def get_smallest_possible(vals):
"""Returns smallest (fewest bits) possible DataType that can represent
value. Prefers unsigned integers where possible."""
vals = np.array(vals)
for v in vals:
assert int(v) == v, "Error float value"
for k in DataType.get_accumulator_dt_cands():
dt = DataType[k]
if dt in [DataType["BIPOLAR"], DataType["TERNARY"], DataType["FLOAT32"]]:
# not currently supported
continue
if (dt.min() <= vals).all() and (vals <= dt.max()).all():
return dt
warnings.warn(
"""InferChannelwiseLinearLayer: Output values may not be
representable with supported data types.
Setting maximum width data type available.
This will lead to errors if there are no constrains on the input
"""
)
if (0 <= vals).all():
return DataType["UINT64"]
else:
return DataType["INT64"]
def get_smallest_possible(self, vals):
"""Returns smallest (fewest bits) possible DataType that can represent
value. Prefers unsigned integers where possible."""
vals = np.array(vals)
for v in vals:
assert int(v) == v, "Error float value"
cands = DataType.get_accumulator_dt_cands()
for k in cands:
dt = DataType[k]
if (dt.min() <= vals).all() and (vals <= dt.max()).all():
return dt
