MLBEDSW-2663: Handle optional tensors

Includes a number of changes:
  * Handle non-existing optional inputs
  * Handle disabled optional inputs (-1 indexed)
  * Added unit tests for parsing operators
  * Add bias tensor to the different Convolutions + FullyConnected if
    it's missing.

Signed-off-by: Jacob Bohlin <jacob.bohlin@arm.com>
Change-Id: Ib88d2b610314b1c886fc0aef4f9da87430ce6ae5

1 files changed, 12 insertions, 5 deletions

diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py
index daa208f1..a2f744d3 100644
--- a/ethosu/vela/tflite_reader.py
+++ b/ethosu/vela/tflite_reader.py
@@ -137,8 +137,8 @@ class TFLiteSubgraph:
 
     def parse_operator(self, op_index, op_data):
         op_type, opt_serializer = self.graph.operator_codes[op_data.OpcodeIndex()]
-        inputs = [self.tensors[idx] for idx in op_data.InputsAsNumpy()]
-        outputs = [self.tensors[idx] for idx in op_data.OutputsAsNumpy()]
+        inputs = [self.tensors[idx] if idx != -1 else None for idx in op_data.InputsAsNumpy()]
+        outputs = [self.tensors[idx] if idx != -1 else None for idx in op_data.OutputsAsNumpy()]
         name = "unknown_op_name"
         if len(outputs):
             name = outputs[0].name
@@ -153,12 +153,19 @@ class TFLiteSubgraph:
 
         if op_type.startswith("DepthwiseConv2d") or op_type.startswith("Conv2D"):
             inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 3, 0))
-            if not op.type.endswith("BackpropInput"):
-                inputs[2] = clone_and_reshape_tensor(inputs[2], (0,))
+            if len(inputs) < 3 or (len(inputs) < 4 and "Backprop" in op_type):
+                # No Bias tensor
+                inputs.append(None)
+            if inputs[-1]:
+                inputs[-1] = clone_and_reshape_tensor(inputs[-1], (0,))
 
         if op_type.startswith("FullyConnected"):
             inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0))
-            inputs[2] = clone_and_reshape_tensor(inputs[2], (0,))
+            if len(inputs) < 3:
+                # No Bias tensor
+                inputs.append(None)
+            if inputs[-1]:
+                inputs[-1] = clone_and_reshape_tensor(inputs[-1], (0,))
 
         if opt_serializer is not None:
             op.attrs = opt_serializer.deserialize(op_data)