np.float32) * 4.0) + output = _layers.batch_norm( + images, + fused=True, + is_training=True, + scale=True, + epsilon=0.5, + param_initializers={ + 'beta': beta, + 'gamma': gamma, + 'moving_mean': mean, + 'moving_variance': variance, + }, + data_format='NCHW') + with self.test_session(use_gpu=True) as sess: sess.run(variables_lib.global_variables_initializer()) + return sess.run(output) + + def testFusedBatchNormFloat16MatchesFloat32(self): + if test.is_gpu_available(cuda_only=True): + shape = [5, 4, 2, 3] + res_32 = self._runFusedBatchNorm(shape, np.float32) + res_16 = self._runFusedBatchNorm(shape, np.float16) + self.assertAllClose(res_32, res_16, rtol=1e-3) + def testAdjustmentCreated(self): # Tests that the adjustment is appropriately passed to and used by the core |