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import torch.nn as nn
import torch.nn.functional as F
from fvcore.common.registry import Registry
import torch
LOSS_REGISTRY = Registry("loss")
def og3d_loss(data_dict):
return F.cross_entropy(data_dict["og3d_logits"], data_dict["tgt_object_id"].squeeze(1))
def og3d_multi_loss(data_dict):
return F.binary_cross_entropy_with_logits(
data_dict["og3d_logits"],
data_dict["tgt_object_id"].float(),
reduction="sum") / float(data_dict["tgt_object_id"].shape[0])
def txt_cls_multi_loss(data_dict):
return F.binary_cross_entropy_with_logits(
data_dict["txt_cls_logits"],
data_dict["tgt_object_label"].float(),
reduction='sum') / float(data_dict["tgt_object_label"].shape[0])
def obj_cls_raw_loss(data_dict):
return (
F.cross_entropy(
data_dict["obj_cls_raw_logits"].permute(0, 2, 1), data_dict["obj_labels"], reduction='none'
) * data_dict["obj_masks"]
).sum() / data_dict["obj_masks"].sum()
def obj_cls_pre_loss(data_dict):
return (
F.cross_entropy(
data_dict["obj_cls_pre_logits"].permute(0, 2, 1), data_dict["obj_labels"], reduction='none'
) * data_dict["obj_masks"]
).sum() / data_dict["obj_masks"].sum()
def obj_cls_post_loss(data_dict):
return (
F.cross_entropy(
data_dict["obj_cls_post_logits"].permute(0, 2, 1), data_dict["obj_labels"], reduction='none'
) * data_dict["obj_masks"]
).sum() / data_dict["obj_masks"].sum()
def answer_loss(data_dict):
return F.binary_cross_entropy_with_logits(
data_dict["answer_scores"], data_dict["answer_label"].float(), reduction='sum'
) / data_dict["answer_scores"].shape[0]
def lm_cls_loss(data_dict):
target_labels = data_dict["masked_lm_labels"]
target_labels = target_labels.view(-1, target_labels.size(-1)) if len(target_labels.size()) == 3 else target_labels
return F.cross_entropy(
data_dict["txt_lm_cls_logits"].permute(0, 2, 1), target_labels, ignore_index=-1
)
def obj_cls_pre_loss_mask(data_dict):
return (
F.cross_entropy(
data_dict["obj_cls_pre_logits"].permute(0, 2, 1), data_dict["obj_labels"], reduction='none'
) * data_dict["obj_masks"] * data_dict["obj_sem_masks"].logical_not()
).sum() / (data_dict["obj_masks"] * data_dict["obj_sem_masks"].logical_not()).sum()
def obj_cls_pre_loss_unmask(data_dict):
return (
F.cross_entropy(
data_dict["obj_cls_pre_logits"].permute(0, 2, 1), data_dict["obj_labels"], reduction='none'
) * data_dict["obj_masks"] * data_dict["obj_sem_masks"]
).sum() / (data_dict["obj_masks"] * data_dict["obj_sem_masks"]).sum()
def obj_cls_post_loss_mask(data_dict):
return (
F.cross_entropy(
data_dict["obj_cls_post_logits"].permute(0, 2, 1), data_dict["obj_labels"], reduction='none'
) * data_dict["obj_masks"] * data_dict["obj_sem_masks"].logical_not()
).sum() / (data_dict["obj_masks"] * data_dict["obj_sem_masks"].logical_not()).sum()
def obj_cls_post_loss_unmask(data_dict):
return (
F.cross_entropy(
data_dict["obj_cls_post_logits"].permute(0, 2, 1), data_dict["obj_labels"], reduction='none'
) * data_dict["obj_masks"] * data_dict["obj_sem_masks"]
).sum() / (data_dict["obj_masks"] * data_dict["obj_sem_masks"]).sum()
def obj_cls_loss(data_dict, smoothing=0.3):
return (
F.cross_entropy(
data_dict["obj_logits"].permute(0, 2, 1), data_dict["obj_labels"],
reduction='none', label_smoothing=smoothing
) * data_dict["obj_masks"]
).sum() / data_dict["obj_masks"].sum()
def mse_loss(data_dict):
return (
((data_dict["pred_images"] - data_dict["target_images"]) ** 2).mean()
)
class Loss(nn.Module):
def __init__(self, cfg, accelerator):
# e.g. refer_loss_v1: ["og3d_loss", "txt_cls_loss", "obj_cls_raw_loss", "obj_cls_pre_loss", "obj_cls_post_loss"]
# qa_loss_v1: ["og3d_loss", "txt_cls_loss", "obj_cls_raw_loss", "obj_cls_pre_loss", "obj_cls_post_loss", "answer_loss"]
# pretrain_loss_v1: ["lm_cls_loss", "obj_cls_raw_loss", "obj_cls_pre_loss", "obj_cls_post_loss", "obj_cls_pre_loss_mask",
# "obj_cls_pre_loss_unmask", "obj_cls_post_loss_mask", "obj_cls_post_loss_unmask"]
super().__init__()
self.all_keys = list(set(cfg.model.vis_loss_list + cfg.model.loss_list))
self.selected_keys = cfg.model.loss_list
self.loss_fn = {}
for k in self.all_keys:
if k in globals().keys():
self.loss_fn[k] = globals()[k]
print(f"Using {k} from loss.globals()")
else:
self.loss_fn[k] = LOSS_REGISTRY.get(k)(cfg, accelerator)
setattr(self, k, self.loss_fn[k]) # register the loss module, otherwise its parameters will not be the same device as the model
print(f"Using {k} from Registry {LOSS_REGISTRY._name}")
def forward(self, data_dict):
all_losses = {}
# Precompute label if needed
if 'txt_cls_loss' in self.loss_fn and 'txt_cls_label' not in data_dict:
data_dict['txt_cls_label'] = data_dict["tgt_object_label"].squeeze(1)
for k, fn in self.loss_fn.items():
# Compute current loss
cur_loss = fn(data_dict)
if isinstance(cur_loss, dict):
all_losses.update(cur_loss)
else:
all_losses[k] = cur_loss
total_loss = sum(all_losses.values())
all_losses["total_loss"] = total_loss
return total_loss, all_losses
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