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_base_ = '../yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py' | |
# dataset settings | |
data_root = 'data/VOCdevkit/' | |
dataset_type = 'YOLOv5VOCDataset' | |
# parameters that often need to be modified | |
num_classes = 20 | |
img_scale = (512, 512) # width, height | |
max_epochs = 50 | |
train_batch_size_per_gpu = 64 | |
train_num_workers = 8 | |
val_batch_size_per_gpu = 1 | |
val_num_workers = 2 | |
# persistent_workers must be False if num_workers is 0. | |
persistent_workers = True | |
lr_factor = 0.15135 | |
affine_scale = 0.75544 | |
# only on Val | |
batch_shapes_cfg = dict(img_size=img_scale[0]) | |
anchors = [[(26, 44), (67, 57), (61, 130)], [(121, 118), (120, 239), | |
(206, 182)], | |
[(376, 161), (234, 324), (428, 322)]] | |
num_det_layers = 3 | |
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth' # noqa | |
tta_img_scales = [img_scale, (416, 416), (640, 640)] | |
# Hyperparameter reference from: | |
# https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.VOC.yaml | |
model = dict( | |
bbox_head=dict( | |
head_module=dict(num_classes=num_classes), | |
prior_generator=dict(base_sizes=anchors), | |
loss_cls=dict( | |
loss_weight=0.21638 * (num_classes / 80 * 3 / num_det_layers), | |
class_weight=0.5), | |
loss_bbox=dict(loss_weight=0.02 * (3 / num_det_layers)), | |
loss_obj=dict( | |
loss_weight=0.51728 * | |
((img_scale[0] / 640)**2 * 3 / num_det_layers), | |
class_weight=0.67198), | |
# Different from COCO | |
prior_match_thr=3.3744), | |
test_cfg=dict(nms=dict(iou_threshold=0.6))) | |
albu_train_transforms = _base_.albu_train_transforms | |
pre_transform = _base_.pre_transform | |
with_mosiac_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_translate_ratio=0.04591, | |
max_shear_degree=0.0, | |
scaling_ratio_range=(1 - affine_scale, 1 + affine_scale), | |
# img_scale is (width, height) | |
border=(-img_scale[0] // 2, -img_scale[1] // 2), | |
border_val=(114, 114, 114)), | |
dict( | |
type='YOLOv5MixUp', | |
prob=0.04266, | |
pre_transform=[ | |
*pre_transform, | |
dict( | |
type='Mosaic', | |
img_scale=img_scale, | |
pad_val=114.0, | |
pre_transform=pre_transform), | |
dict( | |
type='YOLOv5RandomAffine', | |
max_rotate_degree=0.0, | |
max_translate_ratio=0.04591, | |
max_shear_degree=0.0, | |
scaling_ratio_range=(1 - affine_scale, 1 + affine_scale), | |
# img_scale is (width, height) | |
border=(-img_scale[0] // 2, -img_scale[1] // 2), | |
border_val=(114, 114, 114)) | |
]) | |
] | |
without_mosaic_pipeline = [ | |
dict( | |
type='YOLOv5RandomAffine', | |
max_rotate_degree=0.0, | |
max_translate_ratio=0.04591, | |
max_shear_degree=0.0, | |
scaling_ratio_range=(1 - affine_scale, 1 + affine_scale), | |
border=(0, 0), | |
border_val=(114, 114, 114)), | |
dict( | |
type='LetterResize', | |
scale=img_scale, | |
allow_scale_up=True, | |
pad_val=dict(img=114)) | |
] | |
# Because the border parameter is inconsistent when | |
# using mosaic or not, `RandomChoice` is used here. | |
randchoice_mosaic_pipeline = dict( | |
type='RandomChoice', | |
transforms=[with_mosiac_pipeline, without_mosaic_pipeline], | |
prob=[0.85834, 0.14166]) | |
train_pipeline = [ | |
*pre_transform, randchoice_mosaic_pipeline, | |
dict( | |
type='mmdet.Albu', | |
transforms=albu_train_transforms, | |
bbox_params=dict( | |
type='BboxParams', | |
format='pascal_voc', | |
label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), | |
keymap={ | |
'img': 'image', | |
'gt_bboxes': 'bboxes' | |
}), | |
dict( | |
type='YOLOv5HSVRandomAug', | |
hue_delta=0.01041, | |
saturation_delta=0.54703, | |
value_delta=0.27739), | |
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( | |
_delete_=True, | |
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), | |
dataset=dict( | |
type='ConcatDataset', | |
datasets=[ | |
dict( | |
type=dataset_type, | |
data_root=data_root, | |
ann_file='VOC2007/ImageSets/Main/trainval.txt', | |
data_prefix=dict(sub_data_root='VOC2007/'), | |
filter_cfg=dict(filter_empty_gt=False, min_size=32), | |
pipeline=train_pipeline), | |
dict( | |
type=dataset_type, | |
data_root=data_root, | |
ann_file='VOC2012/ImageSets/Main/trainval.txt', | |
data_prefix=dict(sub_data_root='VOC2012/'), | |
filter_cfg=dict(filter_empty_gt=False, min_size=32), | |
pipeline=train_pipeline) | |
], | |
# Use ignore_keys to avoid judging metainfo is | |
# not equal in `ConcatDataset`. | |
ignore_keys='dataset_type'), | |
collate_fn=dict(type='yolov5_collate')) | |
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, | |
ann_file='VOC2007/ImageSets/Main/test.txt', | |
data_prefix=dict(sub_data_root='VOC2007/'), | |
test_mode=True, | |
pipeline=test_pipeline, | |
batch_shapes_cfg=batch_shapes_cfg)) | |
test_dataloader = val_dataloader | |
param_scheduler = None | |
optim_wrapper = dict( | |
optimizer=dict( | |
lr=0.00334, | |
momentum=0.74832, | |
weight_decay=0.00025, | |
batch_size_per_gpu=train_batch_size_per_gpu)) | |
default_hooks = dict( | |
param_scheduler=dict( | |
lr_factor=lr_factor, | |
max_epochs=max_epochs, | |
warmup_epochs=3.3835, | |
warmup_momentum=0.59462, | |
warmup_bias_lr=0.18657)) | |
custom_hooks = [ | |
dict( | |
type='EMAHook', | |
ema_type='ExpMomentumEMA', | |
momentum=0.0001, | |
update_buffers=True, | |
# To load COCO pretrained model, need to set `strict_load=False` | |
strict_load=False, | |
priority=49) | |
] | |
# TODO: Support using coco metric in voc dataset | |
val_evaluator = dict( | |
_delete_=True, type='mmdet.VOCMetric', metric='mAP', eval_mode='area') | |
test_evaluator = val_evaluator | |
train_cfg = dict(max_epochs=max_epochs) | |
# Config for Test Time Augmentation. (TTA) | |
_multiscale_resize_transforms = [ | |
dict( | |
type='Compose', | |
transforms=[ | |
dict(type='YOLOv5KeepRatioResize', scale=s), | |
dict( | |
type='LetterResize', | |
scale=s, | |
allow_scale_up=False, | |
pad_val=dict(img=114)) | |
]) for s in tta_img_scales | |
] | |
tta_pipeline = [ | |
dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
dict( | |
type='TestTimeAug', | |
transforms=[ | |
_multiscale_resize_transforms, | |
[ | |
dict(type='mmdet.RandomFlip', prob=1.), | |
dict(type='mmdet.RandomFlip', prob=0.) | |
], [dict(type='mmdet.LoadAnnotations', with_bbox=True)], | |
[ | |
dict( | |
type='mmdet.PackDetInputs', | |
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
'scale_factor', 'pad_param', 'flip', | |
'flip_direction')) | |
] | |
]) | |
] | |