yolox_s_fast_8xb8-300e_coco.py

_base_ = ['../_base_/default_runtime.py', 'yolox_p5_tta.py']

# ========================Frequently modified parameters======================
# -----data related-----
data_root = 'data/coco/'  # Root path of data
# path of train annotation file
train_ann_file = 'annotations/instances_train2017.json'
train_data_prefix = 'train2017/'  # Prefix of train image path
# path of val annotation file
val_ann_file = 'annotations/instances_val2017.json'
val_data_prefix = 'val2017/'  # Prefix of train image path

num_classes = 80  # Number of classes for classification
# Batch size of a single GPU during training
train_batch_size_per_gpu = 8
# Worker to pre-fetch data for each single GPU during tarining
train_num_workers = 8
# Presistent_workers must be False if num_workers is 0
persistent_workers = True

# -----train val related-----
# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs
base_lr = 0.01
max_epochs = 300  # Maximum training epochs

model_test_cfg = dict(
    yolox_style=True,  # better
    # The config of multi-label for multi-class prediction
    multi_label=True,  # 40.5 -> 40.7
    score_thr=0.001,  # Threshold to filter out boxes
    max_per_img=300,  # Max number of detections of each image
    nms=dict(type='nms', iou_threshold=0.65))  # NMS type and threshold

# ========================Possible modified parameters========================
# -----data related-----
img_scale = (640, 640)  # width, height
# Dataset type, this will be used to define the dataset
dataset_type = 'YOLOv5CocoDataset'
# Batch size of a single GPU during validation
val_batch_size_per_gpu = 1
# Worker to pre-fetch data for each single GPU during validation
val_num_workers = 2

# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 0.33
# The scaling factor that controls the width of the network structure
widen_factor = 0.5
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
# generate new random resize shape interval
batch_augments_interval = 10

# -----train val related-----
weight_decay = 0.0005
loss_cls_weight = 1.0
loss_bbox_weight = 5.0
loss_obj_weight = 1.0
loss_bbox_aux_weight = 1.0
center_radius = 2.5  # SimOTAAssigner
num_last_epochs = 15
random_affine_scaling_ratio_range = (0.1, 2)
mixup_ratio_range = (0.8, 1.6)
# Save model checkpoint and validation intervals
save_epoch_intervals = 10
# The maximum checkpoints to keep.
max_keep_ckpts = 3

ema_momentum = 0.0001

# ===============================Unmodified in most cases====================
# model settings
model = dict(
    type='YOLODetector',
    init_cfg=dict(
        type='Kaiming',
        layer='Conv2d',
        a=2.23606797749979,  # math.sqrt(5)
        distribution='uniform',
        mode='fan_in',
        nonlinearity='leaky_relu'),
    # TODO: Waiting for mmengine support
    use_syncbn=False,
    data_preprocessor=dict(
        type='YOLOv5DetDataPreprocessor',
        pad_size_divisor=32,
        batch_augments=[
            dict(
                type='YOLOXBatchSyncRandomResize',
                random_size_range=(480, 800),
                size_divisor=32,
                interval=batch_augments_interval)
        ]),
    backbone=dict(
        type='YOLOXCSPDarknet',
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        out_indices=(2, 3, 4),
        spp_kernal_sizes=(5, 9, 13),
        norm_cfg=norm_cfg,
        act_cfg=dict(type='SiLU', inplace=True),
    ),
    neck=dict(
        type='YOLOXPAFPN',
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        in_channels=[256, 512, 1024],
        out_channels=256,
        norm_cfg=norm_cfg,
        act_cfg=dict(type='SiLU', inplace=True)),
    bbox_head=dict(
        type='YOLOXHead',
        head_module=dict(
            type='YOLOXHeadModule',
            num_classes=num_classes,
            in_channels=256,
            feat_channels=256,
            widen_factor=widen_factor,
            stacked_convs=2,
            featmap_strides=(8, 16, 32),
            use_depthwise=False,
            norm_cfg=norm_cfg,
            act_cfg=dict(type='SiLU', inplace=True),
        ),
        loss_cls=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
            loss_weight=loss_cls_weight),
        loss_bbox=dict(
            type='mmdet.IoULoss',
            mode='square',
            eps=1e-16,
            reduction='sum',
            loss_weight=loss_bbox_weight),
        loss_obj=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
            loss_weight=loss_obj_weight),
        loss_bbox_aux=dict(
            type='mmdet.L1Loss',
            reduction='sum',
            loss_weight=loss_bbox_aux_weight)),
    train_cfg=dict(
        assigner=dict(
            type='mmdet.SimOTAAssigner',
            center_radius=center_radius,
            iou_calculator=dict(type='mmdet.BboxOverlaps2D'))),
    test_cfg=model_test_cfg)

pre_transform = [
    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
    dict(type='LoadAnnotations', with_bbox=True)
]

train_pipeline_stage1 = [
    *pre_transform,
    dict(
        type='Mosaic',
        img_scale=img_scale,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(
        type='mmdet.RandomAffine',
        scaling_ratio_range=random_affine_scaling_ratio_range,
        # img_scale is (width, height)
        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
    dict(
        type='YOLOXMixUp',
        img_scale=img_scale,
        ratio_range=mixup_ratio_range,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(type='mmdet.YOLOXHSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='mmdet.FilterAnnotations',
        min_gt_bbox_wh=(1, 1),
        keep_empty=False),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]

train_pipeline_stage2 = [
    *pre_transform,
    dict(type='mmdet.Resize', scale=img_scale, keep_ratio=True),
    dict(
        type='mmdet.Pad',
        pad_to_square=True,
        # If the image is three-channel, the pad value needs
        # to be set separately for each channel.
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(type='mmdet.YOLOXHSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='mmdet.FilterAnnotations',
        min_gt_bbox_wh=(1, 1),
        keep_empty=False),
    dict(type='mmdet.PackDetInputs')
]

train_dataloader = dict(
    batch_size=train_batch_size_per_gpu,
    num_workers=train_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    collate_fn=dict(type='yolov5_collate'),
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=train_ann_file,
        data_prefix=dict(img=train_data_prefix),
        filter_cfg=dict(filter_empty_gt=False, min_size=32),
        pipeline=train_pipeline_stage1))

test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
    dict(type='mmdet.Resize', scale=img_scale, keep_ratio=True),
    dict(
        type='mmdet.Pad',
        pad_to_square=True,
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]

val_dataloader = dict(
    batch_size=val_batch_size_per_gpu,
    num_workers=val_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=val_ann_file,
        data_prefix=dict(img=val_data_prefix),
        test_mode=True,
        pipeline=test_pipeline))
test_dataloader = val_dataloader

# Reduce evaluation time
val_evaluator = dict(
    type='mmdet.CocoMetric',
    proposal_nums=(100, 1, 10),
    ann_file=data_root + val_ann_file,
    metric='bbox')

test_evaluator = val_evaluator

# optimizer
# default 8 gpu
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(
        type='SGD',
        lr=base_lr,
        momentum=0.9,
        weight_decay=weight_decay,
        nesterov=True),
    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))

# learning rate
param_scheduler = [
    dict(
        # use quadratic formula to warm up 5 epochs
        # and lr is updated by iteration
        # TODO: fix default scope in get function
        type='mmdet.QuadraticWarmupLR',
        by_epoch=True,
        begin=0,
        end=5,
        convert_to_iter_based=True),
    dict(
        # use cosine lr from 5 to 285 epoch
        type='CosineAnnealingLR',
        eta_min=base_lr * 0.05,
        begin=5,
        T_max=max_epochs - num_last_epochs,
        end=max_epochs - num_last_epochs,
        by_epoch=True,
        convert_to_iter_based=True),
    dict(
        # use fixed lr during last 15 epochs
        type='ConstantLR',
        by_epoch=True,
        factor=1,
        begin=max_epochs - num_last_epochs,
        end=max_epochs,
    )
]

default_hooks = dict(
    checkpoint=dict(
        type='CheckpointHook',
        interval=save_epoch_intervals,
        max_keep_ckpts=max_keep_ckpts,
        save_best='auto'))

custom_hooks = [
    dict(
        type='YOLOXModeSwitchHook',
        num_last_epochs=num_last_epochs,
        new_train_pipeline=train_pipeline_stage2,
        priority=48),
    dict(type='mmdet.SyncNormHook', priority=48),
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=ema_momentum,
        update_buffers=True,
        strict_load=False,
        priority=49)
]

train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=max_epochs,
    val_interval=save_epoch_intervals,
    dynamic_intervals=[(max_epochs - num_last_epochs, 1)])

auto_scale_lr = dict(base_batch_size=8 * train_batch_size_per_gpu)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')