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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') | |