pytorch实现yolo-v3 (源码阅读和复现) -- 002
- pytorch实现yolo-v3 (源码阅读和复现) – 001
- pytorch实现yolo-v3 (源码阅读和复现) – 002
- pytorch实现yolo-v3 (源码阅读和复现) – 003算法分析
- pytorch实现yolo-v3 (源码阅读和复现) – 004算法分析
- pytorch实现yolo-v3 (源码阅读和复现) – 005
上一篇已经介绍了yolov3使用到的网络darknet53每一层的结构,现在这里来完成代码解析和模型创建
本章所有代码: https://github.com/wanghao00/pytorch-yolo-v3/tree/master/001
1. 加载并解析配置文件cfg/yolov3.cfg
配置文件包含6种不同type, 分别为'convolutional', 'net', 'route', 'shortcut', 'upsample', 'yolo',
其中'net'相当于超参数,定义了网络全局配置的相关参数
darnet.py代码如下
"""
python 3.6
Pytorch 0.4
"""
import torch
import torch.nn as nn
def parse_cfg(cfgfile):
"""
输入: 配置文件路径
返回值: list对象,其中每一个item为一个dict类型
对应于一个要建立的神经网络模块
"""
# 加载文件并过滤掉文本中多余内容
with open(cfgfile, 'r') as f:
lines = f.read().split('\n')
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
block = {}
blocks = []
for line in lines:
if line[0] == "[": # 这是一个层(块)的开始
# 上一个块内容如果还没有保存
if len(block) != 0: # 块内已经存了信息, 都是上一个块的信息
blocks.append(block)
block = {} # 新建一个空白块存描述信息
block["type"] = line[1:-1].rstrip() # 块名
else:
key, value = line.split("=")
block[key.rstrip()] = value.lstrip()
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])) # 查看结果可以取消注释
return blocks
# 执行查看返回结果
cfg = parse_cfg("cfg/yolov3.cfg")
print(cfg)blocks的内容如下
{'type': 'net', 'batch': '1', 'subdivisions': '1', 'width': '320', 'height': '320', 'channels': '3', 'momentum': '0.9', 'decay': '0.0005', 'angle': '0', 'saturation': '1.5', 'exposure': '1.5', 'hue': '.1', 'learning_rate': '0.001', 'burn_in': '1000', 'max_batches': '500200', 'policy': 'steps', 'steps': '400000,450000', 'scales': '.1,.1'}
{'type': 'convolutional', 'batch_normalize': '1', 'filters': '32', 'size': '3', 'stride': '1', 'pad': '1', 'activation': 'leaky'}
**省略**
{'type': 'convolutional', 'batch_normalize': '1', 'size': '3', 'stride': '1', 'pad': '1', 'filters': '256', 'activation': 'leaky'}
{'type': 'convolutional', 'size': '1', 'stride': '1', 'pad': '1', 'filters': '255', 'activation': 'linear'}
{'type': 'yolo', 'mask': '0,1,2', 'anchors': '10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326', 'classes': '80', 'num': '9', 'jitter': '.3', 'ignore_thresh': '.5', 'truth_thresh': '1', 'random': '1'}2. 配置项到神经网络层的映射
将上一步的执行结果, 映射成在pytorch中对应的神经网络层
这一步需要我们创建两个层, 其中
EmptyLayer是为了shortcut layer / route layer准备的, 具体公用在Darknet的forward函数中有体现;DetectionLayer是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
class EmptyLayer(nn.Module):
"""
为shortcut layer / route layer 准备, 具体功能不在此实现
"""
def __init__(self):
super(EmptyLayer, self).__init__()
class DetectionLayer(nn.Module):
'''yolo 检测层'''
def __init__(self, anchors):
super(DetectionLayer, self).__init__()
self.anchors = anchors
def forward(self, x, input_dim, num_classes, confidence):
x = x.data
global CUDA
prediction = x
prediction = predict_transform(prediction, input_dim, self.anchors, num_classes, confidence, CUDA)
return prediction
def create_modules(blocks):
# 获取网路输入和预处理相关信息
net_info = blocks[0]
module_list = nn.ModuleList()
index = 0 # route layer 会用到
previous_filters = 3 # 初始值对应于输入数据3通道
output_filters = []
for block in blocks:
container = nn.Sequential()
if block["type"] == "net":
continue
if block["type"] == "convolutional":
''' 1. 卷积层 '''
# 获取激活函数/批归一化/卷积层参数
activation = block["activation"]
try:
batch_normalize = int(block["batch_normalize"])
bias = False
except:
batch_normalize = 0
bias = True
filters = int(block["filters"])
padding = int(block["pad"])
kernel_size = int(block["size"])
stride = int(block["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(previous_filters, filters, kernel_size, stride, pad, bias=bias)
container.add_module("conv_{0}".format(index), conv)
# Add the Batch Norm Layer
if batch_normalize:
bn = nn.BatchNorm2d(filters)
container.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)
container.add_module("leaky_{0}".format(index), activn)
elif block["type"] == "upsample":
'''
2. upsampling layer
没有使用 Bilinear2dUpsampling
实际使用的为最近邻插值
'''
upsample = nn.Upsample(scale_factor=2, mode="nearest")
container.add_module("upsample_{}".format(index), upsample)
# route layer -> Empty layer
elif block["type"] == "route":
block["layers"] = block["layers"].split(',')
#Start of a route
start = int(block["layers"][0])
#end, if there exists one.
try:
end = int(block["layers"][1])
except:
end = 0
#Positive anotation: 正值
if start > 0:
start = start - index
if end > 0:
end = end - index
route = EmptyLayer()
container.add_module("route_{0}".format(index), route)
if end < 0:
filters = output_filters[index + start] + output_filters[index + end]
else:
filters= output_filters[index + start]
# shortcut corresponds to skip connection
elif block["type"] == "shortcut":
from_ = int(block["from"])
shortcut = EmptyLayer()
container.add_module("shortcut_{}".format(index), shortcut)
elif block["type"] == "maxpool":
stride = int(block["stride"])
size = int(block["size"])
maxpool = nn.MaxPool2d(size, stride)
container.add_module("maxpool_{}".format(index), maxpool)
# Yolo is the detection layer
elif block["type"] == "yolo":
mask = block["mask"].split(",")
mask = [int(x) for x in mask]
anchors = block["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
container.add_module("Detection_{}".format(index), detection)
else:
print("...咱未实现的...")
assert False
module_list.append(container)
previous_filters = filters
output_filters.append(filters)
index += 1
return net_info, module_list
blocks = parse_cfg('cfg/yolov3.cfg')
x,y = create_modules(blocks)
print(y)输出节选
ModuleList(
(0): Sequential(
(conv_0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_0): LeakyReLU(negative_slope=0.1, inplace)
)
...省略...
(3): Sequential(
(conv_3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_3): LeakyReLU(negative_slope=0.1, inplace)
)
(4): Sequential(
(shortcut_4): EmptyLayer()
)
...省略...
(97): Sequential(
(upsample_97): Upsample(scale_factor=2, mode=nearest)
)
(98): Sequential(
(route_98): EmptyLayer()
)
...省略...
(104): Sequential(
(conv_104): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_104): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_104): LeakyReLU(negative_slope=0.1, inplace)
)
(105): Sequential(
(conv_105): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1))
)
(106): Sequential(
(Detection_106): DetectionLayer()
)
)3. 构建model
前两步还是未能将所有模块链接到一起形成网络, 下面会创建Darknet类实现,并进行测试
class Darknet(nn.Module):
def __init__(self, cfgfile):
super(Darknet, self).__init__()
self.blocks = parse_cfg(cfgfile)
self.net_info, self.module_list = create_modules(self.blocks)
# 模型版本标志
self.header = torch.IntTensor([0, 0, 0, 0])
self.seen = 0
def get_blocks(self):
return self.blocks # list
def get_module_list(self):
return self.module_list # nn.ModuleList
def forward(self, x, CUDA=True):
detections = []
# 除了net块之外的所有
modules = self.blocks[1:]
# cache output for route layer
outputs = {}
write = False # 拼接检测层结果
for i in range(len(modules)):
module_type = modules[i]["type"]
#
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
#
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
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)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_] # 求和运算
outputs[i] = x
#
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
# Get the input dimensions
inp_dim = int(self.net_info["height"])
# Get the number of classes
num_classes = int(modules[i]["classes"])
# Output the result
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if type(x) == int:
continue
# 将在3个不同level的fm上检测结果
# 存储在 detections 里
if not write:
detections = x
write = True
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
# 网络forward 执行完毕
try:
return detections
except:
return 0
model = Darknet("cfg/yolov3.cfg")
input = torch.sigmoid(torch.rand(1, 3, 416, 416).float())
# 网络输入数据大小
model.net_info["height"] = 416
predictions = model(input, False)
print(predictions.shape) # torch.Size([1, 10647, 85]) 网络会在三个不同level的特征图(?, 255, 13,13)/ (?, 255, 26, 26)/ (?, 255, 52, 52)上进行多尺度预测, 在每个位置处有三组不同尺寸的anchor,最终将三个层次的预测结果合并在一起返回,所以模型直接的预测结为Size([?, 10647, 5+cls]) ,
其中10647= (13*13 + 26*26 + 52*52) * 3 [anchors]
关于在特征图上如何利用给定锚点进行多尺度检测,即DetectionLayer中使用的predict_transform的实现思路,会在下一篇文章中继续阐述.