Add framework unit test generation scripts

And fixes in tosa_verif_run_tests:
* support for no-color printing
* stop double printing of error messages on verbose
* differentiate result code pass from results check

Change-Id: I26e957013a8d18f7d3d3691067dfb778008a1eea
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>

1 files changed, 76 insertions, 0 deletions

diff --git a/verif/frameworks/test_gen_utils.py b/verif/frameworks/test_gen_utils.py
new file mode 100644
index 0000000..2d8e5d6
--- /dev/null
+++ b/verif/frameworks/test_gen_utils.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2020-2022, ARM Limited.
+# SPDX-License-Identifier: Apache-2.0
+from enum import IntEnum
+from enum import unique
+
+import tensorflow as tf
+
+
+# Get a string name for a given shape
+def get_shape_str(shape, dtype):
+    shape_name = None
+    for dim in shape:
+        shape_name = (shape_name + "x" + str(dim)) if shape_name else str(dim)
+
+    if dtype == tf.float32:
+        shape_name = shape_name + "_f32"
+    elif dtype == tf.float16:
+        shape_name = shape_name + "_f16"
+    elif dtype == tf.int32:
+        shape_name = shape_name + "_i32"
+    elif dtype == tf.uint32:
+        shape_name = shape_name + "_u32"
+    elif dtype == tf.bool:
+        shape_name = shape_name + "_bool"
+    elif dtype == tf.quint8:
+        shape_name = shape_name + "_qu8"
+    elif dtype == tf.qint8:
+        shape_name = shape_name + "_qi8"
+    elif dtype == tf.qint16:
+        shape_name = shape_name + "_qi16"
+    elif dtype == tf.quint16:
+        shape_name = shape_name + "_qu16"
+    else:
+        raise Exception("Unsupported type: {}".format(dtype))
+
+    return shape_name
+
+
+@unique
+class QuantType(IntEnum):
+    UNKNOWN = 0
+    ALL_I8 = 1
+    ALL_U8 = 2
+    ALL_I16 = 3
+    # TODO: support QUINT16
+    CONV_U8_U8 = 4
+    CONV_I8_I8 = 5
+    CONV_I8_I4 = 6
+    CONV_I16_I8 = 7
+
+
+def get_tf_dtype(quantized_inference_dtype):
+    if quantized_inference_dtype == QuantType.ALL_I8:
+        return tf.qint8
+    elif quantized_inference_dtype == QuantType.ALL_U8:
+        return tf.quint8
+    elif quantized_inference_dtype == QuantType.ALL_I16:
+        return tf.qint16
+    elif quantized_inference_dtype == QuantType.CONV_U8_U8:
+        return tf.quint8
+    elif quantized_inference_dtype == QuantType.CONV_I8_I8:
+        return tf.qint8
+    elif quantized_inference_dtype == QuantType.CONV_I8_I4:
+        return tf.qint8
+    elif quantized_inference_dtype == QuantType.CONV_I16_I8:
+        return tf.qint16
+    else:
+        return None
+
+
+class TensorScale:
+    def __init__(self, _min, _max, _num_bits, _narrow_range):
+        self.min = _min
+        self.max = _max
+        self.num_bits = _num_bits
+        self.narrow_range = _narrow_range