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yolov7_w-p6_syncbn_fast_8x16b-300e_coco.py
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yolov7_w-p6_syncbn_fast_8x16b-300e_coco.py
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_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'))
]
])
]