def applier()

in apex/apex/amp/_initialize.py [0:0]

• 15 lines of code
• 11 McCabe index (conditional complexity)

def applier(value, fn):
    if isinstance(value, torch.Tensor):
        return fn(value)
    elif isinstance(value, string_classes):
        return value
    elif isinstance(value, np.ndarray):
        return value
    elif hasattr(value, "to"): # Allow handling of custom batch classes
        return fn(value)
    elif isinstance(value, container_abcs.Mapping):
        return {applier(k, fn) : applier(v, fn) for k, v in value.items()}
    elif isinstance(value, container_abcs.Iterable):
        return type(value)(applier(v, fn) for v in value)
    else:
        # Do I want this to fire off even if someone chooses to pass something ordinary like
        # an int or float?  May be more annoying than it's worth.
        # print("Warning:  unrecognized type in applier.  If your input data is a custom class, "
        #     "provide it with a .to(dtype) method which converts its floating-point Tensors to dtype. "
        #     "Amp will check for your custom to() and invoke it to cast the batch's "
        #     "floating-point Tensors to the appropriate type. "
        #     "Also, if your data is a custom class, it is your responsibility to ensure that "
        #     "any Tensors you want to be cuda are already cuda."
        return value