_base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py'] # dataset settings data_root = 'data/coco/' dataset_type = 'YOLOv5CocoDataset' # parameters that often need to be modified img_scale = (640, 640) # width, height deepen_factor = 0.33 widen_factor = 0.5 max_epochs = 80 num_classes = 80 save_epoch_intervals = 5 train_batch_size_per_gpu = 8 train_num_workers = 8 val_batch_size_per_gpu = 1 val_num_workers = 2 # The pretrained model is geted and converted from official PPYOLOE. # https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md load_from = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/ppyoloe_plus_s_obj365_pretrained-bcfe8478.pth' # noqa # persistent_workers must be False if num_workers is 0. persistent_workers = True # Base learning rate for optim_wrapper base_lr = 0.001 strides = [8, 16, 32] model = dict( type='YOLODetector', data_preprocessor=dict( # use this to support multi_scale training type='PPYOLOEDetDataPreprocessor', pad_size_divisor=32, batch_augments=[ dict( type='PPYOLOEBatchRandomResize', random_size_range=(320, 800), interval=1, size_divisor=32, random_interp=True, keep_ratio=False) ], mean=[0., 0., 0.], std=[255., 255., 255.], bgr_to_rgb=True), backbone=dict( type='PPYOLOECSPResNet', deepen_factor=deepen_factor, widen_factor=widen_factor, block_cfg=dict( type='PPYOLOEBasicBlock', shortcut=True, use_alpha=True), norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5), act_cfg=dict(type='SiLU', inplace=True), attention_cfg=dict( type='EffectiveSELayer', act_cfg=dict(type='HSigmoid')), use_large_stem=True), neck=dict( type='PPYOLOECSPPAFPN', in_channels=[256, 512, 1024], out_channels=[192, 384, 768], deepen_factor=deepen_factor, widen_factor=widen_factor, num_csplayer=1, num_blocks_per_layer=3, block_cfg=dict( type='PPYOLOEBasicBlock', shortcut=False, use_alpha=False), norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5), act_cfg=dict(type='SiLU', inplace=True), drop_block_cfg=None, use_spp=True), bbox_head=dict( type='PPYOLOEHead', head_module=dict( type='PPYOLOEHeadModule', num_classes=num_classes, in_channels=[192, 384, 768], widen_factor=widen_factor, featmap_strides=strides, reg_max=16, norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5), act_cfg=dict(type='SiLU', inplace=True), num_base_priors=1), prior_generator=dict( type='mmdet.MlvlPointGenerator', offset=0.5, strides=strides), bbox_coder=dict(type='DistancePointBBoxCoder'), loss_cls=dict( type='mmdet.VarifocalLoss', use_sigmoid=True, alpha=0.75, gamma=2.0, iou_weighted=True, reduction='sum', loss_weight=1.0), loss_bbox=dict( type='IoULoss', iou_mode='giou', bbox_format='xyxy', reduction='mean', loss_weight=2.5, return_iou=False), # Since the dflloss is implemented differently in the official # and mmdet, we're going to divide loss_weight by 4. loss_dfl=dict( type='mmdet.DistributionFocalLoss', reduction='mean', loss_weight=0.5 / 4)), train_cfg=dict( initial_epoch=30, 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, eps=1e-9)), test_cfg=dict( multi_label=True, nms_pre=1000, score_thr=0.01, nms=dict(type='nms', iou_threshold=0.7), max_per_img=300)) train_pipeline = [ dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args), dict(type='LoadAnnotations', with_bbox=True), dict(type='PPYOLOERandomDistort'), dict(type='mmdet.Expand', mean=(103.53, 116.28, 123.675)), dict(type='PPYOLOERandomCrop'), 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, persistent_workers=persistent_workers, pin_memory=True, sampler=dict(type='DefaultSampler', shuffle=True), collate_fn=dict(type='yolov5_collate', use_ms_training=True), dataset=dict( type=dataset_type, data_root=data_root, ann_file='annotations/instances_train2017.json', data_prefix=dict(img='train2017/'), filter_cfg=dict(filter_empty_gt=True, min_size=0), pipeline=train_pipeline)) test_pipeline = [ dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args), dict( type='mmdet.FixShapeResize', width=img_scale[0], height=img_scale[1], keep_ratio=False, interpolation='bicubic'), 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, test_mode=True, data_prefix=dict(img='val2017/'), filter_cfg=dict(filter_empty_gt=True, min_size=0), ann_file='annotations/instances_val2017.json', pipeline=test_pipeline)) test_dataloader = val_dataloader param_scheduler = None optim_wrapper = dict( type='OptimWrapper', optimizer=dict( type='SGD', lr=base_lr, momentum=0.9, weight_decay=5e-4, nesterov=False), paramwise_cfg=dict(norm_decay_mult=0.)) default_hooks = dict( param_scheduler=dict( type='PPYOLOEParamSchedulerHook', warmup_min_iter=1000, start_factor=0., warmup_epochs=5, min_lr_ratio=0.0, total_epochs=int(max_epochs * 1.2)), checkpoint=dict( type='CheckpointHook', interval=save_epoch_intervals, save_best='auto', max_keep_ckpts=3)) custom_hooks = [ dict( type='EMAHook', ema_type='ExpMomentumEMA', momentum=0.0002, update_buffers=True, strict_load=False, priority=49) ] val_evaluator = dict( type='mmdet.CocoMetric', proposal_nums=(100, 1, 10), ann_file=data_root + 'annotations/instances_val2017.json', metric='bbox') test_evaluator = val_evaluator train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=save_epoch_intervals) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop')