目标检测 YOLOv5 - 学习率
flyfish
YOLOv5的学习率有3个
先用简单代码可视化下学习率大概的样子，然后再可视化三个学习率。
学习率大概的样子
from torch.optim import SGD
import torch.optim.lr_scheduler as lr_scheduler
from torchvision.models import shufflenet_v2_x0_5 as testmodel
import math
import matplotlib.pyplot as plt
epochs=300
def one_cycle(y1=0.0, y2=1.0, steps=100):
return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
model = testmodel()
optimizer = SGD(model.parameters(), lr=0.001)
lf = one_cycle(1, 0.2, epochs)
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
lr_t=[]
epoch_t=[]
for epoch in range(epochs):
optimizer.zero_grad()
scheduler.step()
lr=optimizer.param_groups[0]['lr']
print("learning rate: {:.6f}".format(optimizer.param_groups[0]['lr']))
lr_t.append(lr)
epoch_t.append(epoch)
plt.figure()
plt.plot(epoch_t, lr_t, color="r",label='learning rate')
plt.legend()
plt.show()
三个学习率的样子
#关于以下配置内容在从hyp.scrach.yaml
# lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
# lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
# momentum: 0.937 # SGD momentum/Adam beta1
# weight_decay: 0.0005 # optimizer weight decay 5e-4
# warmup_epochs: 3.0 # warmup epochs (fractions ok)
# warmup_momentum: 0.8 # warmup initial momentum
# warmup_bias_lr: 0.1 # warmup initial bias lr
from torch.optim import SGD
import torch.optim.lr_scheduler as lr_scheduler
from torchvision.models import shufflenet_v2_x0_5 as testmodel
import math
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
lr0 = 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf = 0.2 # final OneCycleLR learning rate (lr0 * lrf)
momentum = 0.937 # SGD momentum/Adam beta1
weight_decay = 0.0005 # optimizer weight decay 5e-4
warmup_epochs = 3.0 # warmup epochs (fractions ok)
warmup_momentum = 0.8 # warmup initial momentum
warmup_bias_lr = 0.1 # warmup initial bias lr
epochs = 300
nb = 200 # nb是batch的个数，number of batches = 样本个数 /batch_size
nw = 1000 #nw = max(round(hyp['warmup_epochs'] * nb), 1000) #number of warmup iterations
def one_cycle(y1=0.0, y2=1.0, steps=100):
# lambda function for sinusoidal ramp from y1 to y2
return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
model = testmodel()
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
optimizer = SGD(pg0, lr=lr0, momentum=momentum, nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': weight_decay}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
lf = one_cycle(1, lrf, epochs)
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
lr0,lr1,lr2 ,epoch_t= [], [], [],[]
optimizer.zero_grad()
for epoch in range(0,epochs):
for i in range(nb):
ni = i + nb * epoch
if ni <= nw:#warmpup 预热阶段
xi = [0, nw]
for j, x in enumerate(optimizer.param_groups):
x['lr'] = np.interp(ni, xi, [0.1 if j == 2 else 0.0, 0.01 * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [0.8, 0.937])
lr = [x['lr'] for x in optimizer.param_groups]
lr0.append(lr[0])
lr1.append(lr[1])
lr2.append(lr[2])
scheduler.step()
epoch_t.append(epoch)
# 使用plt.subplot来创建小图.
# plt.subplot(221)表示将整个图像窗口分为2行2列, 当前位置为1.
# plt.subplot(222)表示将整个图像窗口分为2行2列, 当前位置为2.
# plt.subplot(223)表示将整个图像窗口分为2行2列, 当前位置为3.
plt.figure()
plt.subplot(221)
plt.plot(epoch_t, lr0, color="r",label='learning rate 0')
plt.legend()
print(lr0)
plt.subplot(222)
plt.plot(epoch_t, lr1, color="b",label='learning rate 1')
plt.legend()
plt.subplot(223)
plt.plot(epoch_t, lr2,color="g",label='learning rate 2')
plt.legend()
plt.show()
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原文链接：https://blog.csdn.net/flyfish1986/article/details/119875059
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