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Yolov5 model construction source code details | CSDN creation punch in
2022-07-03 05:07:00 【TT ya】
Introduction to deep learning, rookie , I hope it's like taking notes and recording what I've learned , Also hope to help the same entry-level people , I hope the big guys can help correct it ~ Tort made delete .
The whole family bucket part of code analysis comments is just for the convenience of looking at the loop , Those indents of conditional judgment correspond to , And two 、 3、 ... and 、 The four topics are the same . Can be pasted directly into VS code Look inside , It will also be clearer , See what kind of form you like ~
Catalog
One 、 Read the synopsis carefully
1、 Load the parsing yaml The configuration file （ Including network parameters and so on ）
5、 Initialize weights and offsets
5、 ... and 、 Code analysis comments family bucket
One 、 Read the synopsis carefully
We have already introduced YOLOv5 Network framework of , Portal
YOLOv5 Network structure details _tt Ya's blog -CSDN Blog
How to build it concretely ？ Let's start with the source code （YOLOv5 In the source yolo.py Of class Model） Let's get started
Two 、def __init__
Actually, after running __init__, The whole network is set up .
1、 Load the parsing yaml The configuration file （ Including network parameters and so on ）
class Model(nn.Module):
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
super().__init__()
if isinstance(cfg, dict): # Judge cfg Is it right? dict（ Dictionaries ） type
self.yaml = cfg # model dict
# The model dictionary is assigned to self.yamlFirst judgement cfg Is it right? dict（ Dictionaries ） type , If it is , Assign this model dictionary to self.yaml
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name # obtain cfg The name of the file
with open(cfg, encoding='ascii', errors='ignore') as f:# use ascii code , Ignore the wrong form to open the file cfg
self.yaml = yaml.safe_load(f) # model dict
# use yaml Loaded as a file cfg, Assign a value to self.yamlOtherwise import yaml, obtain cfg After the file name of , use ascii code , Ignore the wrong form to open the file cfg, use yaml Loaded as a file cfg, Assign a value to self.yaml.
final yaml Configuration information in the form of a dictionary .
2、self.yaml
Let's first look at what is in the configuration file
The first is the setting of some parameters
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # 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] # P5/32And then there's the Internet backbone part
# 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
]Finally, the network Neck + Head part （ But here it is written directly Head 了 , It's all the same ）
# 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]], # Detect(P3, P4, P5)
]3、 Define the network model
# Define model
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
if nc and nc != self.yaml['nc']:
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
self.yaml['nc'] = nc # override yaml valueJudge the input channel Is it the same as that in the configuration file , If they are different, they become input parameters .
if anchors: LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') self.yaml['anchors'] = round(anchors) # override yaml valuetake anchors To round （ Prevent the input of decimal numbers from reporting errors ）
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelistImport the information in the configuration file into the model and save .
Then let's intersperse here parse_model function
Please look at the catalogue
Four 、 Other required functions —— parse_model function
Let's go back and continue our analysis
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
# Number those categories , from 0 To nc-1
self.inplace = self.yaml.get('inplace', True)self.name： Number those categories , from 0 To nc-1
4、Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 256 # 2x min stride
m.inplace = self.inplace
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
m.anchors /= m.stride.view(-1, 1, 1)
Here we call forward() function ：
Then let's intersperse here forward（） function
Please look at the catalogue
Let's come back and continue to analyze .
Enter a [1, ch, 256, 256] Of tensor, Then get FPN Dimension of output results . Then the multiple of down sampling is calculated stride：8,16,32.
Last use anchor Remove the above value , This way anchor Put and shrink to 3 On different scales .
therefore anchor In the end shape yes [3,3,2].
check_anchor_order(m)
# according to YOLOv5 Detect（） modular m Check the anchoring sequence , Correct if necessary
self.stride = m.stride
self._initialize_biases() # only run once
# Initialize the deviation into Detect modular First of all, according to the YOLOv5 Detect（） modular m Check the anchoring sequence , Correct if necessary , After checking and correcting stride It's settled , Then initialize the deviation into Detect modular （ There is no deviation , Because the initial class frequency cf by None）.
5、 Initialize weights and offsets
initialize_weights(self)
self.info()
LOGGER.info('')3、 ... and 、def forward
1、 The main body
def forward(self, x, augment=False, profile=False, visualize=False):
if augment:
return self._forward_augment(x) # augmented inference, None
return self._forward_once(x, profile, visualize) # single-scale inference, trainIf augment yes True Just call _forward_augment function , Do enhancement .
If augment yes False Just call _forward_once function , No enhancement .
You can see the default augment by False, So what is actually called is _forward_once function .
2、_forward_once function
def _forward_once(self, x, profile=False, visualize=False):
y, dt = [], [] # outputs
for m in self.model:Here, first initialize the list of stored outputs （ among dt Not used , Later on ）, Then enter the cycle , Traverse each module （ layer ）
Once again m Parameters in ：m.f Which floor does it start from ;
m.n Is the default depth of the module ;
m.args Is the parameter of this layer （ It's from yaml From there ）
if m.f != -1: # if not from previous layer
# Not on the next floor , That is, it is not directly connected to the upper layer （ Such as Concat)
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
# Take out the result of the corresponding layer , Prepare the following entry correspondence m Of forward()First, judge ： If not directly connected to the upper layer , Such as Concat, Then we take out the result of the corresponding layer , Prepare the following entry correspondence m Of forward().
（ The result of the corresponding layer and the corresponding m Of forward() I'll say later ）
if profile:
self._profile_one_layer(m, x, dt)Because the default profile yes False, The input is also False, So it doesn't use , Let's not talk about him , next ~
x = m(x) # run
#m It's a module （ Some layer ） It means , therefore x Incoming module , Equivalent to the execution module （ for instance Focus,SPP etc. ） Medium forward（）
# first floor Focus Of m.f yes -1, So jump to this step directly Here is what we said earlier “ Corresponding m Of forward()” La
m It's a module （ Some layer ） It means , therefore x Incoming module , Equivalent to the execution module （ for instance Focus,SPP etc. ） Medium forward（）
Because the first floor Focus Of m.f yes -1, None of the previous judgments has entered , So jump to this step directly .
y.append(x if m.i in self.save else None) # save output
# Save the output results of each layer to yHere is what we said earlier “ The result of the corresponding layer ” La
Here is to save the output results of each layer to y, The preservation here is for people like Concat These modules serve , They are not directly connected to the upper layer , Output results of all layers to be connected are required .
Causality , So not all the output results of all layers should be saved , Only needed , To save .
Here is our self.m It does matter . If you remember , Actually, as we said before , stay parse_model Here in the function
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelistHere is the operation of on-demand allocation （x!=1）
OK, return to our _forward_once function
if visualize:
feature_visualization(x, m.type, m.i, save_dir=visualize)Here with profile equally , Default False, There is no need to use ,pass~
return xreturn It's over
Four 、 Other required functions —— parse_model function
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}")
# Log record , Regardless of him
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)The above is the read configuration dict In the parameters of the , See my previous article for specific parameters
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch outinitialization
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 # Set the module type m Convert to value
for j, a in enumerate(args):# Loop module parameters args
try:
args[j] = eval(a) if isinstance(a, str) else a # Set each module parameter args[j] Convert to value
except NameError:
pass
Iteration loop backbone And Head（Neck,head） Configuration of , And the module type m Convert to value , Parameters of each module args[j] Convert to value
f： From which floor
n： Default depth of the module
m： The type of module
args： Parameters of the module
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
# Network with （n*gd） Depth scaling of the control module .
# Depth here refers to something like CSP The number of iterations of this module , Generally, we refer to the width of the characteristic graph channel.Network with （n*gd） Depth scaling of the control module .
Depth here refers to something like CSP The number of iterations of this module , Generally, we refer to the width of the characteristic graph channel.
if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:
c1, c2 = ch[f], args[0]
#ch It is used to save the output of all previous modules channle（ therefore ch[-1] Represents the output channel of the previous module ）.
# ch[f] It's No f Layer output .args[0] Is the default output channel .ch It is used to save the output of all previous modules channle（ therefore ch[-1] Represents the output channel of the previous module ）.
ch[f] It's No f Layer output .args[0] Is the default output channel .
if c2 != no: # if not output
# If not the final output
c2 = make_divisible(c2 * gw, 8)# Ensure that the output channel is 8 Multiple
def make_divisible(x, divisor):
# Returns nearest x divisible by divisor
# Returns the nearest... Divisible by divisor x
if isinstance(divisor, torch.Tensor):
divisor = int(divisor.max()) # to int
return math.ceil(x / divisor) * divisorIf not the final output , call general.py Medium make_divisible function , Generation can be 8 The nearest division x As c2, This ensures that the output channel is 8 Multiple .
args = [c1, c2, *args[1:]] #args Change to the original args+module The number of input channels （c1）、 Number of output channels （c2）
if m in [BottleneckCSP, C3, C3TR, C3Ghost]: # Only CSP The structure will be based on the depth parameter n To adjust the number of repetitions of this module
args.insert(2, n) # number of repeats
# Module parameter information args Insert n
n = 1#n Reset Yes args Supplement information ：
args Change to the original args+module The number of input channels （c1）、 Number of output channels （c2）
If it is BottleneckCSP, C3 Words , Plus the depth parameter n（ Used to adjust the number of repetitions of this module ）, Then reset n.
elif m is nn.BatchNorm2d:
args = [ch[f]]BN Only the number of input channels , That is, the number of channels remains unchanged
elif m is Concat:
c2 = sum(ch[x] for x in f)Concat：f Is the index of all the layers that need to be spliced , Then the output channel c2 Is the sum of all layers
elif m is Detect:
args.append([ch[x] for x in f])# Fill in the number of input channels for each prediction layer
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
#[list(range(args[1] * 2))]： Initialization list ： The width and height of the prediction box
# Finally, take len(f) , Is to generate the initial height and width of the prediction frame corresponding to all prediction layers Detect class ：
Module information args First fill in the number of input channels of each prediction layer , Then fill in the list of the initial height and width of the prediction box corresponding to all prediction layers .
elif m is Contract:
c2 = ch[f] * args[0] ** 2
elif m is Expand:
c2 = ch[f] // args[0] ** 2
else:
c2 = ch[f]# In other cases, the number of output channels （c2） Is the number of input channels Contract and Expand Class does not exist in the network structure , Let's not talk about him
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
# take args Parameters in to build module m, Then, the number of cycles of the module is expressed as a parameter n control .take args Parameters in to build module m, Then, the number of cycles of the module is expressed as a parameter n control
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
LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print
# The above is the log file information （ Each layer module Build number 、 Parameter quantity, etc ）The above is the log file information （ Each layer module Build number 、 Parameter quantity, etc ）
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)# Save the built module to layers in
if i == 0:
ch = []
ch.append(c2)# Write the number of output channels of this layer ch In the list Save the output of the required layer （ such as Concat Layers need concat Some layers , The results of these layers need to be saved ）, The follow-up will be in _forward_once The function uses
The built modules are saved in layers in , And write the number of output channels of this layer into ch in
# When the cycle is over, build the model
return nn.Sequential(*layers), sorted(save)Build the model after the cycle .
5、 ... and 、 Code analysis comments family bucket
1、Model part
class Model(nn.Module):
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
super().__init__()
if isinstance(cfg, dict): # Judge cfg Is it right? dict（ Dictionaries ） type
self.yaml = cfg # model dict
# The model dictionary is assigned to self.yaml
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name # obtain cfg The name of the file
with open(cfg, encoding='ascii', errors='ignore') as f:# use ascii code , Ignore the wrong form to open the file cfg
self.yaml = yaml.safe_load(f) # model dict
# use yaml Loaded as a file cfg, Assign a value to self.yaml
# Define model
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
if nc and nc != self.yaml['nc']:
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
self.yaml['nc'] = nc # override yaml value
# The above is for judging the input channel Is it the same as that in the configuration file , If they are different, they become input parameters .
if anchors:
LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
self.yaml['anchors'] = round(anchors) # override yaml value
# General anchors To round （ Prevent the input of decimal numbers from reporting errors ）
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
# Number those categories , from 0 To nc-1
self.inplace = self.yaml.get('inplace', True)
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 256 # 2x min stride Step size initialization
m.inplace = self.inplace
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
m.anchors /= m.stride.view(-1, 1, 1)
check_anchor_order(m) # according to YOLOv5 Detect（） modular m Check the anchoring sequence , Correct if necessary
self.stride = m.stride
self._initialize_biases() # only run once
# Initialize the deviation into Detect modular （ There is no deviation , Because the initial class frequency cf by None）
# Init weights, biases
initialize_weights(self)
self.info()
LOGGER.info('')
def forward(self, x, augment=False, profile=False, visualize=False):
if augment:
return self._forward_augment(x) # augmented inference, None
# enhance
return self._forward_once(x, profile, visualize) # single-scale inference, train
# Not enhanced by default 2、_forward_once function
def _forward_once(self, x, profile=False, visualize=False):
y, dt = [], [] # outputs
for m in self.model:
#m Parameters in ：m.f Which floor does it start from ,m.n Is the default depth of the module ,m.args Is the parameter of this layer （ It's from yaml From there ）
if m.f != -1: # if not from previous layer
# Not on the next floor , That is, it is not directly connected to the upper layer （ Such as Concat)
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
# Take out the result of the corresponding layer , Prepare the following entry correspondence m Of forward()
if profile:
self._profile_one_layer(m, x, dt)
x = m(x) # run
#m It's a module （ Some layer ） It means , therefore x Incoming module , Equivalent to the execution module （ for instance Focus,SPP etc. ） Medium forward（）
# first floor Focus Of m.f yes -1, So jump to this step directly
y.append(x if m.i in self.save else None) # save output
# Save the output results of each layer to y
if visualize:
feature_visualization(x, m.type, m.i, save_dir=visualize)
return x3、parse_model function
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}")
# Log record , Regardless of him
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)
# The above is the read configuration dict The parameters inside
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 # Set the module type m Convert to value
for j, a in enumerate(args):# Loop module parameters args
try:
args[j] = eval(a) if isinstance(a, str) else a # Set each module parameter args[j] Convert to value
except NameError:
pass
# Above cycle , Start the iteration loop backbone And head Configuration of
#f： From which floor ;n： Default depth of the module ;m： The type of module ;args： Parameters of the module
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
# Network with （n*gd） Depth scaling of the control module .
# Depth here refers to something like CSP The number of iterations of this module , Generally, we refer to the width of the characteristic graph channel.
if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:
c1, c2 = ch[f], args[0]
#ch It is used to save the output of all previous modules channle（ therefore ch[-1] Represents the output channel of the previous module ）.
# ch[f] It's No f Layer output .args[0] Is the default output channel .
if c2 != no: # if not output
# If not the final output
c2 = make_divisible(c2 * gw, 8)# Ensure that the output channel is 8 Multiple
args = [c1, c2, *args[1:]] #args Change to the original args+module The number of input channels （c1）、 Number of output channels （c2）
if m in [BottleneckCSP, C3, C3TR, C3Ghost]: # Only CSP The structure will be based on the depth parameter n To adjust the number of repetitions of this module
args.insert(2, n) # number of repeats
# Module parameter information args Insert n
n = 1#n Reset
elif m is nn.BatchNorm2d:
args = [ch[f]]#BN Only the number of input channels , That is, the number of channels remains unchanged
elif m is Concat:
c2 = sum(ch[x] for x in f)#Concat：f Is the index of all the layers that need to be spliced , Then the number of output channels c2 Is the sum of all layers
elif m is Detect:
args.append([ch[x] for x in f])# Fill in the number of input channels for each prediction layer
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
#[list(range(args[1] * 2))]： Initialization list ： The width and height of the prediction box
# Finally, take len(f) , Is to generate the initial height and width of the prediction frame corresponding to all prediction layers
elif m is Contract:
c2 = ch[f] * args[0] ** 2
elif m is Expand:
c2 = ch[f] // args[0] ** 2
else:
c2 = ch[f]# In other cases, the number of output channels （c2） Is the number of input channels
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
# take args Parameters in to build module m, Then, the number of cycles of the module is expressed as a parameter n control . The whole is scaled by the width ,C3 The module is depth scaled .
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
LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print
# The above is the log file information （ Each layer module Build number 、 Parameter quantity, etc ）
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
# Save the output of the required layer （ such as Concat Layers need concat Some layers , The results of these layers need to be saved ）
layers.append(m_)# Save the built module to layers in
if i == 0:
ch = []
ch.append(c2)# Write the number of output channels of this layer ch In the list
# When the cycle is over, build the model
return nn.Sequential(*layers), sorted(save)You are welcome to criticize and correct in the comment area ~
At the same time , Originality is not easy. , Hee hee , If you like , Please praise it a little
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