def convert_cuda(self, model, input):
cuda_model = model.cuda()
# input might be nested - we want to move everything to GPU
cuda_input = function._nested_map(
lambda o: isinstance(o, Variable) or torch.is_tensor(o),
lambda o: o.cuda())(input)
return cuda_model, cuda_input
def convert_cuda(self, model, input):
cuda_model = model.cuda()
# input might be nested - we want to move everything to GPU
cuda_input = function._nested_map(
lambda o: isinstance(o, Variable) or torch.is_tensor(o),
lambda o: o.cuda())(input)
return cuda_model, cuda_input
def _clone_inputs(args):
def clone_input(a):
if a is None:
return None
elif isinstance(a, Variable):
v = Variable(a.data.clone(), requires_grad=a.requires_grad)
if a.grad is not None:
v.grad = clone_input(v.grad)
return v
else:
return a.clone()
return function._nested_map(lambda o: isinstance(o, Variable) or torch.is_tensor(o),
clone_input, condition_msg="Variables")(args)
def _clone_inputs(args):
def clone_input(a):
if a is None:
return None
elif isinstance(a, Variable):
v = Variable(a.data.clone(), requires_grad=a.requires_grad)
if a.grad is not None:
v.grad = clone_input(v.grad)
return v
else:
return a.clone()
return function._nested_map(lambda o: isinstance(o, Variable) or torch.is_tensor(o),
clone_input, condition_msg="Variables")(args)
def _clone_inputs(args):
def clone_input(a):
if a is None:
return None
elif isinstance(a, torch.Tensor):
# TODO: figure out one liner to .clone() and set requires_grad
v = Variable(a.data.clone(), requires_grad=a.requires_grad)
if a.grad is not None:
v.grad = clone_input(v.grad)
return v
else:
return a.clone()
return function._nested_map(lambda x: isinstance(x, torch.Tensor),
clone_input, condition_msg="tensors")(args)
def _clone_inputs(args):
def clone_input(a):
if a is None:
return None
elif isinstance(a, torch.Tensor):
# TODO: figure out one liner to .clone() and set requires_grad
v = Variable(a.data.clone(), requires_grad=a.requires_grad)
if a.grad is not None:
v.grad = clone_input(v.grad)
return v
else:
return a.clone()
return function._nested_map(lambda x: isinstance(x, torch.Tensor),
clone_input, condition_msg="tensors")(args)
def _clone_inputs(args):
def clone_input(a):
if a is None:
return None
elif isinstance(a, torch.Tensor):
# TODO: figure out one liner to .clone() and set requires_grad
v = (a.detach().clone(
memory_format=torch.preserve_format).requires_grad_(
a.requires_grad))
if a.grad is not None:
v.grad = clone_input(v.grad)
return v
else:
return a.clone(memory_format=torch.preserve_format)
return function._nested_map(lambda x: isinstance(x, torch.Tensor),
clone_input,
condition_msg="tensors")(args)
def do_input_map(fn, input):
return _nested_map(lambda t: isinstance(t, torch.Tensor), fn)(input)
def _flatten(obj, params=tuple()):
obj_vars = tuple(itertools.chain(function._iter_variables(obj), params))
obj_struct = function._nested_map(lambda o: isinstance(o, Variable),
lambda x: HOLE)(obj)
return obj_vars, obj_struct
