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| import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
import shutil
from torch.utils.tensorboard import SummaryWriter
def set_seed(seed=42):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
set_seed()
original_data_dir = 'rice_images'
data_dir = 'data'
def copy_data(original_dir, target_dir, train_ratio=0.8):
if os.path.exists(target_dir):
shutil.rmtree(target_dir)
os.makedirs(target_dir)
os.makedirs(os.path.join(target_dir, 'train'), exist_ok=True)
os.makedirs(os.path.join(target_dir, 'val'), exist_ok=True)
for class_name in os.listdir(original_dir):
class_dir = os.path.join(original_dir, class_name)
if os.path.isdir(class_dir):
os.makedirs(os.path.join(target_dir, 'train', class_name), exist_ok=True)
os.makedirs(os.path.join(target_dir, 'val', class_name), exist_ok=True)
images = [f for f in os.listdir(class_dir) if os.path.isfile(os.path.join(class_dir, f)) and f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif'))]
num_images = len(images)
num_train = int(train_ratio * num_images)
print(f"(✪ω✪) 正在处理类别 '{class_name}'，共有 {num_images} 张图片，将复制 {num_train} 张到训练集...")
np.random.shuffle(images)
train_images = images[:num_train]
val_images = images[num_train:]
for i, image in enumerate(train_images):
src = os.path.join(class_dir, image)
dst = os.path.join(target_dir, 'train', class_name, image)
shutil.copyfile(src, dst)
if (i + 1) % 100 == 0:
print(f" 已复制 {i + 1}/{num_train} 张训练图片...")
for i, image in enumerate(val_images):
src = os.path.join(class_dir, image)
dst = os.path.join(target_dir, 'val', class_name, image)
shutil.copyfile(src, dst)
if (i + 1) % 50 == 0:
print(f" 已复制 {i + 1}/{len(val_images)} 张验证图片...")
if not os.path.exists(data_dir) or not os.listdir(data_dir):
print("(｡♥‿♥｡) 正在从 'rice_images' 复制数据到 'data'...")
copy_data(original_data_dir, data_dir)
else:
print(" (´｡• ω •｡`) 'data' 文件夹已存在且不为空，跳过数据复制…")
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomRotation(degrees=30),
transforms.RandomAffine(degrees=10, translate=(0.1, 0.1), scale=(0.8, 1.2), shear=5),
transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
transforms.RandomPerspective(distortion_scale=0.3, p=0.5),
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])
]),
}
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=16, shuffle=True, num_workers=0) for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
if torch.cuda.is_available():
device = torch.device("cuda:0")
print(f"(✪ω✪) 正在使用 GPU: {torch.cuda.get_device_name(0)} 进行训练！")
if torch.cuda.get_device_capability(device)[0] < 7:
print(" (´･ω･`) GPU 不支持 FP16，将不会进行混合精度运算")
else:
device = torch.device("cpu")
print("(´｡• ω •｡`) 没有可用的 GPU，将在 CPU 上训练…呜喵")
print(f"(๑•̀ㅂ•́)و✧ 训练集大小: {dataset_sizes['train']}, 验证集大小: {dataset_sizes['val']}")
print("类别:", class_names)
def train_model(model, criterion, optimizer, scheduler, num_epochs=25, start_epoch=0, best_acc=0.0, best_model_wts=None):
since = time.time()
if best_model_wts is not None:
model.load_state_dict(best_model_wts)
writer = SummaryWriter('runs/rice_experiment')
checkpoint_path = 'checkpoints/last_checkpoint.pth'
if os.path.exists(checkpoint_path):
print(f"(๑•̀ㅂ•́)و✧ 正在从检查点恢复: {checkpoint_path}")
checkpoint = torch.load(checkpoint_path)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
best_acc = checkpoint['best_acc']
best_model_wts = model.state_dict()
print(f"已从第 {start_epoch} 个 epoch 恢复, 当前最佳准确率: {best_acc:.4f}")
else:
print("(´･ω･`) 未找到检查点, 从头开始训练.")
best_model_wts = copy.deepcopy(model.state_dict())
for epoch in range(start_epoch, num_epochs):
print(f'Epoch {epoch + 1}/{num_epochs}')
print('-' * 10)
for phase in ['train', 'val']:
if phase == 'train':
print('(ง •̀_•́)ง 开始训练...')
model.train()
else:
print('(✪ω✪) 开始验证...')
model.eval()
running_loss = 0.0
running_corrects = 0
batch_num = 0
for inputs, labels in dataloaders[phase]:
batch_num += 1
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
print(f' [{phase}] Batch {batch_num}/{len(dataloaders[phase])}, Loss: {loss.item():.4f}, Acc: {torch.sum(preds == labels.data).double() / inputs.size(0):.4f}')
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}')
writer.add_scalar(f'Loss/{phase}', epoch_loss, epoch)
writer.add_scalar(f'Accuracy/{phase}', epoch_acc, epoch)
if phase == 'train':
scheduler.step()
if phase == 'val':
if epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print(f"(｡･∀･)ﾉﾞ 新的最佳验证准确率: {best_acc:.4f}, 正在保存最佳检查点...")
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
torch.save({
'epoch': epoch + 1,
'model_state_dict': best_model_wts,
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
'best_acc': best_acc,
}, 'checkpoints/best_checkpoint.pth')
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
torch.save({
'epoch': epoch + 1,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
'best_acc': best_acc,
}, 'checkpoints/last_checkpoint.pth')
print('检查点已保存在 checkpoints/last_checkpoint.pth！')
time_elapsed = time.time() - since
print(f'(｡･∀･)ﾉﾞ训练完成，用时 {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
print(f'历史最佳验证准确率: {best_acc:4f}')
writer.close()
if best_model_wts:
model.load_state_dict(best_model_wts)
return model
print('( •̀ ω •́ )y 加载预训练的 ResNet50 模型...')
model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
for param in model.parameters():
param.requires_grad = True
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, len(class_names))
model = model.to(device)
criterion = nn.CrossEntropyLoss()
learning_rate = 1e-4
optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=1e-4)
scheduler = lr_scheduler.CosineAnnealingWarmRestarts(
optimizer,
T_0=10,
T_mult=2,
eta_min=learning_rate / 100
)
num_epochs = 100
start_epoch = 0
best_acc = 0.0
print('(≧∇≦)/ 开始训练！')
model_ft = train_model(model, criterion, optimizer, scheduler, num_epochs=num_epochs, start_epoch=start_epoch, best_acc=best_acc)
torch.save(model_ft.state_dict(), 'final_model.pth')
print('训练结束，最终模型已保存！')
|
