YOLO系列源码学习
YOLO V4-large
创建模型
V4 large通过yaml文件创建网络模型；yaml文件通过yaml.load(cfg)之后会得到一个字典文件:
self.yaml:
{
nc: ,
depth_multiple: ,
width_multiple: ,
anchors: ,
backbone: ,
head: ,
}
yaml文件中的网络结构主要由四部分组成：
from:该模块的输出来自哪一层
number: 该模块会被重复叠加几次
module：该模块名字
args：该模块参数
其中，对于args参数：
Conv: [output_channels, kernel_size, stride]
点击查看yolov4-p5.yaml
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [13,17, 31,25, 24,51, 61,45] # P3/8
- [48,102, 119,96, 97,189, 217,184] # P4/16
- [171,384, 324,451, 616,618, 800,800] # P5/32
# csp-p5 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, BottleneckCSP, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 3, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 15, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 15, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 7, BottleneckCSP, [1024]], # 10
]
# yolov4-p5 head
# na = len(anchors[0])
head:
[[-1, 1, SPPF, [512]], # 11
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[8, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSP2, [256]], # 16
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[6, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSP2, [128]], # 21
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 16], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSP2, [256]], # 25
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 11], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSP2, [512]], # 29
[-1, 1, Conv, [1024, 3, 1]],
[[22,26,30], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
搭建模型是通过文件yolo.py中的parse_model(d, ch)函数：
点击查看parse_model()代码
def parse_model(d, ch): # model_dict, input_channels(3)
"""
Args:
d(dict):{anchors:, nc:, backbone:[], head:[],}, model dict from yaml
ch:[3],means every layers' outputs channels
"""
print('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
m = eval(m) if isinstance(m, str) else m # eval strings
for j, a in enumerate(args):
try:
args[j] = eval(a) if isinstance(a, str) else a # eval strings
except:
pass
n = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, BottleneckCSP2, SPPCSP, SPPF, VoVCSP, C3]:
c1, c2 = ch[f], args[0]
# Normal
# if i > 0 and args[0] != no: # channel expansion factor
# ex = 1.75 # exponential (default 2.0)
# e = math.log(c2 / ch[1]) / math.log(2)
# c2 = int(ch[1] * ex ** e)
# if m != Focus:
c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
# Experimental
# if i > 0 and args[0] != no: # channel expansion factor
# ex = 1 + gw # exponential (default 2.0)
# ch1 = 32 # ch[1]
# e = math.log(c2 / ch1) / math.log(2) # level 1-n
# c2 = int(ch1 * ex ** e)
# if m != Focus:
# c2 = make_divisible(c2, 8) if c2 != no else c2
args = [c1, c2, *args[1:]]
if m in [BottleneckCSP, BottleneckCSP2, SPPCSP, SPPF, VoVCSP, C3]:
# insert number of repeating times in args:[in, out, number, ...]
args.insert(2, n)
n = 1
elif m in [HarDBlock, HarDBlock2]:
c1 = ch[f]
args = [c1, *args[:]]
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
elif m is Detect:
args.append([ch[x + 1] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
else:
c2 = ch[f]
m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace('__main__.', '') # module type
np = sum([x.numel() for x in m_.parameters()]) # number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
print('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
if m in [HarDBlock, HarDBlock2]:
c2 = m_.get_out_ch()
ch.append(c2)
else:
ch.append(c2)
return nn.Sequential(*layers), sorted(save)
训练
在DDP模式下：
默认机器：opt.batch_size = opt.total_batch_size
其他机器：opt.batch_size = opt.total_batch_size // opt.word_size
