diff options
Diffstat (limited to 'tensorflow/python/keras/layers')
| -rw-r--r-- | tensorflow/python/keras/layers/convolutional.py | 14 | ||||
| -rw-r--r-- | tensorflow/python/keras/layers/convolutional_test.py | 36 |
2 files changed, 48 insertions, 2 deletions
diff --git a/tensorflow/python/keras/layers/convolutional.py b/tensorflow/python/keras/layers/convolutional.py index 8f5872385c..58024677ee 100644 --- a/tensorflow/python/keras/layers/convolutional.py +++ b/tensorflow/python/keras/layers/convolutional.py @@ -1951,6 +1951,7 @@ class UpSampling2D(Layer): It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". + interpolation: A string, one of `nearest` or `bilinear`. Input shape: 4D tensor with shape: @@ -1967,10 +1968,18 @@ class UpSampling2D(Layer): `(batch, channels, upsampled_rows, upsampled_cols)` """ - def __init__(self, size=(2, 2), data_format=None, **kwargs): + def __init__(self, + size=(2, 2), + data_format=None, + interpolation='nearest', + **kwargs): super(UpSampling2D, self).__init__(**kwargs) self.data_format = conv_utils.normalize_data_format(data_format) self.size = conv_utils.normalize_tuple(size, 2, 'size') + if interpolation not in {'nearest', 'bilinear'}: + raise ValueError('`interpolation` argument should be one of `"nearest"` ' + 'or `"bilinear"`.') + self.interpolation = interpolation self.input_spec = InputSpec(ndim=4) def compute_output_shape(self, input_shape): @@ -1992,7 +2001,8 @@ class UpSampling2D(Layer): def call(self, inputs): return backend.resize_images( - inputs, self.size[0], self.size[1], self.data_format) + inputs, self.size[0], self.size[1], self.data_format, + interpolation=self.interpolation) def get_config(self): config = {'size': self.size, 'data_format': self.data_format} 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 |