Add RFFT2d to the reference model

Includes:
* RFFT2d reference implementation
* TFLite framework tests
* Basic TOSA tests
* Serialization submodule upgrade with support for FFT/RFFT

Signed-off-by: Luke Hutton <luke.hutton@arm.com>
Change-Id: I2a687e9cf87fb62a26160ea52439ba9830bea36e

1 files changed, 29 insertions, 1 deletions

diff --git a/verif/frameworks/arg_gen.py b/verif/frameworks/arg_gen.py
index d81c3dd..61a1de0 100644
--- a/verif/frameworks/arg_gen.py
+++ b/verif/frameworks/arg_gen.py
@@ -1,5 +1,7 @@
-# Copyright (c) 2020-2022, ARM Limited.
+# Copyright (c) 2020-2023, ARM Limited.
 # SPDX-License-Identifier: Apache-2.0
+import math
+
 import numpy as np
 
 
@@ -851,3 +853,29 @@ class ArgGen:
             else:
                 axes.append(["_axis_m{}".format(-i), [i]])
         return axes
+
+    def agRFFT2d(op, shape, rng):
+        args = []
+
+        # Must be rank 3 input tensor
+        if len(shape) != 3:
+            return []
+
+        # Check rfft2d with enforced fft_length
+        for fft_length_h in [2, 32]:
+            for fft_length_w in [2, 8, 16]:
+                fft_length = [fft_length_h, fft_length_w]
+                args.append(["_fft_length_{}x{}".format(*fft_length), [fft_length]])
+
+        # Check rfft2d with no fft_length provided (fft_length=None).
+        # In this case, the height and width of the input should be
+        # used for the calculation. Therefore, we need to check that
+        # the input shape is already a power of two.
+        def is_power_of_two(x):
+            return math.log(x, 2).is_integer()
+
+        height, width = shape[1:3]
+        if is_power_of_two(height) and is_power_of_two(width):
+            args.append(["_fft_length_None", [None]])
+
+        return args