def __call__(self, ops):
# boxing inputs if needed
ops = box(ops)
# graph computation or eager?
if self.debug:
input_types = [type(op) for op in ops]
self.print_debug('inputs type:%s' % (input_types))
if issubclass(type(ops[0]), OP):
# graph mode
self.print_debug('graph mode')
input_shapes = []
for op in ops:
assert issubclass(type(op), OP)
self.input_ops.append(op)
input_shapes.append(op.get_output_shapes())
self.compute_output_shape(input_shapes)
return self
else:
# eager mode
self.print_debug('eager mode')
# check inputs are valis
for op in ops:
if not B.is_tensor(op):
raise ValueError("Expecting list(tensors) or a tensor")
# input shapes
input_shapes = []
for op in ops:
input_shapes.append(op.shape)
self.compute_output_shape(input_shapes)
# compute concrete results - dispatch iterate through populations.
return unbox(self.dispatch(ops, self.EAGER))
def _call_from_graph(self, populations):
"Function called during graph executions"
populations = box(populations)
# dispatch take care of iterating through populations
return unbox(self.dispatch(populations, self.GRAPH))
def get_output_shapes(self):
"return the shape of tensor returned by the op"
return unbox(self.output_shapes)
def get_populations(self):
return unbox(self._populations)
def test_box_unbox_tensor():
val = tensor([1, 2, 3])
assert tensor_equal(unbox(box(val)), val)
def test_box_unbox():
vals = ['a', [1, 2, 3]]
for val in vals:
assert unbox(box(val)) == val