Adjust input output sizes when any dim is None (#480)

Whenever inputs and outputs have None as dim (except for batch size), after conversion they will have 0 as the dimension.
Batch size will be converted to 'N'.
input(None, None) -> input(N, 0)

With this fix even other dims will have variables like M1, M2.
input(None, None) -> input(N, M1)

Author: buddhapuneeth

keras2onnx/_parser_tf.py

@@ -54,6 +54,15 @@ def adjust_input_batch_size(var_type):
     return var_type
 
 
+def adjust_input_output_size(var_type, dim_variable_counter):
+    if len(var_type.shape) > 0:
+        for dim in range(1, len(var_type.shape)):
+            if var_type.shape[dim] is None:
+                dim_variable_counter += 1
+                var_type.shape[dim] = 'M' + str(dim_variable_counter)
+    return dim_variable_counter
+
+
 def _get_layer_name(reserved, ts_or_op):
     return ts_or_op.rsplit('/', 1)[0]
 

keras2onnx/parser.py

@@ -15,7 +15,8 @@
 from ._consts import TYPES
 from ._tf_ops import pass_thru_converter
 from ._parser_tf import (infer_variable_type, LayerInfo, is_placeholder_node,
-                         tsname_to_node, on_parsing_keras_layer_v2, adjust_input_batch_size as _adjust_input_batch_size)
+                         tsname_to_node, on_parsing_keras_layer_v2, adjust_input_batch_size as _adjust_input_batch_size,
+                         adjust_input_output_size as _adjust_input_output_size)
 from ._parser_1x import (extract_inbound_nodes,
                          list_input_tensors, list_input_mask, list_output_mask,
                          list_output_tensors, list_input_shapes, list_output_shapes, on_parsing_keras_layer)
@@ -756,6 +757,7 @@ def parse_graph(topo, graph, target_opset, output_names, keras_node_dict):
     """
     top_level = topo.declare_scope('__root')
 
+    dim_variable_counter = 0
     # Create the onnx model input name before parsing to keep ...
     # ... the model input names are identical to the original Keras model.
     for idx_ in range(len(topo.raw_model.model.inputs)):
@@ -765,6 +767,7 @@ def parse_graph(topo, graph, target_opset, output_names, keras_node_dict):
             idx_key = list(topo.raw_model.model.inputs.keys())[idx_]
         input_ts = topo.raw_model.model.inputs[idx_key]
         var_type = _adjust_input_batch_size(infer_variable_type(input_ts, target_opset))
+        dim_variable_counter = _adjust_input_output_size(var_type, dim_variable_counter)
         str_value = input_ts.name
         var0 = None
         if hasattr(topo.raw_model.model, 'input_names'):
@@ -789,6 +792,7 @@ def parse_graph(topo, graph, target_opset, output_names, keras_node_dict):
     for idx_, ts_ in enumerate(output_tensors):
         op = top_level.declare_local_operator(TYPES.Identity)
         var_type = _adjust_input_batch_size(infer_variable_type(ts_, target_opset))
+        dim_variable_counter = _adjust_input_output_size(var_type, dim_variable_counter)
         str_value = ts_.name
         use_ts_name = False
         if hasattr(topo.raw_model.model, 'output_names'):