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| import numpy as np
from torch.utils.data import DataLoader
from torchutils import *
from torchvision import datasets
import os.path as osp
import os
import timm
import torch.nn as nn
import matplotlib.pyplot as plt
from tqdm import tqdm
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
print(f'Using device: {device}')
seed = 42
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
data_path = "data/split"
params = {
'model': 'resnet50d',
"img_size": 224,
"train_dir": osp.join(data_path, "train"),
"val_dir": osp.join(data_path, "val"),
'device': device,
'lr': 1e-3,
'batch_size': 4,
'num_workers': 0,
'epochs': 10,
"save_dir": "checkpoints",
"pretrained": True,
"num_classes": len(os.listdir(osp.join(data_path, "train"))),
'weight_decay': 1e-5
}
class SELFMODEL(nn.Module):
def __init__(self, model_name=params['model'], out_features=params['num_classes'],
pretrained=True):
super().__init__()
self.model = timm.create_model(model_name, pretrained=pretrained)
if model_name[:3] == "res":
n_features = self.model.fc.in_features
self.model.fc = nn.Linear(n_features, out_features)
elif model_name[:3] == "vit":
n_features = self.model.head.in_features
self.model.head = nn.Linear(n_features, out_features)
else:
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, out_features)
print(self.model)
def forward(self, x):
x = self.model(x)
return x
def train(train_loader, model, criterion, optimizer, epoch, params):
metric_monitor = MetricMonitor()
model.train()
nBatch = len(train_loader)
stream = tqdm(train_loader)
for i, (images, target) in enumerate(stream, start=1):
images = images.to(params['device'], non_blocking=True)
target = target.to(params['device'], non_blocking=True)
output = model(images)
loss = criterion(output, target.long())
f1_macro = calculate_f1_macro(output, target)
recall_macro = calculate_recall_macro(output, target)
acc = accuracy(output, target)
metric_monitor.update('Loss', loss.item())
metric_monitor.update('F1', f1_macro)
metric_monitor.update('Recall', recall_macro)
metric_monitor.update('Accuracy', acc)
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr = adjust_learning_rate(optimizer, epoch, params, i, nBatch)
stream.set_description(
"Epoch: {epoch}. Train. {metric_monitor}".format(
epoch=epoch,
metric_monitor=metric_monitor)
)
return metric_monitor.metrics['Accuracy']["avg"], metric_monitor.metrics['Loss']["avg"]
def validate(val_loader, model, criterion, epoch, params):
metric_monitor = MetricMonitor()
model.eval()
stream = tqdm(val_loader)
with torch.no_grad():
for i, (images, target) in enumerate(stream, start=1):
images = images.to(params['device'], non_blocking=True)
target = target.to(params['device'], non_blocking=True)
output = model(images)
loss = criterion(output, target.long())
f1_macro = calculate_f1_macro(output, target)
recall_macro = calculate_recall_macro(output, target)
acc = accuracy(output, target)
metric_monitor.update('Loss', loss.item())
metric_monitor.update('F1', f1_macro)
metric_monitor.update("Recall", recall_macro)
metric_monitor.update('Accuracy', acc)
stream.set_description(
"Epoch: {epoch}. Validation. {metric_monitor}".format(
epoch=epoch,
metric_monitor=metric_monitor)
)
return metric_monitor.metrics['Accuracy']["avg"], metric_monitor.metrics['Loss']["avg"]
def show_loss_acc(acc, loss, val_acc, val_loss, sava_dir):
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.ylabel('Accuracy')
plt.ylim([min(plt.ylim()), 1])
plt.title('Training and Validation Accuracy')
plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.ylabel('Cross Entropy')
plt.title('Training and Validation Loss')
plt.xlabel('epoch')
save_path = osp.join(save_dir, "results.png")
plt.savefig(save_path, dpi=100)
if __name__ == '__main__':
accs = []
losss = []
val_accs = []
val_losss = []
data_transforms = get_torch_transforms(img_size=params["img_size"])
train_transforms = data_transforms['train']
valid_transforms = data_transforms['val']
train_dataset = datasets.ImageFolder(params["train_dir"], train_transforms)
valid_dataset = datasets.ImageFolder(params["val_dir"], valid_transforms)
if params['pretrained'] == True:
save_dir = osp.join(params['save_dir'], params['model'] + "_pretrained_" + str(params["img_size"]))
else:
save_dir = osp.join(params['save_dir'],
params['model'] + "_nopretrained_" + str(params["img_size"]))
if not osp.isdir(save_dir):
os.makedirs(save_dir)
print("save dir {} created".format(save_dir))
train_loader = DataLoader(
train_dataset, batch_size=params['batch_size'], shuffle=True,
num_workers=params['num_workers'], pin_memory=True,
)
val_loader = DataLoader(
valid_dataset, batch_size=params['batch_size'], shuffle=False,
num_workers=params['num_workers'], pin_memory=True,
)
print(train_dataset.classes)
model = SELFMODEL(model_name=params['model'], out_features=params['num_classes'],
pretrained=params['pretrained'])
model = model.to(params['device'])
criterion = nn.CrossEntropyLoss().to(params['device'])
optimizer = torch.optim.AdamW(model.parameters(), lr=params['lr'], weight_decay=params['weight_decay'])
best_acc = 0.0
for epoch in range(1, params['epochs'] + 1):
acc, loss = train(train_loader, model, criterion, optimizer, epoch, params)
val_acc, val_loss = validate(val_loader, model, criterion, epoch, params)
accs.append(acc)
losss.append(loss)
val_accs.append(val_acc)
val_losss.append(val_loss)
if val_acc >= best_acc:
save_path = osp.join(save_dir, f"{params['model']}_{epoch}epochs_accuracy{acc:.5f}_weights.pth")
torch.save(model.state_dict(), save_path)
best_acc = val_acc
show_loss_acc(accs, losss, val_accs, val_losss, save_dir)
print("训练已完成，模型和训练日志保存在: {}".format(save_dir))
|
