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_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', backend_args=_base_.backend_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', backend_args=_base_.backend_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')
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