● 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
● 🍦 参考文章：Pytorch实战 |第P9周：YOLOv5-Backbone模块实现(训练营内部成员可读)
● 🍖 原作者：K同学啊|接辅导、项目定制
类似于上周内容，除了网络结构部分的内容，其余部分的内容和上周一样。
yolov5结构示意图
一、 前期准备
1. 设置GPU
-
import torch
-
import torch.nn
as nn
-
import torchvision.transforms
as transforms
-
import torchvision
-
from torchvision
import transforms, datasets
-
import os,PIL,pathlib,warnings
-
-
warnings.filterwarnings(
"ignore")
#忽略警告信息
-
-
device = torch.device(
"cuda"
if torch.cuda.is_available()
else
"cpu")
-
print(device)
2. 导入数据
-
import os,PIL,random,pathlib
-
-
data_dir =
'./data/'
-
data_dir = pathlib.Path(data_dir)
-
-
data_paths =
list(data_dir.glob(
'*'))
-
classeNames = [
str(path).split(
"\\")[
1]
for path
in data_paths]
-
print(classeNames)
图形变换，输出一下：用到torchvision.transforms.Compose()类
-
train_transforms = transforms.Compose([
-
transforms.Resize([
224,
224]),
# 将输入图片resize成统一尺寸
-
# transforms.RandomHorizontalFlip(), # 随机水平翻转
-
transforms.ToTensor(),
# 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
-
transforms.Normalize(
# 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
-
mean=[
0.485,
0.456,
0.406],
-
std=[
0.229,
0.224,
0.225])
# 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
-
])
-
-
test_transform = transforms.Compose([
-
transforms.Resize([
224,
224]),
# 将输入图片resize成统一尺寸
-
transforms.ToTensor(),
# 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
-
transforms.Normalize(
# 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
-
mean=[
0.485,
0.456,
0.406],
-
std=[
0.229,
0.224,
0.225])
# 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
-
])
-
-
total_data = datasets.ImageFolder(
"./data/",transform=train_transforms)
-
print(total_data.class_to_idx)
3. 划分数据集
-
train_size =
int(
0.8 *
len(total_data))
-
test_size =
len(total_data) - train_size
-
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
-
batch_size =
16
-
-
train_dl = torch.utils.data.DataLoader(train_dataset,
-
batch_size=batch_size,
-
shuffle=
True,
-
num_workers=
0)
-
test_dl = torch.utils.data.DataLoader(test_dataset,
-
batch_size=batch_size,
-
shuffle=
True,
-
num_workers=
0)
-
for X, y
in test_dl:
-
print(
"Shape of X [N, C, H, W]: ", X.shape)
-
print(
"Shape of y: ", y.shape, y.dtype)
-
break
二、搭建YOLOv5-Backbone模型
1. 搭建模型
-
import torch.nn.functional
as F
-
-
def
autopad(
k, p=None):
# kernel, padding
-
# Pad to 'same'
-
if p
is
None:
-
p = k //
2
if
isinstance(k,
int)
else [x //
2
for x
in k]
# auto-pad
-
return p
-
-
class
Conv(nn.Module):
-
# Standard convolution
-
def
__init__(
self, c1, c2, k=1, s=1, p=None, g=1, act=True):
# ch_in, ch_out, kernel, stride, padding, groups
-
super().__init__()
-
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=
False)
-
self.bn = nn.BatchNorm2d(c2)
-
self.act = nn.SiLU()
if act
is
True
else (act
if
isinstance(act, nn.Module)
else nn.Identity())
-
-
def
forward(
self, x):
-
return self.act(self.bn(self.conv(x)))
-
-
class
Bottleneck(nn.Module):
-
# Standard bottleneck
-
def
__init__(
self, c1, c2, shortcut=True, g=1, e=0.5):
# ch_in, ch_out, shortcut, groups, expansion
-
super().__init__()
-
c_ =
int(c2 * e)
# hidden channels
-
self.cv1 = Conv(c1, c_,
1,
1)
-
self.cv2 = Conv(c_, c2,
3,
1, g=g)
-
self.add = shortcut
and c1 == c2
-
-
def
forward(
self, x):
-
return x + self.cv2(self.cv1(x))
if self.add
else self.cv2(self.cv1(x))
-
-
class
C3(nn.Module):
-
# CSP Bottleneck with 3 convolutions
-
def
__init__(
self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
# ch_in, ch_out, number, shortcut, groups, expansion
-
super().__init__()
-
c_ =
int(c2 * e)
# hidden channels
-
self.cv1 = Conv(c1, c_,
1,
1)
-
self.cv2 = Conv(c1, c_,
1,
1)
-
self.cv3 = Conv(
2 * c_, c2,
1)
# act=FReLU(c2)
-
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=
1.0)
for _
in
range(n)))
-
#SSPF模块将经过Conv的x、一次池化后的y1、两次池化后的y2和3次池化后的self.m(y2)先进行拼接，然后再Conv提取特征。 仔细观察不难发现，虽然SSPF对特征图进行了多次池化，但是特征图尺寸并未发生变化，通道数更不会变化，所以后续的4个输出能够在channel维度进行融合.
-
-
def
forward(
self, x):
-
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=
1))
-
-
class
SPPF(nn.Module):
-
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
-
def
__init__(
self, c1, c2, k=5):
# equivalent to SPP(k=(5, 9, 13))
-
super().__init__()
-
c_ = c1 //
2
# hidden channels
-
self.cv1 = Conv(c1, c_,
1,
1)
-
self.cv2 = Conv(c_ *
4, c2,
1,
1)
-
self.m = nn.MaxPool2d(kernel_size=k, stride=
1, padding=k //
2)
-
-
def
forward(
self, x):
-
x = self.cv1(x)
-
with warnings.catch_warnings():
-
warnings.simplefilter(
'ignore')
# suppress torch 1.9.0 max_pool2d() warning
-
y1 = self.m(x)
-
y2 = self.m(y1)
-
return self.cv2(torch.cat([x, y1, y2, self.m(y2)],
1))
-
"""
-
这个是YOLOv5, 6.0版本的主干网络，这里进行复现
-
（注：有部分删改，详细讲解将在后续进行展开）
-
"""
-
class
YOLOv5_backbone(nn.Module):
-
def
__init__(
self):
-
super(YOLOv5_backbone, self).__init__()
-
-
self.Conv_1 = Conv(
3,
64,
3,
2,
2)
-
self.Conv_2 = Conv(
64,
128,
3,
2)
-
self.C3_3 = C3(
128,
128)
-
self.Conv_4 = Conv(
128,
256,
3,
2)
-
self.C3_5 = C3(
256,
256)
-
self.Conv_6 = Conv(
256,
512,
3,
2)
-
self.C3_7 = C3(
512,
512)
-
self.Conv_8 = Conv(
512,
1024,
3,
2)
-
self.C3_9 = C3(
1024,
1024)
-
self.SPPF = SPPF(
1024,
1024,
5)
-
-
# 全连接网络层，用于分类
-
self.classifier = nn.Sequential(
-
nn.Linear(in_features=
65536, out_features=
100),
-
nn.ReLU(),
-
nn.Linear(in_features=
100, out_features=
4)
-
)
-
-
def
forward(
self, x):
-
x = self.Conv_1(x)
-
x = self.Conv_2(x)
-
x = self.C3_3(x)
-
x = self.Conv_4(x)
-
x = self.C3_5(x)
-
x = self.Conv_6(x)
-
x = self.C3_7(x)
-
x = self.Conv_8(x)
-
x = self.C3_9(x)
-
x = self.SPPF(x)
-
-
x = torch.flatten(x, start_dim=
1)
-
x = self.classifier(x)
-
-
return x
-
-
device =
"cuda"
if torch.cuda.is_available()
else
"cpu"
-
print(
"Using {} device".
format(device))
-
-
model = YOLOv5_backbone().to(device)
-
print(model)
-
-
-
-
2. 查看模型详情
这里就不展示了，有兴趣大家琢磨哈哈哈
三、 训练模型
1. 编写训练和测试函数
和之前cnn网络、vgg一样
-
# 训练循环
-
def
train(
dataloader, model, loss_fn, optimizer):
-
size =
len(dataloader.dataset)
# 训练集的大小
-
num_batches =
len(dataloader)
# 批次数目, (size/batch_size，向上取整)
-
-
train_loss, train_acc =
0,
0
# 初始化训练损失和正确率
-
-
for X, y
in dataloader:
# 获取图片及其标签
-
X, y = X.to(device), y.to(device)
-
-
# 计算预测误差
-
pred = model(X)
# 网络输出
-
loss = loss_fn(pred, y)
# 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
-
-
# 反向传播
-
optimizer.zero_grad()
# grad属性归零
-
loss.backward()
# 反向传播
-
optimizer.step()
# 每一步自动更新
-
-
# 记录acc与loss
-
train_acc += (pred.argmax(
1) == y).
type(torch.
float).
sum().item()
-
train_loss += loss.item()
-
-
train_acc /= size
-
train_loss /= num_batches
-
-
return train_acc, train_loss
-
def
test (dataloader, model, loss_fn):
-
size =
len(dataloader.dataset)
# 测试集的大小
-
num_batches =
len(dataloader)
# 批次数目
-
test_loss, test_acc =
0,
0
-
-
# 当不进行训练时，停止梯度更新，节省计算内存消耗
-
with torch.no_grad():
-
for imgs, target
in dataloader:
-
imgs, target = imgs.to(device), target.to(device)
-
-
# 计算loss
-
target_pred = model(imgs)
-
loss = loss_fn(target_pred, target)
-
-
test_loss += loss.item()
-
test_acc += (target_pred.argmax(
1) == target).
type(torch.
float).
sum().item()
-
-
test_acc /= size
-
test_loss /= num_batches
-
-
return test_acc, test_loss
2. 正式训练
这里也设置了训练器，结合前几次实验经验，使用Adam模型
-
import copy
-
-
optimizer = torch.optim.Adam(model.parameters(), lr=
1e-4)
-
loss_fn = nn.CrossEntropyLoss()
# 创建损失函数
-
-
epochs =
20
-
-
train_loss = []
-
train_acc = []
-
test_loss = []
-
test_acc = []
-
-
best_acc =
0
# 设置一个最佳准确率，作为最佳模型的判别指标
-
-
for epoch
in
range(epochs):
-
# 更新学习率（使用自定义学习率时使用）
-
# adjust_learning_rate(optimizer, epoch, learn_rate)
-
-
model.train()
-
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
-
# scheduler.step() # 更新学习率（调用官方动态学习率接口时使用）
-
-
model.
eval()
-
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
-
-
# 保存最佳模型到 best_model
-
if epoch_test_acc > best_acc:
-
best_acc = epoch_test_acc
-
best_model = copy.deepcopy(model)
-
-
train_acc.append(epoch_train_acc)
-
train_loss.append(epoch_train_loss)
-
test_acc.append(epoch_test_acc)
-
test_loss.append(epoch_test_loss)
-
-
# 获取当前的学习率
-
lr = optimizer.state_dict()[
'param_groups'][
0][
'lr']
-
-
template = (
'Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
-
print(template.
format(epoch +
1, epoch_train_acc *
100, epoch_train_loss,
-
epoch_test_acc *
100, epoch_test_loss, lr))
-
-
# 保存最佳模型到文件中
-
PATH =
'./best_model.pth'
# 保存的参数文件名
-
torch.save(model.state_dict(), PATH)
-
-
print(
'Done')
四、 结果可视化
1. Loss与Accuracy图
-
import matplotlib.pyplot
as plt
-
#隐藏警告
-
import warnings
-
warnings.filterwarnings(
"ignore")
#忽略警告信息
-
plt.rcParams[
'font.sans-serif'] = [
'SimHei']
# 用来正常显示中文标签
-
plt.rcParams[
'axes.unicode_minus'] =
False
# 用来正常显示负号
-
plt.rcParams[
'figure.dpi'] =
100
#分辨率
-
-
epochs_range =
range(epochs)
-
-
plt.figure(figsize=(
12,
3))
-
plt.subplot(
1,
2,
1)
-
-
plt.plot(epochs_range, train_acc, label=
'Training Accuracy')
-
plt.plot(epochs_range, test_acc, label=
'Test Accuracy')
-
plt.legend(loc=
'lower right')
-
plt.title(
'Training and Validation Accuracy')
-
-
plt.subplot(
1,
2,
2)
-
plt.plot(epochs_range, train_loss, label=
'Training Loss')
-
plt.plot(epochs_range, test_loss, label=
'Test Loss')
-
plt.legend(loc=
'upper right')
-
plt.title(
'Training and Validation Loss')
-
plt.show()
2. 指定图片进行预测
-
from PIL
import Image
-
-
classes =
list(total_data.class_to_idx)
-
-
-
def
predict_one_image(
image_path, model, transform, classes):
-
test_img = Image.
open(image_path).convert(
'RGB')
-
plt.imshow(test_img)
# 展示预测的图片
-
-
test_img = transform(test_img)
-
img = test_img.to(device).unsqueeze(
0)
-
-
model.
eval()
-
output = model(img)
-
-
_, pred = torch.
max(output,
1)
-
pred_class = classes[pred]
-
print(
f'预测结果是：{pred_class}')
-
-
-
# 预测训练集中的某张照片
-
predict_one_image(image_path=
'./data/sunrise/sunrise8.jpg',
-
model=model,
-
transform=train_transforms,
-
classes=classes)
3. 模型评估
以往都是看看最后几轮得到准确率，但是跳动比较大就不太好找准确率最高的一回，所以我们用函数返回进行比较。
-
best_model.
eval()
-
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
-
print(epoch_test_acc, epoch_test_loss)
-
print(epoch_test_acc)
转载：https://blog.csdn.net/m0_62237233/article/details/128294998
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