Encounter issue while attempting to train Nanodet-plus-m_320

[AlphaIkaros2]

My configuration file:
#nanodet-plus-m_320
#COCO mAP(0.5:0.95) = 0.270
#AP_50 = 0.418
#AP_75 = 0.281
#AP_small = 0.083
#AP_m = 0.278
#AP_l = 0.451
save_dir: workspace/nanodet-plus-m_320
model:
weight_averager:
name: ExpMovingAverager
decay: 0.9998
arch:
name: NanoDetPlus
detach_epoch: 10
backbone:
name: ShuffleNetV2
model_size: 1.0x
out_stages: [2,3,4]
activation: LeakyReLU
fpn:
name: GhostPAN
in_channels: [116, 232, 464]
out_channels: 96
kernel_size: 5
num_extra_level: 1
use_depthwise: True
activation: LeakyReLU
head:
name: NanoDetPlusHead
num_classes: 15
input_channel: 96
feat_channels: 96
stacked_convs: 2
kernel_size: 5
strides: [8, 16, 32, 64]
activation: LeakyReLU
reg_max: 7
norm_cfg:
type: BN
loss:
loss_qfl:
name: QualityFocalLoss
use_sigmoid: True
beta: 2.0
loss_weight: 1.0
loss_dfl:
name: DistributionFocalLoss
loss_weight: 0.25
loss_bbox:
name: GIoULoss
loss_weight: 2.0
#Auxiliary head, only use in training time.
aux_head:
name: SimpleConvHead
num_classes: 15
input_channel: 192
feat_channels: 192
stacked_convs: 4
strides: [8, 16, 32, 64]
activation: LeakyReLU
reg_max: 7
data:
train:
name: CocoDataset
img_path: /content/drive/MyDrive/Nanodet/BFMC2024_2-3/train
ann_path: /content/drive/MyDrive/Nanodet/BFMC2024_2-3/train/_annotations.coco.json
input_size: [320,320] #[w,h]
keep_ratio: False
pipeline:
perspective: 0.0
scale: [0.6, 1.4]
stretch: [[0.8, 1.2], [0.8, 1.2]]
rotation: 0
shear: 0
translate: 0.2
flip: 0.5
brightness: 0.2
contrast: [0.6, 1.4]
saturation: [0.5, 1.2]
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
val:
name: CocoDataset
img_path: /content/drive/MyDrive/Nanodet/BFMC2024_2-3/valid
ann_path: /content/drive/MyDrive/Nanodet/BFMC2024_2-3/valid/_annotations.coco.json
input_size: [320,320] #[w,h]
keep_ratio: False
pipeline:
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
device:
gpu_ids: [0] # Set like [0, 1, 2, 3] if you have multi-GPUs
workers_per_gpu: 10
batchsize_per_gpu: 96
precision: 32 # set to 16 to use AMP training
schedule:
#resume:
#load_model:
optimizer:
name: AdamW
lr: 0.001
weight_decay: 0.05
warmup:
name: linear
steps: 500
ratio: 0.0001
total_epochs: 50
lr_schedule:
name: CosineAnnealingLR
T_max: 300
eta_min: 0.00005
val_intervals: 10
grad_clip: 35
evaluator:
name: CocoDetectionEvaluator
save_key: mAP
log:
interval: 50

class_names: ['car','crosswalk','highway_entry'
,'highway_exit'
,'intersection'
,'no_entry'
,'onewayroad'
,'parking'
,'pedestrian'
,'priority'
,'roundabout'
,'stop'
,'trafficlight_green'
,'trafficlight_red'
,'trafficlight_yellow',]

Output:
../aten/src/ATen/native/cuda/ScatterGatherKernel.cu:365: operator(): block: [0,0,0], thread: [0,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed.