mmyolo/configs/yolov7/yolov7_tiny_syncbn_fast_8x1...

83 lines
2.5 KiB
Python

_base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py'
num_classes = _base_.num_classes
num_det_layers = _base_.num_det_layers
img_scale = _base_.img_scale
pre_transform = _base_.pre_transform
model = dict(
backbone=dict(
arch='Tiny', act_cfg=dict(type='LeakyReLU', negative_slope=0.1)),
neck=dict(
is_tiny_version=True,
in_channels=[128, 256, 512],
out_channels=[64, 128, 256],
block_cfg=dict(
_delete_=True, type='TinyDownSampleBlock', middle_ratio=0.25),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
use_repconv_outs=False),
bbox_head=dict(
head_module=dict(in_channels=[128, 256, 512]),
loss_cls=dict(loss_weight=0.5 *
(num_classes / 80 * 3 / num_det_layers)),
loss_obj=dict(loss_weight=1.0 *
((img_scale[0] / 640)**2 * 3 / num_det_layers))))
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=0.1, # change
scaling_ratio_range=(0.5, 1.6), # change
# 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=0.1, # change
scaling_ratio_range=(0.5, 1.6), # change
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=[0.8, 0.2])
train_pipeline = [
*pre_transform,
randchoice_mosaic_pipeline,
dict(
type='YOLOv5MixUp',
alpha=8.0,
beta=8.0,
prob=0.05, # change
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))
default_hooks = dict(param_scheduler=dict(lr_factor=0.01))