diff options
Diffstat (limited to 'ethosu/vela/weight_compressor.py')
| -rw-r--r-- | ethosu/vela/weight_compressor.py | 27 |
1 files changed, 14 insertions, 13 deletions
diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py index a580fb6..b87a2bf 100644 --- a/ethosu/vela/weight_compressor.py +++ b/ethosu/vela/weight_compressor.py @@ -280,23 +280,24 @@ def _prepare_scale_and_bias(arch, tens, explicit_scaling): # If weight_scales is not already an iterable make it into a list weight_scales = [weight_scales] - # 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 as well as - # for FullyConnected operators - if ifm_dtype == DataType.uint8 or first_consumer_op.original_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) for weight_scale in weight_scales - ] - else: - raise UnsupportedFeatureError(f"Compression of {ifm_dtype} is not implemented; Tensor: '{tens.name}'") - if explicit_scaling: assert len(explicit_scaling.shift) == len(explicit_scaling.multiplier) quantised_scales = [(int(m), int(s)) for s, m in zip(explicit_scaling.shift, explicit_scaling.multiplier)] else: + # 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 as well as + # for FullyConnected operators + if ifm_dtype == DataType.uint8 or first_consumer_op.original_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) + for weight_scale in weight_scales + ] + else: + raise UnsupportedFeatureError(f"Compression of {ifm_dtype} is not implemented; Tensor: '{tens.name}'") + # quantise all of the weight scales into (scale_factor, shift) if ifm_dtype == DataType.int16 and bias_tens.dtype == DataType.int64: # Reference uses reduced scaling for int16 with int64 bias |