mmdetectionをyolox_sで学習したときのコード
ubuntu20.04
core i7
RTX3060 Laptop
CUDA11.6
pytorch1.12.0
python3.8
pytorch1.12.0
mmcv==1.6.0 ←1.7.0だと動かなかった
mmdet==2.25.0
MMDetection V2.25.0
_base_ = ['configs/_base_/schedules/schedule_1x.py', 'configs/_base_/default_runtime.py']
img_scale = (640, 640) # height, width
＃model settings
model = dict(
type='YOLOX',
input_size=img_scale,
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
neck=dict(
type='YOLOXPAFPN',
in_channels=[128, 256, 512],
out_channels=128,
num_csp_blocks=1),
bbox_head=dict(
type='YOLOXHead', num_classes=1, in_channels=128, feat_channels=128),
#num_classesに検出したいclassの数を入れる
train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
# In order to align the source code, the threshold of the val phase is
# 0.01, and the threshold of the test phase is 0.001.
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
#dataset settings
data_root = 'data/coco/' #データセットがおいてあるディレクトリ
dataset_type = 'CocoDataset'
train_pipeline = [
dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-img_scale[0] // 2, -img_scale[1] // 2)),
dict(
type='MixUp',
img_scale=img_scale,
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
# According to the official implementation, multi-scale
# training is not considered here but in the
# 'mmdet/models/detectors/yolox.py'.
dict(type='Resize', img_scale=img_scale, keep_ratio=True),
dict(
type='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='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
train_dataset = dict(
type='MultiImageMixDataset',
dataset=dict(
type=dataset_type,
classes=('あああ',), #classの名前
ann_file=data_root + 'annotations/instances_train2017.json', #trainのアノテーションファイルのパス
img_prefix=data_root + 'train2017/', #trainさせる画像のパス
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_empty_gt=False,
),
pipeline=train_pipeline)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=img_scale,
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=8, ＃GPUメモリが足りないときは下げる
workers_per_gpu=4, ＃GPUメモリが足りないときは下げる
persistent_workers=True,
train=train_dataset,
val=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json', #validのアノテーションファイルのパス
img_prefix='data/coco/val2017/', #validationさせる画像のパス
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
classes=('あああ', )),
test=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
classes=('あああ', )))
＃optimizer
＃ default 8 gpu
optimizer = dict(
type='SGD',
lr=0.01,
momentum=0.9,
weight_decay=5e-4,
nesterov=True,
paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))
optimizer_config = dict(grad_clip=None)
max_epochs = 300 ＃デフォルト 300エポック
num_last_epochs = 15
resume_from = None
interval = 10
＃learning policy
lr_config = dict(
_delete_=True,
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=5, # 5 epoch
num_last_epochs=num_last_epochs,
min_lr_ratio=0.05)
runner = dict(type='EpochBasedRunner', max_epochs=max_epochs) ＃デフォルト 300エポック
custom_hooks = [
dict(
type='YOLOXModeSwitchHook',
num_last_epochs=num_last_epochs,
priority=48),
dict(
type='SyncNormHook',
num_last_epochs=num_last_epochs,
interval=interval,
priority=48),
dict(
type='ExpMomentumEMAHook',
resume_from=resume_from,
momentum=0.0001,
priority=49)
]
checkpoint_config = dict(interval=interval)
evaluation = dict(
save_best='auto',
# The evaluation interval is 'interval' when running epoch is
# less than ‘max_epochs - num_last_epochs’.
# The evaluation interval is 1 when running epoch is greater than
# or equal to ‘max_epochs - num_last_epochs’.
interval=interval,
dynamic_intervals=[(max_epochs - num_last_epochs, 1)],
metric='bbox')
log_config = dict(interval=50)
＃NOTE: `auto_scale_lr` is for automatically scaling LR,
＃USER SHOULD NOT CHANGE ITS VALUES.
＃base_batch_size = (8 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=64) ＃バッチサイズはここで変えることができる
＃MMdetectionから学習済みモデルをダウンロードしてファイルのパス
load_from = 'yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth'