add implementations for Conv2DTranspose and SeparableConv2D

see BenWhetton#55
and BenWhetton#27

src/kerassurgeon/surgeon.py

@@ -437,6 +437,60 @@ def _apply_delete_mask(self, node, inbound_masks):
                 new_layer = type(layer).from_config(config)
             outbound_mask = None
 
+        elif layer_class in ('Conv2DTranspose'):
+            if np.all(inbound_masks):
+                new_layer = layer
+            else:
+                if data_format == 'channels_first':
+                    inbound_masks = np.swapaxes(inbound_masks, 0, -1)
+                # Conv layer: trim down inbound_masks to filter shape
+                k_size = layer.kernel_size
+                index = [slice(None, 1, None) for _ in k_size]
+                inbound_masks = inbound_masks[tuple(index + [slice(None)])]
+                weights = layer.get_weights()
+                # Delete unused weights to obtain new_weights
+                # Each deleted channel was connected to all of the channels
+                # in layer; therefore, the mask must be repeated for each
+                # channel.
+                # `delete_mask`'s size: size(weights[0])
+                delete_mask = np.tile(
+                    inbound_masks[..., np.newaxis], list(k_size) + [1, weights[0].shape[-2]]
+                ).transpose(0, 1, 3, 2)
+                new_shape = list(weights[0].shape)
+                new_shape[-1] = -1  # Input size channels
+                weights[0] = np.reshape(weights[0][delete_mask], new_shape)
+                # Instantiate new layer with new_weights
+                config = layer.get_config()
+                config['weights'] = weights
+                new_layer = type(layer).from_config(config)
+
+            outbound_mask = None
+
+        elif layer_class in ('SeparableConv2D'):
+            if np.all(inbound_masks):
+                new_layer = layer
+            else:
+                if data_format == 'channels_first':
+                    inbound_masks = np.swapaxes(inbound_masks, 0, -1)
+
+                # Conv layer: trim down inbound_masks to filter shape
+                k_size = layer.kernel_size
+                index = [slice(None, dim_size, None) for dim_size in k_size]
+                delete_mask = inbound_masks[tuple(index + [slice(None)])]
+                # Delete unused weights to obtain new_weights
+                weights = layer.get_weights()
+
+                new_shape = list(weights[0].shape)
+                new_shape[-2] = -1  # Weights always have channels_last
+                weights[0] = np.reshape(weights[0][delete_mask], new_shape)
+                weights[1] = weights[1][:, :, delete_mask[0][0], :]
+
+                # Instantiate new layer with new_weights
+                config = layer.get_config()
+                config['weights'] = weights
+                new_layer = type(layer).from_config(config)
+            outbound_mask = None
+
         elif layer_class in (
             'Cropping1D',
             'Cropping2D',
@@ -598,8 +652,6 @@ def _apply_delete_mask(self, node, inbound_masks):
         else:
             # Not implemented:
             # - Lambda
-            # - SeparableConv2D
-            # - Conv2DTranspose
             # - LocallyConnected1D
             # - LocallyConnected2D
             # - TimeDistributed
@@ -660,6 +712,18 @@ def _delete_channel_weights(self, layer, channel_indices):
             channel_indices_lstm = [layer.units * m + i for m in range(4) for i in channel_indices]
             weights = [np.delete(w, channel_indices_lstm, axis=-1) for w in layer.get_weights()]
             weights[1] = np.delete(weights[1], channel_indices, axis=0)
+        elif layer.__class__.__name__ == 'Conv2DTranspose':
+            weights = []
+            allweights = layer.get_weights()
+            w = np.delete(allweights[0], channel_indices, axis=2)
+            weights.append(w)
+            if len(allweights) == 2:
+                b = np.delete(allweights[1], channel_indices, axis=-1)
+                weights.append(b)
+        elif layer.__class__.__name__ == 'SeparableConv2D':
+            weights = layer.get_weights()
+            for i in range(1, len(weights)):
+                weights[i] = np.delete(weights[i], channel_indices, axis=-1)
         else:
             weights = [np.delete(w, channel_indices, axis=-1) for w in layer.get_weights()]
         layer_config['weights'] = weights