文章目录
- [YOLOv5代码解读[02] models/yolov5l.yaml文件解析](#YOLOv5代码解读[02] models/yolov5l.yaml文件解析)
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YOLOv5代码解读[02] models/yolov5l.yaml文件解析
yolov5l.yaml文件
python
复制代码
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 27 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors, False]], # Detect(P3, P4, P5)
]
检测头1--->耦合头
python
复制代码
class Detect(nn.Module):
stride = None
onnx_dynamic = False
export = False
def __init__(self, nc=80, anchors=(), Decoupled=False, ch=(), inplace=True):
super().__init__()
# 是否解耦头
self.decoupled = Decoupled
# 类别数目
self.nc = nc
# 每个anchor输出维度
self.no = nc + 5
# 检测层的输出数量（不同尺度个数）
self.nl = len(anchors)
# 每个尺度特征图的anchor数量
self.na = len(anchors[0]) // 2
# 初始化步长init grid
self.grid = [torch.zeros(1)] * self.nl
# 初始化anchor grid
self.anchor_grid = [torch.zeros(1)] * self.nl
# self.register_buffer("a", torch.ones(2,3))
# register_buffer的作用是将torch.ones(2,3)这个tensor注册到模型的buffers()属性中，并命名为a，
# 这代表a对应的是一个持久态，不会有梯度传播给它，但是能被模型的state_dict记录下来，可以理解为模型的常数。
self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # (3,3,2) == (nl,na,2)
# 检测头head输出卷积
# 如果是解耦头
if self.decoupled:
self.m = nn.ModuleList(DecoupledHead(x, self.nc, anchors) for x in ch)
# 如果是耦合头
else:
self.m = nn.ModuleList(nn.Conv2d(x, self.no*self.na, 1) for x in ch)
# use in-place ops (e.g. slice assignment)
self.inplace = inplace
def forward(self, x):
# inference output
z = []
# 对于每个尺度的特征图来说
for i in range(self.nl):
# conv
# P3: [1, 128, 80, 80]->[1, 3*(nc+5), 80, 80]
# P4: [1, 256, 40, 40]->[1, 3*(nc+5), 40, 40]
# P5: [1, 512, 20, 20]->[1, 3*(nc+5), 20, 20]
x[i] = self.m[i](x[i])
# 以coco数据集为例，x(bs,255,20,20) -> x(bs,3,20,20,85) (x,y,w,h,c,c1,c2,.........)
bs, _, ny, nx = x[i].shape
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
# 推断过程inference
if not self.training:
# self.grid: [tensor([0.]), tensor([0.]), tensor([0.])]
if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
y = x[i].sigmoid()
if self.inplace:
# 中心点xy 网格grid
y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]
# 长宽wh 锚anchor_grid
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
else:
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
y = torch.cat((xy, wh, y[..., 4:]), -1)
z.append(y.view(bs, -1, self.no))
return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
# # 转成caffe时候的代码
# def forward(self, x):
# # inference output
# z = []
# # 对于每个尺度的特征图来说
# for i in range(self.nl):
# # conv
# # P3: [1, 128, 80, 80]->[1, 3*(nc+5), 80, 80]
# # P4: [1, 256, 40, 40]->[1, 3*(nc+5), 40, 40]
# # P5: [1, 512, 20, 20]->[1, 3*(nc+5), 20, 20]
# x[i] = self.m[i](x[i])
# # y = x[i]
# y = x[i].sigmoid()
# z.append(y)
# return z
def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
d = self.anchors[i].device
t = self.anchors[i].dtype
y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
# torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
if torch_1_10:
yv, xv = torch.meshgrid(y, x, indexing='ij')
else:
yv, xv = torch.meshgrid(y, x)
# 网格grid (x, y)
# x[i] --> (bs,3,ny,nx,85)
# grid --> (1,3,ny,nx,2)
grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2))
# 锚anchor (w, h)
# x[i] --> (bs,3,ny,nx,85)
# anchor_grid --> (1,3,ny,nx,2)
# self.stride: tensor([ 8., 16., 32.])
anchor_grid = (self.anchors[i].clone() * self.stride[i]).view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2))
return grid, anchor_grid
检测头2--->解耦头
python
复制代码
class DecoupledHead(nn.Module):
def __init__(self, ch=256, nc=80, anchors=()):
super().__init__()
# 类别个数
self.nc = nc
# 检测层的数量
self.nl = len(anchors)
# 每一层anchor个数
self.na = len(anchors[0]) // 2
self.merge = Conv(ch, 128 , 1, 1) # 默认256
self.cls_convs1 = Conv(128, 64, 3, 1, 1)
self.cls_convs2 = Conv(64, 64, 3, 1, 1)
self.reg_convs1 = Conv(128, 64, 3, 1, 1)
self.reg_convs2 = Conv(64, 64, 3, 1, 1)
self.cls_preds = nn.Conv2d(64 , self.nc*self.na, 1)
self.reg_preds = nn.Conv2d(64 , 4*self.na, 1)
self.obj_preds = nn.Conv2d(64 , 1*self.na, 1)
def forward(self, x):
x = self.merge(x)
x1 = self.cls_convs1(x)
x1 = self.cls_convs2(x1)
x1 = self.cls_preds(x1)
x2 = self.reg_convs1(x)
x2 = self.reg_convs2(x2)
x21 = self.reg_preds(x2)
x22 = self.obj_preds(x2)
out = torch.cat([x21, x22, x1], 1)
return out
检测头3--->ASFF检测头
python
复制代码
class ASFF_Detect(nn.Module):
stride = None
onnx_dynamic = False
def __init__(self, nc=80, anchors=(), ch=(), multiplier=0.5, rfb=False, inplace=True):
super().__init__()
# 类别数目
self.nc = nc
# 每个anchor输出维度
self.no = nc + 5
# 检测层的输出数量（不同尺度个数）
self.nl = len(anchors)
# 每个尺度特征图的anchor数量
self.na = len(anchors[0]) // 2
# 初始化步长init grid
self.grid = [torch.zeros(1)] * self.nl
# init anchor grid
self.anchor_grid = [torch.zeros(1)] * self.nl
# self.register_buffer("a", torch.ones(2,3))
# register_buffer的作用是将torch.ones(2,3)这个tensor注册到模型的buffers()属性中，并命名为a，
# 这代表a对应的是一个持久态，不会有梯度传播给它，但是能被模型的state_dict记录下来，可以理解为模型的常数。
self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # (3,3,2) == (nl,na,2)
# ASFF模块
self.l0_fusion = ASFFV5(level=0, multiplier=multiplier, rfb=rfb)
self.l1_fusion = ASFFV5(level=1, multiplier=multiplier, rfb=rfb)
self.l2_fusion = ASFFV5(level=2, multiplier=multiplier, rfb=rfb)
# 检测头head输出卷积
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)
# use in-place ops (e.g. slice assignment)
self.inplace = inplace
def forward(self, x):
# inference output
z = []
result = []
result.append(self.l2_fusion(x))
result.append(self.l1_fusion(x))
result.append(self.l0_fusion(x))
x = result
# 对于每个尺度的特征图来说
for i in range(self.nl):
# conv
# P3: [1, 128, 80, 80]->[1, 3*(nc+5), 80, 80]
# P4: [1, 256, 40, 40]->[1, 3*(nc+5), 40, 40]
# P5: [1, 512, 20, 20]->[1, 3*(nc+5), 20, 20]
x[i] = self.m[i](x[i])
# 以coco数据集为例，x(bs,255,20,20) -> x(bs,3,20,20,85) (x,y,w,h,c,c1,c2,.........)
bs, _, ny, nx = x[i].shape
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
# 推断过程inference
if not self.training:
# self.grid: [tensor([0.]), tensor([0.]), tensor([0.])]
if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
y = x[i].sigmoid()
# 这块xy的计算存在大量疑惑？？？？？？？？？？？？？？？？？？？？？？？？？
if self.inplace:
# 中心点xy 网格grid
y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]
# 长宽wh 锚anchor_grid
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
y = torch.cat((xy, wh, y[..., 4:]), -1)
z.append(y.view(bs, -1, self.no))
return x if self.training else (torch.cat(z, 1), x)
def _make_grid(self, nx=20, ny=20, i=0):
d = self.anchors[i].device
if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
yv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)], indexing='ij')
else:
yv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)])
# 网格grid (x, y)
# x[i] --> (bs,3,ny,nx,85)
# grid --> (1,3,ny,nx,2)
grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
# 锚anchor (w, h)
# x[i] --> (bs,3,ny,nx,85)
# anchor_grid --> (1,3,ny,nx,2)
# self.stride: tensor([ 8., 16., 32.])
anchor_grid = (self.anchors[i].clone() * self.stride[i]).view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
return grid, anchor_grid
Model类解析
python
复制代码
class Model(nn.Module):
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):
super().__init__()
# 字典dict类型
if isinstance(cfg, dict):
self.yaml = cfg
# yaml文件
else:
self.yaml_file = Path(cfg).name
# 用ascii编码，忽略错误的形式打开文件cfg
with open(cfg, encoding='ascii', errors='ignore') as f:
self.yaml = yaml.safe_load(f)
# 输入通道
ch = self.yaml['ch'] = self.yaml.get('ch', ch)
# 重写yaml文件中的nc
if nc and nc != self.yaml['nc']:
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
self.yaml['nc'] = nc
# 重写yaml文件中的anchors
if anchors:
LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
self.yaml['anchors'] = round(anchors)
# 根据yaml文件的model_dict解析模型
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])
# 默认类别名字 从0到nc-1
self.names = [str(i) for i in range(self.yaml['nc'])]
self.inplace = self.yaml.get('inplace', True)
# 设置Detect()中的inplace, stride, anchors
m = self.model[-1]
if isinstance(m, Detect) or isinstance(m, ASFF_Detect):
s = 256
m.inplace = self.inplace
# 根据前向传播forward 计算步长stride
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])
# 把anchors放缩到了3个不同的尺度
# 这块的形状为什么这样变化？？？？？？
m.anchors /= m.stride.view(-1, 1, 1)
# 根据YOLOv5 Detect()模块m的步幅顺序检查给定锚框顺序，必要时进行纠正。
check_anchor_order(m)
self.stride = m.stride
if m.decoupled:
LOGGER.info('decoupled done')
pass
else:
self._initialize_biases() # only run once
# 初始化权重weights和偏置biases
initialize_weights(self)
self.info()
LOGGER.info('')
def forward(self, x, augment=False, profile=False, visualize=False):
# 推断时增强augmented inference
if augment:
return self._forward_augment(x)
# 单尺度推断single-scale inference 或者训练train
return self._forward_once(x, profile, visualize)
def _forward_augment(self, x):
# height, width
img_size = x.shape[-2:]
s = [1, 0.83, 0.67] # scales
f = [None, 3, None] # flips (2-ud, 3-lr)
y = [] # outputs
for si, fi in zip(s, f):
xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
yi = self._forward_once(xi)[0] # forward
# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
yi = self._descale_pred(yi, fi, si, img_size)
y.append(yi)
y = self._clip_augmented(y) # clip augmented tails
return torch.cat(y, 1), None # augmented inference, train
def _forward_once(self, x, profile=False, visualize=False):
y, dt = [], []
for m in self.model:
# 输入不是来自于上一个层的输出
if m.f != -1:
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]
if profile:
self._profile_one_layer(m, x, dt)
# 计算输出
x = m(x)
y.append(x if m.i in self.save else None)
# 特征可视化
if visualize:
feature_visualization(x, m.type, m.i, save_dir=visualize)
return x
def _descale_pred(self, p, flips, scale, img_size):
# de-scale predictions following augmented inference (inverse operation)
if self.inplace:
p[..., :4] /= scale # de-scale
if flips == 2:
p[..., 1] = img_size[0] - p[..., 1] # de-flip ud
elif flips == 3:
p[..., 0] = img_size[1] - p[..., 0] # de-flip lr
else:
x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale
if flips == 2:
y = img_size[0] - y # de-flip ud
elif flips == 3:
x = img_size[1] - x # de-flip lr
p = torch.cat((x, y, wh, p[..., 4:]), -1)
return p
def _clip_augmented(self, y):
# Clip YOLOv5 augmented inference tails
nl = self.model[-1].nl # number of detection layers (P3-P5)
g = sum(4 ** x for x in range(nl)) # grid points
e = 1 # exclude layer count
i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices
y[0] = y[0][:, :-i] # large
i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices
y[-1] = y[-1][:, i:] # small
return y
def _profile_one_layer(self, m, x, dt):
c = isinstance(m, Detect) or isinstance(m, ASFF_Detect) # is final layer, copy input as inplace fix
o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
t = time_sync()
for _ in range(10):
m(x.copy() if c else x)
dt.append((time_sync() - t) * 100)
if m == self.model[0]:
LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} {'module'}")
LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
if c:
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
# https://arxiv.org/abs/1708.02002 section 3.3
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
m = self.model[-1]
# mi--> Conv2d(128, 255, kernel_size=(1, 1), stride=(1, 1))
# s --> tensor(8.)
for mi, s in zip(m.m, m.stride):
# conv.bias(255) to (3,85)
b = mi.bias.view(m.na, -1)
b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
b.data[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # cls
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
def _print_biases(self):
m = self.model[-1]
for mi in m.m:
b = mi.bias.detach().view(m.na, -1).T
LOGGER.info(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
def _print_weights(self):
for m in self.model.modules():
if type(m) is Bottleneck:
LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
LOGGER.info('Fusing layers... ')
for m in self.model.modules():
if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
delattr(m, 'bn') # remove batchnorm
m.forward = m.forward_fuse # update forward
self.info()
return self
def info(self, verbose=False, img_size=640):
# 打印模型信息
model_info(self, verbose, img_size)
def _apply(self, fn):
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
self = super()._apply(fn)
m = self.model[-1] # Detect()
if isinstance(m, Detect) or isinstance(m, ASFF_Detect) or isinstance(m, Decoupled_Detect):
m.stride = fn(m.stride)
m.grid = list(map(fn, m.grid))
if isinstance(m.anchor_grid, list):
m.anchor_grid = list(map(fn, m.anchor_grid))
return self
parse_model函数
python
复制代码
def parse_model(d, ch):
# model_dict, input_channels(3)
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
# nc:类别数; gd:'depth_multiple'; gw:'width_multiple'
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
# anchor数目, 每层为3
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors
# 每层的输出，na*(classes+5)
no = na * (nc + 5)
# layers, savelist, ch_out
layers, save, c2 = [], [], ch[-1]
# from, number, module, args
# 以[-1, 1, Conv, [64, 6, 2, 2]为例, ch=[3], f=-1, n=1, m=Conv, args=[64, 6, 2, 2]
# [-1, 1, Conv, [128, 3, 2]
# [-1, 3, C3, [128]]
# [-1, 1, SPPF, [1024, 5]]
# [-1, 1, nn.Upsample, [None, 2, 'nearest']]
# [[-1, 6], 1, Concat, [1]]
# [-1, 3, C3, [512, False]]
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):
# 把strings转为本身的类型
m = eval(m) if isinstance(m, str) else m
for j, a in enumerate(args):
try:
# 列表形式
args[j] = eval(a) if isinstance(a, str) else a
except NameError:
pass
# depth_gain 深度缩放因子
n = n_ = max(round(n*gd), 1) if n > 1 else n
# 对于不同类型的卷积模块
if m in [Conv, DWConv, CrossConv, GhostConv, Bottleneck, GhostBottleneck,
BottleneckCSP, MobileBottleneck, SPP, SPPF, MixConv2d, Focus,
InvertedResidual, ConvBNReLU, C3, C3TR, C3SPP, C3Ghost, CoordAtt,
CoordAttv2, OSA_Stage]:
# i=0, c1=3, c2=64;
# i=1, c1=32, c2=128;
# i=2, c1=64, c2=128;
# c1输入通道；c2输出通道；
c1, c2 = ch[f], args[0]
# width_gain 宽度缩放因子
# 说明不是输出
if c2 != no:
# 输出通道数必须为8的倍数
c2 = make_divisible(c2*gw, 8)
# i=0, [3, 32, 6, 2, 2]
# i=1, [32, 64, 3, 2]
# i=2, [64, 64]
args = [c1, c2, *args[1:]]
# 堆叠次数number of repeats
# 注意网络设计理念：stage ---> block ---> layer
if m in [BottleneckCSP, C3, C3TR, C3Ghost]:
args.insert(2, n)
n = 1
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum(ch[x] for x in f)
elif m is Detect:
args.append([ch[x] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
elif m is ASFF_Detect :
args.append([ch[x] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
elif m is Contract:
c2 = ch[f] * args[0] ** 2
elif m is Expand:
c2 = ch[f] // args[0] ** 2
elif m is ConvNeXt_Block:
c2 = args[0]
args = args[1:]
else:
c2 = ch[f]
# module
# Conv(3, 32, 6, 2, 2]
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)
# m ===> <class 'models.common.Conv'>
# str(m)[8:-2] ===> models.common.Conv
t = str(m)[8:-2].replace('__main__.', '')
# 参数(parameters)/模型参数, 由模型通过学习得到的变量，比如权重和偏置.
# m_.parameters(): <generator object Module.parameters at 0x7fcf4c2059d0>
np = sum(x.numel() for x in m_.parameters())
# attach index, 'from' index, type, number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np
LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}')
# savelist [6, 4, 14, 10, 17, 20, 23]
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)
# layers列表
layers.append(m_)
if i == 0:
ch = []
# ch列表
ch.append(c2)
return nn.Sequential(*layers), sorted(save)