Added the mode "bilinear" in the upscaling2D layer.

keras/backend/cntk_backend.py

@@ -1156,17 +1156,20 @@ def permute_dimensions(x, pattern):
     return C.transpose(x, axis)
 
 
-def resize_images(x, height_factor, width_factor, data_format):
-    if data_format == 'channels_first':
-        output = repeat_elements(x, height_factor, axis=2)
-        output = repeat_elements(output, width_factor, axis=3)
-        return output
-    elif data_format == 'channels_last':
-        output = repeat_elements(x, height_factor, axis=1)
-        output = repeat_elements(output, width_factor, axis=2)
-        return output
+def resize_images(x, height_factor, width_factor, data_format, interpolation='nearest'):
+    if interpolation == 'nearest':
+        if data_format == 'channels_first':
+            output = repeat_elements(x, height_factor, axis=2)
+            output = repeat_elements(output, width_factor, axis=3)
+            return output
+        elif data_format == 'channels_last':
+            output = repeat_elements(x, height_factor, axis=1)
+            output = repeat_elements(output, width_factor, axis=2)
+            return output
+        else:
+            raise ValueError('CNTK Backend: Invalid data_format:', data_format)
     else:
-        raise ValueError('CNTK Backend: Invalid data_format:', data_format)
+        raise NotImplementedError('CNTK only supports `nearest` interpolation.')
 
 
 def resize_volumes(x, depth_factor, height_factor, width_factor, data_format):

keras/backend/tensorflow_backend.py

@@ -1962,14 +1962,19 @@ def permute_dimensions(x, pattern):
     return tf.transpose(x, perm=pattern)
 
 
-def resize_images(x, height_factor, width_factor, data_format):
+def resize_images(x,
+                  height_factor,
+                  width_factor,
+                  data_format,
+                  interpolation='nearest'):
     """Resizes the images contained in a 4D tensor.
 
     # Arguments
         x: Tensor or variable to resize.
         height_factor: Positive integer.
         width_factor: Positive integer.
         data_format: string, `"channels_last"` or `"channels_first"`.
+        interpolation: A string, one of `nearest` or `bilinear`.
 
     # Returns
         A tensor.
@@ -1978,25 +1983,39 @@ def resize_images(x, height_factor, width_factor, data_format):
         ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
     """
     if data_format == 'channels_first':
-        original_shape = int_shape(x)
-        new_shape = tf.shape(x)[2:]
-        new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))
+        rows, cols = 2, 3
+    else:
+        rows, cols = 1, 2
+
+    original_shape = int_shape(x)
+    new_shape = tf.shape(x)[rows:cols + 1]
+    new_shape *= tf.constant(np.array([height_factor, width_factor], dtype='int32'))
+
+    if data_format == 'channels_first':
         x = permute_dimensions(x, [0, 2, 3, 1])
+    if interpolation == 'nearest':
         x = tf.image.resize_nearest_neighbor(x, new_shape)
+    elif interpolation == 'bilinear':
+        x = tf.image.resize_bilinear(x, new_shape)
+    else:
+        raise ValueError('interpolation should be one '
+                         'of "nearest" or "bilinear".')
+    if data_format == 'channels_first':
         x = permute_dimensions(x, [0, 3, 1, 2])
-        x.set_shape((None, None, original_shape[2] * height_factor if original_shape[2] is not None else None,
-                     original_shape[3] * width_factor if original_shape[3] is not None else None))
-        return x
-    elif data_format == 'channels_last':
-        original_shape = int_shape(x)
-        new_shape = tf.shape(x)[1:3]
-        new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))
-        x = tf.image.resize_nearest_neighbor(x, new_shape)
-        x.set_shape((None, original_shape[1] * height_factor if original_shape[1] is not None else None,
-                     original_shape[2] * width_factor if original_shape[2] is not None else None, None))
-        return x
+
+    if original_shape[rows] is None:
+        new_height = None
     else:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+        new_height = original_shape[rows] * height_factor
+
+    if original_shape[cols] is None:
+        new_width = None
+    else:
+        new_width = original_shape[cols] * width_factor
+
+    output_shape = (None, new_height, new_width, None)
+    x.set_shape(transpose_shape(output_shape, data_format, spatial_axes=(1, 2)))
+    return x
 
 
 def resize_volumes(x, depth_factor, height_factor, width_factor, data_format):

keras/backend/theano_backend.py

@@ -19,6 +19,7 @@
 except ImportError:
     from theano.sandbox.softsign import softsign as T_softsign
 
+import warnings
 import numpy as np
 from .common import floatx
 from .common import epsilon
@@ -965,24 +966,52 @@ def repeat_elements(x, rep, axis):
     return y
 
 
-def resize_images(x, height_factor, width_factor, data_format):
+def resize_images(x,
+                  height_factor,
+                  width_factor,
+                  data_format,
+                  interpolation='nearest'):
     """Resize the images contained in a 4D tensor of shape
     - [batch, channels, height, width] (for 'channels_first' data_format)
     - [batch, height, width, channels] (for 'channels_last' data_format)
     by a factor of (height_factor, width_factor). Both factors should be
     positive integers.
     """
     if data_format == 'channels_first':
-        output = repeat_elements(x, height_factor, axis=2)
-        output = repeat_elements(output, width_factor, axis=3)
-        return output
+        axis_1 = 2
+        axis_2 = 3
     elif data_format == 'channels_last':
-        output = repeat_elements(x, height_factor, axis=1)
-        output = repeat_elements(output, width_factor, axis=2)
-        return output
+        axis_1 = 1
+        axis_2 = 2
     else:
         raise ValueError('Invalid data_format:', data_format)
 
+    if interpolation == 'nearest':
+        output = repeat_elements(x, height_factor, axis=axis_1)
+        output = repeat_elements(output, width_factor, axis=axis_2)
+    elif interpolation == 'bilinear':
+        if not (height_factor == width_factor == 2):
+            raise NotImplementedError(
+                'Bilinear upscaling with factors other than (2, 2)'
+                'is not available when using the Theano backend.')
+        if data_format == 'channels_last':
+            output = permute_dimensions(x, [0, 3, 1, 2])
+        else:
+            output = x
+        output = T.nnet.abstract_conv.bilinear_upsampling(output,
+                                                          ratio=height_factor)
+        if data_format == 'channels_last':
+            output = permute_dimensions(output, [0, 2, 3, 1])
+        if hasattr(x, '_keras_shape'):
+            output._keras_shape = list(x._keras_shape)
+            output._keras_shape[axis_1] *= height_factor
+            output._keras_shape[axis_2] *= width_factor
+            output._keras_shape = tuple(output._keras_shape)
+    else:
+        raise ValueError('interpolation should be one of "nearest" or "bilinear".')
+
+    return output
+
 
 def resize_volumes(x, depth_factor, height_factor, width_factor, data_format):
     """Resize the volume contained in a 5D tensor of shape

keras/layers/convolutional.py

@@ -1916,6 +1916,9 @@ 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`.
+            Note that CNTK does not support yet the `bilinear` upscaling
+            and that with Theano, only `size=(2, 2)` is possible.
 
     # Input shape
         4D tensor with shape:
@@ -1933,11 +1936,15 @@ class UpSampling2D(Layer):
     """
 
     @interfaces.legacy_upsampling2d_support
-    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 = K.normalize_data_format(data_format)
         self.size = conv_utils.normalize_tuple(size, 2, 'size')
         self.input_spec = InputSpec(ndim=4)
+        if interpolation not in ['nearest', 'bilinear']:
+            raise ValueError('interpolation should be one '
+                             'of "nearest" or "bilinear".')
+        self.interpolation = interpolation
 
     def compute_output_shape(self, input_shape):
         if self.data_format == 'channels_first':
@@ -1957,7 +1964,7 @@ def compute_output_shape(self, input_shape):
 
     def call(self, inputs):
         return K.resize_images(inputs, self.size[0], self.size[1],
-                               self.data_format)
+                               self.data_format, self.interpolation)
 
     def get_config(self):
         config = {'size': self.size,

tests/keras/backend/backend_test.py

@@ -1323,6 +1323,23 @@ def test_resize_images(self):
             K.resize_images(K.variable(xval), 2, 2,
                             data_format='channels_middle')
 
+    @staticmethod
+    def _helper_bilinear(data_format, height_factor, width_factor):
+        x_shape = (2, 3, 4, 5)
+        check_single_tensor_operation('resize_images', x_shape,
+                                      [KTF, KTH],
+                                      height_factor=height_factor,
+                                      width_factor=width_factor,
+                                      data_format=data_format,
+                                      interpolation='bilinear')
+
+    @pytest.mark.skipif(K.backend() == 'cntk', reason='Not supported.')
+    @pytest.mark.parametrize('data_format', ['channels_first', 'channels_last'])
+    def test_resize_images_bilinear(self, data_format):
+        self._helper_bilinear(data_format, 2, 2)
+        with pytest.raises(NotImplementedError):
+            self._helper_bilinear(data_format, 4, 4)
+
     def test_resize_volumes(self):
         for data_format in ['channels_first', 'channels_last']:
             shape = (5, 5, 5)

tests/keras/layers/convolutional_test.py

@@ -862,6 +862,45 @@ def test_upsampling_2d():
                 assert_allclose(np_output, expected_out)
 
 
+@pytest.mark.skipif((K.backend() == 'cntk'),
+                    reason='cntk does not support it yet')
+def test_upsampling_2d_bilinear():
+    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:  # tf
+            inputs = np.random.rand(num_samples, input_num_row, input_num_col,
+                                    stack_size)
+
+        # basic test
+        layer_test(convolutional.UpSampling2D,
+                   kwargs={'size': (2, 2),
+                           'data_format': data_format,
+                           'interpolation': 'bilinear'},
+                   input_shape=inputs.shape)
+
+        for length_row in [2]:
+            for length_col in [2, 3]:
+                layer = convolutional.UpSampling2D(
+                    size=(length_row, length_col),
+                    data_format=data_format)
+                layer.build(inputs.shape)
+                outputs = layer(K.variable(inputs))
+                np_output = K.eval(outputs)
+                if data_format == 'channels_first':
+                    assert np_output.shape[2] == length_row * input_num_row
+                    assert np_output.shape[3] == length_col * input_num_col
+                else:  # tf
+                    assert np_output.shape[1] == length_row * input_num_row
+                    assert np_output.shape[2] == length_col * input_num_col
+
+
 @pytest.mark.skipif((K.backend() == 'cntk'),
                     reason="cntk does not support it yet")
 def test_upsampling_3d():