pytorch yolov3 代码详解_超详细的Pytorch版yolov3代码中文注释详解（一）

from __future__ import division

import torch

import torch.nn as nn

import torch.nn.functional as F

from torch.autograd import Variable

import numpy as np

from util import *

def get_test_input():

img = cv2.imread("dog-cycle-car.png")

img = cv2.resize(img, (416,416)) #Resize to the input dimension

img_ = img[:,:,::-1].transpose((2,0,1)) #img是【h,w,channel】，这里的img[:,:,::-1]是将第三个维度channel从opencv的BGR转化为pytorch的RGB，然后transpose((2,0,1))的意思是将[height,width,channel]->[channel,height,width]

img_ = img_[np.newaxis,:,:,:]/255.0 #Add a channel at 0 (for batch) | Normalise

img_ = torch.from_numpy(img_).float() #Convert to float

img_ = Variable(img_) # Convert to Variable

return img_

def parse_cfg(cfgfile):

"""输入: 配置文件路径返回值: 列表对象,其中每一个元素为一个字典类型对应于一个要建立的神经网络模块(层)"""

# 加载文件并过滤掉文本中多余内容

file = open(cfgfile, 'r')

lines = file.read().split('\n') # store the lines in a list等价于readlines

lines = [x for x in lines if len(x) > 0] # 去掉空行

lines = [x for x in lines if x[0] != '#'] # 去掉以#开头的注释行

lines = [x.rstrip().lstrip() for x in lines] # 去掉左右两边的空格(rstricp是去掉右边的空格，lstrip是去掉左边的空格)

# cfg文件中的每个块用[]括起来最后组成一个列表，一个block存储一个块的内容，即每个层用一个字典block存储。

block = {}

blocks = []

for line in lines:

if line[0] == "[": # 这是cfg文件中一个层(块)的开始

if len(block) != 0: # 如果块内已经存了信息, 说明是上一个块的信息还没有保存

blocks.append(block) # 那么这个块(字典)加入到blocks列表中去

block = {} # 覆盖掉已存储的block,新建一个空白块存储描述下一个块的信息(block是字典)

block["type"] = line[1:-1].rstrip() # 把cfg的[]中的块名作为键type的值

else:

key,value = line.split("=") #按等号分割

block[key.rstrip()] = value.lstrip()#左边是key(去掉右空格)，右边是value(去掉左空格)，形成一个block字典的键值对

blocks.append(block) # 退出循环，将最后一个未加入的block加进去

# print('\n\n'.join([repr(x) for x in blocks]))

return blocks

# 配置文件定义了6种不同type

# 'net': 相当于超参数,网络全局配置的相关参数

# {'convolutional', 'net', 'route', 'shortcut', 'upsample', 'yolo'}

# cfg = parse_cfg("cfg/yolov3.cfg")

# print(cfg)

class EmptyLayer(nn.Module):

"""为shortcut layer / route layer 准备, 具体功能不在此实现，在Darknet类的forward函数中有体现"""

def __init__(self):

super(EmptyLayer, self).__init__()

class DetectionLayer(nn.Module):

'''yolo 检测层的具体实现, 在特征图上使用锚点预测目标区域和类别, 功能函数在predict_transform中'''

def __init__(self, anchors):

super(DetectionLayer, self).__init__()

self.anchors = anchors

def create_modules(blocks):

net_info = blocks[0] # blocks[0]存储了cfg中[net]的信息，它是一个字典，获取网络输入和预处理相关信息

module_list = nn.ModuleList() # module_list用于存储每个block,每个block对应cfg文件中一个块，类似[convolutional]里面就对应一个卷积块

prev_filters = 3 #初始值对应于输入数据3通道，用来存储我们需要持续追踪被应用卷积层的卷积核数量(上一层的卷积核数量(或特征图深度))

output_filters = [] #我们不仅需要追踪前一层的卷积核数量，还需要追踪之前每个层。随着不断地迭代，我们将每个模块的输出卷积核数量添加到 output_filters 列表上。

for index, x in enumerate(blocks[1:]): #这里，我们迭代block[1:] 而不是blocks，因为blocks的第一个元素是一个net块，它不属于前向传播。

module = nn.Sequential()# 这里每个块用nn.sequential()创建为了一个module,一个module有多个层

#check the type of block

#create a new module for the block

#append to module_list

if (x["type"] == "convolutional"):

''' 1. 卷积层 '''

# 获取激活函数/批归一化/卷积层参数(通过字典的键获取值)

activation = x["activation"]

try:

batch_normalize = int(x["batch_normalize"])

bias = False#卷积层后接BN就不需要bias

except:

batch_normalize = 0

bias = True #卷积层后无BN层就需要bias

filters= int(x["filters"])

padding = int(x["pad"])

kernel_size = int(x["size"])

stride = int(x["stride"])

if padding:

pad = (kernel_size - 1) // 2

else:

pad = 0

# 开始创建并添加相应层

# Add the convolutional layer

# nn.Conv2d(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True)

conv = nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias = bias)

module.add_module("conv_{0}".format(index), conv)

#Add the Batch Norm Layer

if batch_normalize:

bn = nn.BatchNorm2d(filters)

module.add_module("batch_norm_{0}".format(index), bn)

#Check the activation.

#It is either Linear or a Leaky ReLU for YOLO

# 给定参数负轴系数0.1

if activation == "leaky":

activn = nn.LeakyReLU(0.1, inplace = True)

module.add_module("leaky_{0}".format(index), activn)

elif (x["type"] == "upsample"):

'''2. upsampling layer没有使用 Bilinear2dUpsampling实际使用的为最近邻插值'''

stride = int(x["stride"])#这个stride在cfg中就是2，所以下面的scale_factor写2或者stride是等价的

upsample = nn.Upsample(scale_factor = 2, mode = "nearest")

module.add_module("upsample_{}".format(index), upsample)

# route layer -> Empty layer

# route层的作用：当layer取值为正时，输出这个正数对应的层的特征，如果layer取值为负数，输出route层向后退layer层对应层的特征

elif (x["type"] == "route"):

x["layers"] = x["layers"].split(',')

#Start of a route

start = int(x["layers"][0])

#end, if there exists one.

try:

end = int(x["layers"][1])

except:

end = 0

#Positive anotation: 正值

if start > 0:

start = start - index

if end > 0:# 若end>0，由于end= end - index，再执行index + end输出的还是第end层的特征

end = end - index

route = EmptyLayer()

module.add_module("route_{0}".format(index), route)

if end < 0: #若end<0，则end还是end，输出index+end(而end<0)故index向后退end层的特征。

filters = output_filters[index + start] + output_filters[index + end]

else: #如果没有第二个参数，end=0，则对应下面的公式，此时若start>0，由于start = start - index，再执行index + start输出的还是第start层的特征;若start<0，则start还是start，输出index+start(而start<0)故index向后退start层的特征。

filters= output_filters[index + start]

#shortcut corresponds to skip connection

elif x["type"] == "shortcut":

shortcut = EmptyLayer() #使用空的层，因为它还要执行一个非常简单的操作(加)。没必要更新 filters 变量,因为它只是将前一层的特征图添加到后面的层上而已。

module.add_module("shortcut_{}".format(index), shortcut)

#Yolo is the detection layer

elif x["type"] == "yolo":

mask = x["mask"].split(",")

mask = [int(x) for x in mask]

anchors = x["anchors"].split(",")

anchors = [int(a) for a in anchors]

anchors = [(anchors[i], anchors[i+1]) for i in range(0, len(anchors),2)]

anchors = [anchors[i] for i in mask]

detection = DetectionLayer(anchors)# 锚点,检测,位置回归,分类，这个类见predict_transform中

module.add_module("Detection_{}".format(index), detection)

module_list.append(module)

prev_filters = filters

output_filters.append(filters)

return (net_info, module_list)

class Darknet(nn.Module):

def __init__(self, cfgfile):

super(Darknet, self).__init__()

self.blocks = parse_cfg(cfgfile) #调用parse_cfg函数

self.net_info, self.module_list = create_modules(self.blocks)#调用create_modules函数

def forward(self, x, CUDA):

modules = self.blocks[1:] # 除了net块之外的所有，forward这里用的是blocks列表中的各个block块字典

outputs = {} #We cache the outputs for the route layer

write = 0#write表示我们是否遇到第一个检测。write=0，则收集器尚未初始化，write=1，则收集器已经初始化，我们只需要将检测图与收集器级联起来即可。

for i, module in enumerate(modules):

module_type = (module["type"])

if module_type == "convolutional" or module_type == "upsample":

x = self.module_list[i](x)

elif module_type == "route":

layers = module["layers"]

layers = [int(a) for a in layers]

if (layers[0]) > 0:

layers[0] = layers[0] - i

# 如果只有一层时。从前面的if (layers[0]) > 0:语句中可知，如果layer[0]>0，则输出的就是当前layer[0]这一层的特征,如果layer[0]<0，输出就是从route层(第i层)向后退layer[0]层那一层得到的特征

if len(layers) == 1:

x = outputs[i + (layers[0])]

#第二个元素同理

else:

if (layers[1]) > 0:

layers[1] = layers[1] - i

map1 = outputs[i + layers[0]]

map2 = outputs[i + layers[1]]

x = torch.cat((map1, map2), 1)#第二个参数设为 1,这是因为我们希望将特征图沿anchor数量的维度级联起来。

elif module_type == "shortcut":

from_ = int(module["from"])

x = outputs[i-1] + outputs[i+from_] # 求和运算，它只是将前一层的特征图添加到后面的层上而已

elif module_type == 'yolo':

anchors = self.module_list[i][0].anchors

#从net_info(实际就是blocks[0]，即[net])中get the input dimensions

inp_dim = int (self.net_info["height"])

#Get the number of classes

num_classes = int (module["classes"])

#Transform

x = x.data # 这里得到的是预测的yolo层feature map

# 在util.py中的predict_transform()函数利用x(是传入yolo层的feature map)，得到每个格子所对应的anchor最终得到的目标

# 坐标与宽高，以及出现目标的得分与每种类别的得分。经过predict_transform变换后的x的维度是(batch_size, grid_size*grid_size*num_anchors, 5+类别数量)

x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)

if not write: #if no collector has been intialised. 因为一个空的tensor无法与一个有数据的tensor进行concatenate操作，

detections = x #所以detections的初始化在有预测值出来时才进行，

write = 1 #用write = 1标记，当后面的分数出来后，直接concatenate操作即可。

else:

'''变换后x的维度是(batch_size, grid_size*grid_size*num_anchors, 5+类别数量)，这里是在维度1上进行concatenate，即按照anchor数量的维度进行连接，对应教程part3中的Bounding Box attributes图的行进行连接。yolov3中有3个yolo层，所以对于每个yolo层的输出先用predict_transform()变成每行为一个anchor对应的预测值的形式(不看batch_size这个维度，x剩下的维度可以看成一个二维tensor)，这样3个yolo层的预测值按照每个方框对应的行的维度进行连接。得到了这张图处所有anchor的预测值，后面的NMS等操作可以一次完成'''

detections = torch.cat((detections, x), 1)# 将在3个不同level的feature map上检测结果存储在 detections 里

outputs[i] = x

return detections

# blocks = parse_cfg('cfg/yolov3.cfg')

# x,y = create_modules(blocks)

# print(y)

def load_weights(self, weightfile):

#Open the weights file

fp = open(weightfile, "rb")

#The first 5 values are header information

# 1. Major version number

# 2. Minor Version Number

# 3. Subversion number

# 4,5. Images seen by the network (during training)

header = np.fromfile(fp, dtype = np.int32, count = 5)# 这里读取first 5 values权重

self.header = torch.from_numpy(header)

self.seen = self.header[3]

weights = np.fromfile(fp, dtype = np.float32)#加载 np.ndarray 中的剩余权重，权重是以float32类型存储的

ptr = 0

for i in range(len(self.module_list)):

module_type = self.blocks[i + 1]["type"] # blocks中的第一个元素是网络参数和图像的描述，所以从blocks[1]开始读入

#If module_type is convolutional load weights

#Otherwise ignore.

if module_type == "convolutional":

model = self.module_list[i]

try:

batch_normalize = int(self.blocks[i+1]["batch_normalize"]) # 当有bn层时，"batch_normalize"对应值为1

except:

batch_normalize = 0

conv = model[0]

if (batch_normalize):

bn = model[1]

#Get the number of weights of Batch Norm Layer

num_bn_biases = bn.bias.numel()

#Load the weights

bn_biases = torch.from_numpy(weights[ptr:ptr + num_bn_biases])

ptr += num_bn_biases

bn_weights = torch.from_numpy(weights[ptr: ptr + num_bn_biases])

ptr += num_bn_biases

bn_running_mean = torch.from_numpy(weights[ptr: ptr + num_bn_biases])

ptr += num_bn_biases

bn_running_var = torch.from_numpy(weights[ptr: ptr + num_bn_biases])

ptr += num_bn_biases

#Cast the loaded weights into dims of model weights.

bn_biases = bn_biases.view_as(bn.bias.data)

bn_weights = bn_weights.view_as(bn.weight.data)

bn_running_mean = bn_running_mean.view_as(bn.running_mean)

bn_running_var = bn_running_var.view_as(bn.running_var)

#Copy the data to model 将从weights文件中得到的权重bn_biases复制到model中(bn.bias.data)

bn.bias.data.copy_(bn_biases)

bn.weight.data.copy_(bn_weights)

bn.running_mean.copy_(bn_running_mean)

bn.running_var.copy_(bn_running_var)

else:#如果 batch_normalize 的检查结果不是 True，只需要加载卷积层的偏置项

#Number of biases

num_biases = conv.bias.numel()

#Load the weights

conv_biases = torch.from_numpy(weights[ptr: ptr + num_biases])

ptr = ptr + num_biases

#reshape the loaded weights according to the dims of the model weights

conv_biases = conv_biases.view_as(conv.bias.data)

#Finally copy the data

conv.bias.data.copy_(conv_biases)

#Let us load the weights for the Convolutional layers

num_weights = conv.weight.numel()

#Do the same as above for weights

conv_weights = torch.from_numpy(weights[ptr:ptr+num_weights])

ptr = ptr + num_weights

conv_weights = conv_weights.view_as(conv.weight.data)

conv.weight.data.copy_(conv_weights)