def __init__(self, trunk_name, output_stride=8, pretrained=True):
super(get_resnet, self).__init__()
if trunk_name == "resnet18":
resnet = resnet18(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnet34":
resnet = resnet34(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnet50":
resnet = resnet50(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnet101":
resnet = resnet101(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnet152":
resnet = resnet152(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnext101_32x8d":
resnet = resnext101_32x8d(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "se_resnet50":
resnet = se_resnext50_32x4d(pretrained=pretrained)
elif trunk_name == "se_resnet101":
resnet = se_resnext101_32x4d(pretrained=pretrained)
else:
raise KeyError("[*] Not a valid network arch")
self.layer0 = resnet.layer0
self.layer1, self.layer2, self.layer3, self.layer4 = \
resnet.layer1, resnet.layer2, resnet.layer3, resnet.layer4
if output_stride == 8:
for n, m in self.layer3.named_modules():
if 'conv2' in n:
m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
for n, m in self.layer4.named_modules():
if 'conv2' in n:
m.dilation, m.padding, m.stride = (4, 4), (4, 4), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
elif output_stride == 16:
for n, m in self.layer4.named_modules():
if 'conv2' in n:
m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
else:
raise KeyError(
"[*] Unsupported output_stride {}".format(output_stride))
def __init__(self, num_classes=256, input_bits=8, embed_dim=8, seq_len=100, backbone='resnet50'):
super(CNN, self).__init__()
self.input_bits = input_bits
self.seq_len = seq_len
# self.embed = nn.Embedding(256, 256)
self.embed = nn.Embedding(2 ** input_bits, embed_dim)
self.backbone = {
'resnet18': resnet18(n_classes=num_classes, input_channels=embed_dim),
'resnet34': resnet34(n_classes=num_classes, input_channels=embed_dim),
'resnet50': resnet50(n_classes=num_classes, input_channels=embed_dim),
'resnet101': resnet101(n_classes=num_classes, input_channels=embed_dim),
'resnet152': resnet152(n_classes=num_classes, input_channels=embed_dim),
}.get(backbone, 'resnet50')
def __init__(self, args, train_transform=None, test_transform=None, val_transform=None):
self.args = args
self.train_transform = train_transform
self.test_transform = test_transform
self.val_transform = val_transform
self.loss_lams = torch.zeros(args.num_classes, args.num_classes, dtype=torch.float32).cuda()
self.loss_lams[:, :] = 1. / args.num_classes
self.loss_lams.requires_grad = False
if self.args.arch == 'resnet50':
self.net = resnet.resnet50(num_classes=args.num_classes)
elif self.args.arch == 'res2net50':
self.net = res2net.res2net50_26w_4s(num_classes=args.num_classes)
elif self.args.arch == 'resnet101':
self.net = resnet.resnet101()
elif self.args.arch == 'resnet18':
self.net = resnet.resnet18()
elif self.args.arch == 'resnet34':
self.net = resnet.resnet34()
elif self.args.arch == 'vgg16':
self.net = vgg_cub.vgg16()
elif self.args.arch == 'vgg16_bn':
self.net = vgg_cub.vgg16_bn()
elif self.args.arch == 'vgg19':
self.net = vgg_cub.vgg19()
elif self.args.arch == 'vgg19_bn':
self.net = vgg_cub.vgg19_bn()
elif self.args.arch == 'vgg16_std':
self.net = vgg_std.vgg16()
elif self.args.arch == 'vgg16_bn_std':
self.net = vgg_std.vgg16_bn()
elif self.args.arch == 'mobilenetv2':
self.net = mobilenetv2.mobilenet_v2(num_classes=args.num_classes)
if self.args.load_model is not None:
self.net.load_state_dict(torch.load(self.args.load_model), strict=True)
print('load model from %s' % self.args.load_model)
elif self.args.pretrained_model is not None:
self.net.load_state_dict(torch.load(self.args.pretrained_model), strict=False)
print('load pretrained model form %s' % self.args.pretrained_model)
else:
print('not load any model, will train from scrach!')
if args.expname is None:
args.expname = 'runs/{}_{}_{}'.format(args.arch, args.dataset, args.method)
os.makedirs(args.expname, exist_ok=True)
def main(args):
random_seed = args.seed
np.random.seed(random_seed)
random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
torch.cuda.manual_seed_all(random_seed)
torch.backends.cudnn.deterministic = True # need to set to True as well
print(
'Using {}\nTest on {}\nRandom Seed {}\nk_cc {}\nk_outlier {}\nevery n epoch {}\n'
.format(args.net, args.dataset, args.seed, args.k_cc, args.k_outlier,
args.every))
# -- training parameters --
if args.dataset == 'cifar10' or args.dataset == 'cifar100':
num_epoch = 180
milestone = [int(x) for x in args.milestone.split(',')]
batch_size = 128
elif args.dataset == 'pc':
num_epoch = 90
milestone = [30, 60]
batch_size = 128
else:
ValueError('Invalid Dataset!')
start_epoch = 0
num_workers = args.nworker
weight_decay = 1e-4
gamma = 0.5
lr = 0.001
which_data_set = args.dataset # 'cifar100', 'cifar10', 'pc'
noise_level = args.noise # noise level
noise_type = args.type # "uniform", "asymmetric"
train_val_ratio = 0.8
which_net = args.net # "resnet34", "resnet18", "pc"
# -- denoising related parameters --
k_cc = args.k_cc
k_outlier = args.k_outlier
when_to_denoise = args.start_clean # starting from which epoch we denoise
denoise_every_n_epoch = args.every # every n epoch, we perform denoising
# -- specify dataset --
# data augmentation
if which_data_set[:5] == 'cifar':
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)),
])
else:
transform_train = None
transform_test = None
if which_data_set == 'cifar10':
trainset = CIFAR10(root='./data',
split='train',
train_ratio=train_val_ratio,
download=True,
transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
worker_init_fn=_init_fn)
valset = CIFAR10(root='./data',
split='val',
train_ratio=train_val_ratio,
download=True,
transform=transform_test)
valloader = torch.utils.data.DataLoader(valset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
testset = CIFAR10(root='./data',
split='test',
download=True,
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
num_class = 10
in_channel = 3
elif which_data_set == 'cifar100':
trainset = CIFAR100(root='./data',
split='train',
train_ratio=train_val_ratio,
download=True,
transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
worker_init_fn=_init_fn)
valset = CIFAR100(root='./data',
split='val',
train_ratio=train_val_ratio,
download=True,
transform=transform_test)
valloader = torch.utils.data.DataLoader(valset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
testset = CIFAR100(root='./data',
split='test',
download=True,
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
num_class = 100
in_channel = 3
elif which_data_set == 'pc':
trainset = ModelNet40(split='train',
train_ratio=train_val_ratio,
num_ptrs=1024,
random_jitter=True)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
worker_init_fn=_init_fn,
drop_last=True)
valset = ModelNet40(split='val',
train_ratio=train_val_ratio,
num_ptrs=1024)
valloader = torch.utils.data.DataLoader(valset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
testset = ModelNet40(split='test', num_ptrs=1024)
testloader = torch.utils.data.DataLoader(testset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
num_class = 40
else:
ValueError('Invalid Dataset!')
print('train data size:', len(trainset))
print('validation data size:', len(valset))
print('test data size:', len(testset))
ntrain = len(trainset)
# -- generate noise --
y_train = trainset.get_data_labels()
y_train = np.array(y_train)
noise_y_train = None
keep_indices = None
p = None
if noise_type == 'none':
pass
else:
if noise_type == "uniform":
noise_y_train, p, keep_indices = noisify_with_P(
y_train,
nb_classes=num_class,
noise=noise_level,
random_state=random_seed)
trainset.update_corrupted_label(noise_y_train)
print("apply uniform noise")
else:
if which_data_set == 'cifar10':
noise_y_train, p, keep_indices = noisify_cifar10_asymmetric(
y_train, noise=noise_level, random_state=random_seed)
elif which_data_set == 'cifar100':
noise_y_train, p, keep_indices = noisify_cifar100_asymmetric(
y_train, noise=noise_level, random_state=random_seed)
elif which_data_set == 'pc':
noise_y_train, p, keep_indices = noisify_modelnet40_asymmetric(
y_train, noise=noise_level, random_state=random_seed)
trainset.update_corrupted_label(noise_y_train)
print("apply asymmetric noise")
print("clean data num:", len(keep_indices))
print("probability transition matrix:\n{}".format(p))
# -- create log file --
file_name = '(' + which_data_set + '_' + which_net + ')' \
+ 'type_' + noise_type + '_noise_' + str(noise_level) \
+ '_k_cc_' + str(k_cc) + '_k_outlier_' + str(k_outlier) + '_start_' + str(when_to_denoise) \
+ '_every_' + str(denoise_every_n_epoch) + '.txt'
log_dir = check_folder('logs/logs_txt_' + str(random_seed))
file_name = os.path.join(log_dir, file_name)
saver = open(file_name, "w")
saver.write(
'noise type: {}\nnoise level: {}\nk_cc: {}\nk_outlier: {}\nwhen_to_apply_epoch: {}\n'
.format(noise_type, noise_level, k_cc, k_outlier, when_to_denoise))
if noise_type != 'none':
saver.write('total clean data num: {}\n'.format(len(keep_indices)))
saver.write('probability transition matrix:\n{}\n'.format(p))
saver.flush()
# -- set network, optimizer, scheduler, etc --
if which_net == 'resnet18':
net = resnet18(in_channel=in_channel, num_classes=num_class)
feature_size = 512
elif which_net == 'resnet34':
net = resnet34(in_channel=in_channel, num_classes=num_class)
feature_size = 512
elif which_net == 'pc':
net = PointNetCls(k=num_class)
feature_size = 256
else:
ValueError('Invalid network!')
optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=weight_decay)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = net.to(device)
################################################
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=milestone,
gamma=gamma)
criterion = nn.NLLLoss() # since the output of network is by log softmax
# -- misc --
best_acc = 0
best_epoch = 0
best_weights = None
curr_trainloader = trainloader
big_comp = set()
patience = args.patience
no_improve_counter = 0
# -- start training --
for epoch in range(start_epoch, num_epoch):
train_correct = 0
train_loss = 0
train_total = 0
exp_lr_scheduler.step()
net.train()
print("current train data size:", len(curr_trainloader.dataset))
for _, (images, labels, _) in enumerate(curr_trainloader):
if images.size(
0
) == 1: # when batch size equals 1, skip, due to batch normalization
continue
images, labels = images.to(device), labels.to(device)
outputs, features = net(images)
log_outputs = torch.log_softmax(outputs, 1)
loss = criterion(log_outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
train_total += images.size(0)
_, predicted = outputs.max(1)
train_correct += predicted.eq(labels).sum().item()
train_acc = train_correct / train_total * 100.
cprint('epoch: {}'.format(epoch), 'white')
cprint(
'train accuracy: {}\ntrain loss:{}'.format(train_acc, train_loss),
'yellow')
# --- compute big connected components ---
net.eval()
features_all = torch.zeros(ntrain, feature_size).to(device)
prob_all = torch.zeros(ntrain, num_class)
labels_all = torch.zeros(ntrain, num_class)
train_gt_labels = np.zeros(ntrain, dtype=np.uint64)
train_pred_labels = np.zeros(ntrain, dtype=np.uint64)
for _, (images, labels, indices) in enumerate(trainloader):
images = images.to(device)
outputs, features = net(images)
softmax_outputs = torch.softmax(outputs, 1)
features_all[indices] = features.detach()
prob_all[indices] = softmax_outputs.detach().cpu()
tmp_zeros = torch.zeros(labels.shape[0], num_class)
tmp_zeros[torch.arange(labels.shape[0]), labels] = 1.0
labels_all[indices] = tmp_zeros
train_gt_labels[indices] = labels.cpu().numpy().astype(np.int64)
train_pred_labels[indices] = labels.cpu().numpy().astype(np.int64)
if epoch >= when_to_denoise and (
epoch - when_to_denoise) % denoise_every_n_epoch == 0:
cprint('\n>> Computing Big Components <<', 'white')
labels_all = labels_all.numpy()
train_gt_labels = train_gt_labels.tolist()
train_pred_labels = np.squeeze(train_pred_labels).ravel().tolist()
_, idx_of_comp_idx2 = calc_topo_weights_with_components_idx(
ntrain,
labels_all,
features_all,
train_gt_labels,
train_pred_labels,
k=k_cc,
use_log=False,
cp_opt=3,
nclass=num_class)
# --- update largest connected component ---
cur_big_comp = list(set(range(ntrain)) - set(idx_of_comp_idx2))
big_comp = big_comp.union(set(cur_big_comp))
# --- remove outliers in largest connected component ---
big_com_idx = list(big_comp)
feats_big_comp = features_all[big_com_idx]
labels_big_comp = np.array(train_gt_labels)[big_com_idx]
knnG_list = calc_knn_graph(feats_big_comp, k=args.k_outlier)
knnG_list = np.array(knnG_list)
knnG_shape = knnG_list.shape
knn_labels = labels_big_comp[knnG_list.ravel()]
knn_labels = np.reshape(knn_labels, knnG_shape)
majority, counts = mode(knn_labels, axis=-1)
majority = majority.ravel()
counts = counts.ravel()
if args.zeta > 1.0: # use majority vote
non_outlier_idx = np.where(majority == labels_big_comp)[0]
outlier_idx = np.where(majority != labels_big_comp)[0]
outlier_idx = np.array(list(big_comp))[outlier_idx]
print(">> majority == labels_big_comp -> size: ",
len(non_outlier_idx))
else: # zeta filtering
non_outlier_idx = np.where((majority == labels_big_comp) & (
counts >= args.k_outlier * args.zeta))[0]
print(">> zeta {}, then non_outlier_idx -> size: {}".format(
args.zeta, len(non_outlier_idx)))
outlier_idx = np.where(majority != labels_big_comp)[0]
outlier_idx = np.array(list(big_comp))[outlier_idx]
cprint(">> The number of outliers: {}".format(len(outlier_idx)),
'red')
cprint(
">> The purity of outliers: {}".format(
np.sum(y_train[outlier_idx] == noise_y_train[outlier_idx])
/ float(len(outlier_idx))), 'red')
big_comp = np.array(list(big_comp))[non_outlier_idx]
big_comp = set(big_comp.tolist())
# --- construct updated dataset set, which contains the collected clean data ---
if which_data_set == 'cifar10':
trainset_ignore_noisy_data = CIFAR10(
root='./data',
split='train',
train_ratio=train_val_ratio,
download=True,
transform=transform_train)
elif which_data_set == 'cifar100':
trainset_ignore_noisy_data = CIFAR100(
root='./data',
split='train',
train_ratio=train_val_ratio,
download=True,
transform=transform_train)
else:
trainset_ignore_noisy_data = ModelNet40(
split='train',
train_ratio=train_val_ratio,
num_ptrs=1024,
random_jitter=True)
trainloader_ignore_noisy_data = torch.utils.data.DataLoader(
trainset_ignore_noisy_data,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
worker_init_fn=_init_fn,
drop_last=True)
curr_trainloader = trainloader_ignore_noisy_data
noisy_data_indices = list(set(range(ntrain)) - big_comp)
trainset_ignore_noisy_data.update_corrupted_label(noise_y_train)
trainset_ignore_noisy_data.ignore_noise_data(noisy_data_indices)
clean_data_num = len(big_comp.intersection(set(keep_indices)))
noise_data_num = len(big_comp) - clean_data_num
print("Big Comp Number:", len(big_comp))
print("Found Noisy Data Number:", noise_data_num)
print("Found True Data Number:", clean_data_num)
# compute purity of the component
cc_size = len(big_comp)
equal = np.sum(
noise_y_train[list(big_comp)] == y_train[list(big_comp)])
ratio = equal / float(cc_size)
print("Purity of current component: {}".format(ratio))
noise_size = len(noisy_data_indices)
equal = np.sum(noise_y_train[noisy_data_indices] ==
y_train[noisy_data_indices])
print("Purity of data outside component: {}".format(
equal / float(noise_size)))
saver.write('Purity {}\tsize{}\t'.format(ratio, cc_size))
# --- validation ---
val_total = 0
val_correct = 0
net.eval()
with torch.no_grad():
for _, (images, labels, _) in enumerate(valloader):
images, labels = images.to(device), labels.to(device)
outputs, _ = net(images)
val_total += images.size(0)
_, predicted = outputs.max(1)
val_correct += predicted.eq(labels).sum().item()
val_acc = val_correct / val_total * 100.
if val_acc > best_acc:
best_acc = val_acc
best_epoch = epoch
best_weights = copy.deepcopy(net.state_dict())
no_improve_counter = 0
else:
no_improve_counter += 1
if no_improve_counter >= patience:
print(
'>> No improvement for {} epochs. Stop at epoch {}'.format(
patience, epoch))
saver.write(
'>> No improvement for {} epochs. Stop at epoch {}'.format(
patience, epoch))
saver.write(
'>> val epoch: {}\n>> current accuracy: {}\n'.format(
epoch, val_acc))
saver.write('>> best accuracy: {}\tbest epoch: {}\n\n'.format(
best_acc, best_epoch))
break
cprint('val accuracy: {}'.format(val_acc), 'cyan')
cprint(
'>> best accuracy: {}\n>> best epoch: {}\n'.format(
best_acc, best_epoch), 'green')
saver.write('{}\n'.format(val_acc))
# -- testing
cprint('>> testing <<', 'cyan')
test_total = 0
test_correct = 0
net.load_state_dict(best_weights)
net.eval()
with torch.no_grad():
for _, (images, labels, _) in enumerate(testloader):
images, labels = images.to(device), labels.to(device)
outputs, _ = net(images)
test_total += images.size(0)
_, predicted = outputs.max(1)
test_correct += predicted.eq(labels).sum().item()
test_acc = test_correct / test_total * 100.
cprint('>> test accuracy: {}'.format(test_acc), 'cyan')
saver.write('>> test accuracy: {}\n'.format(test_acc))
# retest on the validation set, for sanity check
cprint('>> validation <<', 'cyan')
val_total = 0
val_correct = 0
net.eval()
with torch.no_grad():
for _, (images, labels, _) in enumerate(valloader):
images, labels = images.to(device), labels.to(device)
outputs, _ = net(images)
val_total += images.size(0)
_, predicted = outputs.max(1)
val_correct += predicted.eq(labels).sum().item()
val_acc = val_correct / val_total * 100.
cprint('>> validation accuracy: {}'.format(val_acc), 'cyan')
saver.write('>> validation accuracy: {}'.format(val_acc))
saver.close()
return test_acc
def create_model(Model, num_classes):
if Model == "ResNet18":
model = resnet18(pretrained=False)
fc_features = model.fc.in_features
model.fc = nn.Linear(fc_features, num_classes)
model = model.cuda()
elif Model == "ResNet34":
model = resnet34(pretrained=False).cuda()
fc_features = model.fc.in_features
model.fc = nn.Linear(fc_features, num_classes)
model = model.cuda()
elif Model == "ResNet50":
model = resnet50(pretrained=False).cuda()
fc_features = model.fc.in_features
model.fc = nn.Linear(fc_features, num_classes)
model = model.cuda()
elif Model == "ResNet101":
model = resnet101(pretrained=False).cuda()
fc_features = model.fc.in_features
model.fc = nn.Linear(fc_features, num_classes)
model = model.cuda()
elif Model == "MobileNet_v2":
model = mobilenet_v2(num_classes=num_classes, pretrained=False).cuda()
elif Model == "Mobilenetv3":
model = mobilenetv3(n_class=num_classes, pretrained=False).cuda()
elif Model == "shufflenet_v2_x0_5":
model = shufflenet_v2_x0_5(pretrained=False,
num_classes=num_classes).cuda()
elif Model == "shufflenet_v2_x1_0":
model = shufflenet_v2_x1_0(pretrained=False,
num_classes=num_classes).cuda()
elif Model == "shufflenet_v2_x1_5":
model = shufflenet_v2_x1_5(pretrained=False,
num_classes=num_classes).cuda()
elif Model == "shufflenet_v2_x1_5":
model = shufflenet_v2_x2_0(pretrained=False,
num_classes=num_classes).cuda()
elif "efficientnet" in Model:
model = EfficientNet.from_pretrained(Model,
num_classes=num_classes,
pretrained=False).cuda()
elif Model == "inception_v3":
model = inception_v3(pretrained=False, num_classes=num_classes).cuda()
elif Model == "mnasnet0_5":
model = mnasnet0_5(pretrained=False, num_classes=num_classes).cuda()
elif Model == "vgg11":
model = vgg11(pretrained=False, num_classes=num_classes).cuda()
elif Model == "vgg11_bn":
model = vgg11_bn(pretrained=False, num_classes=num_classes).cuda()
elif Model == "vgg19":
model = vgg19(pretrained=False, num_classes=num_classes).cuda()
elif Model == "densenet121":
model = densenet121(pretrained=False).cuda()
elif Model == "ResNeXt29_32x4d":
model = ResNeXt29_32x4d(num_classes=num_classes).cuda()
elif Model == "ResNeXt29_2x64d":
model = ResNeXt29_2x64d(num_classes=num_classes).cuda()
else:
print("model error")
# input = torch.randn(1, 3, 32, 32).cuda()
# flops, params = profile(model, inputs=(input,))
# flops, params = clever_format([flops, params], "%.3f")
# print("------------------------------------------------------------------------")
# print(" ",Model)
# print( " flops:", flops, " params:", params)
# print("------------------------------------------------------------------------")
return model
def main(args):
random_seed = int(np.random.choice(range(1000), 1))
np.random.seed(random_seed)
random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
torch.cuda.manual_seed_all(random_seed)
torch.backends.cudnn.deterministic = True
arg_which_net = args.network
arg_dataset = args.dataset
arg_epoch_start = args.epoch_start
lr = args.lr
arg_gpu = args.gpu
arg_num_gpu = args.n_gpus
arg_every_n_epoch = args.every_n_epoch # interval to perform the correction
arg_epoch_update = args.epoch_update # the epoch to start correction (warm-up period)
arg_epoch_interval = args.epoch_interval # interval between two update of A
noise_level = args.noise_level
noise_type = args.noise_type # "uniform", "asymmetric", "none"
train_val_ratio = 0.9
which_net = arg_which_net # "cnn" "resnet18" "resnet34" "preact_resnet18" "preact_resnet34" "preact_resnet101" "pc"
num_epoch = args.n_epochs # Total training epochs
print('Using {}\nTest on {}\nRandom Seed {}\nevery n epoch {}\nStart at epoch {}'.
format(arg_which_net, arg_dataset, random_seed, arg_every_n_epoch, arg_epoch_start))
# -- training parameters
if arg_dataset == 'mnist':
milestone = [30, 60]
batch_size = 64
in_channels = 1
elif arg_dataset == 'cifar10':
milestone = [60, 180]
batch_size = 128
in_channels = 1
elif arg_dataset == 'cifar100':
milestone = [60, 180]
batch_size = 128
in_channels = 1
elif arg_dataset == 'pc':
milestone = [30, 60]
batch_size = 128
start_epoch = 0
num_workers = 1
#gamma = 0.5
# -- specify dataset
# data augmentation
if arg_dataset == 'cifar10':
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)),
])
elif arg_dataset == 'cifar100':
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.507, 0.487, 0.441), (0.507, 0.487, 0.441)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.507, 0.487, 0.441), (0.507, 0.487, 0.441)),
])
elif arg_dataset == 'mnist':
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
])
else:
transform_train = None
transform_test = None
if arg_dataset == 'cifar10':
trainset = CIFAR10(root='./data', split='train', train_ratio=train_val_ratio, trust_ratio=0, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers, worker_init_fn=_init_fn)
valset = CIFAR10(root='./data', split='val', train_ratio=train_val_ratio, trust_ratio=0, download=True, transform=transform_test)
valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
testset = CIFAR10(root='./data', split='test', download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
images_size = [3, 32, 32]
num_class = 10
in_channel = 3
elif arg_dataset == 'cifar100':
trainset = CIFAR100(root='./data', split='train', train_ratio=train_val_ratio, trust_ratio=0, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers,worker_init_fn=_init_fn)
valset = CIFAR100(root='./data', split='val', train_ratio=train_val_ratio, trust_ratio=0, download=True, transform=transform_test)
valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
testset = CIFAR100(root='./data', split='test', download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
num_class = 100
in_channel = 3
elif arg_dataset == 'mnist':
trainset = MNIST(root='./data', split='train', train_ratio=train_val_ratio, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers,worker_init_fn=_init_fn)
valset = MNIST(root='./data', split='val', train_ratio=train_val_ratio, download=True, transform=transform_test)
valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
testset = MNIST(root='./data', split='test', download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
num_class = 10
in_channel = 1
elif arg_dataset == 'pc':
trainset = ModelNet40(split='train', train_ratio=train_val_ratio, num_ptrs=1024, random_jitter=True)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers, worker_init_fn=_init_fn, drop_last=True)
valset = ModelNet40(split='val', train_ratio=train_val_ratio, num_ptrs=1024)
valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
testset = ModelNet40(split='test', num_ptrs=1024)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
num_class = 40
print('train data size:', len(trainset))
print('validation data size:', len(valset))
print('test data size:', len(testset))
eps = 1e-6 # This is the epsilon used to soft the label (not the epsilon in the paper)
ntrain = len(trainset)
# -- generate noise --
# y_train is ground truth labels, we should not have any access to this after the noisy labels are generated
# algorithm after y_tilde is generated has nothing to do with y_train
y_train = trainset.get_data_labels()
y_train = np.array(y_train)
noise_y_train = None
keep_indices = None
p = None
if(noise_type == 'none'):
pass
else:
if noise_type == "uniform":
noise_y_train, p, keep_indices = noisify_with_P(y_train, nb_classes=num_class, noise=noise_level, random_state=random_seed)
trainset.update_corrupted_label(noise_y_train)
noise_softlabel = torch.ones(ntrain, num_class)*eps/(num_class-1)
noise_softlabel.scatter_(1, torch.tensor(noise_y_train.reshape(-1, 1)), 1-eps)
trainset.update_corrupted_softlabel(noise_softlabel)
print("apply uniform noise")
else:
if arg_dataset == 'cifar10':
noise_y_train, p, keep_indices = noisify_cifar10_asymmetric(y_train, noise=noise_level, random_state=random_seed)
elif arg_dataset == 'cifar100':
noise_y_train, p, keep_indices = noisify_cifar100_asymmetric(y_train, noise=noise_level, random_state=random_seed)
elif arg_dataset == 'mnist':
noise_y_train, p, keep_indices = noisify_mnist_asymmetric(y_train, noise=noise_level, random_state=random_seed)
elif arg_dataset == 'pc':
noise_y_train, p, keep_indices = noisify_modelnet40_asymmetric(y_train, noise=noise_level,
random_state=random_seed)
trainset.update_corrupted_label(noise_y_train)
noise_softlabel = torch.ones(ntrain, num_class) * eps / (num_class - 1)
noise_softlabel.scatter_(1, torch.tensor(noise_y_train.reshape(-1, 1)), 1 - eps)
trainset.update_corrupted_softlabel(noise_softlabel)
print("apply asymmetric noise")
print("clean data num:", len(keep_indices))
print("probability transition matrix:\n{}".format(p))
# -- create log file
file_name = '[' + arg_dataset + '_' + which_net + ']' \
+ 'type:' + noise_type + '_' + 'noise:' + str(noise_level) + '_' \
+ '_' + 'start:' + str(arg_epoch_start) + '_' \
+ 'every:' + str(arg_every_n_epoch) + '_'\
+ 'time:' + str(datetime.datetime.now()) + '.txt'
log_dir = check_folder('new_logs/logs_txt_' + str(random_seed))
file_name = os.path.join(log_dir, file_name)
saver = open(file_name, "w")
saver.write('noise type: {}\nnoise level: {}\nwhen_to_apply_epoch: {}\n'.format(
noise_type, noise_level, arg_epoch_start))
if noise_type != 'none':
saver.write('total clean data num: {}\n'.format(len(keep_indices)))
saver.write('probability transition matrix:\n{}\n'.format(p))
saver.flush()
# -- set network, optimizer, scheduler, etc
if which_net == "cnn":
net_trust = CNN9LAYER(input_channel=in_channel, n_outputs=num_class)
net = CNN9LAYER(input_channel=in_channel, n_outputs=num_class)
net.apply(weight_init)
feature_size = 128
elif which_net == 'resnet18':
net_trust = resnet18(in_channel=in_channel, num_classes=num_class)
net = resnet18(in_channel=in_channel, num_classes=num_class)
feature_size = 512
elif which_net == 'resnet34':
net_trust = resnet34(in_channel=in_channel, num_classes=num_class)
net = resnet34(in_channel=in_channel, num_classes=num_class)
feature_size = 512
elif which_net == 'preact_resnet18':
net_trust = preact_resnet18(num_classes=num_class, num_input_channels=in_channel)
net = preact_resnet18(num_classes=num_class, num_input_channels=in_channel)
feature_size = 256
elif which_net == 'preact_resnet34':
net_trust = preact_resnet34(num_classes=num_class, num_input_channels=in_channel)
net = preact_resnet34(num_classes=num_class, num_input_channels=in_channel)
feature_size = 256
elif which_net == 'preact_resnet101':
net_trust = preact_resnet101()
net = preact_resnet101()
feature_size = 256
elif which_net == 'pc':
net_trust = PointNetCls(k=num_class)
net = PointNetCls(k=num_class)
feature_size = 256
else:
ValueError('Invalid network!')
opt_gpus = [i for i in range(arg_gpu, arg_gpu+int(arg_num_gpu))]
if len(opt_gpus) > 1:
print("Using ", len(opt_gpus), " GPUs")
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str(x) for x in opt_gpus)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
if len(opt_gpus) > 1:
net_trust = torch.nn.DataParallel(net_trust)
net = torch.nn.DataParallel(net)
net_trust.to(device)
net.to(device)
# net.apply(conv_init)
# ------------------------- Initialize Setting ------------------------------
best_acc = 0
best_epoch = 0
patience = 50
no_improve_counter = 0
A = 1/num_class*torch.ones(ntrain, num_class, num_class, requires_grad=False).float().to(device)
h = np.zeros([ntrain, num_class])
criterion_1 = nn.NLLLoss()
pred_softlabels = np.zeros([ntrain, arg_every_n_epoch, num_class], dtype=np.float)
train_acc_record = []
clean_train_acc_record = []
noise_train_acc_record = []
val_acc_record = []
recovery_record = []
noise_ytrain = copy.copy(noise_y_train)
#noise_ytrain = torch.tensor(noise_ytrain).to(device)
cprint("================ Clean Label... ================", "yellow")
for epoch in range(num_epoch): # Add some modification here
train_correct = 0
train_loss = 0
train_total = 0
delta = 1.2 + 0.02*max(epoch - arg_epoch_update + 1, 0)
clean_train_correct = 0
noise_train_correct = 0
optimizer_trust = RAdam(net_trust.parameters(),
lr=learning_rate(lr, epoch),
weight_decay=5e-4)
#optimizer_trust = optim.SGD(net_trust.parameters(), lr=learning_rate(lr, epoch), weight_decay=5e-4,
# nesterov=True, momentum=0.9)
net_trust.train()
# Train with noisy data
for i, (images, labels, softlabels, indices) in enumerate(tqdm(trainloader, ncols=100, ascii=True)):
if images.size(0) == 1: # when batch size equals 1, skip, due to batch normalization
continue
images, labels, softlabels = images.to(device), labels.to(device), softlabels.to(device)
outputs, features = net_trust(images)
log_outputs = torch.log_softmax(outputs, 1).float()
# arg_epoch_start : epoch start to introduce loss retro
# arg_epoch_interval : epochs between two updating of A
if epoch in [arg_epoch_start-1, arg_epoch_start+arg_epoch_interval-1]:
h[indices] = log_outputs.detach().cpu()
normal_outputs = torch.softmax(outputs, 1)
if epoch >= arg_epoch_start: # use loss_retro + loss_ce
A_batch = A[indices].to(device)
loss = sum([-A_batch[i].matmul(softlabels[i].reshape(-1, 1).float()).t().matmul(log_outputs[i])
for i in range(len(indices))]) / len(indices) + \
criterion_1(log_outputs, labels)
else: # use loss_ce
loss = criterion_1(log_outputs, labels)
optimizer_trust.zero_grad()
loss.backward()
optimizer_trust.step()
#arg_every_n_epoch : rolling windows to get eta_tilde
if epoch >= (arg_epoch_update - arg_every_n_epoch):
pred_softlabels[indices, epoch % arg_every_n_epoch, :] = normal_outputs.detach().cpu().numpy()
train_loss += loss.item()
train_total += images.size(0)
_, predicted = outputs.max(1)
train_correct += predicted.eq(labels).sum().item()
# For monitoring purpose, comment the following line out if you have your own dataset that doesn't have ground truth label
train_label_clean = torch.tensor(y_train)[indices].to(device)
train_label_noise = torch.tensor(noise_ytrain[indices]).to(device)
clean_train_correct += predicted.eq(train_label_clean).sum().item()
noise_train_correct += predicted.eq(train_label_noise).sum().item() # acc wrt the original noisy labels
train_acc = train_correct / train_total * 100
clean_train_acc = clean_train_correct/train_total*100
noise_train_acc = noise_train_correct/train_total*100
print(" Train Epoch: [{}/{}] \t Training Acc wrt Corrected {:.3f} \t Train Acc wrt True {:.3f} \t Train Acc wrt Noise {:.3f}".
format(epoch, num_epoch, train_acc, clean_train_acc, noise_train_acc))
# updating A
if epoch in [arg_epoch_start - 1, arg_epoch_start + 40 - 1]:
cprint("+++++++++++++++++ Updating A +++++++++++++++++++", "magenta")
unsolved = 0
infeasible = 0
y_soft = trainset.get_data_softlabel()
with torch.no_grad():
for i in tqdm(range(ntrain), ncols=100, ascii=True):
try:
result, A_opt = updateA(y_soft[i], h[i], rho=0.9)
except:
A[i] = A[i]
unsolved += 1
continue
if (result == np.inf):
A[i] = A[i]
infeasible += 1
else:
A[i] = torch.tensor(A_opt)
print(A[0])
print("Unsolved points: {} | Infeasible points: {}".format(unsolved, infeasible))
# applying lRT scheme
# args_epoch_update : epoch to update labels
if epoch >= arg_epoch_update:
y_tilde = trainset.get_data_labels()
pred_softlabels_bar = pred_softlabels.mean(1)
clean_labels, clean_softlabels = lrt_flip_scheme(pred_softlabels_bar, y_tilde, delta)
trainset.update_corrupted_softlabel(clean_softlabels)
trainset.update_corrupted_label(clean_softlabels.argmax(1))
# validation
if not (epoch % 5):
val_total = 0
val_correct = 0
net_trust.eval()
with torch.no_grad():
for i, (images, labels, _, _) in enumerate(valloader):
images, labels = images.to(device), labels.to(device)
outputs, _ = net_trust(images)
val_total += images.size(0)
_, predicted = outputs.max(1)
val_correct += predicted.eq(labels).sum().item()
val_acc = val_correct / val_total * 100
train_acc_record.append(train_acc)
val_acc_record.append(val_acc)
clean_train_acc_record.append(clean_train_acc)
noise_train_acc_record.append(noise_train_acc)
recovery_acc = np.sum(trainset.get_data_labels() == y_train) / ntrain
recovery_record.append(recovery_acc)
if val_acc > best_acc:
best_acc = val_acc
best_epoch = epoch
no_improve_counter = 0
else:
no_improve_counter += 1
if no_improve_counter >= patience:
print('>> No improvement for {} epochs. Stop at epoch {}'.format(patience, epoch))
saver.write('>> No improvement for {} epochs. Stop at epoch {}'.format(patience, epoch))
saver.write('>> val epoch: {}\n>> current accuracy: {}%\n'.format(epoch, val_acc))
saver.write('>> best accuracy: {}\tbest epoch: {}\n\n'.format(best_acc, best_epoch))
break
cprint('val accuracy: {}'.format(val_acc), 'cyan')
cprint('>> best accuracy: {}\n>> best epoch: {}\n'.format(best_acc, best_epoch), 'green')
cprint('>> final recovery rate: {}\n'.format(recovery_acc),
'green')
saver.write('>> val epoch: {}\n>> current accuracy: {}%\n'.format(epoch, val_acc))
saver.write("outputs: {}\n".format(normal_outputs))
saver.write('>> best accuracy: {}\tbest epoch: {}\n\n'.format(best_acc, best_epoch))
saver.write(
'>> final recovery rate: {}%\n'.format(np.sum(trainset.get_data_labels() == y_train) / ntrain * 100))
saver.flush()
# If want to train the neural network again with corrected labels and original loss_ce again
# set args.two_stage to True
print("Use Two-Stage Model {}".format(args.two_stage))
if args.two_stage==True:
criterion_2 = nn.NLLLoss()
best_acc = 0
best_epoch = 0
patience = 50
no_improve_counter = 0
cprint("================ Normal Training ================", "yellow")
for epoch in range(num_epoch): # Add some modification here
train_correct = 0
train_loss = 0
train_total = 0
optimizer_trust = optim.SGD(net.parameters(), momentum=0.9, nesterov=True,
lr=learning_rate(lr, epoch),
weight_decay=5e-4)
net.train()
for i, (images, labels, softlabels, indices) in enumerate(tqdm(trainloader, ncols=100, ascii=True)):
if images.size(0) == 1: # when batch size equals 1, skip, due to batch normalization
continue
images, labels, softlabels = images.to(device), labels.to(device), softlabels.to(device)
outputs, features = net(images)
log_outputs = torch.log_softmax(outputs, 1).float()
loss = criterion_2(log_outputs, labels)
optimizer_trust.zero_grad()
loss.backward()
optimizer_trust.step()
train_loss += loss.item()
train_total += images.size(0)
_, predicted = outputs.max(1)
train_correct += predicted.eq(labels).sum().item()
train_acc = train_correct / train_total * 100
print(" Train Epoch: [{}/{}] \t Training Accuracy {}%".format(epoch, num_epoch, train_acc))
if not (epoch % 5):
val_total = 0
val_correct = 0
net_trust.eval()
with torch.no_grad():
for i, (images, labels, _, _) in enumerate(valloader):
images, labels = images.to(device), labels.to(device)
outputs, _ = net(images)
val_total += images.size(0)
_, predicted = outputs.max(1)
val_correct += predicted.eq(labels).sum().item()
val_acc = val_correct / val_total * 100
if val_acc > best_acc:
best_acc = val_acc
best_epoch = epoch
no_improve_counter = 0
else:
no_improve_counter += 1
if no_improve_counter >= patience:
print('>> No improvement for {} epochs. Stop at epoch {}'.format(patience, epoch))
saver.write('>> No improvement for {} epochs. Stop at epoch {}'.format(patience, epoch))
saver.write('>> val epoch: {}\n>> current accuracy: {}%\n'.format(epoch, val_acc))
saver.write('>> best accuracy: {}\tbest epoch: {}\n\n'.format(best_acc, best_epoch))
break
cprint('val accuracy: {}'.format(val_acc), 'cyan')
cprint('>> best accuracy: {}\n>> best epoch: {}\n'.format(best_acc, best_epoch), 'green')
cprint('>> final recovery rate: {}\n'.format(np.sum(trainset.get_data_labels() == y_train) / ntrain), 'green')
cprint("================ Start Testing ================", "yellow")
test_total = 0
test_correct = 0
net_trust.eval()
for i, (images, labels, softlabels, indices) in enumerate(testloader):
if images.shape[0] == 1:
continue
images, labels = images.to(device), labels.to(device)
outputs, _ = net_trust(images)
test_total += images.shape[0]
test_correct += outputs.argmax(1).eq(labels).sum().item()
test_acc = test_correct/test_total*100
print("Final test accuracy {} %".format(test_correct/test_total*100))
return test_acc
def main_worker(gpu, ngpus_per_node, args):
global best_acc1, best_loc1, best_epoch, \
loc1_at_best_acc1, acc1_at_best_loc1, \
gtknown_at_best_acc1, gtknown_at_best_loc1
global writer
args.gpu = gpu
log_folder = os.path.join('train_log', args.name, ts)
args.save_dir = log_folder
if args.gpu == 0:
writer = SummaryWriter(logdir=log_folder)
if not os.path.isdir(log_folder):
os.makedirs(log_folder, exist_ok=True)
with open('{}/args.json'.format(log_folder), 'w') as fp:
json.dump(args.__dict__, fp)
Logger(os.path.join(log_folder, 'log.log'))
print('args: ', args)
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
if args.dataset == 'CUB':
num_classes = 200
elif args.dataset == 'tiny_imagenet':
num_classes = 200
elif args.dataset == 'ILSVRC':
num_classes = 1000
else:
raise Exception("Not preferred dataset.")
if args.arch == 'vgg16':
model = vgg.vgg16(pretrained=True, num_classes=num_classes)
elif args.arch == 'vgg16_GAP':
model = vgg.vgg16_GAP(pretrained=True, num_classes=num_classes)
elif args.arch == 'vgg16_ADL':
model = vgg.vgg16_ADL(pretrained=True,
num_classes=num_classes,
ADL_position=args.ADL_position,
drop_rate=args.ADL_rate,
drop_thr=args.ADL_thr)
elif args.arch == 'resnet50_ADL':
model = resnet.resnet50(pretrained=True,
num_classes=num_classes,
ADL_position=args.ADL_position,
drop_rate=args.ADL_rate,
drop_thr=args.ADL_thr)
elif args.arch == 'resnet50':
model = resnet.resnet50(pretrained=True, num_classes=num_classes)
elif args.arch == 'resnet34_ADL':
model = resnet.resnet34(pretrained=True,
num_classes=num_classes,
ADL_position=args.ADL_position,
drop_rate=args.ADL_rate,
drop_thr=args.ADL_thr)
elif args.arch == 'se_resnet50_ADL':
model = resnet.resnet50_se(pretrained=True,
num_classes=num_classes,
ADL_position=args.ADL_position,
drop_rate=args.ADL_rate,
drop_thr=args.ADL_thr)
else:
model = None
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(args.workers / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu], find_unused_parameters=True)
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
param_features = []
param_classifiers = []
if args.arch.startswith('vgg'):
for name, parameter in model.named_parameters():
if 'features.' in name:
param_features.append(parameter)
else:
param_classifiers.append(parameter)
elif args.arch.startswith('resnet') or args.arch.startswith('se'):
for name, parameter in model.named_parameters():
if 'layer4.' in name or 'fc.' in name:
param_classifiers.append(parameter)
else:
param_features.append(parameter)
else:
raise Exception("Fail to recognize the architecture")
optimizer = torch.optim.SGD([{
'params': param_features,
'lr': args.lr
}, {
'params': param_classifiers,
'lr': args.lr * args.lr_ratio
}],
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=args.nest)
# optionally resume from a checkpoint
if args.resume:
model, optimizer = load_model(model, optimizer, args)
# for param_group in optimizer.param_groups:
# param_group['lr'] = args.lr
cudnn.benchmark = True
# CUB-200-2011
train_loader, val_loader, train_sampler = data_loader(args)
if args.cam_curve:
cam_curve(val_loader, model, criterion, writer, args)
return
if args.evaluate:
evaluate(val_loader, model, criterion, args)
return
if args.gpu == 0:
print("Batch Size per Tower: %d" % (args.batch_size))
print(model)
for epoch in range(args.start_epoch, args.epochs):
if args.gpu == 0:
print(
"===========================================================")
print("Start Epoch %d ..." % (epoch + 1))
if args.distributed:
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch, args)
val_acc1 = 0
val_loss = 0
val_gtloc = 0
val_loc = 0
# train for one epoch
train_acc, train_loss, progress_train = \
train(train_loader, model, criterion, optimizer, epoch, args)
if args.gpu == 0:
progress_train.display(epoch + 1)
# evaluate on validation set
if args.task == 'cls':
val_acc1, val_loss = validate(val_loader, model, criterion, epoch,
args)
# evaluate localization on validation set
elif args.task == 'wsol':
val_acc1, val_acc5, val_loss, \
val_gtloc, val_loc = evaluate_loc(val_loader, model, criterion, epoch, args)
# tensorboard
if args.gpu == 0:
writer.add_scalar(args.name + '/train_acc', train_acc, epoch)
writer.add_scalar(args.name + '/train_loss', train_loss, epoch)
writer.add_scalar(args.name + '/val_cls_acc', val_acc1, epoch)
writer.add_scalar(args.name + '/val_loss', val_loss, epoch)
writer.add_scalar(args.name + '/val_gt_loc', val_gtloc, epoch)
writer.add_scalar(args.name + '/val_loc1', val_loc, epoch)
# remember best acc@1 and save checkpoint
is_best = val_acc1 > best_acc1
best_acc1 = max(val_acc1, best_acc1)
if is_best:
best_epoch = epoch + 1
loc1_at_best_acc1 = val_loc
gtknown_at_best_acc1 = val_gtloc
if args.task == 'wsol':
# in case best loc,, Not using this.
is_best_loc = val_loc > best_loc1
best_loc1 = max(val_loc, best_loc1)
if is_best_loc:
best_epoch = epoch + 1
acc1_at_best_loc1 = val_acc1
gtknown_at_best_loc1 = val_gtloc
if args.gpu == 0:
print("\nCurrent Best Epoch: %d" % (best_epoch))
print("Top-1 GT-Known Localization Acc: %.3f \
\nTop-1 Localization Acc: %.3f\
\nTop-1 Classification Acc: %.3f" % \
(gtknown_at_best_acc1, loc1_at_best_acc1, best_acc1))
print("\nEpoch %d finished." % (epoch + 1))
if not args.multiprocessing_distributed or (
args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0):
saving_dir = os.path.join(log_folder)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
}, is_best, saving_dir)
if args.gpu == 0:
save_train(best_acc1, loc1_at_best_acc1, gtknown_at_best_acc1,
best_loc1, acc1_at_best_loc1, gtknown_at_best_loc1, args)
print("===========================================================")
print("Start Evaluation on Best Checkpoint ...")
args.resume = os.path.join(log_folder, 'model_best.pth.tar')
model, _ = load_model(model, optimizer, args)
evaluate(val_loader, model, criterion, args)
cam_curve(val_loader, model, criterion, writer, args)