Spaces:
Sleeping
Sleeping
_base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py' | |
# ========================modified parameters======================== | |
# -----data related----- | |
img_scale = (1280, 1280) # height, width | |
num_classes = 80 # Number of classes for classification | |
# Config of batch shapes. Only on val | |
# It means not used if batch_shapes_cfg is None. | |
batch_shapes_cfg = dict( | |
img_size=img_scale[ | |
0], # The image scale of padding should be divided by pad_size_divisor | |
size_divisor=64) # Additional paddings for pixel scale | |
tta_img_scales = [(1280, 1280), (1024, 1024), (1536, 1536)] | |
# -----model related----- | |
# Basic size of multi-scale prior box | |
anchors = [ | |
[(19, 27), (44, 40), (38, 94)], # P3/8 | |
[(96, 68), (86, 152), (180, 137)], # P4/16 | |
[(140, 301), (303, 264), (238, 542)], # P5/32 | |
[(436, 615), (739, 380), (925, 792)] # P6/64 | |
] | |
strides = [8, 16, 32, 64] # Strides of multi-scale prior box | |
num_det_layers = 4 # # 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 | |
obj_level_weights = [4.0, 1.0, 0.25, 0.06] | |
simota_candidate_topk = 20 | |
# The only difference between P6 and P5 in terms of | |
# hyperparameters is lr_factor | |
lr_factor = 0.2 | |
# ===============================Unmodified in most cases==================== | |
pre_transform = _base_.pre_transform | |
model = dict( | |
backbone=dict(arch='W', out_indices=(2, 3, 4, 5)), | |
neck=dict( | |
in_channels=[256, 512, 768, 1024], | |
out_channels=[128, 256, 384, 512], | |
use_maxpool_in_downsample=False, | |
use_repconv_outs=False), | |
bbox_head=dict( | |
head_module=dict( | |
type='YOLOv7p6HeadModule', | |
in_channels=[128, 256, 384, 512], | |
featmap_strides=strides, | |
norm_cfg=norm_cfg, | |
act_cfg=dict(type='SiLU', inplace=True)), | |
prior_generator=dict(base_sizes=anchors, strides=strides), | |
simota_candidate_topk=simota_candidate_topk, # note | |
# scaled based on number of detection layers | |
loss_cls=dict(loss_weight=loss_cls_weight * | |
(num_classes / 80 * 3 / num_det_layers)), | |
loss_bbox=dict(loss_weight=loss_bbox_weight * (3 / num_det_layers)), | |
loss_obj=dict(loss_weight=loss_obj_weight * | |
((img_scale[0] / 640)**2 * 3 / num_det_layers)), | |
obj_level_weights=obj_level_weights)) | |
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(dataset=dict(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( | |
dataset=dict(pipeline=test_pipeline, batch_shapes_cfg=batch_shapes_cfg)) | |
test_dataloader = val_dataloader | |
default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor)) | |
# 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')) | |
] | |
]) | |
] | |