YOLO v3 PyTorch版本源码解读（一）：模型结构解读
PyTorch 代码链接：https://github.com/ultralytics/yolov3
本篇主要是对代码文件中 models.py的解读，同时由于用到了utils文件夹下 parse_config.py中的两个函数，所以也对其进行了分析。
1. utils文件夹
1.1. parse_config.py
这个py文件中定义了两个函数——parse_model_cfg和parse_data_cfg，其中parse_model_cfg返回的module_defs存储的是所有的网络参数信息，一个list中嵌套了多个dict，每一个dict对应的是网络中的一个子模块——卷积、池化、特征拼接、跨层连接或者 yolo输出层；parse_data_cfg是将rbc.data文件中的内容存储到options这个dict中，获取的时候就可以对这个对象通过key提取。
# 返回的module_defs存储的是所有的网络参数信息，一个list中嵌套了多个dict
def parse_model_cfg(path):
"""Parses the yolo-v3 layer configuration file and returns module definitions"""
file = open(path, 'r')
lines = file.read().split('\n') # 读取cfg文件的每一行存入列表
lines = [x for x in lines if x and not x.startswith('#')] # 删除掉空行或者以"#"开头的行
lines = [x.rstrip().lstrip() for x in lines] # 去掉左右两边的空格
module_defs = []
for line in lines:
if line.startswith('['): # 当遇到 '['时，意味着要新建一个字典了，也就说说每个字典对应一个 新块
module_defs.append({})
module_defs[-1]['type'] = line[1:-1].rstrip()
if module_defs[-1]['type'] == 'convolutional':
module_defs[-1]['batch_normalize'] = 0 # pre-populate with zeros (may be overwritten later)
else:
key, value = line.split("=") # 用"="分割两边的key和value
value = value.strip()
module_defs[-1][key.rstrip()] = value.strip()
return module_defs
# 将rbc.data文件中的内容存储到options这个dict中，获取的时候就可以对这个对象通过key提取value；
def parse_data_cfg(path):
"""Parses the data configuration file"""
options = dict()
options['gpus'] = '0,1,2,3'
options['num_workers'] = '10'
with open(path, 'r') as fp:
lines = fp.readlines()
for line in lines:
line = line.strip()
if line == '' or line.startswith('#'):
continue
key, value = line.split('=')
options[key.strip()] = value.strip()
return options
2. Models.py
该文件主要用于定义yolo v3的网络结构，
2.1 create_modules
根据由cfg返回的嵌套多个dict的列表创建网络结构，当遇到 [route] 和 [shortcut] 时，该层实际上相当于 EmptyLayer层，仅仅做了一个线性映射，不同的地方在于送往下一层之前要做拼接或者短接，而这一点不用在模型结构中体现，在DarkNet的forward中可以看出来。这里需要注意的是在定义nn.Conv2d时，bias = not bn，也就是说如果
卷积层之后有bn层的话，卷积层没有偏置，否则有偏置。
# 根据cfg文件创建模块
def create_modules(module_defs):
"""
Constructs module list of layer blocks from module configuration in module_defs
"""
# 获取网络超参数
hyperparams = module_defs.pop(0)
# 保存上一层中输出feature maps 的卷积核个数，作为下一次操作中卷积核的通道数
output_filters = [int(hyperparams['channels'])]
# 创建一个list，其中存放的是module
module_list = nn.ModuleList()
yolo_index = -1
for i, module_def in enumerate(module_defs):
modules = nn.Sequential()
# 根据不同的层进行不同的设计
# 卷积层
if module_def['type'] == 'convolutional':
bn = int(module_def['batch_normalize'])
filters = int(module_def['filters'])
kernel_size = int(module_def['size'])
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
out_channels=filters,
kernel_size=kernel_size,
stride=int(module_def['stride']),
padding=pad,
bias=not bn))
if bn:
modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters))
if module_def['activation'] == 'leaky':
# modules.add_module('leaky_%d' % i, nn.PReLU(num_parameters=filters, init=0.10))
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1, inplace=True))
# 最大池化模块
elif module_def['type'] == 'maxpool':
kernel_size = int(module_def['size'])
stride = int(module_def['stride'])
if kernel_size == 2 and stride == 1:
modules.add_module('_debug_padding_%d' % i, nn.ZeroPad2d((0, 1, 0, 1)))
maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
modules.add_module('maxpool_%d' % i, maxpool)
# 上采样模块
elif module_def['type'] == 'upsample':
upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')
modules.add_module('upsample_%d' % i, upsample)
# 这里做的是在卷积核个数维度上的拼接
elif module_def['type'] == 'route':
layers = [int(x) for x in module_def['layers'].split(',')]
filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
modules.add_module('route_%d' % i, EmptyLayer())
# 跨层连接
elif module_def['type'] == 'shortcut':
filters = output_filters[int(module_def['from'])]
modules.add_module('shortcut_%d' % i, EmptyLayer())
elif module_def['type'] == 'yolo':
yolo_index += 1
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
# Extract anchors
anchors = [float(x) for x in module_def['anchors'].split(',')]
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
anchors = [anchors[i] for i in anchor_idxs]
# print("anchors = ", anchors)
nc = int(module_def['classes']) # number of classes
img_size = hyperparams['height']
# Define detection layer
modules.add_module('yolo_%d' % i, YOLOLayer(anchors, nc, img_size, yolo_index))
# Register module list and number of output filters
# 将module添加到module_list中
module_list.append(modules)
output_filters.append(filters)
return hyperparams, module_list
# 该层就相当于一个普通的 线性映射层
class EmptyLayer(nn.Module):
"""Placeholder for 'route' and 'shortcut' layers"""
def __init__(self):
super(EmptyLayer, self).__init__()
def forward(self, x):
return x
2.2 定义 YOLOLayer类
当遇到 [yolo] 时，送入 YOLOLayer类，
class YOLOLayer(nn.Module):
def __init__(self, anchors, nc, img_size, yolo_index):
super(YOLOLayer, self).__init__()
self.anchors = torch.Tensor(anchors)
self.na = len(anchors) # number of anchors (3)
self.nc = nc # number of classes (80)
self.nx = 0 # initialize number of x gridpoints，即本层feature map的宽
self.ny = 0 # initialize number of y gridpoints，即本层feature map的高
if ONNX_EXPORT: # grids must be computed in __init__
stride = [32, 16, 8][yolo_index] # stride of this layer
nx = int(img_size[1] / stride) # number x grid points
ny = int(img_size[0] / stride) # number y grid points
create_grids(self, max(img_size), (nx, ny))
# p的维度为 [batch_size, C_out, H, W]
def forward(self, p, img_size, var=None):
if ONNX_EXPORT:
bs = 1 # batch size
else:
bs, ny, nx = p.shape[0], p.shape[-2], p.shape[-1]
if (self.nx, self.ny) != (nx, ny):
create_grids(self, img_size, (nx, ny), p.device)
# p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85) # (bs, anchors, grid, grid, classes + xywh)
p = p.view(bs, self.na, self.nc + 5, self.ny, self.nx).permute(0, 1, 3, 4, 2).contiguous() # prediction
if self.training:
return p
elif ONNX_EXPORT:
# Constants CAN NOT BE BROADCAST, ensure correct shape!
ngu = self.ng.repeat((1, self.na * self.nx * self.ny, 1))
grid_xy = self.grid_xy.repeat((1, self.na, 1, 1, 1)).view((1, -1, 2))
anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view((1, -1, 2)) / ngu
# p = p.view(-1, 5 + self.nc)
# xy = torch.sigmoid(p[..., 0:2]) + grid_xy[0] # x, y
# wh = torch.exp(p[..., 2:4]) * anchor_wh[0] # width, height
# p_conf = torch.sigmoid(p[:, 4:5]) # Conf
# p_cls = F.softmax(p[:, 5:85], 1) * p_conf # SSD-like conf
# return torch.cat((xy / ngu[0], wh, p_conf, p_cls), 1).t()
p = p.view(1, -1, 5 + self.nc)
# 求得bx, by
xy = torch.sigmoid(p[..., 0:2]) + grid_xy # x, y
# 求得bw, bh
wh = torch.exp(p[..., 2:4]) * anchor_wh # width, height
# 得到置信度
p_conf = torch.sigmoid(p[..., 4:5]) # Conf
# 得到类别概率
p_cls = p[..., 5:5 + self.nc]
# Broadcasting only supported on first dimension in CoreML. See onnx-coreml/_operators.py
# p_cls = F.softmax(p_cls, 2) * p_conf # SSD-like conf
p_cls = torch.exp(p_cls).permute((2, 1, 0))
p_cls = p_cls / p_cls.sum(0).unsqueeze(0) * p_conf.permute((2, 1, 0)) # F.softmax() equivalent
p_cls = p_cls.permute(2, 1, 0)
return torch.cat((xy / ngu, wh, p_conf, p_cls), 2).squeeze().t()
else: # inference
# s = 1.5 # scale_xy (pxy = pxy * s - (s - 1) / 2)
io = p.clone() # inference output
io[..., 0:2] = torch.sigmoid(io[..., 0:2]) + self.grid_xy # xy
io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method
# io[..., 2:4] = ((torch.sigmoid(io[..., 2:4]) * 2) ** 3) * self.anchor_wh # wh power method
io[..., 4:] = torch.sigmoid(io[..., 4:]) # p_conf, p_cls
# io[..., 5:] = F.softmax(io[..., 5:], dim=4) # p_cls
io[..., :4] *= self.stride
if self.nc == 1:
io[..., 5] = 1 # single-class model https://github.com/ultralytics/yolov3/issues/235
# reshape from [1, 3, 13, 13, 85] to [1, 507, 85]
return io.view(bs, -1, 5 + self.nc), p
def create_grids(self, img_size=416, ng=(13, 13), device='cpu'):
nx, ny = ng # x and y grid size
self.img_size = img_size
# 该层特征图相对于原始图片的缩放尺寸
self.stride = img_size / max(ng)
# build xy offsets
"""
meshgrid的作用是：根据传入的两个一维数组参数生成两个数组元素的列表。如果第一个参数是xarray，维度是xdimesion；
第二个参数是yarray，维度是ydimesion；那么生成的第一个二维数组是以xarray为行，共ydimesion行的向量；而第二个
二维数组是以yarray的转置为列，共xdimesion列的向量。
x = np.array([1,2,3])
y = np.array([4,5,6,7])
X,Y = np.meshgrid(x,y)
X #以xarray[1,2,3]为行，2行的向量
Y #以yarray转置为列[4,5,6,7]，共3列向量
array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3],
[1, 2, 3]])
array([[4, 4, 4],
[5, 5, 5],
[6, 6, 6],
[7, 7, 7]])
"""
yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
# torch.stack指定了做完stack之后生成结果放置的维度
# 如果 ng = (13, 13)，那么 torch.stack((xv, yv), 2)的维度为 (13, 13, 2) --------> (1, 1, 13, 13, 2)
self.grid_xy = torch.stack((xv, yv), 2).to(device).float().view((1, 1, ny, nx, 2))
# build wh gains
# 将anchor box的 w和h的数值映射到该层feature map上
self.anchor_vec = self.anchors.to(device) / self.stride
# self.anchor_vec: [3, 2] -------> anchor_wh: [1, 3, 1, 1, 2]
self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2).to(device)
self.ng = torch.Tensor(ng).to(device)
self.nx = nx
self.ny = ny
2.3 初始化模型
在 train.py 中通过 model = Darknet(cfg).to(device) 完成对模型的构建；
class Darknet(nn.Module):
"""YOLOv3 object detection model"""
def __init__(self, cfg, img_size=(416, 416)):
super(Darknet, self).__init__()
self.module_defs = parse_model_cfg(cfg)
self.module_defs[0]['cfg'] = cfg
self.module_defs[0]['height'] = img_size
self.hyperparams, self.module_list = create_modules(self.module_defs)
self.yolo_layers = get_yolo_layers(self)
# Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
self.version = np.array([0, 2, 5], dtype=np.int32) # (int32) version info: major, minor, revision
self.seen = np.array([0], dtype=np.int64) # (int64) number of images seen during training
def forward(self, x, var=None):
img_size = max(x.shape[-2:])
layer_outputs = []
output = []
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
mtype = module_def['type']
if mtype in ['convolutional', 'upsample', 'maxpool']:
x = module(x)
elif mtype == 'route':
# 在 C_{out} 维度做拼接
layer_i = [int(x) for x in module_def['layers'].split(',')]
if len(layer_i) == 1:
x = layer_outputs[layer_i[0]]
else:
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
elif mtype == 'shortcut':
# 跨层连接
layer_i = int(module_def['from'])
x = layer_outputs[-1] + layer_outputs[layer_i]
elif mtype == 'yolo':
x = module[0](x, img_size)
output.append(x)
layer_outputs.append(x)
if self.training:
return output
elif ONNX_EXPORT:
output = torch.cat(output, 1) # cat 3 layers 85 x (507, 2028, 8112) to 85 x 10647
nc = self.module_list[self.yolo_layers[0]][0].nc # number of classes
return output[5:5 + nc].t(), output[:4].t() # ONNX scores, boxes
else:
io, p = list(zip(*output)) # inference output, training output
return torch.cat(io, 1), p
def fuse(self):
# Fuse Conv2d + BatchNorm2d layers throughout model
fused_list = nn.ModuleList()
for a in list(self.children())[0]:
for i, b in enumerate(a):
if isinstance(b, nn.modules.batchnorm.BatchNorm2d):
# fuse this bn layer with the previous conv2d layer
conv = a[i - 1]
fused = torch_utils.fuse_conv_and_bn(conv, b)
a = nn.Sequential(fused, *list(a.children())[i + 1:])
break
fused_list.append(a)
self.module_list = fused_list
# model_info(self) # yolov3-spp reduced from 225 to 152 layers
2.4 加载权重——load_darknet_weights()
权重文件有两种—— “.pt” 和 “.weights"结尾的，以”.pt"结尾的文件需要用 torch.load()来读取，以 ".weights"结尾的文件需要用 load_darknet_weights()来读取。
在 train.py 中调用load_darknet_weights()：
if '-tiny.cfg' in cfg:
cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
else:
cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
# 从列表中将权重读出来，并用这些权重初始化网络参数
def load_darknet_weights(self, weights, cutoff=-1):
# Parses and loads the weights stored in 'weights'
# cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
# 获取权重文件名
weights_file = weights.split(os.sep)[-1]
# Try to download weights if not available locally
if not os.path.isfile(weights):
try:
url = 'https://pjreddie.com/media/files/' + weights_file
print('Downloading ' + url)
os.system('curl ' + url + ' -o ' + weights)
except IOError:
print(weights + ' not found.\nTry https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI')
# Establish cutoffs
if weights_file == 'darknet53.conv.74':
cutoff = 75
elif weights_file == 'yolov3-tiny.conv.15':
cutoff = 15
# Read weights file
with open(weights, 'rb') as f:
# Read Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
self.version = np.fromfile(f, dtype=np.int32, count=3) # (int32) version info: major, minor, revision
self.seen = np.fromfile(f, dtype=np.int64, count=1) # (int64) number of images seen during training
weights = np.fromfile(f, dtype=np.float32) # The rest are weights
ptr = 0
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
# 全卷积网络，只有卷积层和bn层有参数，由于bn层的参数是按照 bias, weight, running_mean, running_var的顺序写入列表的，
# 所以读取的时候也应该按照这个顺序，同时由于有bn层的时候卷积层没有偏置，所以不用读取卷积层的偏置
if module_def['type'] == 'convolutional':
conv_layer = module[0]
if module_def['batch_normalize']:
# Load BN bias, weights, running mean and running variance
bn_layer = module[1]
num_b = bn_layer.bias.numel() # Number of biases
# Bias
bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
bn_layer.bias.data.copy_(bn_b)
ptr += num_b
# Weight
bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
bn_layer.weight.data.copy_(bn_w)
ptr += num_b
# Running Mean
bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
bn_layer.running_mean.data.copy_(bn_rm)
ptr += num_b
# Running Var
bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
bn_layer.running_var.data.copy_(bn_rv)
ptr += num_b
else:
# Load conv. bias
num_b = conv_layer.bias.numel()
conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
conv_layer.bias.data.copy_(conv_b)
ptr += num_b
# Load conv. weights
num_w = conv_layer.weight.numel()
conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
conv_layer.weight.data.copy_(conv_w)
ptr += num_w
return cutoff
2.5 保存权重——save_weights()
与 load_darknet_weights()相对应，用save_weights格式保存的权重需用load_darknet_weights()才可以正确读取。
def save_weights(self, path='model.weights', cutoff=-1):
# Converts a PyTorch model to Darket format (*.pt to *.weights)
# Note: Does not work if model.fuse() is applied
with open(path, 'wb') as f:
# Write Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
self.version.tofile(f) # (int32) version info: major, minor, revision
self.seen.tofile(f) # (int64) number of images seen during training
# Iterate through layers
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
if module_def['type'] == 'convolutional':
conv_layer = module[0]
# If batch norm, load bn first
if module_def['batch_normalize']:
bn_layer = module[1]
bn_layer.bias.data.cpu().numpy().tofile(f)
bn_layer.weight.data.cpu().numpy().tofile(f)
bn_layer.running_mean.data.cpu().numpy().tofile(f)
bn_layer.running_var.data.cpu().numpy().tofile(f)
# Load conv bias
else:
conv_layer.bias.data.cpu().numpy().tofile(f)
# Load conv weights
conv_layer.weight.data.cpu().numpy().tofile(f)
2.6 “.pt” 和 ".weights"权重文件的转换
def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights'):
# Converts between PyTorch and Darknet format per extension (i.e. *.weights convert to *.pt and vice versa)
# from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')
# Initialize model
model = Darknet(cfg)
# Load weights and save
if weights.endswith('.pt'): # if PyTorch format
model.load_state_dict(torch.load(weights, map_location='cpu')['model'])
save_weights(model, path='converted.weights', cutoff=-1)
print("Success: converted '%s' to 'converted.weights'" % weights)
elif weights.endswith('.weights'): # darknet format
_ = load_darknet_weights(model, weights)
chkpt = {'epoch': -1,
'best_fitness': None,
'training_results': None,
'model': model.state_dict(),
'optimizer': None}
torch.save(chkpt, 'converted.pt')
print("Success: converted '%s' to 'converted.pt'" % weights)
else:
print('Error: extension not supported.')