diff options
Diffstat (limited to 'tensorflow/python/ops/nn_fused_batchnorm_test.py')
| -rw-r--r-- | tensorflow/python/ops/nn_fused_batchnorm_test.py | 119 |
1 files changed, 61 insertions, 58 deletions
diff --git a/tensorflow/python/ops/nn_fused_batchnorm_test.py b/tensorflow/python/ops/nn_fused_batchnorm_test.py index 1fcd0384da..e72d34d1f7 100644 --- a/tensorflow/python/ops/nn_fused_batchnorm_test.py +++ b/tensorflow/python/ops/nn_fused_batchnorm_test.py @@ -335,22 +335,22 @@ class BatchNormalizationTest(test.TestCase): def testInference(self): x_shape = [1, 1, 6, 1] - if test.is_gpu_available(cuda_only=True): - for dtype in [np.float16, np.float32]: + for dtype in [np.float16, np.float32]: + if test.is_gpu_available(cuda_only=True): self._test_inference( x_shape, dtype, [1], np.float32, use_gpu=True, data_format='NHWC') self._test_inference( x_shape, dtype, [1], np.float32, use_gpu=True, data_format='NCHW') - self._test_inference( - x_shape, np.float32, [1], np.float32, use_gpu=False, data_format='NHWC') + self._test_inference( + x_shape, dtype, [1], np.float32, use_gpu=False, data_format='NHWC') x_shape = [1, 1, 6, 2] if test.is_gpu_available(cuda_only=True): for dtype in [np.float16, np.float32]: self._test_inference( x_shape, dtype, [2], np.float32, use_gpu=True, data_format='NHWC') - self._test_inference( - x_shape, np.float32, [2], np.float32, use_gpu=False, data_format='NHWC') + self._test_inference( + x_shape, dtype, [2], np.float32, use_gpu=False, data_format='NHWC') x_shape = [1, 2, 1, 6] if test.is_gpu_available(cuda_only=True): @@ -359,33 +359,33 @@ class BatchNormalizationTest(test.TestCase): x_shape, dtype, [2], np.float32, use_gpu=True, data_format='NCHW') x_shape = [27, 131, 127, 6] - if test.is_gpu_available(cuda_only=True): - for dtype in [np.float16, np.float32]: + for dtype in [np.float16, np.float32]: + if test.is_gpu_available(cuda_only=True): self._test_inference( x_shape, dtype, [131], np.float32, use_gpu=True, data_format='NCHW') self._test_inference( x_shape, dtype, [6], np.float32, use_gpu=True, data_format='NHWC') - self._test_inference( - x_shape, np.float32, [6], np.float32, use_gpu=False, data_format='NHWC') + self._test_inference( + x_shape, dtype, [6], np.float32, use_gpu=False, data_format='NHWC') def testTraining(self): x_shape = [1, 1, 6, 1] - if test.is_gpu_available(cuda_only=True): - for dtype in [np.float16, np.float32]: + for dtype in [np.float16, np.float32]: + if test.is_gpu_available(cuda_only=True): self._test_training( x_shape, dtype, [1], np.float32, use_gpu=True, data_format='NHWC') self._test_training( x_shape, dtype, [1], np.float32, use_gpu=True, data_format='NCHW') - self._test_training( - x_shape, np.float32, [1], np.float32, use_gpu=False, data_format='NHWC') + self._test_training( + x_shape, dtype, [1], np.float32, use_gpu=False, data_format='NHWC') x_shape = [1, 1, 6, 2] - if test.is_gpu_available(cuda_only=True): - for dtype in [np.float16, np.float32]: + for dtype in [np.float16, np.float32]: + if test.is_gpu_available(cuda_only=True): self._test_training( x_shape, dtype, [2], np.float32, use_gpu=True, data_format='NHWC') - self._test_training( - x_shape, np.float32, [2], np.float32, use_gpu=False, data_format='NHWC') + self._test_training( + x_shape, dtype, [2], np.float32, use_gpu=False, data_format='NHWC') x_shape = [1, 2, 1, 6] if test.is_gpu_available(cuda_only=True): @@ -394,20 +394,20 @@ class BatchNormalizationTest(test.TestCase): x_shape, dtype, [2], np.float32, use_gpu=True, data_format='NCHW') x_shape = [27, 131, 127, 6] - if test.is_gpu_available(cuda_only=True): - for dtype in [np.float16, np.float32]: + for dtype in [np.float16, np.float32]: + if test.is_gpu_available(cuda_only=True): self._test_training( x_shape, dtype, [131], np.float32, use_gpu=True, data_format='NCHW') self._test_training( x_shape, dtype, [6], np.float32, use_gpu=True, data_format='NHWC') - self._test_training( - x_shape, np.float32, [6], np.float32, use_gpu=False, data_format='NHWC') + self._test_training( + x_shape, dtype, [6], np.float32, use_gpu=False, data_format='NHWC') def testBatchNormGrad(self): for is_training in [True, False]: x_shape = [1, 1, 6, 1] - if test.is_gpu_available(cuda_only=True): - for dtype in [np.float16, np.float32]: + for dtype in [np.float16, np.float32]: + if test.is_gpu_available(cuda_only=True): self._test_gradient( x_shape, dtype, [1], @@ -422,17 +422,17 @@ class BatchNormalizationTest(test.TestCase): use_gpu=True, data_format='NCHW', is_training=is_training) - self._test_gradient( - x_shape, - np.float32, [1], - np.float32, - use_gpu=False, - data_format='NHWC', - is_training=is_training) + self._test_gradient( + x_shape, + dtype, [1], + np.float32, + use_gpu=False, + data_format='NHWC', + is_training=is_training) x_shape = [1, 1, 6, 2] - if test.is_gpu_available(cuda_only=True): - for dtype in [np.float16, np.float32]: + for dtype in [np.float16, np.float32]: + if test.is_gpu_available(cuda_only=True): self._test_gradient( x_shape, dtype, [2], @@ -440,13 +440,13 @@ class BatchNormalizationTest(test.TestCase): use_gpu=True, data_format='NHWC', is_training=is_training) - self._test_gradient( - x_shape, - np.float32, [2], - np.float32, - use_gpu=False, - data_format='NHWC', - is_training=is_training) + self._test_gradient( + x_shape, + dtype, [2], + np.float32, + use_gpu=False, + data_format='NHWC', + is_training=is_training) x_shape = [1, 2, 1, 6] if test.is_gpu_available(cuda_only=True): @@ -460,8 +460,8 @@ class BatchNormalizationTest(test.TestCase): is_training=is_training) x_shape = [5, 7, 11, 4] - if test.is_gpu_available(cuda_only=True): - for dtype in [np.float16, np.float32]: + for dtype in [np.float16, np.float32]: + if test.is_gpu_available(cuda_only=True): self._test_gradient( x_shape, dtype, [7], @@ -476,13 +476,13 @@ class BatchNormalizationTest(test.TestCase): use_gpu=True, data_format='NHWC', is_training=is_training) - self._test_gradient( - x_shape, - np.float32, [4], - np.float32, - use_gpu=False, - data_format='NHWC', - is_training=is_training) + self._test_gradient( + x_shape, + dtype, [4], + np.float32, + use_gpu=False, + data_format='NHWC', + is_training=is_training) def _testBatchNormGradGrad(self, config): shape = config['shape'] @@ -506,15 +506,14 @@ class BatchNormalizationTest(test.TestCase): data_format='NCHW', is_training=is_training, err_tolerance=err_tolerance) - if dtype != np.float16: - self._test_grad_grad( - shape, - np.float32, [shape[3]], - np.float32, - use_gpu=False, - data_format='NHWC', - is_training=is_training, - err_tolerance=err_tolerance) + self._test_grad_grad( + shape, + dtype, [shape[3]], + np.float32, + use_gpu=False, + data_format='NHWC', + is_training=is_training, + err_tolerance=err_tolerance) def testBatchNormGradGrad(self): configs = [{ @@ -526,6 +525,10 @@ class BatchNormalizationTest(test.TestCase): 'err_tolerance': 1e-3, 'dtype': np.float32, }, { + 'shape': [2, 3, 4, 5], + 'err_tolerance': 1e-2, + 'dtype': np.float16, + }, { 'shape': [2, 3, 2, 2], 'err_tolerance': 2e-3, 'dtype': np.float16, |