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[Improve] Beauty RTMDet config #531

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Feb 9, 2023
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[Improve] Beauty RTMDet config #531

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71ea28c
beauty rtmdet config
Nioolek Feb 8, 2023
68cd314
format
Nioolek Feb 9, 2023
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143 changes: 98 additions & 45 deletions configs/rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco.py
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Original file line number Diff line number Diff line change
@@ -1,37 +1,92 @@
_base_ = '../_base_/default_runtime.py'

# ========================Frequently modified parameters======================
# -----data related-----
data_root = 'data/coco/'
dataset_type = 'YOLOv5CocoDataset'

img_scale = (640, 640) # width, height
deepen_factor = 1.0
widen_factor = 1.0
max_epochs = 300
stage2_num_epochs = 20
interval = 10
num_classes = 80
# 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 = 10
val_batch_size_per_gpu = 32
val_num_workers = 10
# persistent_workers must be False if num_workers is 0.
persistent_workers = True
strides = [8, 16, 32]

# -----train val related-----
# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs
base_lr = 0.004
max_epochs = 300 # Maximum training epochs
# Change train_pipeline for final 20 epochs (stage 2)
num_epochs_stage2 = 20

# single-scale training is recommended to
# be turned on, which can speed up training.
env_cfg = dict(cudnn_benchmark=True)
model_test_cfg = dict(
# The config of multi-label for multi-class prediction.
multi_label=True,
# The number of boxes before NMS
nms_pre=30000,
score_thr=0.001, # Threshold to filter out boxes.
nms=dict(type='nms', iou_threshold=0.65), # NMS type and threshold
max_per_img=300) # Max number of detections of each image

# only on Val
# ========================Possible modified parameters========================
# -----data related-----
img_scale = (640, 640) # width, height
# ratio range for random resize
random_resize_ratio_range = (0.1, 2.0)
# Cached images number in mosaic
mosaic_max_cached_images = 40
# Number of cached images in mixup
mixup_max_cached_images = 20
# 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 = 32
# Worker to pre-fetch data for each single GPU during validation
val_num_workers = 10

# Config of batch shapes. Only on val.
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 = 1.0
# The scaling factor that controls the width of the network structure
widen_factor = 1.0
# Strides of multi-scale prior box
strides = [8, 16, 32]

norm_cfg = dict(type='BN') # Normalization config

# -----train val related-----
lr_start_factor = 1.0e-5
dsl_topk = 13 # Number of bbox selected in each level
loss_cls_weight = 1.0
loss_bbox_weight = 2.0
qfl_beta = 2.0 # beta of QualityFocalLoss
weight_decay = 0.05

# Save model checkpoint and validation intervals
save_checkpoint_intervals = 10
# validation intervals in stage 2
val_interval_stage2 = 1
# 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(
Expand All @@ -46,7 +101,7 @@
deepen_factor=deepen_factor,
widen_factor=widen_factor,
channel_attention=True,
norm_cfg=dict(type='BN'),
norm_cfg=norm_cfg,
act_cfg=dict(type='SiLU', inplace=True)),
neck=dict(
type='CSPNeXtPAFPN',
Expand All @@ -56,7 +111,7 @@
out_channels=256,
num_csp_blocks=3,
expand_ratio=0.5,
norm_cfg=dict(type='BN'),
norm_cfg=norm_cfg,
act_cfg=dict(type='SiLU', inplace=True)),
bbox_head=dict(
type='RTMDetHead',
Expand All @@ -66,7 +121,7 @@
in_channels=256,
stacked_convs=2,
feat_channels=256,
norm_cfg=dict(type='BN'),
norm_cfg=norm_cfg,
act_cfg=dict(type='SiLU', inplace=True),
share_conv=True,
pred_kernel_size=1,
Expand All @@ -77,24 +132,19 @@
loss_cls=dict(
type='mmdet.QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_bbox=dict(type='mmdet.GIoULoss', loss_weight=2.0)),
beta=qfl_beta,
loss_weight=loss_cls_weight),
loss_bbox=dict(type='mmdet.GIoULoss', loss_weight=loss_bbox_weight)),
train_cfg=dict(
assigner=dict(
type='BatchDynamicSoftLabelAssigner',
num_classes=num_classes,
topk=13,
topk=dsl_topk,
iou_calculator=dict(type='mmdet.BboxOverlaps2D')),
allowed_border=-1,
pos_weight=-1,
debug=False),
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),
test_cfg=model_test_cfg,
)

train_pipeline = [
Expand All @@ -104,20 +154,23 @@
type='Mosaic',
img_scale=img_scale,
use_cached=True,
max_cached_images=40,
max_cached_images=mosaic_max_cached_images,
pad_val=114.0),
dict(
type='mmdet.RandomResize',
# img_scale is (width, height)
scale=(img_scale[0] * 2, img_scale[1] * 2),
ratio_range=(0.1, 2.0),
ratio_range=random_resize_ratio_range,
resize_type='mmdet.Resize',
keep_ratio=True),
dict(type='mmdet.RandomCrop', crop_size=img_scale),
dict(type='mmdet.YOLOXHSVRandomAug'),
dict(type='mmdet.RandomFlip', prob=0.5),
dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
dict(type='YOLOv5MixUp', use_cached=True, max_cached_images=20),
dict(
type='YOLOv5MixUp',
use_cached=True,
max_cached_images=mixup_max_cached_images),
dict(type='mmdet.PackDetInputs')
]

Expand All @@ -127,7 +180,7 @@
dict(
type='mmdet.RandomResize',
scale=img_scale,
ratio_range=(0.1, 2.0),
ratio_range=random_resize_ratio_range,
resize_type='mmdet.Resize',
keep_ratio=True),
dict(type='mmdet.RandomCrop', crop_size=img_scale),
Expand Down Expand Up @@ -162,8 +215,8 @@
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
ann_file=train_ann_file,
data_prefix=dict(img=train_data_prefix),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline))

Expand All @@ -177,8 +230,8 @@
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
ann_file=val_ann_file,
data_prefix=dict(img=val_data_prefix),
test_mode=True,
batch_shapes_cfg=batch_shapes_cfg,
pipeline=test_pipeline))
Expand All @@ -189,22 +242,22 @@
val_evaluator = dict(
type='mmdet.CocoMetric',
proposal_nums=(100, 1, 10),
ann_file=data_root + 'annotations/instances_val2017.json',
ann_file=data_root + val_ann_file,
metric='bbox')
test_evaluator = val_evaluator

# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
optimizer=dict(type='AdamW', lr=base_lr, weight_decay=weight_decay),
paramwise_cfg=dict(
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))

# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0e-5,
start_factor=lr_start_factor,
by_epoch=False,
begin=0,
end=1000),
Expand All @@ -223,8 +276,8 @@
default_hooks = dict(
checkpoint=dict(
type='CheckpointHook',
interval=interval,
max_keep_ckpts=3 # only keep latest 3 checkpoints
interval=save_checkpoint_intervals,
max_keep_ckpts=max_keep_ckpts # only keep latest 3 checkpoints
))

custom_hooks = [
Expand All @@ -237,15 +290,15 @@
priority=49),
dict(
type='mmdet.PipelineSwitchHook',
switch_epoch=max_epochs - stage2_num_epochs,
switch_epoch=max_epochs - num_epochs_stage2,
switch_pipeline=train_pipeline_stage2)
]

train_cfg = dict(
type='EpochBasedTrainLoop',
max_epochs=max_epochs,
val_interval=interval,
dynamic_intervals=[(max_epochs - stage2_num_epochs, 1)])
val_interval=save_checkpoint_intervals,
dynamic_intervals=[(max_epochs - num_epochs_stage2, val_interval_stage2)])

val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
2 changes: 2 additions & 0 deletions configs/rtmdet/rtmdet_m_syncbn_fast_8xb32-300e_coco.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,10 @@
_base_ = './rtmdet_l_syncbn_fast_8xb32-300e_coco.py'

# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75

# =======================Unmodified in most cases==================
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
Expand Down
22 changes: 17 additions & 5 deletions configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,19 @@
_base_ = './rtmdet_l_syncbn_fast_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa

# ========================modified parameters======================
deepen_factor = 0.33
widen_factor = 0.5
img_scale = _base_.img_scale

# ratio range for random resize
random_resize_ratio_range = (0.5, 2.0)
# Number of cached images in mosaic
mosaic_max_cached_images = 40
# Number of cached images in mixup
mixup_max_cached_images = 20

# =======================Unmodified in most cases==================
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
Expand All @@ -30,20 +39,23 @@
type='Mosaic',
img_scale=img_scale,
use_cached=True,
max_cached_images=40,
max_cached_images=mosaic_max_cached_images,
pad_val=114.0),
dict(
type='mmdet.RandomResize',
# img_scale is (width, height)
scale=(img_scale[0] * 2, img_scale[1] * 2),
ratio_range=(0.5, 2.0), # note
ratio_range=random_resize_ratio_range, # note
resize_type='mmdet.Resize',
keep_ratio=True),
dict(type='mmdet.RandomCrop', crop_size=img_scale),
dict(type='mmdet.YOLOXHSVRandomAug'),
dict(type='mmdet.RandomFlip', prob=0.5),
dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
dict(type='YOLOv5MixUp', use_cached=True, max_cached_images=20),
dict(
type='YOLOv5MixUp',
use_cached=True,
max_cached_images=mixup_max_cached_images),
dict(type='mmdet.PackDetInputs')
]

Expand All @@ -53,7 +65,7 @@
dict(
type='mmdet.RandomResize',
scale=img_scale,
ratio_range=(0.5, 2.0), # note
ratio_range=random_resize_ratio_range, # note
resize_type='mmdet.Resize',
keep_ratio=True),
dict(type='mmdet.RandomCrop', crop_size=img_scale),
Expand All @@ -75,6 +87,6 @@
priority=49),
dict(
type='mmdet.PipelineSwitchHook',
switch_epoch=_base_.max_epochs - _base_.stage2_num_epochs,
switch_epoch=_base_.max_epochs - _base_.num_epochs_stage2,
switch_pipeline=train_pipeline_stage2)
]
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