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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 = 16 | |
# 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. Corresponding to 8xb16=64 bs | |
base_lr = 0.01 | |
max_epochs = 500 # Maximum training epochs | |
# Disable mosaic augmentation for final 10 epochs (stage 2) | |
close_mosaic_epochs = 10 | |
model_test_cfg = dict( | |
# The config of multi-label for multi-class prediction. | |
multi_label=True, | |
# The number of boxes before NMS | |
nms_pre=30000, | |
score_thr=0.001, # Threshold to filter out boxes. | |
nms=dict(type='nms', iou_threshold=0.7), # NMS type and threshold | |
max_per_img=300) # Max number of detections of each image | |
# ========================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. | |
# We tested YOLOv8-m will get 0.02 higher than not using it. | |
batch_shapes_cfg = None | |
# You can turn on `batch_shapes_cfg` by uncommenting the following lines. | |
# batch_shapes_cfg = dict( | |
# type='BatchShapePolicy', | |
# batch_size=val_batch_size_per_gpu, | |
# img_size=img_scale[0], | |
# # The image scale of padding should be divided by pad_size_divisor | |
# size_divisor=32, | |
# # Additional paddings for pixel scale | |
# 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 | |
# Strides of multi-scale prior box | |
strides = [8, 16, 32] | |
# The output channel of the last stage | |
last_stage_out_channels = 1024 | |
num_det_layers = 3 # The number of model output scales | |
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001) # Normalization config | |
# -----train val related----- | |
affine_scale = 0.5 # YOLOv5RandomAffine scaling ratio | |
# YOLOv5RandomAffine aspect ratio of width and height thres to filter bboxes | |
max_aspect_ratio = 100 | |
tal_topk = 10 # Number of bbox selected in each level | |
tal_alpha = 0.5 # A Hyper-parameter related to alignment_metrics | |
tal_beta = 6.0 # A Hyper-parameter related to alignment_metrics | |
# TODO: Automatically scale loss_weight based on number of detection layers | |
loss_cls_weight = 0.5 | |
loss_bbox_weight = 7.5 | |
# Since the dfloss is implemented differently in the official | |
# and mmdet, we're going to divide loss_weight by 4. | |
loss_dfl_weight = 1.5 / 4 | |
lr_factor = 0.01 # Learning rate scaling factor | |
weight_decay = 0.0005 | |
# Save model checkpoint and validation intervals in stage 1 | |
save_epoch_intervals = 10 | |
# validation intervals in stage 2 | |
val_interval_stage2 = 1 | |
# The maximum checkpoints to keep. | |
max_keep_ckpts = 2 | |
# 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='YOLOv8CSPDarknet', | |
arch='P5', | |
last_stage_out_channels=last_stage_out_channels, | |
deepen_factor=deepen_factor, | |
widen_factor=widen_factor, | |
norm_cfg=norm_cfg, | |
act_cfg=dict(type='SiLU', inplace=True)), | |
neck=dict( | |
type='YOLOv8PAFPN', | |
deepen_factor=deepen_factor, | |
widen_factor=widen_factor, | |
in_channels=[256, 512, last_stage_out_channels], | |
out_channels=[256, 512, last_stage_out_channels], | |
num_csp_blocks=3, | |
norm_cfg=norm_cfg, | |
act_cfg=dict(type='SiLU', inplace=True)), | |
bbox_head=dict( | |
type='YOLOv8Head', | |
head_module=dict( | |
type='YOLOv8HeadModule', | |
num_classes=num_classes, | |
in_channels=[256, 512, last_stage_out_channels], | |
widen_factor=widen_factor, | |
reg_max=16, | |
norm_cfg=norm_cfg, | |
act_cfg=dict(type='SiLU', inplace=True), | |
featmap_strides=strides), | |
prior_generator=dict( | |
type='mmdet.MlvlPointGenerator', offset=0.5, strides=strides), | |
bbox_coder=dict(type='DistancePointBBoxCoder'), | |
# scaled based on number of detection layers | |
loss_cls=dict( | |
type='mmdet.CrossEntropyLoss', | |
use_sigmoid=True, | |
reduction='none', | |
loss_weight=loss_cls_weight), | |
loss_bbox=dict( | |
type='IoULoss', | |
iou_mode='ciou', | |
bbox_format='xyxy', | |
reduction='sum', | |
loss_weight=loss_bbox_weight, | |
return_iou=False), | |
loss_dfl=dict( | |
type='mmdet.DistributionFocalLoss', | |
reduction='mean', | |
loss_weight=loss_dfl_weight)), | |
train_cfg=dict( | |
assigner=dict( | |
type='BatchTaskAlignedAssigner', | |
num_classes=num_classes, | |
use_ciou=True, | |
topk=tal_topk, | |
alpha=tal_alpha, | |
beta=tal_beta, | |
eps=1e-9)), | |
test_cfg=model_test_cfg) | |
albu_train_transforms = [ | |
dict(type='Blur', p=0.01), | |
dict(type='MedianBlur', p=0.01), | |
dict(type='ToGray', p=0.01), | |
dict(type='CLAHE', p=0.01) | |
] | |
pre_transform = [ | |
dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
dict(type='LoadAnnotations', with_bbox=True) | |
] | |
last_transform = [ | |
dict( | |
type='mmdet.Albu', | |
transforms=albu_train_transforms, | |
bbox_params=dict( | |
type='BboxParams', | |
format='pascal_voc', | |
label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), | |
keymap={ | |
'img': 'image', | |
'gt_bboxes': 'bboxes' | |
}), | |
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 = [ | |
*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_shear_degree=0.0, | |
scaling_ratio_range=(1 - affine_scale, 1 + affine_scale), | |
max_aspect_ratio=max_aspect_ratio, | |
# img_scale is (width, height) | |
border=(-img_scale[0] // 2, -img_scale[1] // 2), | |
border_val=(114, 114, 114)), | |
*last_transform | |
] | |
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=114.0)), | |
dict( | |
type='YOLOv5RandomAffine', | |
max_rotate_degree=0.0, | |
max_shear_degree=0.0, | |
scaling_ratio_range=(1 - affine_scale, 1 + affine_scale), | |
max_aspect_ratio=max_aspect_ratio, | |
border_val=(114, 114, 114)), *last_transform | |
] | |
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'), | |
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 | |
param_scheduler = None | |
optim_wrapper = dict( | |
type='OptimWrapper', | |
clip_grad=dict(max_norm=10.0), | |
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='linear', | |
lr_factor=lr_factor, | |
max_epochs=max_epochs), | |
checkpoint=dict( | |
type='CheckpointHook', | |
interval=save_epoch_intervals, | |
save_best='auto', | |
max_keep_ckpts=max_keep_ckpts)) | |
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 - close_mosaic_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 - close_mosaic_epochs), | |
val_interval_stage2)]) | |
val_cfg = dict(type='ValLoop') | |
test_cfg = dict(type='TestLoop') | |