1 files changed, 81 insertions, 0 deletions
diff --git a/model-optimizer/extensions/front/tf/deconv_ext.py b/model-optimizer/extensions/front/tf/deconv_ext.py
new file mode 100644
index 000000000..67de85650
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+++ b/model-optimizer/extensions/front/tf/deconv_ext.py
@@ -0,0 +1,81 @@
+"""
+ Copyright (c) 2018 Intel Corporation
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+"""
+import numpy as np
+
+from mo.front.common.partial_infer.utils import convert_tf_padding_to_str, int64_array
+from mo.front.extractor import FrontExtractorOp
+from mo.front.tf.extractors.utils import tf_data_format_spatial, tf_data_format_channel, tf_data_format_batch, \
+    tf_int_list
+from mo.ops.deconvolution import Deconvolution
+from mo.ops.op import PermuteAttrs
+
+
+class Conv2DBackpropInputFrontExtractor(FrontExtractorOp):
+    op = 'Conv2DBackpropInput'
+    enabled = True
+
+    @staticmethod
+    def extract(node):
+        attrs = tf_create_attrs(node, 3, 2)
+        attrs.update({'op': __class__.op,
+                      'get_weights_permute': PermuteAttrs.Permutation(perm=int64_array([3, 2, 0, 1]),
+                                                                      inv=int64_array([2, 3, 1, 0]))
+                      })
+
+        # update the attributes of the node
+        Deconvolution.update_node_stat(node, attrs)
+        return __class__.enabled
+
+
+class Conv3DBackpropInputV2InputFrontExtractor(FrontExtractorOp):
+    op = 'Conv3DBackpropInputV2'
+    enabled = True
+
+    @staticmethod
+    def extract(node):
+        attrs = tf_create_attrs(node, 4, 3)
+        attrs.update({'op': __class__.op,
+                      'get_weights_permute': PermuteAttrs.Permutation(perm=int64_array([3, 4, 0, 1, 2]),
+                                                                      inv=int64_array([2, 3, 4, 0, 1]))
+                      })
+
+        # update the attributes of the node
+        Deconvolution.update_node_stat(node, attrs)
+        return __class__.enabled
+
+
+def tf_create_attrs(node, input_feature_channel, output_feature_channel):
+    data_format = node.pb.attr["data_format"]
+
+    return {
+        'auto_pad': convert_tf_padding_to_str(node.pb.attr['padding']),
+        'bias_addable': True,
+        'bias_term': False,
+        'spatial_dims': tf_data_format_spatial(data_format),
+        'channel_dims': tf_data_format_channel(data_format),
+        'batch_dims': tf_data_format_batch(data_format),
+        'pad': None,  # will be inferred when input shape is known
+        'pad_spatial_shape': None,
+        'output_spatial_shape': None,
+        'output_shape': None,
+        'output': None,
+        'stride': tf_int_list(node.pb.attr["strides"].list),
+        'type': None,  # don't set type until we are sure it is really translated to correct IR; see infer function
+        'group': None,
+        'layout': data_format.s.decode(),
+        'input_feature_channel': input_feature_channel,
+        'output_feature_channel': output_feature_channel,
+    }