Add test for len1_array_to_scalar function

Moved len1_array_to_scalar from a nested function to a staticmethod
of TFLiteSubgraph.

Change-Id: I182f0b70f03070855c1a4478d26644892c1ebb15
Signed-off-by: Diego Russo <diego.russo@arm.com>

1 files changed, 16 insertions, 17 deletions

diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py
index 4f9bd7d0..7e158aac 100644
--- a/ethosu/vela/tflite_reader.py
+++ b/ethosu/vela/tflite_reader.py
@@ -91,28 +91,15 @@ class TFLiteSubgraph:
         shape = list(np_shape) if type(np_shape) is np.ndarray else []
         name = decode_str(tens_data.Name())
         dtype = datatype_map[tens_data.Type()]
-
         tens = Tensor(shape, dtype, name)
-
         quant = tens_data.Quantization()
 
-        def len1_array_to_scalar(arr):
-            # The following flatbuffer quantisation fields all return a scalar value of 0 if they are not definied in
-            # the input buffer. This is represented in Vela by using None.
-            # Otherwise, the fields returned are a single or multi-element array. In which case, single element arrays
-            # are converted to scalars
-            if isinstance(arr, int) and arr == 0:
-                return None
-            if len(arr) == 1:
-                return arr[0]
-            return arr
-
         tens.quantization = QuantizationParameters()
         if quant is not None:
-            tens.quantization.min = len1_array_to_scalar(quant.MinAsNumpy())
-            tens.quantization.max = len1_array_to_scalar(quant.MaxAsNumpy())
-            tens.quantization.scale_f32 = len1_array_to_scalar(quant.ScaleAsNumpy())
-            tens.quantization.zero_point = len1_array_to_scalar(quant.ZeroPointAsNumpy())
+            tens.quantization.min = self.len1_array_to_scalar(quant.MinAsNumpy())
+            tens.quantization.max = self.len1_array_to_scalar(quant.MaxAsNumpy())
+            tens.quantization.scale_f32 = self.len1_array_to_scalar(quant.ScaleAsNumpy())
+            tens.quantization.zero_point = self.len1_array_to_scalar(quant.ZeroPointAsNumpy())
 
         if dtype == DataType.uint8:
             tens.quantization.quant_min = 0
@@ -199,6 +186,18 @@ class TFLiteSubgraph:
             op.outputs[0] = intermediate_tens
             act_op.inputs = [intermediate_tens]
 
+    @staticmethod
+    def len1_array_to_scalar(arr):
+        # The following flatbuffer quantisation fields all return a scalar value of 0 if they are not definied in
+        # the input buffer. This is represented in Vela by using None.
+        # Otherwise, the fields returned are a single or multi-element array. In which case, single element arrays
+        # are converted to scalars
+        if isinstance(arr, int) and arr == 0:
+            return None
+        if len(arr) == 1:
+            return arr[0]
+        return arr
+
 
 class TFLiteGraph:
     def __init__(