def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("using {} device.".format(device))
batch_size = 16
epochs = 200
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 = LeNet(num_classes=5)
# load pretrained weights
# download url: https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth
# download url: https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth
# model_weight_path = "weights/LeNet_pretrained.pth"
# assert os.path.exists(model_weight_path), "file {} dose not exist.".format(model_weight_path)
# pre_weights = torch.load(model_weight_path, map_location=device)
# delete classifier weights
# pre_dict = {k: v for k, v in pre_weights.items() if net.state_dict()[k].numel() == v.numel()}
# missing_keys, unexpected_keys = net.load_state_dict(pre_dict, strict=False)
#
# # freeze features weights
# for param in net.conv_stem.parameters():
# param.requires_grad = False
#
# for param in net.bn1.parameters():
# param.requires_grad = False
#
# for param in net.act1.parameters():
# param.requires_grad = False
#
# for param in net.blocks.parameters():
# param.requires_grad = False
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/lenet.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')
help='Learning rate decay',
type=float)
parser.add_argument('--batch_size', default=80, help='Batch Size', type=int)
parser.add_argument('--data_augmentation',
default=True,
help='Using data augmentation',
type=int)
parser.add_argument('--grayscale',
default=True,
help='Using data augmentation',
type=int)
parser.add_argument('--keep_prob',
default=0.8,
help='Keep probability for dropout',
type=int)
config = parser.parse_args()
trainBG = BatchGenerator(X_train, y_train, config.batch_size, config.grayscale,
'train')
validBG = BatchGenerator(X_valid, y_valid, config.batch_size, config.grayscale)
testBG = BatchGenerator(X_valid, y_valid, config.batch_size, config.grayscale)
config.decay_steps = trainBG.num_batches * config.num_epoch
label_dict = {}
with open('signnames.csv') as f:
reader = csv.DictReader(f)
for row in reader:
label_dict[row['ClassId']] = row['SignName']
#vgg = VGGsimple(config, label_dict)
#vgg.train(trainBG, validBG, config.num_epoch)
lenet = LeNet(config, label_dict)
lenet.train(trainBG, validBG, config.num_epoch)
def train_and_test(flags,
corruption_level=0,
gold_fraction=0.5,
get_C=uniform_mix_C):
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
C = get_C(corruption_level)
gold, silver = prepare_data(C, gold_fraction)
print("Gold shape = {}, Silver shape = {}".format(gold.images.shape,
silver.images.shape))
# TODO : test on whole set
test_x = torch.from_numpy(mnist.test.images[:500].reshape([-1, 1, 28, 28]))
test_y = torch.from_numpy(mnist.test.labels[:500]).type(torch.LongTensor)
print("Test shape = {}".format(test_x.shape))
model = LeNet()
optimizer = torch.optim.Adam([p for p in model.parameters()], lr=0.001)
for step in range(flags.num_steps):
x, y = silver.next_batch(flags.batch_size)
y, y_true = np.array([l[0] for l in y]), np.array([l[1] for l in y])
x_val, y_val = gold.next_batch(min(flags.batch_size, flags.nval))
x, y = torch.from_numpy(x.reshape(
[-1, 1, 28, 28])), torch.from_numpy(y).type(torch.LongTensor)
x_val, y_val = torch.from_numpy(x_val.reshape(
[-1, 1, 28, 28])), torch.from_numpy(y_val).type(torch.LongTensor)
# forward
if flags.method == "l2w":
ex_wts = reweight_autodiff(model, x, y, x_val, y_val)
logits, loss = model.loss(x, y, ex_wts)
if step % dbg_steps == 0:
tbrd.log_histogram("ex_wts", ex_wts, step=step)
tbrd.log_value("More_than_0.01",
sum([x > 0.01 for x in ex_wts]),
step=step)
tbrd.log_value("More_than_0.05",
sum([x > 0.05 for x in ex_wts]),
step=step)
tbrd.log_value("More_than_0.1",
sum([x > 0.1 for x in ex_wts]),
step=step)
mean_on_clean_labels = np.mean(
[ex_wts[i] for i in range(len(y)) if y[i] == y_true[i]])
mean_on_dirty_labels = np.mean(
[ex_wts[i] for i in range(len(y)) if y[i] != y_true[i]])
tbrd.log_value("mean_on_clean_labels",
mean_on_clean_labels,
step=step)
tbrd.log_value("mean_on_dirty_labels",
mean_on_dirty_labels,
step=step)
else:
logits, loss = model.loss(x, y)
print("Loss = {}".format(loss))
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
tbrd.log_value("loss", loss, step=step)
if step % dbg_steps == 0:
model.eval()
pred = torch.max(model.forward(test_x), 1)[1]
test_acc = torch.sum(torch.eq(pred, test_y)).item() / float(
test_y.shape[0])
model.train()
print("Test acc = {}.".format(test_acc))
tbrd.log_value("test_acc", test_acc, step=step)
def train(queue, config):
from model import LeNet
model = LeNet(config)
model.train()
net = LeNet()
if MULTI_GPU:
net = nn.DataParallel(net,device_ids=device_ids)
net.to(device)
criteon = nn.CrossEntropyLoss()
optimizer=optim.Adam(net.parameters(), lr=1e-3)
scheduler = StepLR(optimizer, step_size=100, gamma=0.1)
if MULTI_GPU:
optimizer = nn.DataParallel(optimizer, device_ids=device_ids)
scheduler = nn.DataParallel(scheduler, device_ids=device_ids)
# print(net)
for epoch in range(epoch_num):
for batchidx, (label, img) in enumerate(cifar_train_loader):
net.train()
logits = net(img.to(device))
loss = criteon(logits, label.long().to(device))
optimizer.zero_grad()
loss.backward()
optimizer.module.step()
scheduler.module.step()
print("epoch:{} loss:{}".format(epoch, loss.item()))
write.add_scalar(tag='train_loss', global_step=epoch, scalar_value=loss.item())
net.eval()
with torch.no_grad():
total_num = 0
total_correct = 0
for label, img in cifar_test_loader:
logits = net(img.to(device))
""" 该代码定义了LeNet模型的训练过程
"""
import torch
import torch.nn as nn
from model import LeNet
# ... 此处略去定义训练数据载入器的代码，具体可参考代码4.3
model = LeNet() # 定义LeNet模型
model.train() # 切换模型到训练状态
lr = 0.01 # 定义学习率
criterion = nn.CrossEntropyLoss() # 定义损失函数
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9,
weight_decay=5e-4) # 定义随机梯度下降优化器
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(data_train_loader):
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
def main(args):
# Data Loader (Input Pipeline)
model_str = args.dataset + '_%s_' % args.model_type + args.noise_type + '_' + str(args.noise_rate) + '_' + str(args.seed)
txtfile = save_dir + "/" + model_str + ".txt"
nowTime = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
if os.path.exists(txtfile):
os.system('mv %s %s' % (txtfile, txtfile + ".bak-%s" % nowTime))
# Data Loader (Input Pipeline)
print('loading dataset...')
train_dataset, val_dataset, test_dataset = load_data(args)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
drop_last=False,
shuffle=True)
val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
drop_last=False,
shuffle=False)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
drop_last=False,
shuffle=False)
# Define models
print('building model...')
if args.dataset == 'mnist':
clf1 = LeNet()
optimizer1 = torch.optim.SGD(clf1.parameters(), lr=learning_rate, weight_decay=args.weight_decay, momentum=0.9)
scheduler1 = MultiStepLR(optimizer1, milestones=[10, 20], gamma=0.1)
elif args.dataset == 'fmnist':
clf1 = resnet.ResNet50(input_channel=1, num_classes=10)
optimizer1 = torch.optim.SGD(clf1.parameters(), lr=learning_rate, weight_decay=args.weight_decay, momentum=0.9)
scheduler1 = MultiStepLR(optimizer1, milestones=[10, 20], gamma=0.1)
elif args.dataset == 'cifar10':
clf1 = resnet.ResNet50(input_channel=3, num_classes=10)
optimizer1 = torch.optim.SGD(clf1.parameters(), lr=learning_rate, weight_decay=args.weight_decay, momentum=0.9)
scheduler1 = MultiStepLR(optimizer1, milestones=[40, 80], gamma=0.1)
elif args.dataset == 'cifar100':
clf1 = resnet.ResNet50(input_channel=3, num_classes=100)
optimizer1 = torch.optim.SGD(clf1.parameters(), lr=learning_rate, weight_decay=args.weight_decay, momentum=0.9)
scheduler1 = MultiStepLR(optimizer1, milestones=[40, 80], gamma=0.1)
clf1.cuda()
with open(txtfile, "a") as myfile:
myfile.write('epoch train_acc1 val_acc1 test_acc1\n')
epoch = 0
train_acc1 = 0
# evaluate models with random weights
val_acc1 = evaluate(val_loader, clf1)
print('Epoch [%d/%d] Val Accuracy on the %s val data: Model1 %.4f %%' % (
epoch + 1, args.n_epoch, len(val_dataset), val_acc1))
test_acc1 = evaluate(test_loader, clf1)
print('Epoch [%d/%d] Test Accuracy on the %s test data: Model1 %.4f %%' % (
epoch + 1, args.n_epoch, len(test_dataset), test_acc1))
# save results
with open(txtfile, "a") as myfile:
myfile.write(str(int(epoch)) + ' ' + str(train_acc1) + ' ' + str(val_acc1) + ' ' + str(test_acc1) + "\n")
val_acc_list = []
test_acc_list = []
for epoch in range(0, args.n_epoch):
scheduler1.step()
print(optimizer1.state_dict()['param_groups'][0]['lr'])
clf1.train()
train_acc1 = train(train_loader, epoch, clf1, optimizer1, args)
val_acc1 = evaluate(val_loader, clf1)
val_acc_list.append(val_acc1)
test_acc1 = evaluate(test_loader, clf1)
test_acc_list.append(test_acc1)
# save results
print('Epoch [%d/%d] Test Accuracy on the %s test data: Model1 %.4f %% ' % (
epoch + 1, args.n_epoch, len(test_dataset), test_acc1))
with open(txtfile, "a") as myfile:
myfile.write(str(int(epoch)) + ' ' + str(train_acc1) + ' ' + str(val_acc1) + ' ' + str(test_acc1) + "\n")
id = np.argmax(np.array(val_acc_list))
test_acc_max = test_acc_list[id]
return test_acc_max
top5_valid_accuracy_log = []
# History for Confusion Matrix
pred_list = torch.zeros(0, dtype=torch.long, device='cpu')
label_list = torch.zeros(0, dtype=torch.long, device='cpu')
for epoch in range(epochs):
train_loss = 0
train_correct = 0
top5_train_correct = 0
top5_valid_correct = 0
validation_loss = 0
validation_correct = 0
train_total = 0
valid_total = 0
model.train()
for inputs, labels in train_loader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
_, preds = torch.max(outputs, 1)
_, top5_preds = torch.topk(outputs, 5)
train_loss += loss.item()
def train():
device = torch.device("cuda:0" if opt.cuda else "cpu")
utils.set_seed()
# ============================ step 1/5 数据 ============================
norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]
train_transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
valid_transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
# 构建MyDataset
train_data = RMBDataset(data_dir=opt.train_dir, transform=train_transform)
valid_data = RMBDataset(data_dir=opt.valid_dir, transform=valid_transform)
# 构建DataLoader
train_loader = DataLoader(dataset=train_data,
batch_size=opt.batch_size,
shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=opt.batch_size)
# ============================ step 2/5 模型 ============================
net = LeNet(classes=2)
net.to(device)
# net.initialize_weights()
# ============================ step 3/5 损失函数 ============================
criterion = nn.CrossEntropyLoss()
# ============================ step 4/5 优化器 ============================
optimizer = optim.SGD(net.parameters(), lr=opt.lr, momentum=0.9)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
# ============================ step 5/5 训练 ============================
train_curve = list()
valid_curve = list()
for epoch in range(opt.epochs):
loss_mean = 0.
correct = 0.
total = 0.
net.train()
for i, data in enumerate(train_loader):
inputs, labels = data
inputs = inputs.to(device)
labels = labels.to(device)
outputs = net(inputs)
optimizer.zero_grad()
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 统计分类情况
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).squeeze().sum().to("cpu").numpy()
# 打印训练信息
loss_mean += loss.item()
train_curve.append(loss.item())
if (i + 1) % opt.log_interval == 0:
loss_mean = loss_mean / opt.log_interval
print(
"Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}"
.format(epoch, opt.epochs, i + 1, len(train_loader),
loss_mean, correct / total))
loss_mean = 0.
scheduler.step() # 更新学习率
if (epoch + 1) % opt.val_interval == 0:
correct_val = 0.
total_val = 0.
loss_val = 0.
net.eval()
with torch.no_grad():
for j, data in enumerate(valid_loader):
inputs, labels = data
inputs = inputs.to(device)
labels = labels.to(device)
outputs = net(inputs)
loss = criterion(outputs, labels)
_, predicted = torch.max(outputs.data, 1)
total_val += labels.size(0)
correct_val += (
predicted == labels).squeeze().sum().to("cpu").numpy()
loss_val += loss.item()
valid_curve.append(loss_val)
print(
"Valid:\t Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}"
.format(epoch, opt.epochs, j + 1, len(valid_loader),
loss_val, correct / total))
utils.loss_picture(train_curve, train_loader, valid_curve,
opt.val_interval)
# 保存模型参数
net_state_dict = net.state_dict()
torch.save(net_state_dict, opt.path_state_dict)
print("模型保存成功")
# transforms.ToTensor()
# ]), download=True) #从internet上下载MNIST测试数据集，并Resize成32x32大小，转为Tensor
data_train_loader = DataLoader(data_train,
batch_size=256,
shuffle=True,
num_workers=0) #8个线程处理，随机抽取，batch=256
# data_test_loader = DataLoader(data_test, batch_size=1024, num_workers=8)
## 数据载入
# from data import data_train_loader
#### 定义模型训练参数部分
model = LeNet()
model.cpu()
model.train() ## 切换到模型的训练模式
lr = 0.001
loss_define = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=5e-4)
train_loss = 0 ## 训练损失
correct = 0 ## 识别正确个数
total = 0 ## 总个数
# momentum=0.9,
epoch_num = 10
# int(input('input epoch:'))
loss_plot = []
for epoch in range(epoch_num):
####
for batch_idx, (inputs, targets) in enumerate(data_train_loader):
