forked from ailab/resnet-dogcat
151 lines
4.7 KiB
Python
151 lines
4.7 KiB
Python
import os
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
from torch.utils.data import DataLoader, Dataset
|
|
from torchvision import models, transforms
|
|
from PIL import Image
|
|
from safetensors.torch import save_file
|
|
|
|
class CatDogDataset(Dataset):
|
|
def __init__(self, root_dir, transform=None):
|
|
self.root_dir = root_dir
|
|
self.transform = transform
|
|
self.image_paths = []
|
|
self.labels = []
|
|
|
|
for filename in os.listdir(root_dir):
|
|
if 'cat' in filename:
|
|
self.image_paths.append(os.path.join(root_dir, filename))
|
|
self.labels.append(0) # cat
|
|
elif 'dog' in filename:
|
|
self.image_paths.append(os.path.join(root_dir, filename))
|
|
self.labels.append(1) # dog
|
|
|
|
def __len__(self):
|
|
return len(self.image_paths)
|
|
|
|
def __getitem__(self, idx):
|
|
img_path = self.image_paths[idx]
|
|
image = Image.open(img_path).convert('RGB')
|
|
label = self.labels[idx]
|
|
|
|
if self.transform:
|
|
image = self.transform(image)
|
|
|
|
return image, label
|
|
|
|
# 数据预处理
|
|
data_transforms = {
|
|
'train': transforms.Compose([
|
|
transforms.RandomResizedCrop(224),
|
|
transforms.RandomHorizontalFlip(),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
|
]),
|
|
'val': transforms.Compose([
|
|
transforms.Resize(256),
|
|
transforms.CenterCrop(224),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
|
]),
|
|
}
|
|
|
|
data_dir = 'dog-cat'
|
|
image_datasets = {x: CatDogDataset(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
|
|
dataloaders = {x: DataLoader(image_datasets[x], batch_size=32, shuffle=True, num_workers=4) for x in ['train', 'val']}
|
|
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
|
|
class_names = ['cat', 'dog']
|
|
|
|
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
|
|
|
# 加载预训练的 ResNet-50 模型并进行微调
|
|
model_ft = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
|
|
num_ftrs = model_ft.fc.in_features
|
|
model_ft.fc = nn.Linear(num_ftrs, len(class_names))
|
|
|
|
model_ft = model_ft.to(device)
|
|
|
|
criterion = nn.CrossEntropyLoss()
|
|
|
|
# 优化器
|
|
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
|
|
|
|
# 学习率调度器
|
|
exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
|
|
|
|
# 训练模型
|
|
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
|
|
best_model_wts = model.state_dict()
|
|
best_acc = 0.0
|
|
|
|
for epoch in range(num_epochs):
|
|
print(f'Epoch {epoch}/{num_epochs - 1}')
|
|
print('-' * 10)
|
|
|
|
# 每个epoch都有训练和验证阶段
|
|
for phase in ['train', 'val']:
|
|
if phase == 'train':
|
|
model.train() # 设置模型为训练模式
|
|
else:
|
|
model.eval() # 设置模型为评估模式
|
|
|
|
running_loss = 0.0
|
|
running_corrects = 0
|
|
|
|
# 遍历数据
|
|
for inputs, labels in dataloaders[phase]:
|
|
inputs = inputs.to(device)
|
|
labels = labels.to(device)
|
|
|
|
# 清零参数梯度
|
|
optimizer.zero_grad()
|
|
|
|
# 前向传播
|
|
with torch.set_grad_enabled(phase == 'train'):
|
|
outputs = model(inputs)
|
|
_, preds = torch.max(outputs, 1)
|
|
loss = criterion(outputs, labels)
|
|
|
|
# 只有在训练阶段才反向传播+优化
|
|
if phase == 'train':
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
# 统计
|
|
running_loss += loss.item() * inputs.size(0)
|
|
running_corrects += torch.sum(preds == labels.data)
|
|
|
|
if phase == 'train':
|
|
scheduler.step()
|
|
|
|
epoch_loss = running_loss / dataset_sizes[phase]
|
|
epoch_acc = running_corrects.double() / dataset_sizes[phase]
|
|
|
|
print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
|
|
|
|
# 深拷贝模型
|
|
if phase == 'val' and epoch_acc > best_acc:
|
|
best_acc = epoch_acc
|
|
best_model_wts = model.state_dict()
|
|
|
|
print()
|
|
|
|
print(f'Best val Acc: {best_acc:4f}')
|
|
|
|
# 加载最佳模型权重
|
|
model.load_state_dict(best_model_wts)
|
|
return model
|
|
|
|
# 训练和评估模型
|
|
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)
|
|
|
|
# 保存模型为 .pt 文件
|
|
torch.save(model_ft.state_dict(), 'pytorch_model.bin')
|
|
|
|
metadata = {"format": "pt"}
|
|
|
|
# 保存模型为 .safetensors 文件
|
|
save_file(model_ft.state_dict(), 'model.safetensors', metadata=metadata)
|
|
|