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_base_ = 'yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
# ========================modified parameters======================
img_scale = (1280, 1280) # width, height
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)
# 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 of multi-scale prior box
strides = [8, 16, 32, 64]
num_det_layers = 4 # The number of model output scales
loss_cls_weight = 0.5
loss_bbox_weight = 0.05
loss_obj_weight = 1.0
# The obj loss weights of the three output layers
obj_level_weights = [4.0, 1.0, 0.25, 0.06]
affine_scale = 0.5 # YOLOv5RandomAffine scaling ratio
tta_img_scales = [(1280, 1280), (1024, 1024), (1536, 1536)]
# =======================Unmodified in most cases==================
model = dict(
backbone=dict(arch='P6', out_indices=(2, 3, 4, 5)),
neck=dict(
in_channels=[256, 512, 768, 1024], out_channels=[256, 512, 768, 1024]),
bbox_head=dict(
head_module=dict(
in_channels=[256, 512, 768, 1024], featmap_strides=strides),
prior_generator=dict(base_sizes=anchors, strides=strides),
# 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))
pre_transform = _base_.pre_transform
albu_train_transforms = _base_.albu_train_transforms
train_pipeline = [
*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_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='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'),
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
# 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'))
]
])
]
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