diff options
Diffstat (limited to 'ethosu/vela/weight_compressor.py')
| -rw-r--r-- | ethosu/vela/weight_compressor.py | 14 |
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) |