4.yolov5的损失函数的代码注释
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代码注释如下：
import math
from copy import deepcopy
from functools import partial
import numpy as np
import torch
import torch.nn as nn
#yolo_loss(l, outputs[l], targets, y_trues[l])
class YOLOLoss(nn.Module):
def __init__(self, anchors, num_classes, input_shape, cuda, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]], label_smoothing = 0):
super(YOLOLoss, self).__init__()
#-----------------------------------------------------------#
# 20x20的特征层对应的anchor是[116,90],[156,198],[373,326]
# 40x40的特征层对应的anchor是[30,61],[62,45],[59,119]
# 80x80的特征层对应的anchor是[10,13],[16,30],[33,23]
#-----------------------------------------------------------#
self.anchors = anchors
self.num_classes = num_classes
self.bbox_attrs = 5 + num_classes
self.input_shape = input_shape
self.anchors_mask = anchors_mask
self.label_smoothing = label_smoothing
self.threshold = 4
self.balance = [0.4, 1.0, 4]
self.box_ratio = 0.05
self.obj_ratio = 1 * (input_shape[0] * input_shape[1]) / (640 ** 2)
self.cls_ratio = 0.5 * (num_classes / 80)
self.cuda = cuda
#交叉熵损失函数的对数里的值不能为0，所以这个函数确保交叉熵损失函数的pred值介于(0,1)且不等于0或者1.
def clip_by_tensor(self, t, t_min, t_max):
t = t.float()
result = (t >= t_min).float() * t + (t < t_min).float() * t_min
result = (result <= t_max).float() * result + (result > t_max).float() * t_max
return result
#均方损失函数：实际值减去预测值的平方
def MSELoss(self, pred, target):
return torch.pow(pred - target, 2)
#交叉熵损失:loss=-ylog(pred)-(1-y)log(1-pred)
def BCELoss(self, pred, target):
epsilon = 1e-7
pred = self.clip_by_tensor(pred, epsilon, 1.0 - epsilon)
output = - target * torch.log(pred) - (1.0 - target) * torch.log(1.0 - pred)
return output
#置信损失函数
def box_giou(self, b1, b2):
"""
输入为：
----------
b1: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh (x,y)为坐标 (w,h)为预测框的宽和长
b2: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh (x,y)为坐标 (w,h)为真实框的宽和长
返回为：
-------
giou: tensor, shape=(batch, feat_w, feat_h, anchor_num, 1)
"""
#----------------------------------------------------#
# 求出预测框左上角右下角
#----------------------------------------------------#
#b1:[4, 3, 80, 80, 4],[4, 3, 40, 40, 4],[4, 3, 20, 20, 4]
b1_xy = b1[..., :2]#(x,y):三种网格的每个格的左上角的坐标
b1_wh = b1[..., 2:4]#(w,h):三种网格的每个格的宽和高
b1_wh_half = b1_wh/2.#取三种网格的宽和高的一半
b1_mins = b1_xy - b1_wh_half#格子左上角向左或者向上平移半个格子的长度
b1_maxes = b1_xy + b1_wh_half#格子左上角向右或者向下平移半个格子的长度
# print(">>>>>>>>>>>>>>>>>>>>>b1",b1.shape)
# print(">>>>>>>>>>>>>>>>>>>>>b", b1[..., :2])
#----------------------------------------------------#
# 求出真实框左上角右下角
#----------------------------------------------------#
#以下真实框和预测框的注释相同
b2_xy = b2[..., :2]
b2_wh = b2[..., 2:4]
b2_wh_half = b2_wh/2.
b2_mins = b2_xy - b2_wh_half
b2_maxes = b2_xy + b2_wh_half
#----------------------------------------------------#
# 求真实框和预测框所有的iou（置信度）
#----------------------------------------------------#
#iou=(AnB)/(AUB),其中A表示预测框，B表示真实框，式子表示(真实框与预测框的交集的面积)除以(真实框与预测框的并集的面积)
intersect_mins = torch.max(b1_mins, b2_mins)
intersect_maxes = torch.min(b1_maxes, b2_maxes)
intersect_wh = torch.max(intersect_maxes - intersect_mins, torch.zeros_like(intersect_maxes))
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] #(AnB)：真实框与预测框的交集的面积
b1_area = b1_wh[..., 0] * b1_wh[..., 1]
b2_area = b2_wh[..., 0] * b2_wh[..., 1]
union_area = b1_area + b2_area - intersect_area #(AUB)：真实框与预测框的并集的面积
iou = intersect_area / union_area #iou=(AnB)/(AUB)
#----------------------------------------------------#
# 找到包裹两个框的最小框的左上角和右下角
#----------------------------------------------------#
enclose_mins = torch.min(b1_mins, b2_mins)#[4, 3, 40, 40, 2],[4, 3, 80, 80, 2]
enclose_maxes = torch.max(b1_maxes, b2_maxes)
#print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>enclose_mins",enclose_mins.shape)
enclose_wh = torch.max(enclose_maxes - enclose_mins, torch.zeros_like(intersect_maxes))#[4, 3, 40, 40, 2],[4, 3, 80, 80, 2]，大框的高和宽
#print("-------------enclose_wh",enclose_wh.shape,enclose_wh)
#----------------------------------------------------#
# 计算对角线距离
#----------------------------------------------------#
enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]#enclose_area:表示真实框和预测框的最左上角的点和最右下角的点围成的矩形的面积，具体看YOLOV5的置信损失函数
giou = iou - (enclose_area - union_area) / enclose_area
return giou
#---------------------------------------------------#
# 平滑标签
#---------------------------------------------------#
#平滑标签的目的是让神经网络不那么自信，例如，softmax的结果：（1,0）->[1,0]*(1-0.1)+0.05=[0.95,0.05]，在这里，label_smoothing=0.1，num_classes=2
#label_smoothing：平滑标签系数； num_classes：预测的目标类别数量
def smooth_labels(self, y_true, label_smoothing, num_classes):
return y_true * (1.0 - label_smoothing) + label_smoothing / num_classes
def forward(self, l, input, targets=None, y_true=None):
#----------------------------------------------------#
# l 代表使用的是第几个有效特征层
# input的shape为 bs, 3*(5+num_classes), 20, 20
# bs, 3*(5+num_classes), 40, 40
# bs, 3*(5+num_classes), 80, 80
# targets 真实框的标签情况 [batch_size, num_gt, 5]
#----------------------------------------------------#
#--------------------------------#
# 获得图片数量，特征层的高和宽
# 20, 20
#--------------------------------#
bs = input.size(0)
in_h = input.size(2)
in_w = input.size(3)
#-----------------------------------------------------------------------#
# 计算步长
# 每一个特征点对应原来的图片上多少个像素点
# [640, 640] 高的步长为640 / 20 = 32，宽的步长为640 / 20 = 32
# 如果特征层为20x20的话，一个特征点就对应原来的图片上的32个像素点
# 如果特征层为40x40的话，一个特征点就对应原来的图片上的16个像素点
# 如果特征层为80x80的话，一个特征点就对应原来的图片上的8个像素点
# stride_h = stride_w = 32、16、8
#-----------------------------------------------------------------------#
stride_h = self.input_shape[0] / in_h
stride_w = self.input_shape[1] / in_w
#-------------------------------------------------#
# 此时获得的scaled_anchors大小是相对于特征层的
#-------------------------------------------------#
scaled_anchors = [(a_w / stride_w, a_h / stride_h) for a_w, a_h in self.anchors]#三个特征层，每个特征层有3个anchor
#-------------------------------------------------#
# 输入的input一共有三个，他们的shape分别是
# bs, 3 * (5+num_classes), 20, 20 => bs, 3, 5 + num_classes, 20, 20 => batch_size, 3, 20, 20, 5 + num_classes
# batch_size, 3, 20, 20, 5 + num_classes
# batch_size, 3, 40, 40, 5 + num_classes
# batch_size, 3, 80, 80, 5 + num_classes
#-----------------------------------------------#
prediction = input.view(bs, len(self.anchors_mask[l]), self.bbox_attrs, in_h, in_w).permute(0, 1, 3, 4, 2).contiguous()
#-----------------------------------------------#
# 先验框的中心位置的调整参数
# prediction：
# batch_size, 3, 20, 20, 5 + num_classes
# batch_size, 3, 40, 40, 5 + num_classes
# batch_size, 3, 80, 80, 5 + num_classes
#-----------------------------------------------#
x = torch.sigmoid(prediction[..., 0])
y = torch.sigmoid(prediction[..., 1])
#-----------------------------------------------#
# 先验框的宽高调整参数
#-----------------------------------------------#
w = torch.sigmoid(prediction[..., 2])
h = torch.sigmoid(prediction[..., 3])
#-----------------------------------------------#
# 获得置信度，是否有物体
#-----------------------------------------------#
conf = torch.sigmoid(prediction[..., 4])#[4, 3, 80, 80]
# print(">>>>>>>>>>>>>>>conf",conf.shape,conf)
#-----------------------------------------------#
# 种类置信度
#-----------------------------------------------#
pred_cls = torch.sigmoid(prediction[..., 5:])#[4, 3, 80, 80, 1]
# print(">>>>>>>>>>>>>>>pred_cls",pred_cls.shape ,pred_cls)
#-----------------------------------------------#
# self.get_target已经合并到dataloader中
# 原因是在这里执行过慢，会大大延长训练时间
#-----------------------------------------------#
# y_true, noobj_mask = self.get_target(l, targets, scaled_anchors, in_h, in_w)
#---------------------------------------------------------------#
# 将预测结果进行解码，判断预测结果和真实值的重合程度
# 如果重合程度过大则忽略，因为这些特征点属于预测比较准确的特征点
# 作为负样本不合适
#----------------------------------------------------------------#
pred_boxes = self.get_pred_boxes(l, x, y, h, w, targets, scaled_anchors, in_h, in_w)
if self.cuda:
y_true = y_true.type_as(x)
#print(">>>>>>>>>>>>>>>x",x)
#print("..........y_true",y_true)
loss = 0
n = torch.sum(y_true[..., 4] == 1)
#print(">>>>>>>>>>>>>>",n)
if n != 0:
#---------------------------------------------------------------#
# 计算预测结果和真实结果的giou，计算对应有真实框的先验框的giou损失
# loss_cls计算对应有真实框的先验框的分类损失
#----------------------------------------------------------------#
giou = self.box_giou(pred_boxes, y_true[..., :4]).type_as(x)#交叉熵损失,[4, 3, 40, 40],[4, 3, 80, 80]
#print(">>>>>>>>>>>>>>>giou",giou.shape,giou)
loss_loc = torch.mean((1 - giou)[y_true[..., 4] == 1])
loss_cls = torch.mean(self.BCELoss(pred_cls[y_true[..., 4] == 1], self.smooth_labels(y_true[..., 5:][y_true[..., 4] == 1], self.label_smoothing, self.num_classes)))
loss += loss_loc * self.box_ratio + loss_cls * self.cls_ratio
#-----------------------------------------------------------#
# 计算置信度的loss
# 也就意味着先验框对应的预测框预测的更准确
# 它才是用来预测这个物体的。
#-----------------------------------------------------------#
tobj = torch.where(y_true[..., 4] == 1, giou.detach().clamp(0), torch.zeros_like(y_true[..., 4]))
else:
tobj = torch.zeros_like(y_true[..., 4])
loss_conf = torch.mean(self.BCELoss(conf, tobj))
ss=self.BCELoss(conf, tobj)#[4, 3, 20, 20],[4, 3, 40, 40],[4, 3, 80, 80]
#print(">>>>>>>>>>>>ss",ss.shape)
loss += loss_conf * self.balance[l] * self.obj_ratio
# if n != 0:
# print(loss_loc * self.box_ratio, loss_cls * self.cls_ratio, loss_conf * self.balance[l] * self.obj_ratio)
#print(">>>>>>>>>>>>>>loss",loss)
return loss
def get_near_points(self, x, y, i, j):
sub_x = x - i
sub_y = y - j
if sub_x > 0.5 and sub_y > 0.5:
return [[0, 0], [1, 0], [0, 1]]
elif sub_x < 0.5 and sub_y > 0.5:
return [[0, 0], [-1, 0], [0, 1]]
elif sub_x < 0.5 and sub_y < 0.5:
return [[0, 0], [-1, 0], [0, -1]]
else:
return [[0, 0], [1, 0], [0, -1]]
def get_target(self, l, targets, anchors, in_h, in_w):
#-----------------------------------------------------#
# 计算一共有多少张图片
#-----------------------------------------------------#
bs = len(targets)
#-----------------------------------------------------#
# 用于选取哪些先验框不包含物体
# bs, 3, 20, 20
#-----------------------------------------------------#
noobj_mask = torch.ones(bs, len(self.anchors_mask[l]), in_h, in_w, requires_grad = False)
#-----------------------------------------------------#
# 帮助找到每一个先验框最对应的真实框
#-----------------------------------------------------#
box_best_ratio = torch.zeros(bs, len(self.anchors_mask[l]), in_h, in_w, requires_grad = False)
#-----------------------------------------------------#
# batch_size, 3, 20, 20, 5 + num_classes
#-----------------------------------------------------#
y_true = torch.zeros(bs, len(self.anchors_mask[l]), in_h, in_w, self.bbox_attrs, requires_grad = False)
for b in range(bs):
if len(targets[b])==0:
continue
batch_target = torch.zeros_like(targets[b])
#-------------------------------------------------------#
# 计算出正样本在特征层上的中心点
# 获得真实框相对于特征层的大小
#-------------------------------------------------------#
batch_target[:, [0,2]] = targets[b][:, [0,2]] * in_w
batch_target[:, [1,3]] = targets[b][:, [1,3]] * in_h
batch_target[:, 4] = targets[b][:, 4]
batch_target = batch_target.cpu()
#-----------------------------------------------------------------------------#
# batch_target : num_true_box, 5
# batch_target[:, 2:4] : num_true_box, 2
# torch.unsqueeze(batch_target[:, 2:4], 1) : num_true_box, 1, 2
# anchors : 9, 2
# torch.unsqueeze(torch.FloatTensor(anchors), 0) : 1, 9, 2
# ratios_of_gt_anchors : num_true_box, 9, 2
# ratios_of_anchors_gt : num_true_box, 9, 2
#
# ratios : num_true_box, 9, 4
# max_ratios : num_true_box, 9
# max_ratios每一个真实框和每一个先验框的最大宽高比！
#------------------------------------------------------------------------------#
ratios_of_gt_anchors = torch.unsqueeze(batch_target[:, 2:4], 1) / torch.unsqueeze(torch.FloatTensor(anchors), 0)
ratios_of_anchors_gt = torch.unsqueeze(torch.FloatTensor(anchors), 0) / torch.unsqueeze(batch_target[:, 2:4], 1)
ratios = torch.cat([ratios_of_gt_anchors, ratios_of_anchors_gt], dim = -1)
max_ratios, _ = torch.max(ratios, dim = -1)
for t, ratio in enumerate(max_ratios):
#-------------------------------------------------------#
# ratio : 9
#-------------------------------------------------------#
over_threshold = ratio < self.threshold
over_threshold[torch.argmin(ratio)] = True
for k, mask in enumerate(self.anchors_mask[l]):
if not over_threshold[mask]:
continue
#----------------------------------------#
# 获得真实框属于哪个网格点
# x 1.25 => 1
# y 3.75 => 3
#----------------------------------------#
i = torch.floor(batch_target[t, 0]).long()
j = torch.floor(batch_target[t, 1]).long()
offsets = self.get_near_points(batch_target[t, 0], batch_target[t, 1], i, j)
for offset in offsets:
local_i = i + offset[0]
local_j = j + offset[1]
if local_i >= in_w or local_i < 0 or local_j >= in_h or local_j < 0:
continue
if box_best_ratio[b, k, local_j, local_i] != 0:
if box_best_ratio[b, k, local_j, local_i] > ratio[mask]:
y_true[b, k, local_j, local_i, :] = 0
else:
continue
#----------------------------------------#
# 取出真实框的种类
#----------------------------------------#
c = batch_target[t, 4].long()
#----------------------------------------#
# noobj_mask代表无目标的特征点
#----------------------------------------#
noobj_mask[b, k, local_j, local_i] = 0
#----------------------------------------#
# tx、ty代表中心调整参数的真实值
#----------------------------------------#
y_true[b, k, local_j, local_i, 0] = batch_target[t, 0]
y_true[b, k, local_j, local_i, 1] = batch_target[t, 1]
y_true[b, k, local_j, local_i, 2] = batch_target[t, 2]
y_true[b, k, local_j, local_i, 3] = batch_target[t, 3]
y_true[b, k, local_j, local_i, 4] = 1
y_true[b, k, local_j, local_i, c + 5] = 1
#----------------------------------------#
# 获得当前先验框最好的比例
#----------------------------------------#
box_best_ratio[b, k, local_j, local_i] = ratio[mask]
return y_true, noobj_mask
def get_pred_boxes(self, l, x, y, h, w, targets, scaled_anchors, in_h, in_w):
#-----------------------------------------------------#
# 计算一共有多少张图片
#-----------------------------------------------------#
bs = len(targets)
#-----------------------------------------------------#
# 生成网格，先验框中心，网格左上角
#-----------------------------------------------------#
grid_x = torch.linspace(0, in_w - 1, in_w).repeat(in_h, 1).repeat(
int(bs * len(self.anchors_mask[l])), 1, 1).view(x.shape).type_as(x)
grid_y = torch.linspace(0, in_h - 1, in_h).repeat(in_w, 1).t().repeat(
int(bs * len(self.anchors_mask[l])), 1, 1).view(y.shape).type_as(x)
# 生成先验框的宽高
scaled_anchors_l = np.array(scaled_anchors)[self.anchors_mask[l]]
anchor_w = torch.Tensor(scaled_anchors_l).index_select(1, torch.LongTensor([0])).type_as(x)
anchor_h = torch.Tensor(scaled_anchors_l).index_select(1, torch.LongTensor([1])).type_as(x)
anchor_w = anchor_w.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(w.shape)
anchor_h = anchor_h.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(h.shape)
#-------------------------------------------------------#
# 计算调整后的先验框中心与宽高
#-------------------------------------------------------#
pred_boxes_x = torch.unsqueeze(x * 2. - 0.5 + grid_x, -1)
pred_boxes_y = torch.unsqueeze(y * 2. - 0.5 + grid_y, -1)
pred_boxes_w = torch.unsqueeze((w * 2) ** 2 * anchor_w, -1)
pred_boxes_h = torch.unsqueeze((h * 2) ** 2 * anchor_h, -1)
pred_boxes = torch.cat([pred_boxes_x, pred_boxes_y, pred_boxes_w, pred_boxes_h], dim = -1)
return pred_boxes
def is_parallel(model):
# Returns True if model is of type DP or DDP
return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
def de_parallel(model):
# De-parallelize a model: returns single-GPU model if model is of type DP or DDP
return model.module if is_parallel(model) else model
def copy_attr(a, b, include=(), exclude=()):
# Copy attributes from b to a, options to only include [...] and to exclude [...]
for k, v in b.__dict__.items():
if (len(include) and k not in include) or k.startswith('_') or k in exclude:
continue
else:
setattr(a, k, v)
class ModelEMA:
""" Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
Keeps a moving average of everything in the model state_dict (parameters and buffers)
For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
"""
def __init__(self, model, decay=0.9999, tau=2000, updates=0):
# Create EMA
self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA
# if next(model.parameters()).device.type != 'cpu':
# self.ema.half() # FP16 EMA
self.updates = updates # number of EMA updates
self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)
for p in self.ema.parameters():
p.requires_grad_(False)
def update(self, model):
# Update EMA parameters
with torch.no_grad():
self.updates += 1
d = self.decay(self.updates)
msd = de_parallel(model).state_dict() # model state_dict
for k, v in self.ema.state_dict().items():
if v.dtype.is_floating_point:
v *= d
v += (1 - d) * msd[k].detach()
def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
# Update EMA attributes
copy_attr(self.ema, model, include, exclude)
def weights_init(net, init_type='normal', init_gain = 0.02):
def init_func(m):
classname = m.__class__.__name__
if hasattr(m, 'weight') and classname.find('Conv') != -1:
if init_type == 'normal':
torch.nn.init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
torch.nn.init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
torch.nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
torch.nn.init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
elif classname.find('BatchNorm2d') != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
print('initialize network with %s type' % init_type)
net.apply(init_func)
def get_lr_scheduler(lr_decay_type, lr, min_lr, total_iters, warmup_iters_ratio = 0.05, warmup_lr_ratio = 0.1, no_aug_iter_ratio = 0.05, step_num = 10):
def yolox_warm_cos_lr(lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter, iters):
if iters <= warmup_total_iters:
# lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start
lr = (lr - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2
) + warmup_lr_start
elif iters >= total_iters - no_aug_iter:
lr = min_lr
else:
lr = min_lr + 0.5 * (lr - min_lr) * (
1.0
+ math.cos(
math.pi
* (iters - warmup_total_iters)
/ (total_iters - warmup_total_iters - no_aug_iter)
)
)
return lr
def step_lr(lr, decay_rate, step_size, iters):
if step_size < 1:
raise ValueError("step_size must above 1.")
n = iters // step_size
out_lr = lr * decay_rate ** n
return out_lr
if lr_decay_type == "cos":
warmup_total_iters = min(max(warmup_iters_ratio * total_iters, 1), 3)
warmup_lr_start = max(warmup_lr_ratio * lr, 1e-6)
no_aug_iter = min(max(no_aug_iter_ratio * total_iters, 1), 15)
func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter)
else:
decay_rate = (min_lr / lr) ** (1 / (step_num - 1))
step_size = total_iters / step_num
func = partial(step_lr, lr, decay_rate, step_size)
return func
def set_optimizer_lr(optimizer, lr_scheduler_func, epoch):
lr = lr_scheduler_func(epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
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