[WIP][Feature] Support RTMDet-Ins fast training

configs/rtmdet_ins/rtmdet-ins_l_syncbn_fast_8xb32-300e_coco.py

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+_base_ = ['../_base_/default_runtime.py']
+
+# ========================Frequently modified parameters======================
+# -----data related-----
+data_root = 'data/coco/'
+# 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 = 10
+# persistent_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 = 0.004
+max_epochs = 300  # Maximum training epochs
+# Change train_pipeline for final 20 epochs (stage 2)
+num_epochs_stage2 = 20
+
+model_test_cfg = dict(
+    # The config of multi-label for multi-class prediction.
+    multi_label=True,
+    # The number of boxes before NMS
+    nms_pre=1000,
+    score_thr=0.05,  # Threshold to filter out boxes.
+    nms=dict(type='nms', iou_threshold=0.6),  # NMS type and threshold
+    max_per_img=100,  # Max number of detections of each image
+    mask_thr_binary=0.5)  # Threshold of binary mask
+
+# ========================Possible modified parameters========================
+# -----data related-----
+img_scale = (640, 640)  # width, height
+# ratio range for random resize
+random_resize_ratio_range = (0.1, 2.0)
+# Cached images number in mosaic
+mosaic_max_cached_images = 40
+# Number of cached images in mixup
+mixup_max_cached_images = 20
+# 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 = 32
+# Worker to pre-fetch data for each single GPU during validation
+val_num_workers = 10
+use_mask2refine = True
+copypaste_prob = 0.3
+mask_downsample_stride = 4
+
+# Config of batch shapes. Only on val.
+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 = 1.0
+# The scaling factor that controls the width of the network structure
+widen_factor = 1.0
+# Strides of multi-scale prior box
+strides = [8, 16, 32]
+
+norm_cfg = dict(type='BN')  # Normalization config
+
+# -----train val related-----
+lr_start_factor = 1.0e-5
+dsl_topk = 13  # Number of bbox selected in each level
+loss_cls_weight = 1.0
+loss_bbox_weight = 2.0
+loss_mask_weight = 2.0
+qfl_beta = 2.0  # beta of QualityFocalLoss
+weight_decay = 0.05
+
+# Save model checkpoint and validation intervals
+save_checkpoint_intervals = 10
+# validation intervals in stage 2
+val_interval_stage2 = 1
+# 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=[103.53, 116.28, 123.675],
+        std=[57.375, 57.12, 58.395],
+        bgr_to_rgb=False),
+    backbone=dict(
+        type='CSPNeXt',
+        arch='P5',
+        expand_ratio=0.5,
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        channel_attention=True,
+        norm_cfg=norm_cfg,
+        act_cfg=dict(type='SiLU', inplace=True)),
+    neck=dict(
+        type='CSPNeXtPAFPN',
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        in_channels=[256, 512, 1024],
+        out_channels=256,
+        num_csp_blocks=3,
+        expand_ratio=0.5,
+        norm_cfg=norm_cfg,
+        act_cfg=dict(type='SiLU', inplace=True)),
+    bbox_head=dict(
+        type='RTMDetInsHead',
+        head_module=dict(
+            type='RTMDetInsSepBNHeadModule',
+            num_classes=num_classes,
+            in_channels=256,
+            stacked_convs=2,
+            feat_channels=256,
+            norm_cfg=norm_cfg,
+            act_cfg=dict(type='SiLU', inplace=True),
+            share_conv=True,
+            pred_kernel_size=1,
+            featmap_strides=strides),
+        prior_generator=dict(
+            type='mmdet.MlvlPointGenerator', offset=0, strides=strides),
+        bbox_coder=dict(type='DistancePointBBoxCoder'),
+        loss_cls=dict(
+            type='mmdet.QualityFocalLoss',
+            use_sigmoid=True,
+            beta=qfl_beta,
+            loss_weight=loss_cls_weight),
+        loss_bbox=dict(type='mmdet.GIoULoss', loss_weight=loss_bbox_weight),
+        loss_mask=dict(
+            type='mmdet.DiceLoss',
+            loss_weight=loss_mask_weight,
+            eps=5e-6,
+            reduction='mean'),
+        mask_loss_stride=mask_downsample_stride),
+    train_cfg=dict(
+        assigner=dict(
+            type='BatchDynamicSoftLabelAssigner',
+            num_classes=num_classes,
+            topk=dsl_topk,
+            iou_calculator=dict(type='mmdet.BboxOverlaps2D')),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=model_test_cfg,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        mask2bbox=use_mask2refine),
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        use_cached=True,
+        max_cached_images=mosaic_max_cached_images,
+        pad_val=114.0),
+    dict(type='YOLOv5CopyPaste', prob=copypaste_prob),
+    dict(
+        type='mmdet.RandomResize',
+        # img_scale is (width, height)
+        scale=(img_scale[0] * 2, img_scale[1] * 2),
+        ratio_range=random_resize_ratio_range,
+        resize_type='mmdet.Resize',
+        keep_ratio=True),
+    dict(
+        type='mmdet.RandomCrop',
+        crop_size=img_scale,
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='mmdet.FilterAnnotations', min_gt_bbox_wh=(1, 1)),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type='YOLOv5MixUp',
+        use_cached=True,
+        max_cached_images=mixup_max_cached_images),
+    dict(type='Mask2Tensor', downsample_stride=mask_downsample_stride),
+    dict(type='mmdet.PackDetInputs')
+]
+
+train_pipeline_stage2 = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        mask2bbox=use_mask2refine),
+    dict(
+        type='mmdet.RandomResize',
+        scale=img_scale,
+        ratio_range=random_resize_ratio_range,
+        resize_type='mmdet.Resize',
+        keep_ratio=True),
+    dict(
+        type='mmdet.RandomCrop',
+        crop_size=img_scale,
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='mmdet.FilterAnnotations', min_gt_bbox_wh=(1, 1)),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(type='Mask2Tensor', downsample_stride=mask_downsample_stride),
+    dict(type='mmdet.PackDetInputs')
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_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,
+        with_mask=True,
+        _scope_='mmdet'),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'pad_param'))
+]
+
+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=True, min_size=32),
+        pipeline=train_pipeline))
+
+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,
+        batch_shapes_cfg=batch_shapes_cfg,
+        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', 'segm'])
+test_evaluator = val_evaluator
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=weight_decay),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=lr_start_factor,
+        by_epoch=False,
+        begin=0,
+        end=1000),
+    dict(
+        # use cosine lr from 150 to 300 epoch
+        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),
+]
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=save_checkpoint_intervals,
+        max_keep_ckpts=max_keep_ckpts  # only keep latest 3 checkpoints
+    ))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        strict_load=False,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - num_epochs_stage2,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+train_cfg = dict(
+    type='EpochBasedTrainLoop',
+    max_epochs=max_epochs,
+    val_interval=save_checkpoint_intervals,
+    dynamic_intervals=[(max_epochs - num_epochs_stage2, val_interval_stage2)])
+
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')