Эффективность D0. Огромные значения функции потерь ⇐ Python

[Anonymous]

Я пытаюсь обучить свою модель EfficientdetD0, но значения функции потерь исчисляются миллиардами.
Мой код:

Код: Выделить всё

import torch
from torch.utils.data import DataLoader
from torchvision.datasets import CocoDetection
from effdet import get_efficientdet_config, EfficientDet, DetBenchTrain
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from torchvision import transforms as T
from pytorch_lightning.loggers import CSVLogger

class EfficientDetDataModule(pl.LightningDataModule):
def __init__(self, train_dir, val_dir, train_ann, val_ann, batch_size=2, num_workers=4):
super().__init__()
self.train_dir = train_dir
self.val_dir = val_dir
self.train_ann = train_ann
self.val_ann = val_ann
self.batch_size = batch_size
self.num_workers = num_workers

def setup(self, stage=None):
self.coco_train = CocoDetection(
root=self.train_dir,
annFile=self.train_ann,
transform=self.train_transforms()
)
self.coco_val = CocoDetection(
root=self.val_dir,
annFile=self.val_ann,
transform=self.val_transforms()
)

def train_transforms(self):
return T.Compose([
T.Resize((512, 512)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

def val_transforms(self):
return T.Compose([
T.Resize((512, 512)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

def collate_fn(self, batch):
images, targets = zip(*batch)
images = torch.stack(images)

bboxes = []
classes = []
img_scales = []
img_sizes = []

for target_per_image in targets:
bboxes_per_image = []
classes_per_image = []

for obj in target_per_image:
bbox_tensor = torch.tensor(obj['bbox']).float()
class_tensor = torch.tensor([obj['category_id']]).long()

x_min, y_min, width, height = bbox_tensor
x_max = x_min + width
y_max = y_min + height

if x_max 

Подробнее здесь: [url]https://stackoverflow.com/questions/79203835/efficientdetd0-the-huge-values-of-the-loss-function[/url]