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T4
_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') | |