MLBEDSW-7427 Fix scale calculations for FullyConnected

Fixed scale calculations for FullyConnected to match the reference.
Also removed unused low_precision_scaling.

Change-Id: I4b766febff4a0010acd3de708bb49be458d22bf3
Signed-off-by: Fredrik Svedberg <fredrik.svedberg@arm.com>

1 files changed, 4 insertions, 10 deletions

diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py
index e56cc5e5..ab22e94f 100644
--- a/ethosu/vela/weight_compressor.py
+++ b/ethosu/vela/weight_compressor.py
@@ -266,17 +266,11 @@ def _prepare_scale_and_bias(arch, tens, rescale_for_faf, explicit_scaling):
 
     # Convert scales to np.double (from np.float32) to conform to TensorFlow Lite which
     # uses double during scaling calculations
-    # TensorFlow Lite casts the scales slightly differently for uint8 and int8
+    # TensorFlow Lite casts the scales slightly differently for uint8 and int8 as well as
+    # for FullyConnected operators
     if not rescale_for_faf:
-        if ifm_dtype == DataType.uint8:
-            # for some cases of the Mean operator, the scale must be calculated differently to match reference
-            if first_consumer_op.low_precision_scaling:
-                scales = [
-                    np.double(np.single(ifm_scale) / (np.single(weight_scale) * np.single(ofm_scale)))
-                    for weight_scale in weight_scales
-                ]
-            else:
-                scales = [np.double(ifm_scale * weight_scale) / np.double(ofm_scale) for weight_scale in weight_scales]
+        if ifm_dtype == DataType.uint8 or first_consumer_op.type == Op.FullyConnected:
+            scales = [np.double(ifm_scale * weight_scale) / np.double(ofm_scale) for weight_scale in weight_scales]
         elif ifm_dtype == DataType.int8 or ifm_dtype == DataType.int16:
             scales = [
                 (np.double(ifm_scale) * np.double(weight_scale)) / np.double(ofm_scale)