Add SeparableConv1D (#8851)

* Add SeparableConv1D

* Minor updates

keras/backend/cntk_backend.py

@@ -1502,6 +1502,11 @@ def conv2d(x, kernel, strides=(1, 1), padding='valid',
     return _postprocess_conv2d_output(x, data_format)
 
 
+def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
+                     padding='valid', data_format=None, dilation_rate=1):
+    raise NotImplementedError
+
+
 def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
                      padding='valid', data_format=None, dilation_rate=(1, 1)):
     raise NotImplementedError

keras/backend/tensorflow_backend.py

@@ -3162,6 +3162,27 @@ def in_top_k(predictions, targets, k):
 # CONVOLUTIONS
 
 
+def _preprocess_conv1d_input(x, data_format):
+    """Transpose and cast the input before the conv1d.
+
+    # Arguments
+        x: input tensor.
+        data_format: string, `"channels_last"` or `"channels_first"`.
+
+    # Returns
+        A tensor.
+    """
+    if dtype(x) == 'float64':
+        x = tf.cast(x, 'float32')
+    tf_data_format = 'NHWC'  # to pass TF Conv2dNative operations
+    if data_format == 'channels_first':
+        if not _has_nchw_support():
+            x = tf.transpose(x, (0, 2, 1))  # NCW -> NWC
+        else:
+            tf_data_format = 'NCHW'
+    return x, tf_data_format
+
+
 def _preprocess_conv2d_input(x, data_format):
     """Transpose and cast the input before the conv2d.
 
@@ -3362,6 +3383,57 @@ def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
     return x
 
 
+def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
+                     padding='valid', data_format=None, dilation_rate=1):
+    """1D convolution with separable filters.
+
+    # Arguments
+        x: input tensor
+        depthwise_kernel: convolution kernel for the depthwise convolution.
+        pointwise_kernel: kernel for the 1x1 convolution.
+        strides: stride integer.
+        padding: string, `"same"` or `"valid"`.
+        data_format: string, `"channels_last"` or `"channels_first"`.
+        dilation_rate: integer dilation rate.
+
+    # Returns
+        Output tensor.
+
+    # Raises
+        ValueError: if `data_format` is neither `channels_last` or `channels_first`.
+    """
+    if data_format is None:
+        data_format = image_data_format()
+    if data_format not in {'channels_first', 'channels_last'}:
+        raise ValueError('Unknown data_format ' + str(data_format))
+
+    x, tf_data_format = _preprocess_conv1d_input(x, data_format)
+    padding = _preprocess_padding(padding)
+    if tf_data_format == 'NHWC':
+        spatial_start_dim = 1
+        strides = (1, 1) + strides + (1,)
+    else:
+        spatial_start_dim = 2
+        strides = (1, 1, 1) + strides
+    x = tf.expand_dims(x, spatial_start_dim)
+    depthwise_kernel = tf.expand_dims(depthwise_kernel, 0)
+    pointwise_kernel = tf.expand_dims(pointwise_kernel, 0)
+    dilation_rate = (1,) + dilation_rate
+
+    x = tf.nn.separable_conv2d(x, depthwise_kernel, pointwise_kernel,
+                               strides=strides,
+                               padding=padding,
+                               rate=dilation_rate,
+                               data_format=tf_data_format)
+
+    x = tf.squeeze(x, [spatial_start_dim])
+
+    if data_format == 'channels_first' and tf_data_format == 'NHWC':
+        x = tf.transpose(x, (0, 2, 1))  # NWC -> NCW
+
+    return x
+
+
 def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
                      padding='valid', data_format=None, dilation_rate=(1, 1)):
     """2D convolution with separable filters.

keras/backend/theano_backend.py

@@ -1972,6 +1972,11 @@ def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
     return conv_out
 
 
+def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
+                     padding='valid', data_format=None, dilation_rate=1):
+    raise NotImplementedError
+
+
 def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
                      padding='valid', data_format=None, dilation_rate=(1, 1)):
     raise NotImplementedError