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Running
on
T4
_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') | |