diff options
Diffstat (limited to 'tensorflow/python/keras/layers/convolutional_test.py')
| -rw-r--r-- | tensorflow/python/keras/layers/convolutional_test.py | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/tensorflow/python/keras/layers/convolutional_test.py b/tensorflow/python/keras/layers/convolutional_test.py index f88d632ab5..bdc175b8b9 100644 --- a/tensorflow/python/keras/layers/convolutional_test.py +++ b/tensorflow/python/keras/layers/convolutional_test.py @@ -790,6 +790,42 @@ class UpSamplingTest(test.TestCase): np.testing.assert_allclose(np_output, expected_out) @tf_test_util.run_in_graph_and_eager_modes + def test_upsampling_2d_bilinear(self): + num_samples = 2 + stack_size = 2 + input_num_row = 11 + input_num_col = 12 + for data_format in ['channels_first', 'channels_last']: + if data_format == 'channels_first': + inputs = np.random.rand(num_samples, stack_size, input_num_row, + input_num_col) + else: + inputs = np.random.rand(num_samples, input_num_row, input_num_col, + stack_size) + + testing_utils.layer_test(keras.layers.UpSampling2D, + kwargs={'size': (2, 2), + 'data_format': data_format, + 'interpolation': 'bilinear'}, + input_shape=inputs.shape) + + if not context.executing_eagerly(): + for length_row in [2]: + for length_col in [2, 3]: + layer = keras.layers.UpSampling2D( + size=(length_row, length_col), + data_format=data_format) + layer.build(inputs.shape) + outputs = layer(keras.backend.variable(inputs)) + np_output = keras.backend.eval(outputs) + if data_format == 'channels_first': + self.assertEqual(np_output.shape[2], length_row * input_num_row) + self.assertEqual(np_output.shape[3], length_col * input_num_col) + else: + self.assertEqual(np_output.shape[1], length_row * input_num_row) + self.assertEqual(np_output.shape[2], length_col * input_num_col) + + @tf_test_util.run_in_graph_and_eager_modes def test_upsampling_3d(self): num_samples = 2 stack_size = 2 |