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_base_ = ['../_base_/default_runtime.py', 'yolox_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 train image path | |
num_classes = 80 # Number of classes for classification | |
# Batch size of a single GPU during training | |
train_batch_size_per_gpu = 8 | |
# Worker to pre-fetch data for each single GPU during tarining | |
train_num_workers = 8 | |
# Presistent_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 = 300 # Maximum training epochs | |
model_test_cfg = dict( | |
yolox_style=True, # better | |
# The config of multi-label for multi-class prediction | |
multi_label=True, # 40.5 -> 40.7 | |
score_thr=0.001, # Threshold to filter out boxes | |
max_per_img=300, # Max number of detections of each image | |
nms=dict(type='nms', iou_threshold=0.65)) # NMS type and threshold | |
# ========================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 | |
# -----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 | |
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001) | |
# generate new random resize shape interval | |
batch_augments_interval = 10 | |
# -----train val related----- | |
weight_decay = 0.0005 | |
loss_cls_weight = 1.0 | |
loss_bbox_weight = 5.0 | |
loss_obj_weight = 1.0 | |
loss_bbox_aux_weight = 1.0 | |
center_radius = 2.5 # SimOTAAssigner | |
num_last_epochs = 15 | |
random_affine_scaling_ratio_range = (0.1, 2) | |
mixup_ratio_range = (0.8, 1.6) | |
# Save model checkpoint and validation intervals | |
save_epoch_intervals = 10 | |
# The maximum checkpoints to keep. | |
max_keep_ckpts = 3 | |
ema_momentum = 0.0001 | |
# ===============================Unmodified in most cases==================== | |
# model settings | |
model = dict( | |
type='YOLODetector', | |
init_cfg=dict( | |
type='Kaiming', | |
layer='Conv2d', | |
a=2.23606797749979, # math.sqrt(5) | |
distribution='uniform', | |
mode='fan_in', | |
nonlinearity='leaky_relu'), | |
# TODO: Waiting for mmengine support | |
use_syncbn=False, | |
data_preprocessor=dict( | |
type='YOLOv5DetDataPreprocessor', | |
pad_size_divisor=32, | |
batch_augments=[ | |
dict( | |
type='YOLOXBatchSyncRandomResize', | |
random_size_range=(480, 800), | |
size_divisor=32, | |
interval=batch_augments_interval) | |
]), | |
backbone=dict( | |
type='YOLOXCSPDarknet', | |
deepen_factor=deepen_factor, | |
widen_factor=widen_factor, | |
out_indices=(2, 3, 4), | |
spp_kernal_sizes=(5, 9, 13), | |
norm_cfg=norm_cfg, | |
act_cfg=dict(type='SiLU', inplace=True), | |
), | |
neck=dict( | |
type='YOLOXPAFPN', | |
deepen_factor=deepen_factor, | |
widen_factor=widen_factor, | |
in_channels=[256, 512, 1024], | |
out_channels=256, | |
norm_cfg=norm_cfg, | |
act_cfg=dict(type='SiLU', inplace=True)), | |
bbox_head=dict( | |
type='YOLOXHead', | |
head_module=dict( | |
type='YOLOXHeadModule', | |
num_classes=num_classes, | |
in_channels=256, | |
feat_channels=256, | |
widen_factor=widen_factor, | |
stacked_convs=2, | |
featmap_strides=(8, 16, 32), | |
use_depthwise=False, | |
norm_cfg=norm_cfg, | |
act_cfg=dict(type='SiLU', inplace=True), | |
), | |
loss_cls=dict( | |
type='mmdet.CrossEntropyLoss', | |
use_sigmoid=True, | |
reduction='sum', | |
loss_weight=loss_cls_weight), | |
loss_bbox=dict( | |
type='mmdet.IoULoss', | |
mode='square', | |
eps=1e-16, | |
reduction='sum', | |
loss_weight=loss_bbox_weight), | |
loss_obj=dict( | |
type='mmdet.CrossEntropyLoss', | |
use_sigmoid=True, | |
reduction='sum', | |
loss_weight=loss_obj_weight), | |
loss_bbox_aux=dict( | |
type='mmdet.L1Loss', | |
reduction='sum', | |
loss_weight=loss_bbox_aux_weight)), | |
train_cfg=dict( | |
assigner=dict( | |
type='mmdet.SimOTAAssigner', | |
center_radius=center_radius, | |
iou_calculator=dict(type='mmdet.BboxOverlaps2D'))), | |
test_cfg=model_test_cfg) | |
pre_transform = [ | |
dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
dict(type='LoadAnnotations', with_bbox=True) | |
] | |
train_pipeline_stage1 = [ | |
*pre_transform, | |
dict( | |
type='Mosaic', | |
img_scale=img_scale, | |
pad_val=114.0, | |
pre_transform=pre_transform), | |
dict( | |
type='mmdet.RandomAffine', | |
scaling_ratio_range=random_affine_scaling_ratio_range, | |
# img_scale is (width, height) | |
border=(-img_scale[0] // 2, -img_scale[1] // 2)), | |
dict( | |
type='YOLOXMixUp', | |
img_scale=img_scale, | |
ratio_range=mixup_ratio_range, | |
pad_val=114.0, | |
pre_transform=pre_transform), | |
dict(type='mmdet.YOLOXHSVRandomAug'), | |
dict(type='mmdet.RandomFlip', prob=0.5), | |
dict( | |
type='mmdet.FilterAnnotations', | |
min_gt_bbox_wh=(1, 1), | |
keep_empty=False), | |
dict( | |
type='mmdet.PackDetInputs', | |
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', | |
'flip_direction')) | |
] | |
train_pipeline_stage2 = [ | |
*pre_transform, | |
dict(type='mmdet.Resize', scale=img_scale, keep_ratio=True), | |
dict( | |
type='mmdet.Pad', | |
pad_to_square=True, | |
# If the image is three-channel, the pad value needs | |
# to be set separately for each channel. | |
pad_val=dict(img=(114.0, 114.0, 114.0))), | |
dict(type='mmdet.YOLOXHSVRandomAug'), | |
dict(type='mmdet.RandomFlip', prob=0.5), | |
dict( | |
type='mmdet.FilterAnnotations', | |
min_gt_bbox_wh=(1, 1), | |
keep_empty=False), | |
dict(type='mmdet.PackDetInputs') | |
] | |
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=False, min_size=32), | |
pipeline=train_pipeline_stage1)) | |
test_pipeline = [ | |
dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
dict(type='mmdet.Resize', scale=img_scale, keep_ratio=True), | |
dict( | |
type='mmdet.Pad', | |
pad_to_square=True, | |
pad_val=dict(img=(114.0, 114.0, 114.0))), | |
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, | |
ann_file=val_ann_file, | |
data_prefix=dict(img=val_data_prefix), | |
test_mode=True, | |
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') | |
test_evaluator = val_evaluator | |
# optimizer | |
# default 8 gpu | |
optim_wrapper = dict( | |
type='OptimWrapper', | |
optimizer=dict( | |
type='SGD', | |
lr=base_lr, | |
momentum=0.9, | |
weight_decay=weight_decay, | |
nesterov=True), | |
paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.)) | |
# learning rate | |
param_scheduler = [ | |
dict( | |
# use quadratic formula to warm up 5 epochs | |
# and lr is updated by iteration | |
# TODO: fix default scope in get function | |
type='mmdet.QuadraticWarmupLR', | |
by_epoch=True, | |
begin=0, | |
end=5, | |
convert_to_iter_based=True), | |
dict( | |
# use cosine lr from 5 to 285 epoch | |
type='CosineAnnealingLR', | |
eta_min=base_lr * 0.05, | |
begin=5, | |
T_max=max_epochs - num_last_epochs, | |
end=max_epochs - num_last_epochs, | |
by_epoch=True, | |
convert_to_iter_based=True), | |
dict( | |
# use fixed lr during last 15 epochs | |
type='ConstantLR', | |
by_epoch=True, | |
factor=1, | |
begin=max_epochs - num_last_epochs, | |
end=max_epochs, | |
) | |
] | |
default_hooks = dict( | |
checkpoint=dict( | |
type='CheckpointHook', | |
interval=save_epoch_intervals, | |
max_keep_ckpts=max_keep_ckpts, | |
save_best='auto')) | |
custom_hooks = [ | |
dict( | |
type='YOLOXModeSwitchHook', | |
num_last_epochs=num_last_epochs, | |
new_train_pipeline=train_pipeline_stage2, | |
priority=48), | |
dict(type='mmdet.SyncNormHook', priority=48), | |
dict( | |
type='EMAHook', | |
ema_type='ExpMomentumEMA', | |
momentum=ema_momentum, | |
update_buffers=True, | |
strict_load=False, | |
priority=49) | |
] | |
train_cfg = dict( | |
type='EpochBasedTrainLoop', | |
max_epochs=max_epochs, | |
val_interval=save_epoch_intervals, | |
dynamic_intervals=[(max_epochs - num_last_epochs, 1)]) | |
auto_scale_lr = dict(base_batch_size=8 * train_batch_size_per_gpu) | |
val_cfg = dict(type='ValLoop') | |
test_cfg = dict(type='TestLoop') | |