def main():
start_epoch = args.start_epoch # start from epoch 0 or last checkpoint epoch
# Data
print('==> Preparing dataset %s' % args.dataset)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
if args.dataset == 'cifar10':
dataloader = datasets.CIFAR10
num_classes = 10
else:
dataloader = datasets.CIFAR100
num_classes = 100
trainset = dataloader(root=args.dataroot, train=True, download=True, transform=transform_train)
sampler = torch.utils.data.distributed.DistributedSampler(trainset,num_replicas=hvd.size(), rank=hvd.rank())
trainloader = data.DataLoader(dataset=trainset, batch_size=args.train_batch * world_size, shuffle=False, sampler=sampler)
testset = dataloader(root=args.dataroot, train=False, download=False, transform=transform_test)
testloader = data.DataLoader(testset, batch_size=args.test_batch * world_size, shuffle=False, num_workers=args.workers)
# Model
print("==> creating model '{}'".format("Alexnet"))
model = AlexNet(num_classes=num_classes)
device = torch.device('cuda', local_rank)
model = model.to(device)
# model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank)
print('Model on cuda:%d' % local_rank)
print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# 用horovod封装优化器
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
# 广播参数
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
# Train and val
for epoch in range(start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
train_loss, train_acc = train(trainloader, model, criterion, optimizer, epoch, use_cuda)
test_loss, test_acc = test(testloader, model, criterion, epoch, use_cuda)
print('Rank:{} Epoch[{}/{}]: LR: {:.3f}, Train loss: {:.5f}, Test loss: {:.5f}, Train acc: {:.2f}, Test acc: {:.2f}.'.format(local_rank,epoch+1, args.epochs, state['lr'],
train_loss, test_loss, train_acc, test_acc))
def train(pertrained=False, resume_file=None):
if pertrained:
from model import alexnet
net = alexnet(pretrained=True, num_classes=NUMBER_CLASSES)
else:
from model import AlexNet
net = AlexNet(num_classes=NUMBER_CLASSES)
valid_precision = 0
policies = net.parameters()
optimizer = optim.SGD(policies,
lr=LR,
momentum=MOMENTUM,
weight_decay=WEIGHT_DECAY)
train_log = open(
"logs/train_logs_{}.log".format(
time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())), "w")
valid_log = open(
"logs/valid_logs_{}.log".format(
time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())), "w")
train_log.write("{}\t{}\t{}\n".format("epoch", "losses ", "correct"))
valid_log.write("{}\t{}\t{}\n".format("epoch", "losses ", "correct"))
# 恢复训练
if resume_file:
if os.path.isfile(resume_file):
print(("=> loading checkpoint '{}'".format(resume_file)))
checkpoint = torch.load(resume_file)
start_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['model_state_dict'])
print(("=> loaded checkpoint '{}' (epoch {})".format(
resume_file, checkpoint['epoch'])))
else:
start_epoch = 0
print(("=> no checkpoint found at '{}'".format(resume_file)))
# valid_precision = valid(net)
for epoch in range(start_epoch, EPOCHES):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
correct = AverageMeter()
end = time.time()
optimizer = adjust_learning_rate(optimizer, epoch, LR, LR_steps,
WEIGHT_DECAY)
for i_batch, sample_batched in enumerate(train_dataloader):
# measure data loading time
data_time.update(time.time() - end)
inputs, labels = sample_batched
if CUDA_AVALIABLE:
outputs = net.forward(inputs.cuda())
labels = labels.long().flatten().cuda()
else:
outputs = net.forward(inputs)
labels = labels.long().flatten()
outputs = outputs.reshape([-1, NUMBER_CLASSES])
loss = criterion(outputs, labels)
# 更新统计数据
losses.update(loss.item(), inputs.size(0))
_, predicted = torch.max(outputs.data, 1)
# 计算准确率
correct.update(
(predicted == labels.long()).sum().item() / len(labels),
inputs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i_batch % 10 == 0:
print(('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'.format(
epoch,
i_batch,
len(train_dataloader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=correct,
lr=optimizer.param_groups[-1]['lr'])))
train_log.write("{:5d}\t{:.5f}\t{:.5f}\n".format(
epoch, losses.avg, correct.avg))
train_log.flush()
if epoch % 1 == 0:
valid_precision = valid(net, epoch, valid_log)
# 保存网络
if (epoch > 0 and epoch % 10 == 0) or epoch == EPOCHES - 1:
save_path = os.path.join(
"models",
"{:d}_{}_{:d}_{:d}_{:.5f}.pt".format(int(time.time()),
"alexnet", epoch,
BATCHSIZE,
valid_precision))
print("[INFO] Save weights to " + save_path)
torch.save(
{
'epoch': epoch,
'model_state_dict': net.state_dict(),
'optimizer_state_dir': optimizer.state_dict,
'loss': loss
}, save_path)
train_log.close()
valid_log.close()
loss = loss_function(outputs, labels)
running_loss += loss
loss.backward()
optimizer.step()
rate = index / train_data_loader.__len__()
a = "*" * int(rate * 50)
b = "." * int((1 - rate) * 50)
print("\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(
int(rate * 100), a, b, loss),
end="")
print("\n time.perf_counter()-t1")
model.eval()
acc = 0.0
with torch.no_grad():
for data in valid_data_loader:
imgs, labels = data
outputs = model(imgs)
acc += (torch.max(outputs, dim=1)[1] == labels).sum().item()
acc = acc / valid_data_loader.dataset.__len__()
if acc > best_acc:
best_acc = acc
print("Saving Model")
torch.save(model.state_dict(), 'AlexNet_weights.pth')
torch.save(model, 'AlexNet.pth')
print('[epoch %d] train_loss: %.3f test_accuracy: %.3f' %
(epoch + 1, running_loss, acc))
print('Finished Training')
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #指定设备
print("using {} device.".format(device))
data_transform = { #数据预处理
"train": transforms.Compose([transforms.RandomResizedCrop(224),# key 为trian 返回这些方法 随机裁剪 224*224
transforms.RandomHorizontalFlip(),#随机反转
transforms.ToTensor(),#转成
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),#标准化处理
"val": transforms.Compose([transforms.Resize((224, 224)), # cannot 224, must (224, 224)
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}
data_root = os.path.abspath(os.path.join(os.getcwd(), "../..")) # get data root path
image_path = os.path.join(data_root, "data_set", "dog_data") # flower data set path
assert os.path.exists(image_path), "{} path does not exist.".format(image_path)
train_dataset = datasets.ImageFolder(root=os.path.join(image_path, "train"),
transform=data_transform["train"])#数据预处理
train_num = len(train_dataset) #个数
# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx #获取名称所对应索引
cla_dict = dict((val, key) for key, val in flower_list.items()) #遍历 key value 对调
# write dict into json file
json_str = json.dumps(cla_dict, indent=4)
with open('class_indices.json', 'w') as json_file:#生成json 便于打开
json_file.write(json_str)
batch_size = 32
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
print('Using {} dataloader workers every process'.format(nw))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size, shuffle=True,
num_workers=nw) #加载
validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"),
transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,
batch_size=4, shuffle=False,
num_workers=nw)
print("using {} images for training, {} images fot validation.".format(train_num,
val_num))
# test_data_iter = iter(validate_loader)
# test_image, test_label = test_data_iter.next()
#
# def imshow(img):
# img = img / 2 + 0.5 # unnormalize
# npimg = img.numpy()
# plt.imshow(np.transpose(npimg, (1, 2, 0)))
# plt.show()
#
# print(' '.join('%5s' % cla_dict[test_label[j].item()] for j in range(4)))
# imshow(utils.make_grid(test_image))
net = AlexNet(num_classes=5, init_weights=True) #类别5
net.to(device) #网络设备
loss_function = nn.CrossEntropyLoss() #损失函数
# pata = list(net.parameters())
optimizer = optim.Adam(net.parameters(), lr=0.0002) #adam优化器 对象是网络中可训练参数 学习率 自己调参
save_path = './AlexNet.pth' #保存模型路径
best_acc = 0.0
for epoch in range(10):#训练
# train
net.train() #管理神经元失活
running_loss = 0.0 #统计平均损失
t1 = time.perf_counter() #训练时间
for step, data in enumerate(train_loader, start=0): #遍历数据集
images, labels = data #分为图像 标签
optimizer.zero_grad() #清空梯度信息
outputs = net(images.to(device)) #正向传播 指定设备
loss = loss_function(outputs, labels.to(device)) #损失
loss.backward() #反向传播
optimizer.step() #更新结点参数
# print statistics
running_loss += loss.item() #损失累加
# print train process
rate = (step + 1) / len(train_loader) #打印训练进度
a = "*" * int(rate * 50)
b = "." * int((1 - rate) * 50)
print("\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(int(rate * 100), a, b, loss), end="")
print()
print(time.perf_counter()-t1)
# validate
net.eval() #关闭失活
acc = 0.0 # accumulate accurate number / epoch
with torch.no_grad():
for val_data in validate_loader:
val_images, val_labels = val_data
outputs = net(val_images.to(device))
predict_y = torch.max(outputs, dim=1)[1] #最大就是类别
acc += (predict_y == val_labels.to(device)).sum().item() #预测与真实对比 累加
val_accurate = acc / val_num #准确率
if val_accurate > best_acc: #如果准确率大于历史最优
best_acc = val_accurate #更新
torch.save(net.state_dict(), save_path) #保存权重
print('[epoch %d] train_loss: %.3f test_accuracy: %.3f' % #打印信息
(epoch + 1, running_loss / step, val_accurate))
print('Finished Training')
def train(args):
device = torch.device(f"cuda:{args.device_id}")
model = AlexNet(n_cls=100, useLRN=args.useLRN, useDropOut=args.useDropOut)
# model = AlexNet(num_classes= 100)
criterion = nn.CrossEntropyLoss()
model.to(device)
optimizer = Adam(model.parameters(), lr=args.lr)
train_loader, valid_loader = getLoaders(split="train",
batch_size=args.batch_size,
num_workers=args.num_workers,
aug=args.useAug)
train_loss_arr = []
valid_loss_arr = []
valid_acc_arr = []
valid_top5_arr = []
n_iter = 0
best_loss = float('inf')
best_top1_acc = 0
best_top5_acc = 0
for ep in range(args.epoch):
model.train()
for _, (img, label) in tqdm(enumerate(train_loader),
total=len(train_loader)):
img, label = img.to(device), label.to(device)
optimizer.zero_grad()
pred = model(img)
loss = criterion(pred, label)
# loss = model.criterion(pred, label)
loss.backward()
optimizer.step()
train_loss_arr.append(loss.item())
n_iter += 1
model.eval()
ep_valid_loss_arr = []
ep_acc_arr = []
ep_top5_arr = []
with torch.no_grad():
for _, (img, label) in tqdm(enumerate(valid_loader),
total=len(valid_loader)):
img, label = img.to(device), label.to(device)
pred = model(img)
loss = criterion(pred, label)
# loss = model.criterion(pred, label)
acc = utils.top_k_acc(k=1,
pred=pred.detach().cpu().numpy(),
label=label.detach().cpu().numpy())
acc5 = utils.top_k_acc(k=5,
pred=pred.detach().cpu().numpy(),
label=label.detach().cpu().numpy())
ep_acc_arr.append(acc)
ep_top5_arr.append(acc5)
ep_valid_loss_arr.append(loss.item())
valid_loss = np.mean(ep_valid_loss_arr)
valid_acc = np.mean(ep_acc_arr)
valid_top5 = np.mean(ep_top5_arr)
train_loss = np.mean(train_loss_arr[-len(train_loader):])
valid_loss_arr.append(valid_loss)
if valid_loss < best_loss:
best_loss = valid_loss
best_top1_acc = valid_acc
best_top5_acc = valid_top5
model.cpu()
torch.save(model.state_dict(), "best_model.pth")
model.to(device)
if (ep + 1) % 10 == 0:
model.cpu()
torch.save(
{
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"train_loss": train_loss_arr,
"valid_loss": valid_loss_arr,
"valid_acc": valid_acc_arr,
"valid_top5": valid_top5_arr,
"best_loss": best_loss,
"ep": ep,
"n_iter": n_iter,
}, "model_checkpoint.pth")
model.to(device)
print(
f"[{ep}, {n_iter}] train: {train_loss:.4f}, valid: {valid_loss:.4f}, acc: {valid_acc:.4f}, top5: {valid_top5:.4f}"
)
with open("exp_result.txt", "a+") as f:
f.write(
f"{args}, loss: {best_loss:.4f}, top1: {best_top1_acc*100:.1f}, top5: {best_top5_acc*100:.1f}\n"
)
def main():
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print("using {} device.".format(device))
tbwriter = SummaryWriter(log_dir="./logs")
data_transform = {
"train":
transforms.Compose([
transforms.RandomResizedCrop(360),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]),
"val":
transforms.Compose([
transforms.Resize(360, 360), # cannot 360, must (360,360)
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
}
data_root = os.path.abspath(os.path.join(os.getcwd(),
"./DATA")) # get data root path
image_path = os.path.join(data_root, "male") # flower data set path
assert os.path.exists(image_path), "{} path does not exist.".format(
image_path)
train_dataset = datasets.ImageFolder(root=os.path.join(
image_path, "train"),
transform=data_transform["train"])
train_num = len(train_dataset)
flower_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in flower_list.items())
# write dict into json file
json_str = json.dumps(cla_dict, indent=2)
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)
batch_size = 8
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
print('Using {} dataloader workers every process'.format(nw))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=nw)
validate_dataset = datasets.ImageFolder(root=os.path.join(
image_path, "val"),
transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,
batch_size=8,
shuffle=True,
num_workers=nw)
print("using {} images for training, {} images fot validation.".format(
train_num, val_num))
if os.path.exists("./log360.pth"):
net = AlexNet()
#net.load_state_dict(torch.load("./log360.pth", map_location='cuda:2'))
net = torch.load("./log360.pth", 'cpu')
print("continue training")
else:
net = AlexNet(num_classes=3, init_weights=True)
net.to(device)
print("start training anew")
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.98)
epochs = 2000
save_path = './AlexNet.pth'
best_acc = 0.0
train_steps = len(train_loader)
#json_path = './class_indices.json'
#json_file = open(json_path, "r")
#class_indict = json.load(json_file)
#model = AlexNet(num_classed=6).to(device)
trainLOSS = [] #save loss
testLOSS = [] #save loss
valACC = [] #save val acc
for epoch in range(epochs):
scheduler.step()
print('LR:{}'.format(scheduler.get_lr()[0]))
# train
net.train()
running_loss = 0.0
train_bar = tqdm(train_loader)
for step, data in enumerate(train_bar):
images, labels = data
optimizer.zero_grad()
outputs = net(images.to(device))
loss = loss_function(outputs, labels.to(device))
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(
epoch + 1, epochs, loss)
# validate
net.eval()
acc = 0.0 # accumulate accurate number / epoch
with torch.no_grad():
val_bar = tqdm(validate_loader, colour='green')
for val_data in val_bar:
val_images, val_labels = val_data
outputs = net(val_images.to(device))
predict_y = torch.max(outputs, dim=1)[1]
acc += torch.eq(predict_y, val_labels.to(device)).sum().item()
val_accurate = acc / val_num
tbwriter.add_scalar('train/loss', running_loss / train_steps, epoch)
tbwriter.add_scalar('val/acc', val_accurate, epoch)
trainLOSS.append(running_loss / train_steps)
valACC.append(val_accurate)
print('[epoch %d] train_loss: %.3f val_accuracy: %.3f' %
(epoch + 1, running_loss / train_steps, val_accurate))
print(' ')
if val_accurate > best_acc:
best_acc = val_accurate
torch.save(net.state_dict(), save_path)
#predict
#weights_path="./AlexNet.pth"
#model.load_state_dict(torch.load(weights_path))
#model.eval()
#with torch.no_grad():
# putput = torch.squeeze(model(img.to(device))).cpu()
# predict = torch.softmax(output, dim=0)
# predict_cla = torch.argmax(predict.numpy)
npLOSS = np.array(trainLOSS)
npVALACC = np.array(valACC)
np.save('./save/loss_epoch_{}'.format(epoch), npLOSS)
np.save('./save/valacc_epoch_{}'.format(epoch), npVALACC)
print('Finished Training')
if cuda:
xv, yv = xv.cuda(), yv.cuda()
v_feature, v_score, v_pred = model.forward(xv)
v_pred_label = torch.max(v_score, 1)[1]
v_equal = torch.eq(v_pred_label, yv).float()
zeros = zeros.scatter_add(0, yv, v_equal)
zeros_classes = zeros_classes.scatter_add(
0, yv, torch.ones_like(yv, dtype=torch.float))
v_correct += torch.sum(v_equal).item()
v_sum += len(yv)
v_acc = v_correct / v_sum
output('validation: {}, {}'.format(v_correct, v_acc, zeros))
output('class: {}'.format(zeros.tolist()))
output('class: {}'.format(zeros_classes.tolist()))
output('source: {}, target: {}, batch_size: {}, init_lr: {}'.format(
s_name, t_name, batch_size, init_lr))
output('lr_mult: {}, lr_mult_D: {}'.format(lr_mult, lr_mult_D))
output(' ======= START TRAINING ======= ')
# save model
if epoch % 1000 == 0 and epoch != 0:
torch.save(
{
'epoch': epoch + 1,
'model': model.state_dict(),
'opt': opt.state_dict(),
'opt_D': opt_D.state_dict()
}, checkpoint_save_path)
epoch += 1
def main():
# 设置运行设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("using {} device.".format(device))
# 数据处理
data_transform = {
"train":
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]),
"val":
transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
}
# 存放train与val的路径
image_path = '/home/xulei/数据集大本营/5_flower_data/flower_data' # flower data root path
# 若该目录不存在，在报错并终止程序
assert os.path.exists(image_path), "{} path does not exist.".format(
image_path)
# 定义训练数据集
train_dataset = datasets.ImageFolder(root=os.path.join(
image_path, "train"),
transform=data_transform["train"])
# 训练数据集的文件数量
train_num = len(train_dataset)
# flower_list: {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx
# cla_dict : {0: 'daisy', 1: 'dandelion', 2: 'roses', 3: 'sunflowers', 4: 'tulips'}
cla_dict = dict((val, key) for key, val in flower_list.items())
# write dict into json file
# 要输出json格式，需要对json数据进行编码，要用到函数：json.dumps
# indent=4, 的作用是让字典的内容逐行显示，每个key占一行
# json_str :
# '{
# "0": "daisy",
# "1": "dandelion",
# "2": "roses",
# "3": "sunflowers",
# "4": "tulips"
# }'
json_str = json.dumps(cla_dict, indent=4)
with open('class_idices.json', 'w') as json_file:
json_file.write(json_str)
batch_size = 128
nw = min(os.cpu_count(), batch_size if batch_size > 1 else 0,
8) # number of workers nw: 8 ?????
print("using {} dataloader workers every process".format(nw))
train_loader = datas.DataLoader(train_dataset,
batch_size,
shuffle=True,
num_workers=nw)
validate_dataset = datasets.ImageFolder(root=os.path.join(
image_path, "val"),
transform=data_transform["val"])
# val_num: 364
val_num = len(validate_dataset)
validate_loader = datas.DataLoader(validate_dataset,
batch_size,
shuffle=False,
num_workers=nw)
print("using {} images for trainning, {} images for validation.".format(
train_num, val_num))
net = AlexNet(num_classes=5).to(device)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.00004)
epoches = 20
save_path = './AlexNet.pth'
best_acc = 0.0
# train_steps : 26 len(train_loader)= training_images_num/batch_size
train_steps = len(train_loader)
for epoch in range(epoches):
net.train()
running_loss = 0.0
train_bar = tqdm(train_loader) # 进度条
for step, data in enumerate(train_bar):
images, labels = data
optimizer.zero_grad()
outputs = net(images.to(device))
loss = loss_function(outputs, labels.to(device))
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(
epoch + 1, epoches, loss)
# validata
net.eval()
acc = 0.0 # accumulate accurate number / epoch
with torch.no_grad():
val_bar = tqdm(validate_loader) # , colour='green'
for val_data in val_bar:
val_images, val_labels = val_data
outputs = net(val_images.to(device))
predict_y = torch.max(outputs, dim=1)[1]
acc += torch.eq(predict_y, val_labels.to(device)).sum().item()
val_accurate = acc / val_num
print('\n[epoch %d] train_loss: %.3f val_accuracy: %.3f' %
(epoch + 1, running_loss / train_steps, val_accurate))
if val_accurate > best_acc:
best_acc = val_accurate
torch.save(net.state_dict(), save_path)
print("Finshed Training")
train_loss = []
for epoch in range(num_epochs):
for idx, (inputs, labels) in enumerate(train_loader):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss.append(loss.item())
if ((idx + 1) % 100 == 0):
print("epoch is {}/{} Step is: {}/{} loss is: {}".format(
epoch, num_epochs, idx, num_batches, loss.item()))
model.eval()
with torch.no_grad():
correct = 0
total = 0
for idx, (inputs, labels) in enumerate(test_loader):
inputs = inputs.to(device)
labels = labels.to(device)
preds = model(inputs)
values, indices = torch.max(preds, 1)
total += labels.shape[0]
correct += (labels == indices).sum().item()
print("Accuracy of the network is: {}%".format(100 * correct / total))
torch.save(model.state_dict(), 'model.pth')
print()
with open(os.path.join("train.log"), "a") as log:
log.write(str('%f s' % (time.perf_counter() - time_start)) + "\n")
print('%f s' % (time.perf_counter() - time_start))
########################################### validate ###########################################
net.eval() # 验证过程中关闭 Dropout
acc = 0.0
with torch.no_grad():
for val_data in validate_loader:
val_images, val_labels = val_data
outputs = net(val_images.to(device))
predict_y = torch.max(outputs,
dim=1)[1] # 以output中值最大位置对应的索引（标签）作为预测输出
acc += (predict_y == val_labels.to(device)).sum().item()
val_accurate = acc / val_num
# 保存准确率最高的那次网络参数
if val_accurate > best_acc:
best_acc = val_accurate
torch.save(net.state_dict(), save_path)
with open(os.path.join("train.log"), "a") as log:
log.write(
str('[epoch %d] train_loss: %.3f test_accuracy: %.3f \n' %
(epoch + 1, running_loss / step, val_accurate)) + "\n")
print('[epoch %d] train_loss: %.3f test_accuracy: %.3f \n' %
(epoch + 1, running_loss / step, val_accurate))
with open(os.path.join("train.log"), "a") as log:
log.write(str('Finished Training') + "\n")
print('Finished Training')
class Solver(object):
def __init__(self, config):
self.model = None
self.lr = config.lr
self.epochs = config.epoch
self.train_batch_size = config.trainBatchSize
self.test_batch_size = config.testBatchSize
self.criterion = None
self.optimizer = None
self.scheduler = None
self.device = None
self.cuda = config.cuda
self.train_loader = None
self.test_loader = None
self.is_board = False
def load_data(self):
train_transform = transforms.Compose(
[transforms.RandomHorizontalFlip(),
transforms.ToTensor()])
test_transform = transforms.Compose([transforms.ToTensor()])
train_set = torchvision.datasets.CIFAR10(
root='/mnt/disk50/datasets/cifar',
train=True,
download=True,
transform=train_transform)
self.train_loader = torch.utils.data.DataLoader(
dataset=train_set, batch_size=self.train_batch_size, shuffle=True)
test_set = torchvision.datasets.CIFAR10(
root='/mnt/disk50/datasets/cifar',
train=False,
download=True,
transform=test_transform)
self.test_loader = torch.utils.data.DataLoader(
dataset=test_set, batch_size=self.test_batch_size, shuffle=False)
def load_model_from_pth(self, model_path):
"""Load the pre-trained model weight
:param model_path:
:return:
"""
checkpoint = torch.load(model_path,
map_location=self.device_name)['model']
# TODO:这里需要具体了解原因在哪里?
checkpoint_parameter_name = list(checkpoint.keys())[0]
model_parameter_name = next(self.model.named_parameters())[0]
is_checkpoint = checkpoint_parameter_name.startswith('module.')
is_model = model_parameter_name.startswith('module.')
if is_checkpoint and not is_model:
# 移除checkpoint模型里面参数
new_parameter_check = OrderedDict()
for key, value in checkpoint.items():
if key.startswith('module.'):
new_parameter_check[key[7:]] = value
self.model.load_state_dict(new_parameter_check)
elif not is_checkpoint and is_model:
# 添加module.参数
new_parameter_dict = OrderedDict()
for key, value in checkpoint.items():
if not key.startswith('module.'):
key = 'module.' + key
new_parameter_dict[key] = value
else:
self.model.load_state_dict(checkpoint)
return self.model
def load_model(self):
if self.cuda:
self.device = torch.device('cuda:0')
cudnn.benchmark = True
else:
self.device = torch.device('cpu')
# self.model = LeNet().to(self.device)
self.model = AlexNet().to(self.device)
self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer,
milestones=[75, 150],
gamma=0.5)
self.criterion = nn.CrossEntropyLoss().to(self.device)
def train(self, writer=None):
print("train:")
self.model.train()
train_loss = 0
train_correct = 0
total = 0
for batch_num, (data, target) in enumerate(self.train_loader):
data, target = data.to(self.device), target.to(self.device)
self.optimizer.zero_grad()
output = self.model(data)
loss = self.criterion(output, target)
loss.backward()
self.optimizer.step()
train_loss += loss.item()
prediction = torch.max(
output,
1) # second param "1" represents the dimension to be reduced
total += target.size(0)
# train_correct incremented by one if predicted right
train_correct += np.sum(
prediction[1].cpu().numpy() == target.cpu().numpy())
progress_bar(
batch_num, len(self.train_loader),
'Loss: %.4f | Acc: %.3f%% (%d/%d)' %
(train_loss / (batch_num + 1), 100. * train_correct / total,
train_correct, total))
# if not writer:
# writer.add_scalar
return train_loss, train_correct / total
def test(self):
print("test:")
self.model.eval()
test_loss = 0
test_correct = 0
total = 0
start = time.time()
with torch.no_grad():
for batch_num, (data, target) in enumerate(self.test_loader):
data, target = data.to(self.device), target.to(self.device)
output = self.model(data)
loss = self.criterion(output, target)
test_loss += loss.item()
prediction = torch.max(output, 1)
total += target.size(0)
test_correct += np.sum(
prediction[1].cpu().numpy() == target.cpu().numpy())
progress_bar(
batch_num, len(self.test_loader),
'Loss: %.4f | Acc: %.3f%% (%d/%d)' %
(test_loss / (batch_num + 1), 100. * test_correct / total,
test_correct, total))
end = time.time()
time_used = end - start
return test_loss, test_correct / total, time_used
def save(self):
model_out_path = "./best_model_new.pkl"
torch.save(self.model.state_dict(), model_out_path)
print("Checkpoint saved to {}".format(model_out_path))
def run(self):
self.load_data()
self.load_model()
# for k, v in self.model.state_dict():
# print('layer{}'.k)
# print(v)
accuracy = 0
writer = SummaryWriter()
for epoch in range(1, self.epochs + 1):
self.scheduler.step(epoch)
print("\n===> epoch: %d/200" % epoch)
train_loss, train_acc = self.train()
test_loss, test_acc = self.test()
# writer.add_scalar('loss_group',{'train_loss':train_loss.numpy(),
# 'test_loss':test_loss.numpy()},epoch)
# writer.add_scalar('acc_group',{'train_acc':train_acc.numpy(),
# 'test_acc':test_acc.numpy()}, epoch)
if test_acc > accuracy:
accuracy = test_acc
self.save()
elif epoch == self.epochs:
print("===> BEST ACC. PERFORMANCE: %.3f%%" % (accuracy * 100))
self.save()
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("using {} device.".format(device))
batch_size = 16
epochs = 20
data_transform = {
"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_root = os.path.abspath(os.path.join(os.getcwd(),
".")) # get data root path
image_path = os.path.join(data_root, "data_set",
"flower_data") # flower data set path
assert os.path.exists(image_path), "{} path does not exist.".format(
image_path)
train_dataset = datasets.ImageFolder(root=os.path.join(
image_path, "train"),
transform=data_transform["train"])
train_num = len(train_dataset)
# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in flower_list.items())
# write dict into json file
json_str = json.dumps(cla_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
print('Using {} dataloader workers every process'.format(nw))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=nw)
validate_dataset = datasets.ImageFolder(root=os.path.join(
image_path, "val"),
transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=nw)
print("using {} images for training, {} images for validation.".format(
train_num, val_num))
# create model
net = AlexNet(num_classes=5)
net.to(device)
# define loss function
loss_function = nn.CrossEntropyLoss()
# construct an optimizer
params = [p for p in net.parameters() if p.requires_grad]
optimizer = optim.Adam(params, lr=0.0001)
best_acc = 0.0
save_path = 'weights/alexnet.pth'
train_steps = len(train_loader)
for epoch in range(epochs):
# train
net.train()
running_loss = 0.0
train_bar = tqdm(train_loader)
for step, data in enumerate(train_bar):
images, labels = data
optimizer.zero_grad()
logits = net(images.to(device))
loss = loss_function(logits, labels.to(device))
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(
epoch + 1, epochs, loss)
# validate
net.eval()
acc = 0.0 # accumulate accurate number / epoch
with torch.no_grad():
val_bar = tqdm(validate_loader)
for val_data in val_bar:
val_images, val_labels = val_data
outputs = net(val_images.to(device))
# loss = loss_function(outputs, test_labels)
predict_y = torch.max(outputs, dim=1)[1]
acc += torch.eq(predict_y, val_labels.to(device)).sum().item()
val_bar.desc = "valid epoch[{}/{}]".format(epoch + 1, epochs)
val_accurate = acc / val_num
print('[epoch %d] train_loss: %.3f val_accuracy: %.3f' %
(epoch + 1, running_loss / train_steps, val_accurate))
if val_accurate > best_acc:
best_acc = val_accurate
torch.save(net.state_dict(), save_path)
print('Finished Training')
if __name__ == "__main__":
# init seed value
seed = torch.initial_seed()
# TensorboardX
tbwriter = SummaryWriter(log_dir=LOG_DIR)
print("TensorboardX summary writer created")
# create model
alexnet = AlexNet(num_classes=NUM_CLASSES)
# load pretrained model
if pretrained:
alexnet_dict = alexnet.state_dict()
# print(alexnet_dict.keys())
alexnet_pretrained = models.alexnet(pretrained=True)
pretrained_dict = alexnet_pretrained.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in alexnet_dict}
# print(pretrained_dict.keys())
pretrained_dict.pop("classifier.6.weight")
pretrained_dict.pop("classifier.6.bias")
alexnet_dict.update(pretrained_dict)
alexnet.load_state_dict(alexnet_dict)
# print(alexnet_dict.keys())
print("Load from pretrained")
# Freeze parameter
if freeze_layer:
for name, value in alexnet.named_parameters():
def train():
try:
os.makedirs(opt.checkpoints_dir)
except OSError:
pass
if torch.cuda.device_count() > 1:
model = torch.nn.parallel.DataParallel(
AlexNet(num_classes=opt.num_classes))
else:
model = AlexNet(num_classes=opt.num_classes)
if os.path.exists(MODEL_PATH):
model.load_state_dict(
torch.load(MODEL_PATH, map_location=lambda storage, loc: storage))
model.to(device)
################################################
# Set loss function and Adam optimizer
################################################
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=opt.lr)
for epoch in range(opt.epochs):
# train for one epoch
print(f"\nBegin Training Epoch {epoch + 1}")
# Calculate and return the top-k accuracy of the model
# so that we can track the learning process.
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
for i, data in enumerate(train_dataloader):
# get the inputs; data is a list of [inputs, labels]
inputs, targets = data
inputs = inputs.to(device)
targets = targets.to(device)
# compute output
output = model(inputs)
loss = criterion(output, targets)
# measure accuracy and record loss
prec1, prec5 = accuracy(output, targets, topk=(1, 2))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1, inputs.size(0))
top5.update(prec5, inputs.size(0))
# compute gradients in a backward pass
optimizer.zero_grad()
loss.backward()
# Call step of optimizer to update model params
optimizer.step()
print(
f"Epoch [{epoch + 1}] [{i + 1}/{len(train_dataloader)}]\t"
f"Loss {loss.item():.4f}\t"
f"Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t"
f"Prec@5 {top5.val:.3f} ({top5.avg:.3f})",
end="\r")
# save model file
torch.save(model.state_dict(), MODEL_PATH)
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
data_transform = {
"train":
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]),
"val":
transforms.Compose([
transforms.Resize((224, 224)), # cannot 224, must (224, 224)
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
}
data_root = os.path.abspath(os.path.join(os.getcwd(), "./"))
image_path = os.path.join(data_root, "flower_data")
train_dataset = datasets.ImageFolder(root=image_path + "/train",
transform=data_transform['train'])
train_num = len(train_dataset)
# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in flower_list.items())
# write dict into json file
json_str = json.dumps(cla_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)
batch_size = 8
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
validate_dataset = datasets.ImageFolder(root=image_path + "/val",
transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,
batch_size=4,
shuffle=True,
num_workers=0)
# test_data_iter = iter(validate_loader)
# test_image, test_label = test_data_iter.next()
#
# def imshow(img):
# img = img / 2 + 0.5 # unnormalize
# npimg = img.numpy()
# plt.imshow(np.transpose(npimg, (1, 2, 0)))
# plt.show()
#
# print(' '.join('%5s' % cla_dict[test_label[j].item()] for j in range(4)))
# imshow(utils.make_grid(test_image))
net = AlexNet(num_class=5)
print(net)
net.to(device)
loss_function = nn.CrossEntropyLoss()
# pata = list(net.parameters())
optimizer = optim.Adam(net.parameters(), lr=0.0002)
save_path = './AlexNet.pth'
best_acc = 0.0
for epoch in range(10):
# train
net.train()
running_loss = 0.0
t1 = time.perf_counter()
for step, data in enumerate(train_loader, start=0):
images, labels = data
optimizer.zero_grad()
outputs = net(images.to(device))
loss = loss_function(outputs, labels.to(device))
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
# print train process
rate = (step + 1) / len(train_loader)
a = "*" * int(rate * 50)
b = "." * int((1 - rate) * 50)
print("\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(
int(rate * 100), a, b, loss),
end="")
print()
print(time.perf_counter() - t1)
# validate
net.eval()
acc = 0.0 # accumulate accurate number / epoch
# 验证过程中不计算损失梯度
with torch.no_grad():
for val_data in validate_loader:
val_images, val_labels = val_data
outputs = net(val_images.to(device))
predict_y = torch.max(outputs, dim=1)[1]
acc += (predict_y == val_labels.to(device)).sum().item()
val_accurate = acc / val_num
if val_accurate > best_acc:
best_acc = val_accurate
torch.save(net.state_dict(), save_path)
print('[epoch %d] train_loss: %.3f test_accuracy: %.3f' %
(epoch + 1, running_loss / step, val_accurate))
print('Finished Training')
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("using {} device.".format(device))
data_transform = {
"train":
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]),
"val":
transforms.Compose([
transforms.Resize((224, 224)), # cannot 224, must (224, 224)
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
}
data_root = os.path.abspath(os.path.join(os.getcwd(),
"../..")) # get data root path
image_path = os.path.join(data_root, "data_set",
"flower_data") # flower data set path
assert os.path.exists(image_path), "{} path does not exist.".format(
image_path)
train_dataset = datasets.ImageFolder(root=os.path.join(
image_path, "train"),
transform=data_transform["train"])
train_num = len(train_dataset)
# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in flower_list.items())
# write dict into json file
json_str = json.dumps(cla_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)
batch_size = 32
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
print('Using {} dataloader workers every process'.format(nw))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=nw)
validate_dataset = datasets.ImageFolder(root=os.path.join(
image_path, "val"),
transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,
batch_size=4,
shuffle=False,
num_workers=nw)
print("using {} images for training, {} images for validation.".format(
train_num, val_num))
# test_data_iter = iter(validate_loader)
# test_image, test_label = test_data_iter.next()
#
# def imshow(img):
# img = img / 2 + 0.5 # unnormalize
# npimg = img.numpy()
# plt.imshow(np.transpose(npimg, (1, 2, 0)))
# plt.show()
#
# print(' '.join('%5s' % cla_dict[test_label[j].item()] for j in range(4)))
# imshow(utils.make_grid(test_image))
net = AlexNet(num_classes=5, init_weights=True)
net.to(device)
loss_function = nn.CrossEntropyLoss()
# pata = list(net.parameters())
optimizer = optim.Adam(net.parameters(), lr=0.0002)
epochs = 10
save_path = './AlexNet.pth'
best_acc = 0.0
train_steps = len(train_loader)
for epoch in range(epochs):
# train
net.train()
running_loss = 0.0
train_bar = tqdm(train_loader)
for step, data in enumerate(train_bar):
images, labels = data
optimizer.zero_grad()
outputs = net(images.to(device))
loss = loss_function(outputs, labels.to(device))
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(
epoch + 1, epochs, loss)
# validate
net.eval()
acc = 0.0 # accumulate accurate number / epoch
with torch.no_grad():
val_bar = tqdm(validate_loader)
for val_data in val_bar:
val_images, val_labels = val_data
outputs = net(val_images.to(device))
predict_y = torch.max(outputs, dim=1)[1]
acc += torch.eq(predict_y, val_labels.to(device)).sum().item()
val_accurate = acc / val_num
print('[epoch %d] train_loss: %.3f val_accuracy: %.3f' %
(epoch + 1, running_loss / train_steps, val_accurate))
if val_accurate > best_acc:
best_acc = val_accurate
torch.save(net.state_dict(), save_path)
print('Finished Training')
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else
"cpu") # torch.device规定训练中所使用的设备
print("using {} device.".format(device))
data_transform = { # data_transform数据预处理
"train":
transforms.Compose([
transforms.RandomResizedCrop(224), # 随机裁剪为224*224
transforms.RandomHorizontalFlip(), # 水平方向随机翻转
transforms.ToTensor(), # 转化为tensor
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]), # 标准化处理
"val":
transforms.Compose([
transforms.Resize((224, 224)), # * cannot 224, must (224, 224)
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
}
print(os.getcwd())
# data_root = os.path.abspath(os.path.join(os.getcwd(), "../..")) # get data root path
# 先获取数据集所在的根目录os.getcwd()
# ^ os.getcwd() 返回当前进程的工作目录，并非当前文件所在的目录
# "../.."表示的是上两层目录，这个要看具体的情况，这是一个相对路径的写法
# ^ os.path.join 路径拼接，拼接后得到的就是当前目录的上两级目录
# ^ os.path.abspath() 获取指定文件或目录的绝对路径（完整路径）
data_root = os.path.abspath(os.getcwd())
image_path = os.path.join(data_root, "data_set",
"flower_data") # flower data set path
# 等价于 image_path = data_root + "data_set/flower_data"
# assert os.path.exists(image_path), "{} path does not exist.".format(image_path)
train_dataset = datasets.ImageFolder(
root=os.path.join(image_path, "train"), # 下载数据集 ，"train"表示是训练集数据
transform=data_transform["train"]) # 使用"train"的预处理方式
train_num = len(train_dataset) # 查看训练集有多少张图片
# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx # * .class_to_idx 得到分类名称对应的索引
cla_dict = dict(
(val, key) for key, val in flower_list.items()) # * 将刚刚字典的键值对 变为 值键对
# write dict into json file
json_str = json.dumps(cla_dict, indent=4) # 将刚刚的字典变为json形式
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)
batch_size = 32
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
print('Using {} dataloader workers every process'.format(nw))
train_loader = torch.utils.data.DataLoader(
train_dataset, # 加载数据集
batch_size=batch_size,
shuffle=True, # 通过batchsize和随机参数从样本中获取一批批数据
num_workers=nw) # wins下num_workers一般设置为0，linux下num_workers设置可以分布式计算
validate_dataset = datasets.ImageFolder(
root=os.path.join(
image_path, "val"
), # root=os.path.join(image_path, "val")等价于 root=image_path+"val"
transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(
validate_dataset,
batch_size=batch_size,
shuffle=False, # batch_size=4, shuffle=True,
num_workers=nw)
print("using {} images for training, {} images for validation.".format(
train_num, val_num))
# 下面是查看数据集的demo
# 注意，第60行的batch_size=4, shuffle=True再查看：
# test_data_iter = iter(validate_loader)
# test_image, test_label = test_data_iter.next()
# def imshow(img):
# img = img / 2 + 0.5 # unnormalize
# npimg = img.numpy()
# plt.imshow(np.transpose(npimg, (1, 2, 0)))
# plt.show()
# print(' '.join('%5s' % cla_dict[test_label[j].item()] for j in range(4)))
# imshow(utils.make_grid(test_image))
net = AlexNet(num_classes=5, init_weights=True) # 5个类别的花数据集，初始化权重为True
# 实例化模型对象 net
net.to(device) # ^ net.to(device)将网络放入刚刚指定的设备中
loss_function = nn.CrossEntropyLoss() # 定义损失函数，多类别的交叉熵函数
# pata = list(net.parameters()) # 调试所用，查看模型的参数
optimizer = optim.Adam(
net.parameters(),
lr=0.0002) # 定义Adam优化器，优化对象是网络中所有的可训练参数net.parameters(),以及学习了lr=0.0002
epochs = 10
save_path = './AlexNet.pth' # 保存权重的路径
best_acc = 0.0 # 最佳准确率 best_acc，首先初始化为0，后面再更新
train_steps = len(train_loader)
for epoch in range(epochs): # 迭代10次
# * 因为使用了dropout，只在训练中使用，预测中不使用
# train # & 训练阶段
net.train() # 调用net.train()进入训练阶段，同时使用 dropout 方法
running_loss = 0.0 # 统计训练中的平均损失
train_bar = tqdm(train_loader) # 为了统计训练一个epoch所需时间
for step, data in enumerate(train_bar): # 遍历数据集；数据集分为图像和标签
images, labels = data
optimizer.zero_grad() # 梯度清0
outputs = net(
images.to(device)) # 正向传播，图像放入设备中，然后实例化AlexNet的网络net中
loss = loss_function(
outputs, labels.to(device)) # 计算损失，计算预测值与真实值的损失，这里label也要放入设备中
loss.backward() # 反向传播到每一个节点
optimizer.step() # 更新每一个节点的参数
# print statistics
running_loss += loss.item() # 累加loss值
train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(
epoch + 1, epochs, loss) # 为了或者训练进度
# validate # & 测试阶段
net.eval() # 调用net.eval() 进入测试阶段，同时关闭 dropout 方法
acc = 0.0 # accumulate accurate number / epoch
with torch.no_grad(): # * with torch.no_grad() 禁止参数跟踪：验证中不计算损失梯度
val_bar = tqdm(validate_loader)
for val_data in val_bar:
val_images, val_labels = val_data # 数据划分为图片和对应的标签
outputs = net(
val_images.to(device)) # 放入网络net中得到输出，输出的维度是 [batch， 10]
predict_y = torch.max(
outputs, dim=1
)[1] # 求出输出的第1个维度（dim=1类别维度）max（只关注最大值对应的位置[1]，不关心数值 ），得到预测值 predict_y
acc += torch.eq(predict_y, val_labels.to(
device)).sum().item() # 统计预测正确的个数 # ^ 通过.item()得到相应的数值
# acc += (predict_y == val_labels.to(device)).sum().item() # 等价的
val_accurate = acc / val_num # 累加的准确率除以样本个数，得到平均准确率
print('[epoch %d] train_loss: %.3f val_accuracy: %.3f' %
(epoch + 1, running_loss / train_steps, val_accurate))
if val_accurate > best_acc: # 如果当前准确率大于历史最优准确率
best_acc = val_accurate # 更新
torch.save(net.state_dict(), save_path)
print('Finished Training')
