def main():
model = Net()
if torch.cuda.is_available():
model.cuda()
else:
pass
model.apply(weights_init)
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'"
.format(args.resume))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# 数据处理
# 直接在train里面处理
# dataParser = DataParser(batch_size)
loss_function = nn.L1Loss()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
# train_scheduler = optim.lr_scheduler.MultiStepLR(optimizer,milestones=settings.MILESTONES,gamma=0.2)#learning rate decay
scheduler = lr_scheduler.StepLR(optimizer, step_size=args.stepsize, gamma=args.gamma)
log = Logger(join(TMP_DIR, '%s-%d-log.txt' % ('Adam', args.lr)))
sys.stdout = log
train_loss = []
train_loss_detail = []
for epoch in range(args.start_epoch, args.maxepoch):
if epoch == 0:
print("Performing initial testing...")
# 暂时空着
tr_avg_loss, tr_detail_loss = train(model = model,optimizer = optimizer,epoch= epoch,save_dir=join(TMP_DIR, 'epoch-%d-training-record' % epoch))
test()
log.flush()
# Save checkpoint
save_file = os.path.join(TMP_DIR, 'checkpoint_epoch{}.pth'.format(epoch))
save_checkpoint({'epoch': epoch, 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict()})
scheduler.step() # 自动调整学习率
train_loss.append(tr_avg_loss)
train_loss_detail += tr_detail_loss
import torchvision.datasets as dsets
from pathlib import Path
import os
from tqdm import tqdm
import preprocessing
torch.cuda.set_device(0) #使用 GPU
EVAL_DATA_PATH = Path('./data/eval') #验证集路径
TRAIN_DATA_PATH = Path('./data/train') #训练集路径
BATCH_SIZE = 64 #批大小
from model import model
model = model.cuda()
print(model)
#定义训练和验证集 和 dataloader
eval_dataset = dsets.ImageFolder(EVAL_DATA_PATH,
transform=preprocessing.transform)
train_dataset = dsets.ImageFolder(TRAIN_DATA_PATH,
transform=preprocessing.transform)
eval_dataloader = DataLoader(eval_dataset, batch_size=BATCH_SIZE, shuffle=True)
train_dataloader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
#使用交叉熵为 loss，使用 SGD 优化方法
criterion = NN.CrossEntropyLoss()
model = model(name=name)
'''
for name, child in model.named_children():
if name in ['fc','layer4','layer3']:
print(name + ' is unfrozen')
for param in child.parameters():
param.requires_grad = True
else:
print(name + ' is frozen')
for param in child.parameters():
param.requires_grad = False
'''
if use_cuda:
print('Using GPU')
model.cuda()
else:
print('Using CPU')
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
#best_acc = 0
for epoch in range(1, epoch + 1):
train(epoch)
validation()
##学習
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)
num_epochs = 1
#GPUのキャッシュクリア
import torch
torch.cuda.empty_cache()
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.cuda()##
#既存手法
#engin.pyを参考にしてる
model.train()#学習モードに移行
for epoch in range(num_epochs):
#model.train()#これから学習しますよ
for i, batch in enumerate(train_dataloader):
