diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 704c15d..8b930b7 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -31,7 +31,7 @@
 
     -   id: pytest-cov
         name: pytest
-        stages: [commit]
+        stages: [push]
         language: system
         entry: pytest -v --cov=ethosu --cov-fail-under=0
         types: [python]
diff --git a/ethosu/vela/test/test_tflite_reader.py b/ethosu/vela/test/test_tflite_reader.py
new file mode 100644
index 0000000..898e384
--- /dev/null
+++ b/ethosu/vela/test/test_tflite_reader.py
@@ -0,0 +1,36 @@
+# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+# 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
+#
+# 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.
+# Description:
+# Contains unit tests for tflite_reader
+import pytest
+from ethosu.vela.tflite_reader import TFLiteSubgraph
+
+
+class TestTFLiteSubgraph:
+
+    # Generate some data for testing len1_array_to_scalar
+    len1_testdata = [
+        (0, None),
+        pytest.param(1, None, marks=pytest.mark.xfail),
+        ([1, 2, 3], [1, 2, 3]),
+        ([10], 10),
+        ([], []),
+    ]
+
+    @pytest.mark.parametrize("test_input,expected", len1_testdata)
+    def test_len1_array_to_scalar(self, test_input, expected):
+        output = TFLiteSubgraph.len1_array_to_scalar(test_input)
+        assert output == expected
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py
index 4f9bd7d..7e158aa 100644
--- a/ethosu/vela/tflite_reader.py
+++ b/ethosu/vela/tflite_reader.py
@@ -91,28 +91,15 @@
         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 @@
             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__(