Training Logging Per "Step" not Epoch

[RobinGerster]

I created a simple config for rtmdet and log my results to wandb. It seams that training metrics like loss get logged every step and not per epoch. Similairly, my validation results get logged per epoch but the x-axis on wandb still shows "Step". (Note I use the newest version of MMDetection).

My config is below:
########## My Addtitions Start ##########
#Modified by ConfigWriter to enable wandb logging
experiment_name = "default"
condition_name = "default"
dataset_name = "default"
model_name = "rtmdet-tiny"
fold = 1

#Automatically determined to enable wandb logging
group_name = f"{model_name}{dataset_name}{condition_name}"
tags = [condition_name, dataset_name, model_name, fold]

#Modified by ConfigWriter to enable loading checkpoints
load_from = None

########## My Additions End ##########

Inherit and overwrite part of the config based on this config

base = '../mmdetection/configs/rtmdet/rtmdet_tiny_8xb32-300e_coco.py'

data_root = 'data/' # dataset root

batch_size_per_gpu = 32
num_workers = 8
max_epochs = 2
base_lr = 0.00008

metainfo = {
'classes': ("Bus", "Car", "Lamp", "Motorcycle", "People", "Truck"),
'palette': [
(220, 20, 60), # Vibrant Red
(210, 180, 140), # Warm Beige/Tan
(192, 192, 192), # Classic Silver/Grey
(0, 191, 255), # Bright Blue
(255, 223, 0), # Soft Yellow/Light Orange
(34, 139, 34) # Forest Green
]
}

train_dataloader = dict(
batch_size=batch_size_per_gpu,
num_workers=num_workers,
dataset=dict(
data_root=data_root,
metainfo=metainfo,
data_prefix=dict(img='train_images/'),
ann_file='train.json'))

val_dataloader = dict(
batch_size=batch_size_per_gpu,
num_workers=num_workers,
dataset=dict(
data_root=data_root,
metainfo=metainfo,
data_prefix=dict(img='validation_images/'),
ann_file='valid.json'))

test_dataloader = val_dataloader

val_evaluator = dict(ann_file=data_root + 'valid.json')

test_evaluator = val_evaluator

model = dict(bbox_head=dict(num_classes=6))

train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{base.backend_args}}),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='PackDetInputs')
]

test_pipeline = [
dict(type='LoadImageFromFile', backend_args={{base.backend_args}}),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]

learning rate

param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0e-5,
by_epoch=False,
begin=0,
end=10),
dict(
type='CosineAnnealingLR',
eta_min=base_lr * 0.05,
begin=max_epochs // 2,
end=max_epochs,
T_max=max_epochs // 2,
by_epoch=True,
convert_to_iter_based=True),
]

optimizer

optim_wrapper = dict(
delete=True,
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
paramwise_cfg=dict(
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))

default_hooks = dict(
checkpoint=dict(
interval=1,
max_keep_ckpts=1,
save_best='auto'
),
logger=dict(type='LoggerHook', interval=1))

custom_hooks = [
]

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)

vis_backends = [
dict(type='LocalVisBackend'),
dict(type='WandbVisBackend',
init_kwargs={
'project': experiment_name,
"entity": "mm-gerster",
"name": f"fold{fold}",
"group": group_name,
"tags": [condition_name, dataset_name, model_name, f"fold_{fold}"],
"reinit": True,
})
]
visualizer = dict(
type='DetLocalVisualizer',
vis_backends=vis_backends,
name='visualizer')