An error occurs while trying to compute loss in the classification head. To the best I can tell, my input data is in the structure required by the torch vision RetinaNet. However, I still get this error that prevents me from training.
I’ve replaced the example minimally below. The error message you receive should be “index 1 is out of bounds for dimension 1 with size 1” in compute_loss(args) method of the classification head.
Thank you in advance for any help.
Minimal code:

''' Adapted from example in PyTorch code '''
import torch
import torchvision
from torchvision.models.detection.anchor_utils import AnchorGenerator
from torchvision.models.detection.backbone_utils import resnet_fpn_backbone
from torchvision.models.detection import RetinaNet

''' Backbone '''
backbone = resnet_fpn_backbone('resnet18', pretrained=False, trainable_layers=4)
backbone.out_channels = 256

''' Anchor Generator '''
anchor_sizes = ((32,), (64,), (128,), (256,), (512,)) 
aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes) 
anchor_generator = AnchorGenerator(sizes=anchor_sizes, aspect_ratios=aspect_ratios)

''' Model '''
model = RetinaNet(backbone,
                  num_classes=1,
                  anchor_generator=anchor_generator)

def __getitem__():
    img = torch.rand(3, 256, 256)
    bboxes = []
    bboxes = [[15, 15, 20, 20]]*20
    bboxes = torch.FloatTensor(bboxes)
    labels = torch.LongTensor(np.ones(len(bboxes), dtype=int))
    targets = {'boxes':bboxes, 'labels':torch.LongTensor(labels)}
    return img, targets

def retinanet_collate_fn(batch_size=2):
    img_batch = []
    targets_batch = []
    for i in range(batch_size):
        img, targets = __getitem__()
        img_batch.append(img)
        targets_batch.append(targets)
    return img_batch, targets_batch
    

img_batch, targets_batch = retinanet_collate_fn(batch_size=2)

outputs = model(img_batch, targets_batch)