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_base_ = ['../_base_/default_runtime.py', '../_base_/det_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 val image path
num_classes = 80 # Number of classes for classification
# Batch size of a single GPU during training
train_batch_size_per_gpu = 16
# Worker to pre-fetch data for each single GPU during training
train_num_workers = 8
# persistent_workers must be False if num_workers is 0
persistent_workers = True
# -----model related-----
# Basic size of multi-scale prior box
anchors = [
[(12, 16), (19, 36), (40, 28)], # P3/8
[(36, 75), (76, 55), (72, 146)], # P4/16
[(142, 110), (192, 243), (459, 401)] # P5/32
]
# -----train val related-----
# Base learning rate for optim_wrapper. Corresponding to 8xb16=128 bs
base_lr = 0.01
max_epochs = 300 # Maximum training epochs
num_epoch_stage2 = 30 # The last 30 epochs switch evaluation interval
val_interval_stage2 = 1 # Evaluation interval
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
# ========================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
# Config of batch shapes. Only on val.
# It means not used if batch_shapes_cfg is None.
batch_shapes_cfg = dict(
type='BatchShapePolicy',
batch_size=val_batch_size_per_gpu,
img_size=img_scale[0],
# The image scale of padding should be divided by pad_size_divisor
size_divisor=32,
# Additional paddings for pixel scale
extra_pad_ratio=0.5)
# -----model related-----
strides = [8, 16, 32] # Strides of multi-scale prior box
num_det_layers = 3 # The number of model output scales
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
# Data augmentation
max_translate_ratio = 0.2 # YOLOv5RandomAffine
scaling_ratio_range = (0.1, 2.0) # YOLOv5RandomAffine
mixup_prob = 0.15 # YOLOv5MixUp
randchoice_mosaic_prob = [0.8, 0.2]
mixup_alpha = 8.0 # YOLOv5MixUp
mixup_beta = 8.0 # YOLOv5MixUp
# -----train val related-----
loss_cls_weight = 0.3
loss_bbox_weight = 0.05
loss_obj_weight = 0.7
# BatchYOLOv7Assigner params
simota_candidate_topk = 10
simota_iou_weight = 3.0
simota_cls_weight = 1.0
prior_match_thr = 4. # Priori box matching threshold
obj_level_weights = [4., 1.,
0.4] # The obj loss weights of the three output layers
lr_factor = 0.1 # Learning rate scaling factor
weight_decay = 0.0005
save_epoch_intervals = 1 # Save model checkpoint and validation intervals
max_keep_ckpts = 3 # The maximum checkpoints to keep.
# 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(
type='YOLOv5DetDataPreprocessor',
mean=[0., 0., 0.],
std=[255., 255., 255.],
bgr_to_rgb=True),
backbone=dict(
type='YOLOv7Backbone',
arch='L',
norm_cfg=norm_cfg,
act_cfg=dict(type='SiLU', inplace=True)),
neck=dict(
type='YOLOv7PAFPN',
block_cfg=dict(
type='ELANBlock',
middle_ratio=0.5,
block_ratio=0.25,
num_blocks=4,
num_convs_in_block=1),
upsample_feats_cat_first=False,
in_channels=[512, 1024, 1024],
# The real output channel will be multiplied by 2
out_channels=[128, 256, 512],
norm_cfg=norm_cfg,
act_cfg=dict(type='SiLU', inplace=True)),
bbox_head=dict(
type='YOLOv7Head',
head_module=dict(
type='YOLOv7HeadModule',
num_classes=num_classes,
in_channels=[256, 512, 1024],
featmap_strides=strides,
num_base_priors=3),
prior_generator=dict(
type='mmdet.YOLOAnchorGenerator',
base_sizes=anchors,
strides=strides),
# scaled based on number of detection layers
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=loss_cls_weight *
(num_classes / 80 * 3 / num_det_layers)),
loss_bbox=dict(
type='IoULoss',
iou_mode='ciou',
bbox_format='xywh',
reduction='mean',
loss_weight=loss_bbox_weight * (3 / num_det_layers),
return_iou=True),
loss_obj=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=loss_obj_weight *
((img_scale[0] / 640)**2 * 3 / num_det_layers)),
prior_match_thr=prior_match_thr,
obj_level_weights=obj_level_weights,
# BatchYOLOv7Assigner params
simota_candidate_topk=simota_candidate_topk,
simota_iou_weight=simota_iou_weight,
simota_cls_weight=simota_cls_weight),
test_cfg=model_test_cfg)
pre_transform = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True)
]
mosiac4_pipeline = [
dict(
type='Mosaic',
img_scale=img_scale,
pad_val=114.0,
pre_transform=pre_transform),
dict(
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
max_translate_ratio=max_translate_ratio, # note
scaling_ratio_range=scaling_ratio_range, # note
# img_scale is (width, height)
border=(-img_scale[0] // 2, -img_scale[1] // 2),
border_val=(114, 114, 114)),
]
mosiac9_pipeline = [
dict(
type='Mosaic9',
img_scale=img_scale,
pad_val=114.0,
pre_transform=pre_transform),
dict(
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
max_translate_ratio=max_translate_ratio, # note
scaling_ratio_range=scaling_ratio_range, # note
# img_scale is (width, height)
border=(-img_scale[0] // 2, -img_scale[1] // 2),
border_val=(114, 114, 114)),
]
randchoice_mosaic_pipeline = dict(
type='RandomChoice',
transforms=[mosiac4_pipeline, mosiac9_pipeline],
prob=randchoice_mosaic_prob)
train_pipeline = [
*pre_transform,
randchoice_mosaic_pipeline,
dict(
type='YOLOv5MixUp',
alpha=mixup_alpha, # note
beta=mixup_beta, # note
prob=mixup_prob,
pre_transform=[*pre_transform, randchoice_mosaic_pipeline]),
dict(type='YOLOv5HSVRandomAug'),
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'), # FASTER
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))
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='YOLOv5KeepRatioResize', scale=img_scale),
dict(
type='LetterResize',
scale=img_scale,
allow_scale_up=False,
pad_val=dict(img=114)),
dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'pad_param'))
]
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=val_data_prefix),
ann_file=val_ann_file,
pipeline=test_pipeline,
batch_shapes_cfg=batch_shapes_cfg))
test_dataloader = val_dataloader
param_scheduler = None
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(
type='SGD',
lr=base_lr,
momentum=0.937,
weight_decay=weight_decay,
nesterov=True,
batch_size_per_gpu=train_batch_size_per_gpu),
constructor='YOLOv7OptimWrapperConstructor')
default_hooks = dict(
param_scheduler=dict(
type='YOLOv5ParamSchedulerHook',
scheduler_type='cosine',
lr_factor=lr_factor, # note
max_epochs=max_epochs),
checkpoint=dict(
type='CheckpointHook',
save_param_scheduler=False,
interval=save_epoch_intervals,
save_best='auto',
max_keep_ckpts=max_keep_ckpts))
custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0001,
update_buffers=True,
strict_load=False,
priority=49)
]
val_evaluator = dict(
type='mmdet.CocoMetric',
proposal_nums=(100, 1, 10), # Can be accelerated
ann_file=data_root + val_ann_file,
metric='bbox')
test_evaluator = val_evaluator
train_cfg = dict(
type='EpochBasedTrainLoop',
max_epochs=max_epochs,
val_interval=save_epoch_intervals,
dynamic_intervals=[(max_epochs - num_epoch_stage2, val_interval_stage2)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
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