mirror of https://github.com/open-mmlab/mmyolo.git
335 lines
11 KiB
Python
335 lines
11 KiB
Python
_base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
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# ========================Frequently modified parameters======================
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# -----data related-----
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data_root = 'data/coco/' # Root path of data
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# Path of train annotation file
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train_ann_file = 'annotations/instances_train2017.json'
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train_data_prefix = 'train2017/' # Prefix of train image path
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# Path of val annotation file
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val_ann_file = 'annotations/instances_val2017.json'
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val_data_prefix = 'val2017/' # Prefix of val image path
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num_classes = 80 # Number of classes for classification
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# Batch size of a single GPU during training
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train_batch_size_per_gpu = 16
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# Worker to pre-fetch data for each single GPU during training
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train_num_workers = 8
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# persistent_workers must be False if num_workers is 0
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persistent_workers = True
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# -----train val related-----
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# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs
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base_lr = 0.01
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max_epochs = 500 # Maximum training epochs
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# Disable mosaic augmentation for final 10 epochs (stage 2)
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close_mosaic_epochs = 10
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model_test_cfg = dict(
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# The config of multi-label for multi-class prediction.
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multi_label=True,
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# The number of boxes before NMS
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nms_pre=30000,
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score_thr=0.001, # Threshold to filter out boxes.
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nms=dict(type='nms', iou_threshold=0.7), # NMS type and threshold
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max_per_img=300) # Max number of detections of each image
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# ========================Possible modified parameters========================
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# -----data related-----
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img_scale = (640, 640) # width, height
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# Dataset type, this will be used to define the dataset
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dataset_type = 'YOLOv5CocoDataset'
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# Batch size of a single GPU during validation
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val_batch_size_per_gpu = 1
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# Worker to pre-fetch data for each single GPU during validation
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val_num_workers = 2
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# Config of batch shapes. Only on val.
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# We tested YOLOv8-m will get 0.02 higher than not using it.
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batch_shapes_cfg = None
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# You can turn on `batch_shapes_cfg` by uncommenting the following lines.
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# batch_shapes_cfg = dict(
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# type='BatchShapePolicy',
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# batch_size=val_batch_size_per_gpu,
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# img_size=img_scale[0],
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# # The image scale of padding should be divided by pad_size_divisor
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# size_divisor=32,
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# # Additional paddings for pixel scale
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# extra_pad_ratio=0.5)
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# -----model related-----
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# The scaling factor that controls the depth of the network structure
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deepen_factor = 0.33
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# The scaling factor that controls the width of the network structure
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widen_factor = 0.5
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# Strides of multi-scale prior box
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strides = [8, 16, 32]
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# The output channel of the last stage
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last_stage_out_channels = 1024
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num_det_layers = 3 # The number of model output scales
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norm_cfg = dict(type='BN', momentum=0.03, eps=0.001) # Normalization config
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# -----train val related-----
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affine_scale = 0.5 # YOLOv5RandomAffine scaling ratio
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# YOLOv5RandomAffine aspect ratio of width and height thres to filter bboxes
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max_aspect_ratio = 100
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tal_topk = 10 # Number of bbox selected in each level
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tal_alpha = 0.5 # A Hyper-parameter related to alignment_metrics
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tal_beta = 6.0 # A Hyper-parameter related to alignment_metrics
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# TODO: Automatically scale loss_weight based on number of detection layers
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loss_cls_weight = 0.5
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loss_bbox_weight = 7.5
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# Since the dfloss is implemented differently in the official
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# and mmdet, we're going to divide loss_weight by 4.
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loss_dfl_weight = 1.5 / 4
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lr_factor = 0.01 # Learning rate scaling factor
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weight_decay = 0.0005
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# Save model checkpoint and validation intervals in stage 1
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save_epoch_intervals = 10
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# validation intervals in stage 2
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val_interval_stage2 = 1
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# The maximum checkpoints to keep.
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max_keep_ckpts = 2
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# Single-scale training is recommended to
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# be turned on, which can speed up training.
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env_cfg = dict(cudnn_benchmark=True)
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# ===============================Unmodified in most cases====================
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model = dict(
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type='YOLODetector',
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data_preprocessor=dict(
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type='YOLOv5DetDataPreprocessor',
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mean=[0., 0., 0.],
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std=[255., 255., 255.],
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bgr_to_rgb=True),
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backbone=dict(
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type='YOLOv8CSPDarknet',
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arch='P5',
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last_stage_out_channels=last_stage_out_channels,
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='SiLU', inplace=True)),
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neck=dict(
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type='YOLOv8PAFPN',
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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in_channels=[256, 512, last_stage_out_channels],
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out_channels=[256, 512, last_stage_out_channels],
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num_csp_blocks=3,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='SiLU', inplace=True)),
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bbox_head=dict(
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type='YOLOv8Head',
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head_module=dict(
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type='YOLOv8HeadModule',
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num_classes=num_classes,
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in_channels=[256, 512, last_stage_out_channels],
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widen_factor=widen_factor,
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reg_max=16,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='SiLU', inplace=True),
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featmap_strides=strides),
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prior_generator=dict(
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type='mmdet.MlvlPointGenerator', offset=0.5, strides=strides),
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bbox_coder=dict(type='DistancePointBBoxCoder'),
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# scaled based on number of detection layers
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loss_cls=dict(
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type='mmdet.CrossEntropyLoss',
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use_sigmoid=True,
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reduction='none',
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loss_weight=loss_cls_weight),
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loss_bbox=dict(
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type='IoULoss',
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iou_mode='ciou',
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bbox_format='xyxy',
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reduction='sum',
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loss_weight=loss_bbox_weight,
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return_iou=False),
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loss_dfl=dict(
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type='mmdet.DistributionFocalLoss',
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reduction='mean',
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loss_weight=loss_dfl_weight)),
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train_cfg=dict(
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assigner=dict(
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type='BatchTaskAlignedAssigner',
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num_classes=num_classes,
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use_ciou=True,
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topk=tal_topk,
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alpha=tal_alpha,
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beta=tal_beta,
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eps=1e-9)),
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test_cfg=model_test_cfg)
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albu_train_transforms = [
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dict(type='Blur', p=0.01),
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dict(type='MedianBlur', p=0.01),
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dict(type='ToGray', p=0.01),
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dict(type='CLAHE', p=0.01)
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]
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pre_transform = [
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dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
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dict(type='LoadAnnotations', with_bbox=True)
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]
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last_transform = [
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dict(
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type='mmdet.Albu',
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transforms=albu_train_transforms,
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bbox_params=dict(
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type='BboxParams',
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format='pascal_voc',
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label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
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keymap={
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'img': 'image',
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'gt_bboxes': 'bboxes'
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}),
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dict(type='YOLOv5HSVRandomAug'),
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dict(type='mmdet.RandomFlip', prob=0.5),
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dict(
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type='mmdet.PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
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'flip_direction'))
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]
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train_pipeline = [
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*pre_transform,
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dict(
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type='Mosaic',
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img_scale=img_scale,
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pad_val=114.0,
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pre_transform=pre_transform),
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dict(
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type='YOLOv5RandomAffine',
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max_rotate_degree=0.0,
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max_shear_degree=0.0,
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scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
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max_aspect_ratio=max_aspect_ratio,
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# img_scale is (width, height)
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border=(-img_scale[0] // 2, -img_scale[1] // 2),
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border_val=(114, 114, 114)),
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*last_transform
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]
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train_pipeline_stage2 = [
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*pre_transform,
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dict(type='YOLOv5KeepRatioResize', scale=img_scale),
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dict(
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type='LetterResize',
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scale=img_scale,
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allow_scale_up=True,
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pad_val=dict(img=114.0)),
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dict(
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type='YOLOv5RandomAffine',
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max_rotate_degree=0.0,
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max_shear_degree=0.0,
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scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
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max_aspect_ratio=max_aspect_ratio,
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border_val=(114, 114, 114)), *last_transform
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]
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train_dataloader = dict(
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batch_size=train_batch_size_per_gpu,
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num_workers=train_num_workers,
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persistent_workers=persistent_workers,
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pin_memory=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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collate_fn=dict(type='yolov5_collate'),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file=train_ann_file,
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data_prefix=dict(img=train_data_prefix),
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filter_cfg=dict(filter_empty_gt=False, min_size=32),
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pipeline=train_pipeline))
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test_pipeline = [
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dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
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dict(type='YOLOv5KeepRatioResize', scale=img_scale),
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dict(
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type='LetterResize',
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scale=img_scale,
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allow_scale_up=False,
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pad_val=dict(img=114)),
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dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
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dict(
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type='mmdet.PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor', 'pad_param'))
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]
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val_dataloader = dict(
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batch_size=val_batch_size_per_gpu,
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num_workers=val_num_workers,
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persistent_workers=persistent_workers,
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pin_memory=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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test_mode=True,
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data_prefix=dict(img=val_data_prefix),
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ann_file=val_ann_file,
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pipeline=test_pipeline,
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batch_shapes_cfg=batch_shapes_cfg))
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test_dataloader = val_dataloader
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param_scheduler = None
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optim_wrapper = dict(
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type='OptimWrapper',
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clip_grad=dict(max_norm=10.0),
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optimizer=dict(
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type='SGD',
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lr=base_lr,
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momentum=0.937,
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weight_decay=weight_decay,
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nesterov=True,
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batch_size_per_gpu=train_batch_size_per_gpu),
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constructor='YOLOv5OptimizerConstructor')
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default_hooks = dict(
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param_scheduler=dict(
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type='YOLOv5ParamSchedulerHook',
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scheduler_type='linear',
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lr_factor=lr_factor,
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max_epochs=max_epochs),
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checkpoint=dict(
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type='CheckpointHook',
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interval=save_epoch_intervals,
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save_best='auto',
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max_keep_ckpts=max_keep_ckpts))
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custom_hooks = [
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dict(
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type='EMAHook',
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ema_type='ExpMomentumEMA',
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momentum=0.0001,
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update_buffers=True,
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strict_load=False,
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priority=49),
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dict(
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type='mmdet.PipelineSwitchHook',
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switch_epoch=max_epochs - close_mosaic_epochs,
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switch_pipeline=train_pipeline_stage2)
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]
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val_evaluator = dict(
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type='mmdet.CocoMetric',
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proposal_nums=(100, 1, 10),
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ann_file=data_root + val_ann_file,
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metric='bbox')
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test_evaluator = val_evaluator
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train_cfg = dict(
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type='EpochBasedTrainLoop',
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max_epochs=max_epochs,
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val_interval=save_epoch_intervals,
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dynamic_intervals=[((max_epochs - close_mosaic_epochs),
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val_interval_stage2)])
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val_cfg = dict(type='ValLoop')
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test_cfg = dict(type='TestLoop')
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