第P9周-Pytorch实现YOLOv5-Backbone模块
- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊
目标
- 优化代码结构
- 了解YOLOv5-backbone模块
具体实现
（一）环境
语言环境：Python 3.10
编 译 器: PyCharm
框 架: Pytorch
（二）具体步骤
代码结构如下：
1. Utils.py
import torch
import pathlib
import matplotlib.pyplot as plt
from torchvision.transforms import transforms
# 第一步：设置GPU
def USE_GPU():
if torch.cuda.is_available():
print('CUDA is available, will use GPU')
device = torch.device("cuda")
else:
print('CUDA is not available. Will use CPU')
device = torch.device("cpu")
return device
temp_dict = dict()
def recursive_iterate(path):
"""
根据所提供的路径遍历该路径下的所有子目录，列出所有子目录下的文件
:param path: 路径
:return: 返回最后一级目录的数据
""" path = pathlib.Path(path)
for file in path.iterdir():
if file.is_file():
temp_key = str(file).split('\\')[-2]
if temp_key in temp_dict:
temp_dict.update({temp_key: temp_dict[temp_key] + 1})
else:
temp_dict.update({temp_key: 1})
# print(file)
elif file.is_dir():
recursive_iterate(file)
return temp_dict
def data_from_directory(directory, train_dir=None, test_dir=None, show=False):
"""
提供是的数据集是文件形式的，提供目录方式导入数据，简单分析数据并返回数据分类
:param test_dir: 是否设置了测试集目录
:param train_dir: 是否设置了训练集目录
:param directory: 数据集所在目录
:param show: 是否需要以柱状图形式显示数据分类情况,默认显示
:return: 数据分类列表,类型: list
""" global total_image
print("数据目录：{}".format(directory))
data_dir = pathlib.Path(directory)
# for d in data_dir.glob('**/*'): # **/*通配符可以遍历所有子目录
# if d.is_dir():
# print(d) class_name = []
total_image = 0
# temp_sum = 0
if train_dir is None or test_dir is None:
data_path = list(data_dir.glob('*'))
class_name = [str(path).split('\\')[-1] for path in data_path]
print("数据分类: {}, 类别数量：{}".format(class_name, len(list(data_dir.glob('*')))))
total_image = len(list(data_dir.glob('*/*')))
print("图片数据总数: {}".format(total_image))
else:
temp_dict.clear()
train_data_path = directory + '/' + train_dir
train_data_info = recursive_iterate(train_data_path)
print("{}目录:{},{}".format(train_dir, train_data_path, train_data_info))
temp_dict.clear()
test_data_path = directory + '/' + test_dir
print("{}目录:{},{}".format(test_dir, test_data_path, recursive_iterate(test_data_path)))
class_name = temp_dict.keys()
if show:
# 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
for i in class_name:
data = len(list(pathlib.Path((directory + '\\' + i + '\\')).glob('*')))
plt.title('数据分类情况')
plt.grid(ls='--', alpha=0.5)
plt.bar(i, data)
plt.text(i, data, str(data), ha='center', va='bottom')
print("类别-{}:{}".format(i, data))
# temp_sum += data
plt.show()
# if temp_sum == total_image:
# print("图片数据总数检查一致")
# else: # print("数据数据总数检查不一致，请检查数据集是否正确！")
return class_name
def get_transforms_setting(size):
"""
获取transforms的初始设置
:param size: 图片大小
:return: transforms.compose设置
""" transform_setting = {
'train': transforms.Compose([
transforms.Resize(size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'test': transforms.Compose([
transforms.Resize(size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
}
return transform_setting
# 训练循环
def train(dataloader, device, 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, device, model, loss_fn):
size = len(dataloader.dataset) # 测试集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
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
from PIL import Image
def predict_one_image(image_path, device, model, transform, classes):
"""
预测单张图片
:param image_path: 图片路径
:param device: CPU or GPU :param model: cnn模型
:param transform: :param classes: :return:
""" 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}')
2. config.py
import argparse
def get_options(parser=argparse.ArgumentParser()):
parser.add_argument('--workers', type=int, default=0, help='Number of parallel workers')
parser.add_argument('--batch-size', type=int, default=4, help='input batch size, default=32')
parser.add_argument('--size', type=tuple, default=(224, 224), help='input image size')
parser.add_argument('--lr', type=float, default=1e-4, help='learning rate, default=0.0001')
parser.add_argument('--epochs', type=int, default=20, help='number of epochs')
parser.add_argument('--seed', type=int, default=112, help='random seed')
parser.add_argument('--save-path', type=str, default='./models/', help='path to save checkpoints')
opt = parser.parse_args()
if opt:
print(f'num_workers:{opt.workers}')
print(f'batch_size:{opt.batch_size}')
print(f'learn rate:{opt.lr}')
print(f'epochs:{opt.epochs}')
print(f'random seed:{opt.seed}')
print(f'save_path:{opt.save_path}')
return opt
if __name__ == '__main__':
opt = get_options()
3. dataset.py
import os
import torch
from PIL import Image
from torch.utils.data import Dataset
class CaptchaDataset(Dataset):
def __init__(self, data_dir, transform, characters):
self.file_list = list() # 保存每个训练数据的路径
files = os.listdir(data_dir) # 获取data_dir中的全部文件
for file in files:
path = os.path.join(data_dir, file)
self.file_list.append(path)
self.transform = transform # 将数据转换对象保存到类中
# 设置chars等于0-9，表示验证码中可能会出现的字符
# chars = '0123456789'
self.char2int = {} # 创建一个字符到数据的字典
for i, char in enumerate(characters):
self.char2int[char] = i
def __len__(self):
"""
直接返回数据集中样本数量
:return: 数据集中样本数量
""" return len(self.file_list)
def __getitem__(self, index):
"""
传入索引index,得到与该索引对应的数据与标签
:param index: 索引
:return: 数据与标签
""" file_path = self.file_list[index] # 获取数据的路径
# 因为不需要通过颜色来识别字符，因为转换为灰色后，可提升模型的鲁棒性
image = Image.open(file_path).convert('L')
# 使用transform转换数据，将图片数据转换为张量
image = self.transform(image)
# 获取该数据图片的标签
label_char = os.path.basename(file_path).split('_')[0]
label = list()
for char in label_char:
label.append(self.char2int[char]) # 将其中的字符转换为数字
label = torch.tensor(label, dtype=torch.long)
return image, label
import matplotlib.pyplot as plt
def show_image(data, label):
# 将每个小批量数量中的8个图片和对应的标签显示出来
for i in range(len(data)):
plt.subplot(2, 4, i + 1)
plt.imshow(data[i].squeeze())
plt.title(label[i].item())
plt.axis('off')
plt.show()
from torch.utils.data import DataLoader
from torchvision import transforms
if __name__ == '__main__':
transform = transforms.Compose([
transforms.Resize((128, 128)), # 将图片缩放到指定的大小
transforms.ToTensor(), # 将图片数据转换为张量
])
# 定义CapchaDataset对象dataset
dataset = CaptchaDataset('data/captcha/4digits/', transform)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
# 编写一个循环，模板小批量梯度下降迭代时的数据读取
for epoch in range(3):
print("epoch = %d" % epoch)
for batch_idx, (data, label) in enumerate(dataloader):
print("batch_idx = %d, label = %s" % (batch_idx, label))
show_image(data, label)
4.** model.py (P8周的网络模块也在这里)
import warnings
import torch
import torch.nn as nn
# 搭建模型
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)))
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))
class model_K(nn.Module):
def __init__(self):
super(model_K, self).__init__()
# 卷积模块
self.Conv = Conv(3, 32, 3, 2)
# C3模块1
self.C3_1 = C3(32, 64, 3, 2)
# 全连接网络层，用于分类
self.classifier = nn.Sequential(
nn.Linear(in_features=802816, out_features=100),
nn.ReLU(),
nn.Linear(in_features=100, out_features=4)
)
def forward(self, x):
x = self.Conv(x)
x = self.C3_1(x)
x = torch.flatten(x, start_dim=1)
x = self.classifier(x)
return x
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
5. train.py
import os
import torch
from torch import optim, nn
from dataset import WeatherDataset
from model import YOLOv5_backbone
from config import get_options
from Utils import data_from_directory, get_transforms_setting, USE_GPU, train, test
device = USE_GPU()
opt = get_options()
DATA_DIR = "./data/weather_photos"
classNames = data_from_directory(DATA_DIR)
transform = get_transforms_setting(opt.size)
model_save_path = opt.save_path
model_name = 'weather-v5.pth'
model_save_name = model_save_path + '/' + model_name
# 创建模型文件夹
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
total_data= WeatherDataset(DATA_DIR, transform['train'])
train_dataset = total_data.__getds__('train')
test_dataset = total_data.__getds__('test')
train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=opt.batch_size, shuffle=True)
test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=True)
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
# 创建模型对象
model = YOLOv5_backbone().to(device)
print(model)
# 查看模型详情
import torchsummary as summary
summary.summary(model, (3, 224, 224))
# 正式训练
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):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, device, model, loss_fn, optimizer)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, device, 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
torch.save(model.state_dict(), model_save_name)
print('Done')
# 模型训练结果可视化
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
from datetime import datetime
current_time = datetime.now() # 获取当前时间
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.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效
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()
（三）正式训练
E:\dev\AI\Pytorch\.venv\Scripts\python.exe E:\dev\AI\Pytorch\实战\P8&P9-YOLO实现\train.py
CUDA is available, will use GPU
num_workers:0
batch_size:4
learn rate:0.0001
epochs:20
random seed:112
save_path:./models/
数据目录：./data/weather_photos
数据分类: ['cloudy', 'rain', 'shine', 'sunrise'], 类别数量：4
图片数据总数: 1125
Dataset ImageFolder
Number of datapoints: 1125
Root location: ./data/weather_photos
StandardTransform
Transform: Compose(
Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=True)
ToTensor()
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
)
<torch.utils.data.dataset.Subset object at 0x00000189468193A0> <torch.utils.data.dataset.Subset object at 0x0000018946819400>
Shape of X[N, C, H, W]: torch.Size([4, 3, 224, 224])
Shape of y: torch.Size([4]) torch.int64
YOLOv5_backbone(
(Conv_1): Conv(
(conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(Conv_2): Conv(
(conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(C3_3): C3(
(cv1): Conv(
(conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv3): Conv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): Sequential(
(0): Bottleneck(
(cv1): Conv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
)
)
(Conv_4): Conv(
(conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(C3_5): C3(
(cv1): Conv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv3): Conv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): Sequential(
(0): Bottleneck(
(cv1): Conv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
)
)
(Conv_6): Conv(
(conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(C3_7): C3(
(cv1): Conv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv3): Conv(
(conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): Sequential(
(0): Bottleneck(
(cv1): Conv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
)
)
(Conv_8): Conv(
(conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(C3_9): C3(
(cv1): Conv(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv3): Conv(
(conv): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): Sequential(
(0): Bottleneck(
(cv1): Conv(
(conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
)
)
)
(SPPF): SPPF(
(cv1): Conv(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(cv2): Conv(
(conv): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): SiLU()
)
(m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Linear(in_features=65536, out_features=100, bias=True)
(1): ReLU()
(2): Linear(in_features=100, out_features=4, bias=True)
)
)
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 113, 113] 1,728
BatchNorm2d-2 [-1, 64, 113, 113] 128
SiLU-3 [-1, 64, 113, 113] 0
Conv-4 [-1, 64, 113, 113] 0
Conv2d-5 [-1, 128, 57, 57] 73,728
BatchNorm2d-6 [-1, 128, 57, 57] 256
SiLU-7 [-1, 128, 57, 57] 0
Conv-8 [-1, 128, 57, 57] 0
Conv2d-9 [-1, 64, 57, 57] 8,192
BatchNorm2d-10 [-1, 64, 57, 57] 128
SiLU-11 [-1, 64, 57, 57] 0
Conv-12 [-1, 64, 57, 57] 0
Conv2d-13 [-1, 64, 57, 57] 4,096
BatchNorm2d-14 [-1, 64, 57, 57] 128
SiLU-15 [-1, 64, 57, 57] 0
Conv-16 [-1, 64, 57, 57] 0
Conv2d-17 [-1, 64, 57, 57] 36,864
BatchNorm2d-18 [-1, 64, 57, 57] 128
SiLU-19 [-1, 64, 57, 57] 0
Conv-20 [-1, 64, 57, 57] 0
Bottleneck-21 [-1, 64, 57, 57] 0
Conv2d-22 [-1, 64, 57, 57] 8,192
BatchNorm2d-23 [-1, 64, 57, 57] 128
SiLU-24 [-1, 64, 57, 57] 0
Conv-25 [-1, 64, 57, 57] 0
Conv2d-26 [-1, 128, 57, 57] 16,384
BatchNorm2d-27 [-1, 128, 57, 57] 256
SiLU-28 [-1, 128, 57, 57] 0
Conv-29 [-1, 128, 57, 57] 0
C3-30 [-1, 128, 57, 57] 0
Conv2d-31 [-1, 256, 29, 29] 294,912
BatchNorm2d-32 [-1, 256, 29, 29] 512
SiLU-33 [-1, 256, 29, 29] 0
Conv-34 [-1, 256, 29, 29] 0
Conv2d-35 [-1, 128, 29, 29] 32,768
BatchNorm2d-36 [-1, 128, 29, 29] 256
SiLU-37 [-1, 128, 29, 29] 0
Conv-38 [-1, 128, 29, 29] 0
Conv2d-39 [-1, 128, 29, 29] 16,384
BatchNorm2d-40 [-1, 128, 29, 29] 256
SiLU-41 [-1, 128, 29, 29] 0
Conv-42 [-1, 128, 29, 29] 0
Conv2d-43 [-1, 128, 29, 29] 147,456
BatchNorm2d-44 [-1, 128, 29, 29] 256
SiLU-45 [-1, 128, 29, 29] 0
Conv-46 [-1, 128, 29, 29] 0
Bottleneck-47 [-1, 128, 29, 29] 0
Conv2d-48 [-1, 128, 29, 29] 32,768
BatchNorm2d-49 [-1, 128, 29, 29] 256
SiLU-50 [-1, 128, 29, 29] 0
Conv-51 [-1, 128, 29, 29] 0
Conv2d-52 [-1, 256, 29, 29] 65,536
BatchNorm2d-53 [-1, 256, 29, 29] 512
SiLU-54 [-1, 256, 29, 29] 0
Conv-55 [-1, 256, 29, 29] 0
C3-56 [-1, 256, 29, 29] 0
Conv2d-57 [-1, 512, 15, 15] 1,179,648
BatchNorm2d-58 [-1, 512, 15, 15] 1,024
SiLU-59 [-1, 512, 15, 15] 0
Conv-60 [-1, 512, 15, 15] 0
Conv2d-61 [-1, 256, 15, 15] 131,072
BatchNorm2d-62 [-1, 256, 15, 15] 512
SiLU-63 [-1, 256, 15, 15] 0
Conv-64 [-1, 256, 15, 15] 0
Conv2d-65 [-1, 256, 15, 15] 65,536
BatchNorm2d-66 [-1, 256, 15, 15] 512
SiLU-67 [-1, 256, 15, 15] 0
Conv-68 [-1, 256, 15, 15] 0
Conv2d-69 [-1, 256, 15, 15] 589,824
BatchNorm2d-70 [-1, 256, 15, 15] 512
SiLU-71 [-1, 256, 15, 15] 0
Conv-72 [-1, 256, 15, 15] 0
Bottleneck-73 [-1, 256, 15, 15] 0
Conv2d-74 [-1, 256, 15, 15] 131,072
BatchNorm2d-75 [-1, 256, 15, 15] 512
SiLU-76 [-1, 256, 15, 15] 0
Conv-77 [-1, 256, 15, 15] 0
Conv2d-78 [-1, 512, 15, 15] 262,144
BatchNorm2d-79 [-1, 512, 15, 15] 1,024
SiLU-80 [-1, 512, 15, 15] 0
Conv-81 [-1, 512, 15, 15] 0
C3-82 [-1, 512, 15, 15] 0
Conv2d-83 [-1, 1024, 8, 8] 4,718,592
BatchNorm2d-84 [-1, 1024, 8, 8] 2,048
SiLU-85 [-1, 1024, 8, 8] 0
Conv-86 [-1, 1024, 8, 8] 0
Conv2d-87 [-1, 512, 8, 8] 524,288
BatchNorm2d-88 [-1, 512, 8, 8] 1,024
SiLU-89 [-1, 512, 8, 8] 0
Conv-90 [-1, 512, 8, 8] 0
Conv2d-91 [-1, 512, 8, 8] 262,144
BatchNorm2d-92 [-1, 512, 8, 8] 1,024
SiLU-93 [-1, 512, 8, 8] 0
Conv-94 [-1, 512, 8, 8] 0
Conv2d-95 [-1, 512, 8, 8] 2,359,296
BatchNorm2d-96 [-1, 512, 8, 8] 1,024
SiLU-97 [-1, 512, 8, 8] 0
Conv-98 [-1, 512, 8, 8] 0
Bottleneck-99 [-1, 512, 8, 8] 0
Conv2d-100 [-1, 512, 8, 8] 524,288
BatchNorm2d-101 [-1, 512, 8, 8] 1,024
SiLU-102 [-1, 512, 8, 8] 0
Conv-103 [-1, 512, 8, 8] 0
Conv2d-104 [-1, 1024, 8, 8] 1,048,576
BatchNorm2d-105 [-1, 1024, 8, 8] 2,048
SiLU-106 [-1, 1024, 8, 8] 0
Conv-107 [-1, 1024, 8, 8] 0
C3-108 [-1, 1024, 8, 8] 0
Conv2d-109 [-1, 512, 8, 8] 524,288
BatchNorm2d-110 [-1, 512, 8, 8] 1,024
SiLU-111 [-1, 512, 8, 8] 0
Conv-112 [-1, 512, 8, 8] 0
MaxPool2d-113 [-1, 512, 8, 8] 0
MaxPool2d-114 [-1, 512, 8, 8] 0
MaxPool2d-115 [-1, 512, 8, 8] 0
Conv2d-116 [-1, 1024, 8, 8] 2,097,152
BatchNorm2d-117 [-1, 1024, 8, 8] 2,048
SiLU-118 [-1, 1024, 8, 8] 0
Conv-119 [-1, 1024, 8, 8] 0
SPPF-120 [-1, 1024, 8, 8] 0
Linear-121 [-1, 100] 6,553,700
ReLU-122 [-1, 100] 0
Linear-123 [-1, 4] 404
================================================================
Total params: 21,729,592
Trainable params: 21,729,592
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 137.59
Params size (MB): 82.89
Estimated Total Size (MB): 221.06
----------------------------------------------------------------
Epoch: 1, Train_acc:55.0%, Train_loss:1.114, Test_acc:62.7%, Test_loss:1.046, Lr:1.00E-04
Epoch: 2, Train_acc:66.0%, Train_loss:0.803, Test_acc:66.2%, Test_loss:0.785, Lr:1.00E-04
Epoch: 3, Train_acc:69.2%, Train_loss:0.712, Test_acc:63.6%, Test_loss:0.883, Lr:1.00E-04
Epoch: 4, Train_acc:71.7%, Train_loss:0.722, Test_acc:76.0%, Test_loss:0.598, Lr:1.00E-04
Epoch: 5, Train_acc:74.8%, Train_loss:0.657, Test_acc:74.2%, Test_loss:0.610, Lr:1.00E-04
Epoch: 6, Train_acc:80.8%, Train_loss:0.499, Test_acc:78.7%, Test_loss:0.472, Lr:1.00E-04
Epoch: 7, Train_acc:84.3%, Train_loss:0.399, Test_acc:87.1%, Test_loss:0.360, Lr:1.00E-04
Epoch: 8, Train_acc:86.1%, Train_loss:0.343, Test_acc:86.2%, Test_loss:0.340, Lr:1.00E-04
Epoch: 9, Train_acc:88.3%, Train_loss:0.317, Test_acc:82.2%, Test_loss:0.479, Lr:1.00E-04
Epoch:10, Train_acc:88.4%, Train_loss:0.308, Test_acc:84.4%, Test_loss:0.368, Lr:1.00E-04
Epoch:11, Train_acc:90.0%, Train_loss:0.260, Test_acc:83.1%, Test_loss:0.392, Lr:1.00E-04
Epoch:12, Train_acc:89.3%, Train_loss:0.288, Test_acc:84.9%, Test_loss:0.349, Lr:1.00E-04
Epoch:13, Train_acc:89.6%, Train_loss:0.301, Test_acc:85.3%, Test_loss:0.440, Lr:1.00E-04
Epoch:14, Train_acc:91.0%, Train_loss:0.216, Test_acc:89.8%, Test_loss:0.344, Lr:1.00E-04
Epoch:15, Train_acc:93.2%, Train_loss:0.170, Test_acc:92.4%, Test_loss:0.211, Lr:1.00E-04
Epoch:16, Train_acc:94.8%, Train_loss:0.165, Test_acc:86.7%, Test_loss:0.371, Lr:1.00E-04
Epoch:17, Train_acc:92.9%, Train_loss:0.191, Test_acc:92.0%, Test_loss:0.252, Lr:1.00E-04
Epoch:18, Train_acc:96.1%, Train_loss:0.111, Test_acc:85.3%, Test_loss:0.388, Lr:1.00E-04
Epoch:19, Train_acc:94.1%, Train_loss:0.175, Test_acc:83.6%, Test_loss:0.396, Lr:1.00E-04
Epoch:20, Train_acc:95.7%, Train_loss:0.116, Test_acc:90.2%, Test_loss:0.292, Lr:1.00E-04
Done
进程已结束，退出代码为 0
