Additional code is as follows:
######################################################
input_size = (1024, 512)
dataset_type = 'CustomDataset'
num_classes_dict = {'CocoDataset':80, 'VOCDataset':20, 'CityscapesDataset':8, 'CustomDataset': 6}
num_classes = num_classes_dict[dataset_type]
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) #imagenet mean/std

_base_ = [
    f'../_xbase_/datasets/{dataset_type.lower()}.py',
    '../_xbase_/hyper_params/common_config.py',
    '../_xbase_/hyper_params/ssd_config.py',
    '../_xbase_/hyper_params/schedule.py',
]

######################################################
# settings for qat or calibration - uncomment after doing floating point training
# also change dataset_repeats in the dataset config to 1 for fast learning
quantize = False #'training' #'calibration'
initial_learning_rate = 8e-2
samples_per_gpu = 4
if quantize:
  load_from = './work_dirs/ssd_vgg/latest.pth'
  optimizer = dict(type='SGD', lr=initial_learning_rate/10.0, momentum=0.9, weight_decay=1e-4)
  total_epochs = 1 if quantize == 'calibration' else 6
else:
  optimizer = dict(type='SGD', lr=initial_learning_rate, momentum=0.9, weight_decay=1e-4)
#

######################################################
backbone_type = 'SSDVGG'
backbone_depth = 16
pretrained='torchvision://vgg16'
bacbone_out_channels=[512, 1024, 512, 256, 256, 256, 256]
backbone_out_indices = (3, 4)

basesize_ratio_range = (0.1, 0.9)

conv_cfg = None
norm_cfg = dict(type='BN')

model = dict(
    type='SingleStageDetector',
    pretrained=pretrained,
    backbone=dict(
        type='SSDVGG',
        depth=backbone_depth,
        input_size = input_size
        out_indices=backbone_out_indices,
        norm_eval=False,
        style='pytorch'),
    bbox_head=dict(
        type='SSDHead',
        in_channels=bacbone_out_channels,
        num_classes=num_classes,
        anchor_generator=dict(
            type='SSDAnchorGenerator',
            scale_major=False,
            input_size=input_size,
            basesize_ratio_range=basesize_ratio_range,
            strides=[8, 16, 32, 64, 128, 256, 512],
            ratios=[[2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[0.1, 0.1, 0.2, 0.2])))

# dataset settings
train_pipeline = [
    dict(type='LoadImageFromFile', to_float32=True),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PhotoMetricDistortion',
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18) if not quantize else dict(type='Bypass'),
    dict(
        type='Expand',
        mean=img_norm_cfg['mean'],
        to_rgb=img_norm_cfg['to_rgb'],
        ratio_range=(1, 4)),
    dict(
        type='MinIoURandomCrop',
        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
        min_crop_size=0.3),
    dict(type='Resize', img_scale=input_size, keep_ratio=False),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=input_size,
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=False),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]

data = dict(
    samples_per_gpu=samples_per_gpu,
    workers_per_gpu=0,
    train=dict(dataset=dict(pipeline=train_pipeline)),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline))