def from_backend_value(self, v, t):
"""Convert a backend value to an intermediate value."""
if isinstance(t, abstract.AbstractScalar):
return self.to_scalar(v)
elif isinstance(t, abstract.AbstractArray):
# Convert torch tensor to numpy tensor.
output = self.to_numpy(v)
# If possible and necessary, cast numpy tensor to expected tensor.
array_type = t.element.xtype()
if array_type and array_type not in _type_map:
# Probably u16, u32 or u64. Let's cast.
output = output.astype(type_to_np_dtype(array_type))
return output
elif isinstance(t, abstract.AbstractTuple):
return tuple(
self.from_backend_value(ve, te)
for ve, te in zip(v, t.elements))
elif isinstance(t, abstract.AbstractTaggedUnion):
return TaggedValue(
v.tag, self.from_backend_value(v.value, t.options.get(v.tag)))
elif isinstance(t, abstract.AbstractRandomState):
return RandomStateWrapper(self.to_numpy(v))
elif isinstance(t, abstract.AbstractType):
if isinstance(t.element, abstract.AbstractHandle):
return HandleInstance
else:
myia_type = t.element.xtype()
if myia_type in _type_map:
return getattr(np, type_to_np_dtype(myia_type))
else:
return v
else:
raise NotImplementedError(f"Don't know what to do for {t}")
def convert_random_state(self, v, t):
return RandomStateWrapper(tuple(el.asnumpy().item() for el in v))