# Copyright (c) Facebook, Inc. and its affiliates.
"""
Losses module contains implementations for various losses used generally
in vision and language space. One can register custom losses to be detected by
MMF using the following example.
.. code::
from mmf.common.registry import registry
from torch import nn
@registry.register_loss("custom")
class CustomLoss(nn.Module):
...
Then in your model's config you can specify ``losses`` attribute to use this loss
in the following way:
.. code::
model_config:
some_model:
losses:
- type: custom
- params: {}
"""
import collections
import warnings
from dataclasses import dataclass
from typing import Any, Dict, List, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmf.common.registry import registry
from mmf.utils.distributed import gather_tensor_along_batch_with_backward, get_rank
from mmf.utils.logger import log_class_usage
from omegaconf import MISSING
from packaging import version
from torch import Tensor
from torch.nn.utils.rnn import pack_padded_sequence
[docs]@dataclass
class LossConfig:
type: str = MISSING
params: Dict[str, Any] = MISSING
[docs]class Losses(nn.Module):
"""``Losses`` acts as an abstraction for instantiating and calculating
losses. ``BaseModel`` instantiates this class based on the `losses`
attribute in the model's configuration `model_config`. ``loss_list``
needs to be a list for each separate loss containing `type` and `params`
attributes.
Args:
loss_list (ListConfig): Description of parameter `loss_list`.
Example::
# losses:
# - type: logit_bce
# Can also contain `params` to specify that particular loss's init params
# - type: combined
config = [{"type": "logit_bce"}, {"type": "combined"}]
losses = Losses(config)
.. note::
Since, ``Losses`` is instantiated in the ``BaseModel``, normal end user
mostly doesn't need to use this class.
Attributes:
losses: List containing instantiations of each loss
passed in config
"""
# TODO: Union types are not supported in OmegaConf.
# Later investigate for a workaround.for
def __init__(self, loss_list: List[Union[str, LossConfig]]):
super().__init__()
self.losses = nn.ModuleList()
config = registry.get("config")
self._evaluation_predict = False
if config:
self._evaluation_predict = config.get("evaluation", {}).get(
"predict", False
)
for loss in loss_list:
self.losses.append(MMFLoss(loss))
[docs] def forward(self, sample_list: Dict[str, Tensor], model_output: Dict[str, Tensor]):
"""Takes in the original ``SampleList`` returned from DataLoader
and `model_output` returned from the model and returned a Dict containing
loss for each of the losses in `losses`.
Args:
sample_list (SampleList): SampleList given be the dataloader.
model_output (Dict): Dict returned from model as output.
Returns:
Dict: Dictionary containing loss value for each of the loss.
"""
output = {}
if "targets" not in sample_list:
if not self._evaluation_predict:
warnings.warn(
"Sample list has not field 'targets', are you "
"sure that your ImDB has labels? you may have "
"wanted to run with evaluation.predict=true"
)
return output
for loss in self.losses:
output.update(loss(sample_list, model_output))
if not torch.jit.is_scripting():
registry_loss_key = "{}.{}.{}".format(
"losses", sample_list["dataset_name"], sample_list["dataset_type"]
)
# Register the losses to registry
registry.register(registry_loss_key, output)
return output
[docs]class MMFLoss(nn.Module):
"""Internal MMF helper and wrapper class for all Loss classes.
It makes sure that the value returned from a Loss class is a dict and
contain proper dataset type in keys, so that it is easy to figure out
which one is the val loss and which one is train loss.
For example: it will return ``{"val/vqa2/logit_bce": 27.4}``, in case
`logit_bce` is used and SampleList is from `val` set of dataset `vqa2`.
Args:
params (type): Description of parameter `params`.
.. note::
Since, ``MMFLoss`` is used by the ``Losses`` class, end user
doesn't need to worry about it.
"""
def __init__(self, params=None):
super().__init__()
if params is None:
params = {}
is_mapping = isinstance(params, collections.abc.MutableMapping)
if is_mapping:
if "type" not in params:
raise ValueError(
"Parameters to loss must have 'type' field to"
"specify type of loss to instantiate"
)
else:
loss_name = params["type"]
else:
assert isinstance(
params, str
), "loss must be a string or dictionary with 'type' key"
loss_name = params
self.name = loss_name
loss_class = registry.get_loss_class(loss_name)
log_class_usage("Loss", loss_class)
if loss_class is None:
raise ValueError(f"No loss named {loss_name} is registered to registry")
# Special case of multi as it requires an array
if loss_name.startswith("multi"):
assert is_mapping
self.loss_criterion = loss_class(params)
else:
if is_mapping:
loss_params = params.get("params", {})
else:
loss_params = {}
self.loss_criterion = loss_class(**loss_params)
[docs] def forward(self, sample_list: Dict[str, Tensor], model_output: Dict[str, Tensor]):
loss_dict = {}
loss_result = self.loss_criterion(sample_list, model_output)
if not isinstance(loss_result, collections.abc.Mapping):
loss_result = {"": loss_result}
for child_loss_name, child_loss_result in loss_result.items():
if not isinstance(child_loss_result, torch.Tensor):
child_loss_result = torch.tensor(child_loss_result, dtype=torch.float)
if child_loss_result.dim() == 0:
child_loss_result = child_loss_result.view(1)
if not torch.jit.is_scripting():
key = "{}/{}/{}".format(
sample_list.dataset_type, sample_list.dataset_name, self.name
)
else:
key = f"{self.name}"
key = f"{key}/{child_loss_name}" if child_loss_name else key
loss_dict[key] = child_loss_result
return loss_dict
[docs]@registry.register_loss("logit_bce")
class LogitBinaryCrossEntropy(nn.Module):
"""Returns Binary Cross Entropy for logits.
Attention:
`Key`: logit_bce
"""
def __init__(self):
super().__init__()
[docs] def forward(self, sample_list, model_output):
"""Calculates and returns the binary cross entropy for logits
Args:
sample_list (SampleList): SampleList containing `targets` attribute.
model_output (Dict): Model output containing `scores` attribute.
Returns:
torch.FloatTensor: Float value for loss.
"""
scores = model_output["scores"]
targets = sample_list["targets"]
loss = F.binary_cross_entropy_with_logits(scores, targets, reduction="mean")
return loss * targets.size(1)
[docs]@registry.register_loss("triple_logit_bce")
class TripleLogitBinaryCrossEntropy(nn.Module):
"""
This is used for Three-branch fusion only. We predict scores and compute
cross entropy loss for each of branches.
"""
def __init__(self):
super().__init__()
[docs] def forward(self, sample_list, model_output):
"""Calculates and returns the binary cross entropy for logits
Args:
sample_list (SampleList): SampleList containing `targets` attribute.
model_output (Dict): Model output containing `scores` attribute.
Returns:
torch.FloatTensor: Float value for loss.
"""
scores = model_output["scores"]
targets = sample_list["targets"]
if scores.dim() == 3:
loss = (
F.binary_cross_entropy_with_logits(
scores[:, 0], targets, reduction="mean"
)
+ F.binary_cross_entropy_with_logits(
scores[:, 1], targets, reduction="mean"
)
+ F.binary_cross_entropy_with_logits(
scores[:, 2], targets, reduction="mean"
)
)
else:
loss = F.binary_cross_entropy_with_logits(scores, targets, reduction="mean")
return loss * targets.size(-1)
[docs]@registry.register_loss("bce")
class BinaryCrossEntropyLoss(nn.Module):
def __init__(self):
super().__init__()
[docs] def forward(self, sample_list, model_output):
"""Calculates and returns the binary cross entropy.
Args:
sample_list (SampleList): SampleList containing `targets` attribute.
model_output (Dict): Model output containing `scores` attribute.
Returns:
torch.FloatTensor: Float value for loss.
"""
scores = model_output["scores"]
targets = sample_list["targets"]
loss = F.binary_cross_entropy(scores, targets, reduction="mean")
return loss * targets.size(1)
[docs]@registry.register_loss("caption_cross_entropy")
class CaptionCrossEntropyLoss(nn.Module):
def __init__(self):
super().__init__()
[docs] def forward(self, sample_list, model_output):
"""Calculates and returns the cross entropy loss for captions.
Args:
sample_list (SampleList): SampleList containing `targets` attribute.
model_output (Dict): Model output containing `scores` attribute.
Returns:
torch.FloatTensor: Float value for loss.
"""
scores = model_output["scores"]
targets = sample_list["targets"]
# If no captions(test dataset) then assume decode length to be uniform
if hasattr(sample_list, "caption_len"):
caption_lengths, _ = sample_list.caption_len.sort(dim=0, descending=True)
decode_lengths = (caption_lengths - 1).tolist()
else:
decode_lengths = [targets.size(1)] * targets.size(0)
if version.parse(torch.__version__) >= version.parse("1.1"):
scores = pack_padded_sequence(scores, decode_lengths, batch_first=True).data
targets = pack_padded_sequence(
targets, decode_lengths, batch_first=True
).data
else:
scores, _ = pack_padded_sequence(scores, decode_lengths, batch_first=True)
targets, _ = pack_padded_sequence(targets, decode_lengths, batch_first=True)
loss = F.cross_entropy(scores, targets)
return loss
[docs]@registry.register_loss("nll_loss")
class NLLLoss(nn.Module):
"""Negative log likelikehood loss."""
def __init__(self):
super().__init__()
[docs] def forward(self, sample_list, model_output):
"""Calculates and returns the negative log likelihood.
Args:
sample_list (SampleList): SampleList containing `targets` attribute.
model_output (Dict): Model output containing `scores` attribute.
Returns:
torch.FloatTensor: Float value for loss.
"""
scores = model_output["scores"]
targets = sample_list["targets"]
_, idx = targets.max(dim=1)
loss = F.nll_loss(scores, idx, reduction="mean")
return loss * targets.size(1)
def kl_div(log_x, y):
y_is_0 = torch.eq(y.data, 0)
y.data.masked_fill_(y_is_0, 1)
log_y = torch.log(y)
y.data.masked_fill_(y_is_0, 0)
res = y * (log_y - log_x)
return torch.sum(res, dim=1, keepdim=True)
[docs]@registry.register_loss("multi")
class MultiLoss(nn.Module):
"""A loss for combining multiple losses with weights.
Args:
params (List(Dict)): A list containing parameters for each different loss
and their weights.
Example::
# MultiLoss works with config like below where each loss's params and
# weights are defined
losses:
- type: multi
params:
- type: logit_bce
weight: 0.3
params: {}
- type: attention_supervision
weight: 0.7
params: {}
"""
def __init__(self, params):
super().__init__()
self.losses = []
self.losses_weights = []
self.loss_names = []
for loss_params in params["params"]:
self.loss_names.append(loss_params["type"])
loss_fn = MMFLoss(loss_params)
loss_weight = loss_params.get("weight", {})
self.losses.append(loss_fn)
self.losses_weights.append(loss_weight)
[docs] def forward(self, sample_list, model_output, *args, **kwargs):
"""Calculates and returns the multi loss.
Args:
sample_list (SampleList): SampleList containing `attentions` attribute.
model_output (Dict): Model output containing `attention_supervision`
attribute.
Returns:
torch.FloatTensor: Float value for loss.
"""
loss = 0
for idx, loss_fn in enumerate(self.losses):
value = loss_fn(sample_list, model_output, *args, **kwargs)
loss += self.losses_weights[idx] * list(value.values())[0]
return loss
[docs]@registry.register_loss("attention_supervision")
class AttentionSupervisionLoss(nn.Module):
"""Loss for attention supervision. Used in case you want to make attentions
similar to some particular values.
"""
def __init__(self):
super().__init__()
self.loss_fn = lambda *args, **kwargs: nn.functional.binary_cross_entropy(
*args, **kwargs
)
[docs] def forward(self, sample_list, model_output):
"""Calculates and returns the multi loss.
Args:
sample_list (SampleList): SampleList containing `targets` attribute.
model_output (Dict): Model output containing `scores` attribute.
Returns:
torch.FloatTensor: Float value for loss.
"""
context_attentions = model_output["attentions"]
attention_supervision = sample_list["info"]["attention_supervision"]
loss = self.loss_fn(
context_attentions[0],
attention_supervision.float(),
weight=attention_supervision.float(),
)
# Multiply average loss back with target size to get actual loss
return loss * attention_supervision.size(1)
[docs]@registry.register_loss("weighted_softmax")
class WeightedSoftmaxLoss(nn.Module):
def __init__(self):
super().__init__()
[docs] def forward(self, sample_list, model_output):
pred_score = model_output["scores"]
target_score = sample_list["targets"]
tar_sum = torch.sum(target_score, dim=1, keepdim=True)
tar_sum_is_0 = torch.eq(tar_sum, 0)
tar_sum.masked_fill_(tar_sum_is_0, 1.0e-06)
tar = target_score / tar_sum
res = F.log_softmax(pred_score, dim=1)
loss = kl_div(res, tar)
loss = loss * tar_sum
loss = torch.sum(loss) / loss.size(0)
return loss
[docs]@registry.register_loss("softmax_kldiv")
class SoftmaxKlDivLoss(nn.Module):
def __init__(self):
super().__init__()
[docs] def forward(self, sample_list, model_output):
pred_score = model_output["scores"]
target_score = sample_list["targets"]
tar_sum = torch.sum(target_score, dim=1, keepdim=True)
tar_sum_is_0 = torch.eq(tar_sum, 0)
tar_sum.masked_fill_(tar_sum_is_0, 1.0e-06)
tar = target_score / tar_sum
res = F.log_softmax(pred_score, dim=1)
loss = kl_div(res, tar)
loss = torch.sum(loss) / loss.size(0)
return loss
[docs]@registry.register_loss("wrong")
class WrongLoss(nn.Module):
def __init__(self):
super().__init__()
[docs] def forward(self, sample_list, model_output):
pred_score = model_output["scores"]
target_score = sample_list["targets"]
tar_sum = torch.sum(target_score, dim=1, keepdim=True)
tar_sum_is_0 = torch.eq(tar_sum, 0)
tar_sum.masked_fill_(tar_sum_is_0, 1.0e-06)
tar = target_score / tar_sum
res = F.log_softmax(pred_score, dim=1)
loss = F.kl_div(res, tar, reduction="mean")
loss *= target_score.size(1)
return loss
[docs]@registry.register_loss("bce_kl_combined")
class CombinedLoss(nn.Module):
def __init__(self, weight_softmax):
super().__init__()
self.weight_softmax = weight_softmax
[docs] def forward(self, sample_list, model_output):
pred_score = model_output["scores"]
target_score = sample_list["targets"]
tar_sum = torch.sum(target_score, dim=1, keepdim=True)
tar_sum_is_0 = torch.eq(tar_sum, 0)
tar_sum.masked_fill_(tar_sum_is_0, 1.0e-06)
tar = target_score / tar_sum
res = F.log_softmax(pred_score, dim=1)
loss1 = kl_div(res, tar)
loss1 = torch.sum(loss1) / loss1.size(0)
loss2 = F.binary_cross_entropy_with_logits(
pred_score, target_score, reduction="mean"
)
loss2 *= target_score.size(1)
loss = self.weight_softmax * loss1 + loss2
return loss
[docs]@registry.register_loss("m4c_decoding_bce_with_mask")
class M4CDecodingBCEWithMaskLoss(nn.Module):
def __init__(self):
super().__init__()
self.one = torch.Tensor([1.0])
[docs] def forward(self, sample_list, model_output):
scores = model_output["scores"]
targets = sample_list["targets"]
loss_mask = sample_list["train_loss_mask"]
assert scores.dim() == 3 and loss_mask.dim() == 2
losses = F.binary_cross_entropy_with_logits(scores, targets, reduction="none")
losses *= loss_mask.unsqueeze(-1)
count = torch.max(torch.sum(loss_mask), self.one.to(losses.device))
loss = torch.sum(losses) / count
return loss
[docs]@registry.register_loss("cross_entropy")
class CrossEntropyLoss(nn.Module):
def __init__(self, **params):
super().__init__()
self.loss_fn = nn.CrossEntropyLoss(**params)
[docs] def forward(self, sample_list, model_output):
return self.loss_fn(model_output["scores"], sample_list["targets"])
[docs]@registry.register_loss("soft_label_cross_entropy")
class SoftLabelCrossEntropyLoss(nn.Module):
def __init__(self, ignore_index=-100, reduction="mean", normalize_targets=True):
assert reduction in (
"mean",
"sum",
), "Argument `reduction` only supports `mean` and `sum`"
super().__init__()
self.ignore_index = ignore_index
self.reduction = reduction
self.normalize_targets = normalize_targets
self.eps = torch.finfo(torch.float32).eps
@staticmethod
def convert_to_one_hot(targets, n_classes):
one_hot_targets = torch.zeros(
(targets.size(0), n_classes), dtype=torch.long, device=targets.device
)
one_hot_targets.scatter_(1, targets.long().view(-1, 1), 1)
return one_hot_targets
[docs] def compute_loss(self, targets, scores):
"""for N examples and C classes
- scores: N x C these are raw outputs (without softmax/sigmoid)
- targets: N x C or N corresponding targets
Target elements set to ignore_index contribute 0 loss.
Samples where all entries are ignore_index do not contribute to the loss
reduction.
"""
assert targets.size(0) == scores.size(
0
), "`targets` and `scores` should have the same batch size"
if targets.dim() == 1:
targets = targets.unsqueeze(1)
mask = targets.ne(self.ignore_index).float() # mask out `ignore_index`
else:
mask = targets.sum(-1, keepdim=True).ne(0).float() # mask out zero rows
if targets.size(1) == 1:
targets = self.convert_to_one_hot(targets, scores.size(1))
targets = targets.float() * mask
if self.normalize_targets:
targets /= self.eps + targets.sum(dim=1, keepdim=True)
per_sample_per_target_loss = -targets * F.log_softmax(scores, dim=-1)
per_sample_loss = torch.sum(per_sample_per_target_loss, -1)
loss = per_sample_loss.sum()
# perform reduction
if self.reduction == "mean":
# normalize based on the number of samples with > 0 non-ignored targets
loss /= torch.sum(torch.sum(mask, -1) > 0).clamp(min=1)
return loss
[docs] def forward(self, sample_list, model_output):
return self.compute_loss(sample_list["targets"], model_output["scores"])
[docs]@registry.register_loss("label_smoothing_cross_entropy")
class LabelSmoothingCrossEntropyLoss(SoftLabelCrossEntropyLoss):
"""Cross-entropy loss with label smoothing. If `label_smoothing` = 0, then
it's canonical cross entropy.
The smoothed one-hot encoding is 1 - label_smoothing for true label and
label_smoothing / (num_classes - 1) for the rest.
Reference: https://stackoverflow.com/questions/55681502/label-smoothing-in-pytorch
"""
def __init__(self, label_smoothing=0.1, reduction="mean", ignore_index=-100):
assert (
0 <= label_smoothing < 1
), "value of argument `label_smoothing` must be in range [0, 1)."
super().__init__(ignore_index, reduction, False)
self.label_smoothing = label_smoothing
def smooth_targets(self, targets, n_classes):
if targets.dim() == 1:
targets = targets.unsqueeze(1)
mask = targets.ne(self.ignore_index)
smoothing_value = self.label_smoothing / (n_classes - 1)
one_hot = torch.full(
(n_classes,), smoothing_value, device=targets.device
).repeat(targets.size(0), 1)
# mask out target with `ignore_index` to avoid error `index out of bounds`
one_hot.scatter_(1, targets * mask.long(), 1 - self.label_smoothing)
return one_hot * mask.float()
[docs] def forward(self, sample_list, model_output):
scores = model_output["scores"]
one_hot = self.smooth_targets(sample_list["targets"], scores.size(1))
loss = self.compute_loss(one_hot, scores)
return loss
[docs]@registry.register_loss("in_batch_hinge")
class InBatchHinge(nn.Module):
"""
Based on the code from https://github.com/fartashf/vsepp/blob/master/model.py
"""
def __init__(self, margin: float = 0.0, hard: bool = False):
super().__init__()
self.margin = margin
self.hard = hard
def _compute_loss(self, correlations: Tensor):
diagonal = correlations.diag()[:, None]
d1 = diagonal.expand_as(correlations)
d2 = diagonal.t().expand_as(correlations)
# compare every diagonal score to scores in its column
# caption retrieval
cost_s = (self.margin + correlations - d1).clamp(min=0)
# compare every diagonal score to scores in its row
# image retrieval
cost_im = (self.margin + correlations - d2).clamp(min=0)
# clear diagonals
mask = 1 - torch.eye(correlations.size(0), device=correlations.device)
cost_s = cost_s * mask
cost_im = cost_im * mask
if self.hard:
cost_s = cost_s.max(1)[0]
cost_im = cost_im.max(0)[0]
return cost_s.sum() + cost_im.sum()
[docs] def forward(self, sample_list: Dict[str, Tensor], model_output: Dict[str, Tensor]):
image_embeddings = model_output["scores"]
text_embeddings = model_output["targets"]
if image_embeddings.shape[0] == text_embeddings.shape[0]:
# Training/Single-GT loss
correlations = image_embeddings @ text_embeddings.t()
loss = self._compute_loss(correlations)
else:
# Evaluation/Multi-GT loss
assert text_embeddings.shape[0] % image_embeddings.shape[0] == 0
batch_size, dim_size = image_embeddings.shape
factor = text_embeddings.shape[0] // image_embeddings.shape[0]
text_embeddings = text_embeddings.reshape(batch_size, factor, dim_size)
correlations = image_embeddings @ text_embeddings.permute(1, 2, 0) # FxBxB
loss = 0
for corr in correlations:
loss += self._compute_loss(corr)
return loss
[docs]@registry.register_loss("contrastive_loss")
class ContrastiveLoss(nn.Module):
"""
This is a generic contrastive loss typically used for pretraining. No modality
assumptions are made here.
"""
def __init__(self):
super().__init__()
[docs] def forward(self, sample_list: Dict[str, Tensor], model_output: Dict[str, Tensor]):
assert (
"embedding_1" in model_output and "embedding_2" in model_output
), "Embedding names must be available before loss calculation"
embedding_1 = model_output["embedding_1"]
embedding_2 = model_output["embedding_2"]
assert embedding_1.size(0) == embedding_2.size(0), "batch size must match"
per_gpu_batch_size = embedding_1.size(0)
embedding_1_all_gpus = gather_tensor_along_batch_with_backward(embedding_1)
embedding_2_all_gpus = gather_tensor_along_batch_with_backward(embedding_2)
temperature = model_output["temperature"]
logits_1 = (
torch.matmul(embedding_1, embedding_2_all_gpus.transpose(0, 1))
/ temperature
)
logits_2 = (
torch.matmul(embedding_2, embedding_1_all_gpus.transpose(0, 1))
/ temperature
)
labels = per_gpu_batch_size * get_rank() + torch.arange(
per_gpu_batch_size, device=temperature.device
)
loss_1 = F.cross_entropy(logits_1, labels)
loss_2 = F.cross_entropy(logits_2, labels)
return (loss_1 + loss_2) / 2
[docs]@registry.register_loss("mse")
class MSELoss(nn.Module):
"""Mean Squared Error loss"""
def __init__(self):
super().__init__()
self.loss_fn = nn.MSELoss()
[docs] def forward(self, sample_list, model_output):
targets = sample_list["targets"]
scores = model_output["scores"]
loss = self.loss_fn(scores, targets)
return loss
[docs]@registry.register_loss("cos_emb_loss")
class CosineEmbeddingLoss(nn.Module):
"""Cosine embedding loss"""
def __init__(self):
super().__init__()
self.loss_fn = nn.CosineEmbeddingLoss()
[docs] def forward(self, sample_list, model_output):
targets = sample_list["targets"]
scores = model_output["scores"]
y = torch.ones(targets.size(0)).to(targets.device)
loss = self.loss_fn(scores, targets, y)
return loss
[docs]@registry.register_loss("bce_kl")
class BCEAndKLLoss(nn.Module):
"""binary_cross_entropy_with_logits and kl divergence loss.
Calculates both losses and returns a dict with string keys.
Similar to bce_kl_combined, but returns both losses.
"""
def __init__(self, weight_softmax):
super().__init__()
self.weight_softmax = weight_softmax
[docs] def forward(self, sample_list, model_output):
pred_score = model_output["scores"]
target_score = sample_list["targets"]
tar_sum = torch.sum(target_score, dim=1, keepdim=True)
tar_sum_is_0 = torch.eq(tar_sum, 0)
tar_sum.masked_fill_(tar_sum_is_0, 1.0e-06)
tar = target_score / tar_sum
res = F.log_softmax(pred_score, dim=1)
loss1 = kl_div(res, tar)
loss1 = torch.sum(loss1) / loss1.size(0)
loss2 = F.binary_cross_entropy_with_logits(
pred_score, target_score, reduction="mean"
)
loss2 *= target_score.size(1)
loss = {"kl": self.weight_softmax * loss1, "bce": loss2}
return loss
