add separable conv3d op

theano/tensor/nnet/abstract_conv.py

@@ -662,6 +662,46 @@ def separable_conv2d(input,
     return pointwise_op
 
 
+def separable_conv3d(input,
+                     depthwise_filters,
+                     pointwise_filters,
+                     num_channels,
+                     input_shape=None,
+                     depthwise_filter_shape=None,
+                     pointwise_filter_shape=None,
+                     border_mode='valid',
+                     subsample=(1, 1, 1),
+                     filter_flip=True,
+                     filter_dilation=(1, 1, 1)):
+
+    input = as_tensor_variable(input)
+    depthwise_filters = as_tensor_variable(depthwise_filters)
+    conv_op = AbstractConv3d(imshp=input_shape,
+                             kshp=depthwise_filter_shape,
+                             border_mode=border_mode,
+                             subsample=subsample,
+                             filter_flip=filter_flip,
+                             filter_dilation=filter_dilation,
+                             num_groups=num_channels)
+
+    if input_shape is None or depthwise_filter_shape is None:
+        depthwise_op_shape = None
+    else:
+        depthwise_op_shape = conv_op.infer_shape(None, [input_shape, depthwise_filter_shape])[0]
+    depthwise_op = conv_op(input, depthwise_filters)
+
+    pointwise_op = conv3d(input=depthwise_op,
+                          filters=pointwise_filters,
+                          input_shape=depthwise_op_shape,
+                          filter_shape=pointwise_filter_shape,
+                          border_mode='valid',
+                          subsample=(1, 1, 1),
+                          filter_flip=filter_flip,
+                          filter_dilation=(1, 1, 1),
+                          num_groups=1)
+    return pointwise_op
+
+
 def conv3d(input,
            filters,
            input_shape=None,