def margin_ranking_loss(
input1: TensorLikeType,
input2: TensorLikeType,
target: TensorLikeType,
margin: float = 0.0,
reduction: str = "mean",
) -> TensorLikeType:
# Formula of loss (implementation gets confusing with all the refs.foo)
# loss_without_reduction = max(0, −target * (input1 − input2) + margin)
if input1.ndim != input2.ndim or input1.ndim != target.ndim:
raise RuntimeError(
(
"margin_ranking_loss : All input tensors should have same dimension but got sizes: "
"input1: {}, input2: {}, target: {} ".format(
input1.shape, input2.shape, target.shape
)
)
)
_check_reduction_value(reduction)
neg_target = refs.neg(target)
input_diff = refs.sub(input1, input2)
mul_target_input = refs.mul(neg_target, input_diff)
add_margin = refs.add(mul_target_input, margin)
loss = refs.maximum(add_margin, 0)
return _apply_loss_reduction(loss, reduction)
def logit(self: TensorLikeType, eps: Optional[float] = None) -> TensorLikeType:
if eps is None:
eps = -1.0
lo = eps
hi = 1 - eps
self = refs.clamp(self, lo, hi)
return refs.log(refs.true_divide(self, refs.sub(1, self)))
def tanhshrink(a: TensorLikeType) -> TensorLikeType:
"""
Reference implementation of torch.nn.functional.tanhshrink
"""
if not isinstance(a, TensorLike):
raise RuntimeError(
"Expected a tensor input for an elementwise unary operation!")
return refs.sub(a, refs.tanh(a))
def hinge_embedding_loss(
input: TensorLikeType,
target: TensorLikeType,
margin: float = 1.0,
reduction: str = "mean",
) -> TensorLikeType:
# Formula of loss (implementation gets confusing with all the refs.foo)
# loss_without_reduction = input if y == 1
# = max(0, margin - input) if y == -1
_check_reduction_value(reduction)
margin_clamp = refs.maximum(refs.sub(margin, input), 0)
output_margin = refs.where(refs.ne(target, 1), margin_clamp, 0)
output_self = refs.where(refs.ne(target, -1), input, 0)
loss = refs.add(output_margin, output_self)
return _apply_loss_reduction(loss, reduction)
