[FEATURE] Support YOLOv6 3.0 inference

configs/yolov6/yolov6_v3_l_syncbn_fast_8xb32-300e_coco.py

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+_base_ = './yolov6_v3_m_syncbn_fast_8xb32-300e_coco.py'
+
+# ======================= Possible modified parameters =======================
+# -----model related-----
+# The scaling factor that controls the depth of the network structure
+deepen_factor = 1
+# The scaling factor that controls the width of the network structure
+widen_factor = 1
+
+# ============================== Unmodified in most cases ===================
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        hidden_ratio=1. / 2,
+        block_cfg=dict(
+            type='ConvWrapper',
+            norm_cfg=dict(type='BN', momentum=0.03, eps=0.001)),
+        act_cfg=dict(type='SiLU', inplace=True)),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        hidden_ratio=1. / 2,
+        block_cfg=dict(
+            type='ConvWrapper',
+            norm_cfg=dict(type='BN', momentum=0.03, eps=0.001)),
+        block_act_cfg=dict(type='SiLU', inplace=True)),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))

configs/yolov6/yolov6_v3_m_syncbn_fast_8xb32-300e_coco.py

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+_base_ = './yolov6_v3_s_syncbn_fast_8xb32-300e_coco.py'
+
+# ======================= Possible modified parameters =======================
+# -----model related-----
+# The scaling factor that controls the depth of the network structure
+deepen_factor = 0.6
+# The scaling factor that controls the width of the network structure
+widen_factor = 0.75
+
+# -----train val related-----
+affine_scale = 0.9  # YOLOv5RandomAffine scaling ratio
+
+# ============================== Unmodified in most cases ===================
+model = dict(
+    backbone=dict(
+        type='YOLOv6CSPBep',
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        hidden_ratio=2. / 3,
+        block_cfg=dict(type='RepVGGBlock'),
+        act_cfg=dict(type='ReLU', inplace=True)),
+    neck=dict(
+        type='YOLOv6CSPRepBiPAFPN',
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        block_cfg=dict(type='RepVGGBlock'),
+        hidden_ratio=2. / 3,
+        block_act_cfg=dict(type='ReLU', inplace=True)),
+    bbox_head=dict(
+        type='YOLOv6Head',
+        head_module=dict(reg_max=16, widen_factor=widen_factor)))
+
+mosaic_affine_pipeline = [
+    dict(
+        type='Mosaic',
+        img_scale=_base_.img_scale,
+        pad_val=114.0,
+        pre_transform=_base_.pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        # img_scale is (width, height)
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114))
+]
+
+train_pipeline = [
+    *_base_.pre_transform, *mosaic_affine_pipeline,
+    dict(
+        type='YOLOv5MixUp',
+        prob=0.1,
+        pre_transform=[*_base_.pre_transform, *mosaic_affine_pipeline]),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

configs/yolov6/yolov6_v3_n_syncbn_fast_8xb32-300e_coco.py

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+_base_ = './yolov6_v3_s_syncbn_fast_8xb32-300e_coco.py'
+
+# ======================= Possible modified parameters =======================
+# -----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 = 0.25
+
+# -----train val related-----
+lr_factor = 0.02  # Learning rate scaling factor
+
+# ============================== Unmodified in most cases ===================
+model = dict(
+    backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    bbox_head=dict(
+        head_module=dict(widen_factor=widen_factor),
+        loss_bbox=dict(iou_mode='siou')))
+
+default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))

configs/yolov6/yolov6_v3_s_syncbn_fast_8xb32-300e_coco.py

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+_base_ = ['../_base_/default_runtime.py', '../_base_/det_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 val image path
+
+num_classes = 80  # Number of classes for classification
+# Batch size of a single GPU during training
+train_batch_size_per_gpu = 32
+# Worker to pre-fetch data for each single GPU during training
+train_num_workers = 8
+# persistent_workers must be False if num_workers is 0
+persistent_workers = True
+
+# -----train val related-----
+# Base learning rate for optim_wrapper
+base_lr = 0.01
+max_epochs = 300  # Maximum training epochs
+num_last_epochs = 15  # Last epoch number to switch training pipeline
+
+# ======================= 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
+
+# Config of batch shapes. Only on val.
+# It means not used if batch_shapes_cfg is None.
+batch_shapes_cfg = dict(
+    type='BatchShapePolicy',
+    batch_size=val_batch_size_per_gpu,
+    img_size=img_scale[0],
+    size_divisor=32,
+    extra_pad_ratio=0.5)
+
+# -----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
+
+# -----train val related-----
+affine_scale = 0.5  # YOLOv5RandomAffine scaling ratio
+lr_factor = 0.01  # Learning rate scaling factor
+weight_decay = 0.0005
+# Save model checkpoint and validation intervals
+save_epoch_intervals = 10
+# The maximum checkpoints to keep.
+max_keep_ckpts = 3
+# Single-scale training is recommended to
+# be turned on, which can speed up training.
+env_cfg = dict(cudnn_benchmark=True)
+
+# ============================== Unmodified in most cases ===================
+model = dict(
+    type='YOLODetector',
+    data_preprocessor=dict(
+        type='YOLOv5DetDataPreprocessor',
+        mean=[0., 0., 0.],
+        std=[255., 255., 255.],
+        bgr_to_rgb=True),
+    backbone=dict(
+        type='YOLOv6EfficientRep',
+        out_indices=[1, 2, 3, 4],
+        use_cspsppf=True,
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+        act_cfg=dict(type='ReLU', inplace=True)),
+    neck=dict(
+        type='YOLOv6RepBiPAFPN',
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        in_channels=[128, 256, 512, 1024],
+        out_channels=[128, 256, 512],
+        num_csp_blocks=12,
+        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+        act_cfg=dict(type='ReLU', inplace=True),
+    ),
+    bbox_head=dict(
+        type='YOLOv6Head',
+        head_module=dict(
+            type='YOLOv6HeadModule',
+            num_classes=num_classes,
+            in_channels=[128, 256, 512],
+            widen_factor=widen_factor,
+            norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+            act_cfg=dict(type='SiLU', inplace=True),
+            featmap_strides=[8, 16, 32]),
+        loss_bbox=dict(
+            type='IoULoss',
+            iou_mode='giou',
+            bbox_format='xyxy',
+            reduction='mean',
+            loss_weight=2.5,
+            return_iou=False)),
+    train_cfg=dict(
+        initial_epoch=4,
+        initial_assigner=dict(
+            type='BatchATSSAssigner',
+            num_classes=num_classes,
+            topk=9,
+            iou_calculator=dict(type='mmdet.BboxOverlaps2D')),
+        assigner=dict(
+            type='BatchTaskAlignedAssigner',
+            num_classes=num_classes,
+            topk=13,
+            alpha=1,
+            beta=6),
+    ),
+    test_cfg=dict(
+        multi_label=True,
+        nms_pre=30000,
+        score_thr=0.001,
+        nms=dict(type='nms', iou_threshold=0.65),
+        max_per_img=300))
+
+# The training pipeline of YOLOv6 is basically the same as YOLOv5.
+# The difference is that Mosaic and RandomAffine will be closed in the last 15 epochs. # noqa
+pre_transform = [
+    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
+    dict(type='LoadAnnotations', with_bbox=True)
+]
+
+train_pipeline = [
+    *pre_transform,
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_translate_ratio=0.1,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        # img_scale is (width, height)
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
+        border_val=(114, 114, 114),
+        max_shear_degree=0.0),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+train_pipeline_stage2 = [
+    *pre_transform,
+    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
+    dict(
+        type='LetterResize',
+        scale=img_scale,
+        allow_scale_up=True,
+        pad_val=dict(img=114)),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_translate_ratio=0.1,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        max_shear_degree=0.0,
+    ),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    collate_fn=dict(type='yolov5_collate'),
+    persistent_workers=persistent_workers,
+    pin_memory=True,
+    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))
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
+    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
+    dict(
+        type='LetterResize',
+        scale=img_scale,
+        allow_scale_up=False,
+        pad_val=dict(img=114)),
+    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'pad_param'))
+]
+
+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,
+        test_mode=True,
+        data_prefix=dict(img=val_data_prefix),
+        ann_file=val_ann_file,
+        pipeline=test_pipeline,
+        batch_shapes_cfg=batch_shapes_cfg))
+
+test_dataloader = val_dataloader
+
+# Optimizer and learning rate scheduler of YOLOv6 are basically the same as YOLOv5. # noqa
+# The difference is that the scheduler_type of YOLOv6 is cosine.
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(
+        type='SGD',
+        lr=base_lr,
+        momentum=0.937,
+        weight_decay=weight_decay,
+        nesterov=True,
+        batch_size_per_gpu=train_batch_size_per_gpu),
+    constructor='YOLOv5OptimizerConstructor')
+
+default_hooks = dict(
+    param_scheduler=dict(
+        type='YOLOv5ParamSchedulerHook',
+        scheduler_type='cosine',
+        lr_factor=lr_factor,
+        max_epochs=max_epochs),
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=save_epoch_intervals,
+        max_keep_ckpts=max_keep_ckpts,
+        save_best='auto'))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0001,
+        update_buffers=True,
+        strict_load=False,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - num_last_epochs,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+val_evaluator = dict(
+    type='mmdet.CocoMetric',
+    proposal_nums=(100, 1, 10),
+    ann_file=data_root + val_ann_file,
+    metric='bbox')
+test_evaluator = val_evaluator
+
+train_cfg = dict(
+    type='EpochBasedTrainLoop',
+    max_epochs=max_epochs,
+    val_interval=save_epoch_intervals,
+    dynamic_intervals=[(max_epochs - num_last_epochs, 1)])
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')