def __init__(self, in_channel=3, depth_multiples=0.33, width_multiples=0.50):
super(YOLOv5Backbone, self).__init__()
channel_64 = width_grow(64, width_multiples)
channel_128 = width_grow(128, width_multiples)
channel_256 = width_grow(256, width_multiples)
channel_512 = width_grow(512, width_multiples)
channel_1024 = width_grow(1024, width_multiples)
self.out_channels = [channel_256, channel_512, channel_1024]
self.stem = Focus(in_channel, channel_64, 3)
self.layer1 = nn.Sequential(
CBR(channel_64, channel_128, 3, 2),
BottleNeckCSP(channel_128, channel_128, depth_grow(3, depth_multiples))
)
self.layer2 = nn.Sequential(
CBR(channel_128, channel_256, 3, 2),
BottleNeckCSP(channel_256, channel_256, depth_grow(9, depth_multiples))
)
self.layer3 = nn.Sequential(
CBR(channel_256, channel_512, 3, 2),
BottleNeckCSP(channel_512, channel_512, depth_grow(9, depth_multiples))
)
self.layer4 = nn.Sequential(
CBR(channel_512, channel_1024, 3, 2),
SPP(channel_1024, channel_1024, (5, 9, 13)),
BottleNeckCSP(channel_1024, channel_1024, depth_grow(3, depth_multiples), shortcut=False)
)
def __init__(self, **cfg):
super(YOLOv4, self).__init__()
self.cfg = {**default_cfg, **cfg}
depth_multiples, width_multiples = model_scale(self.cfg['scale_name'])
act_func = MemoryEfficientMish
self.backbones = YOLOv4Backbone(self.cfg['in_channel'],
depth_multiples,
width_multiples,
act=act_func)
c2, c3, c4, c5 = self.backbones.out_channels
self.neck = YOLOv4Neck(c2,
c3,
c4,
c5,
blocks=depth_grow(2, depth_multiples))
self.head = YOLOv4Head(c3,
c4,
c5,
num_cls=self.cfg['num_cls'],
strides=self.cfg['strides'],
anchors=self.cfg['anchors'])
self.loss = PedYOLOv5Loss(iou_weights=self.cfg['iou_weights'],
obj_weights=self.cfg['obj_weights'],
iou_ratio=self.cfg['iou_ratio'],
iou_type=self.cfg['iou_type'])
def __init__(self,
in_channel=3,
depth_multiples=0.33,
width_multiples=0.50,
act=MemoryEfficientMish):
super(YOLOv4Backbone, self).__init__()
channel_32 = width_grow(32, width_multiples)
channel_64 = width_grow(64, width_multiples)
channel_128 = width_grow(128, width_multiples)
channel_256 = width_grow(256, width_multiples)
channel_512 = width_grow(512, width_multiples)
channel_1024 = width_grow(1024, width_multiples)
self.out_channels = [
channel_128, channel_256, channel_512, channel_1024
]
self.layer1 = nn.Sequential(
CBR(in_channel, channel_32, 3, 1, act=act),
CBR(channel_32, channel_64, 3, 2, act=act),
BottleNeck(channel_64, channel_64, act=act))
self.layer2 = nn.Sequential(
CBR(channel_64, channel_128, 3, 2, act=act),
BottleNeckCSP(channel_128,
channel_128,
depth_grow(2, depth_multiples),
act=act))
self.layer3 = nn.Sequential(
CBR(channel_128, channel_256, 3, 2, act=act),
BottleNeckCSP(channel_256,
channel_256,
depth_grow(8, depth_multiples),
act=act))
self.layer4 = nn.Sequential(
CBR(channel_256, channel_512, 3, 2, act=act),
BottleNeckCSP(channel_512,
channel_512,
depth_grow(8, depth_multiples),
act=act))
self.layer5 = nn.Sequential(
CBR(channel_512, channel_1024, 3, 2, act=act),
BottleNeckCSP(channel_1024,
channel_1024,
depth_grow(4, depth_multiples),
act=act))
def __init__(self,
**cfg):
super(YOLOv5, self).__init__()
self.cfg = {**default_cfg, **cfg}
depth_multiples, width_multiples = model_scale(self.cfg['scale_name'])
self.backbones = YOLOv5Backbone(self.cfg['in_channel'], depth_multiples, width_multiples)
c3, c4, c5 = self.backbones.out_channels
self.neck = YOLOv5Neck(c3, c4, c5, blocks=depth_grow(3, depth_multiples))
self.head = YOLOv5Head(c3, c4, c5, num_cls=self.cfg['num_cls'],
strides=self.cfg['strides'],
anchors=self.cfg['anchors'])
self.loss = PedYOLOv5Loss()
def __init__(self,
in_channel=3,
depth_multiples=0.33,
width_multiples=0.50,
act=MemoryEfficientMish(),
extra_len=3):
super(YOLOV4BackBone, self).__init__()
channel_32 = width_grow(32, width_multiples)
channel_64 = width_grow(64, width_multiples)
channel_128 = width_grow(128, width_multiples)
channel_256 = width_grow(256, width_multiples)
channel_512 = width_grow(512, width_multiples)
channel_1024 = width_grow(1024, width_multiples)
self.stem = nn.Sequential(
CBR(in_channel, channel_32, 3, 1, act=act),
CBR(channel_32, channel_64, 3, 2, act=act),
BottleNeckCSP(channel_64,
channel_64,
depth_grow(1, depth_multiples),
act=act), CBR(channel_64, channel_128, 3, 2,
act=act),
BottleNeckCSP(channel_128,
channel_128,
depth_grow(3, depth_multiples),
act=act))
self.layer3 = nn.Sequential(
CBR(channel_128, channel_256, 3, 2, act=act),
BottleNeckCSP(channel_256,
channel_256,
depth_grow(15, depth_multiples),
act=act))
self.layer4 = nn.Sequential(
CBR(channel_256, channel_512, 3, 2, act=act),
BottleNeckCSP(channel_512,
channel_512,
depth_grow(15, depth_multiples),
act=act))
self.layer5 = nn.Sequential(
CBR(channel_512, channel_1024, 3, 2, act=act),
BottleNeckCSP(channel_1024,
channel_1024,
depth_grow(7, depth_multiples),
act=act))
self.extra = list()
for _ in range(extra_len - 3):
self.extra.append(
nn.Sequential(
CBR(channel_1024, channel_1024, 3, 2, act=act),
BottleNeckCSP(channel_1024,
channel_1024,
depth_grow(7, depth_multiples),
act=act)))
self.extra = None if len(self.extra) == 0 else nn.ModuleList(
self.extra)
self.output_channels = [channel_256, channel_512, channel_1024
] + [channel_1024] * (extra_len - 3)
def __init__(self, **kwargs):
super(YOLOV4, self).__init__()
self.cfg = {**default_cfg, **kwargs}
extra_len = len(self.cfg['strides'])
assert extra_len >= 3
assert len(self.cfg['strides']) == len(self.cfg['anchors'])
depth_multiples, width_multiples = model_scale(self.cfg['scale_name'])
act = self.cfg['act']
self.backbones = YOLOV4BackBone(self.cfg['in_channel'],
depth_multiples=depth_multiples,
width_multiples=width_multiples,
act=act,
extra_len=extra_len)
out_channels = self.backbones.output_channels
self.neck = YOLOV4Neck(in_channels=out_channels,
blocks=depth_grow(3, depth_multiples),
k=self.cfg['k'],
act=act)
for m in self.modules():
t = type(m)
if t is nn.Conv2d:
pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif t is nn.BatchNorm2d:
m.eps = 1e-3
m.momentum = 0.03
elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
m.inplace = True
self.head = YOLOV4Head(in_channels=out_channels,
num_cls=self.cfg['num_cls'],
strides=self.cfg['strides'],
anchors=self.cfg['anchors'])
self.loss = YOLOv5Loss(ratio_thresh=self.cfg['ratio_thresh'],
expansion_bias=self.cfg['expansion_bias'],
layer_balance=self.cfg['balance'][extra_len],
cls_pw=self.cfg['cls_pw'],
obj_pw=self.cfg['obj_pw'],
iou_type=self.cfg['iou_type'],
coord_type=self.cfg['coord_type'],
iou_ratio=self.cfg['iou_ratio'],
iou_weights=self.cfg['iou_weights'],
cls_weights=self.cfg['cls_weights'],
obj_weights=self.cfg['obj_weights'])