Help required YOLOX training

[saadyousuf45]

Config:
optimizer = dict(
type='SGD',
lr=0.01,
momentum=0.9,
weight_decay=0.0005,
nesterov=True,
paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=5,
num_last_epochs=15,
min_lr_ratio=0.05)
runner = dict(type='EpochBasedRunner', max_epochs=300)
checkpoint_config = dict(interval=10)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
custom_hooks = [
dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
dict(type='SyncNormHook', num_last_epochs=15, interval=10, priority=48),
dict(
type='ExpMomentumEMAHook',
resume_from=None,
momentum=0.0001,
priority=49)
]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = '/content/drive/MyDrive/Colab Notebooks/Rad_files/Pre-trained Model Checkpoint/yolox_x_8x8_300e_coco.py'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=64)
img_scale = (640, 640)
model = dict(
type='YOLOX',
input_size=(640, 640),
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=1.33, widen_factor=1.25),
neck=dict(
type='YOLOXPAFPN',
in_channels=[320, 640, 1280],
out_channels=320,
num_csp_blocks=4),
bbox_head=dict(
type='YOLOXHead', num_classes=162, in_channels=320, feat_channels=320),
train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
data_root = 'data/'
dataset_type = 'NewDataset'
train_pipeline = [
dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomAffine', scaling_ratio_range=(0.1, 2),
border=(-320, -320)),
dict(
type='MixUp',
img_scale=(640, 640),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
train_dataset = dict(
type='MultiImageMixDataset',
dataset=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_train2017.json',
img_prefix='data/coco/train2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_empty_gt=False),
pipeline=[
dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-320, -320)),
dict(
type='MixUp',
img_scale=(640, 640),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(
type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
])
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
persistent_workers=True,
train=dict(
type='RepeatDataset',
dataset=dict(
type='NewDataset',
ann_file='/content/mmdetection/data/train.txt',
img_prefix=
'/content/drive/MyDrive/Colab Notebooks/Rad_files/Final_testing_images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_empty_gt=False,
data_root='data/'),
pipeline=[
dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-320, -320)),
dict(
type='MixUp',
img_scale=(640, 640),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(
type='FilterAnnotations',
min_gt_bbox_wh=(1, 1),
keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]),
val=dict(
type='NewDataset',
ann_file='test.txt',
img_prefix=
'/content/drive/MyDrive/Colab Notebooks/Rad_files/Final_testing_images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
data_root='data/'),
test=dict(
type='NewDataset',
ann_file='train.txt',
img_prefix=
'/content/drive/MyDrive/Colab Notebooks/Rad_files/Final_testing_images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
data_root='data/'))
max_epochs = 300
num_last_epochs = 15
interval = 10
evaluation = dict(
save_best='auto', interval=10, dynamic_intervals=[(285, 1)], metric='mAP')
work_dir = '/content/drive/MyDrive/Colab Notebooks/Final thesis implementation/testing for HPC/'
seed = 0
gpu_ids = range(0, 1)

The error i get:
:80: DeprecationWarning: np.long is a deprecated alias for np.compat.long. To silence this warning, use np.compat.long by itself. In the likely event your code does not need to work on Python 2 you can use the builtin int for which np.compat.long is itself an alias. Doing this will not modify any behaviour and is safe. When replacing np.long, you may wish to use e.g. np.int64 or np.int32 to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
labels = np.array(gt_labels, dtype = np.long),
:82: DeprecationWarning: np.long is a deprecated alias for np.compat.long. To silence this warning, use np.compat.long by itself. In the likely event your code does not need to work on Python 2 you can use the builtin int for which np.compat.long is itself an alias. Doing this will not modify any behaviour and is safe. When replacing np.long, you may wish to use e.g. np.int64 or np.int32 to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
labels_ignore = np.array(gt_labels_ignore, dtype = np.long))

KeyError Traceback (most recent call last)
in
5
6
----> 7 datasets = [build_dataset(cfg.data.train)]
8
9 model = build_detector(

1 frames
/usr/local/lib/python3.8/dist-packages/mmcv/utils/config.py in missing(self, name)
36
37 def missing(self, name):
---> 38 raise KeyError(name)
39
40 def getattr(self, name):

KeyError: 'times'