if value.requires_grad:
if 'classifier' not in key:
params += [{'params': [value], 'lr': args.multi,
'weight_decay': 0.0005}]
else:
params += [{'params': [value], 'lr': args.multi * 10,
'weight_decay': 0.0005}]
F1 = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
weights_init(F1)
G = nn.DataParallel(G)
F1 = nn.DataParallel(F1)
G = G.to(device)
F1 = F1.to(device)
opt = {}
opt["logs_file"] = logs_file
opt["checkpath"] = checkpath
opt["class_list"] = class_list
lr = args.lr
source_loader = torch.utils.data.DataLoader(source_dataset, batch_size=bs, num_workers=3, shuffle=True, drop_last=True)
target_loader = torch.utils.data.DataLoader(target_dataset, batch_size=min(bs, len(target_dataset)), num_workers=3,
shuffle=True, drop_last=True)
target_loader_unl = torch.utils.data.DataLoader(target_dataset_unl, batch_size=bs * 2, num_workers=3, shuffle=True,
drop_last=True)
target_loader_val = torch.utils.data.DataLoader(target_dataset_val, batch_size=min(bs, len(target_dataset_val)),
def main():
# Training settings
parser = argparse.ArgumentParser(description='SSDA Classification')
parser.add_argument('--steps',
type=int,
default=50000,
metavar='N',
help='maximum number of iterations '
'to train (default: 50000)')
parser.add_argument(
'--method',
type=str,
default='MME',
choices=['S+T', 'ENT', 'MME'],
help='MME is proposed method, ENT is entropy minimization,'
' S+T is training only on labeled examples')
parser.add_argument('--lr',
type=float,
default=0.01,
metavar='LR',
help='learning rate (default: 0.001)')
parser.add_argument('--multi',
type=float,
default=0.1,
metavar='MLT',
help='learning rate multiplication')
parser.add_argument('--T',
type=float,
default=0.05,
metavar='T',
help='temperature (default: 0.05)')
parser.add_argument('--lamda',
type=float,
default=0.1,
metavar='LAM',
help='value of lamda')
parser.add_argument('--save_check',
action='store_true',
default=False,
help='save checkpoint or not')
parser.add_argument('--checkpath',
type=str,
default='./save_model_ssda',
help='dir to save checkpoint')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval',
type=int,
default=100,
metavar='N',
help='how many batches to wait before logging '
'training status')
parser.add_argument('--save_interval',
type=int,
default=500,
metavar='N',
help='how many batches to wait before saving a model')
parser.add_argument('--net',
type=str,
default='alexnet',
help='which network to use')
parser.add_argument('--source',
type=str,
default='real',
help='source domain')
parser.add_argument('--target',
type=str,
default='sketch',
help='target domain')
parser.add_argument('--dataset',
type=str,
default='multi',
choices=['multi', 'office', 'office_home'],
help='the name of dataset')
parser.add_argument('--num',
type=int,
default=3,
help='number of labeled examples in the target')
parser.add_argument('--patience',
type=int,
default=5,
metavar='S',
help='early stopping to wait for improvment '
'before terminating. (default: 5 (5000 iterations))')
parser.add_argument('--early',
action='store_false',
default=True,
help='early stopping on validation or not')
parser.add_argument('--loss',
type=str,
default='CE',
choices=['CE', 'FL', 'CBFL'],
help='classifier loss function')
parser.add_argument(
'--attribute',
type=str,
default=None,
choices=[
'word2vec', 'glove.6B.100d.txt', 'glove.6B.300d.txt',
'glove_anurag', 'fasttext_anurag', 'glove.840B.300d.txt',
'glove.twitter.27B.200d.txt', 'glove.twitter.27B.50d.txt',
'glove.42B.300d.txt', 'glove.6B.200d.txt', 'glove.6B.50d.txt',
'glove.twitter.27B.100d.txt', 'glove.twitter.27B.25d.txt'
],
help='semantic attribute feature vector to be used')
parser.add_argument(
'--dim',
type=int,
default=300,
help=
'dimensionality of the feature vector - make sure this in sync with the dim of the semantic attribute vector'
)
parser.add_argument(
'--deep',
type=int,
default=0,
help='type of classification predictor - 0 for shallow, 1 for deep')
parser.add_argument('--mode',
type=str,
default='train',
choices=['train', 'infer'],
help='mode of script train or infer')
args = parser.parse_args()
print('Dataset %s Source %s Target %s Labeled num perclass %s Network %s' %
(args.dataset, args.source, args.target, args.num, args.net))
source_loader, target_loader, target_loader_unl, target_loader_val, \
target_loader_test, class_num_list, class_list = return_dataset(args) # class num list is returned for CBFL
use_gpu = torch.cuda.is_available()
record_dir = 'record/%s/%s' % (args.dataset, args.method)
if not os.path.exists(record_dir):
os.makedirs(record_dir)
record_file = os.path.join(
record_dir, '%s_net_%s_%s_to_%s_num_%s' %
(args.method, args.net, args.source, args.target, args.num))
if use_gpu:
device = 'cuda'
else:
device = 'cpu'
print("Device: %s Loss: %s Attributes: %s" %
(device, args.loss, args.attribute))
if use_gpu:
torch.cuda.manual_seed(args.seed)
else:
torch.manual_seed(args.seed)
if args.net == 'resnet34':
G = resnet34()
inc = 512
elif args.net == "alexnet":
G = AlexNetBase()
inc = 4096
elif args.net == "vgg":
G = VGGBase()
inc = 4096
else:
raise ValueError('Model cannot be recognized.')
params = []
for key, value in dict(G.named_parameters()).items():
if value.requires_grad:
if 'classifier' not in key:
params += [{
'params': [value],
'lr': 0.1,
'weight_decay': 0.0005
}]
else:
params += [{
'params': [value],
'lr': 1,
'weight_decay': 0.0005
}]
# Setting the predictor layer
if args.attribute is not None:
if args.deep:
F1 = Predictor_deep_attributes(num_class=len(class_list),
inc=inc,
feat_dim=args.dim)
print("Using: Predictor_deep_attributes")
else:
F1 = Predictor_attributes(num_class=len(class_list),
inc=inc,
feat_dim=args.dim)
print("Using: Predictor_attributes")
else:
if args.deep:
F1 = Predictor_deep(num_class=len(class_list), inc=inc)
print("Using: Predictor_deep")
else:
F1 = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
print("Using: Predictor")
# Initializing the weights of the prediction layer
weights_init(F1)
# Setting the prediction layer weights as the semantic attributes
if args.attribute is not None:
att = np.load('attributes/%s_%s.npy' % (args.dataset, args.attribute))
if use_gpu:
att = nn.Parameter(torch.cuda.FloatTensor(att))
else:
att = nn.Parameter(torch.FloatTensor(att, device="cpu"))
if args.deep:
F1.fc3.weight = att
else:
F1.fc2.weight = att
print("attribute shape is: ", att.shape)
lr = args.lr
# loading the model checkpoint - and printing some parameters relating to the checkpoints
main_dict = torch.load(args.checkpath + "/" + args.net + "_" +
args.method + "_" + args.source + "_" +
args.target + ".ckpt.best.pth.tar")
G.load_state_dict(main_dict['G_state_dict'])
F1.load_state_dict(main_dict['F1_state_dict'])
print("Loaded pretrained model weights")
print("Loaded weights from step: ", main_dict['step'])
print("Current best test acc is: ", main_dict['best_acc_test'])
G.to(device)
F1.to(device)
# Loading the txt file having the weights and paths of the image file as a data frame
df = pd.read_csv(args.method + '_' + main_dict['arch'] + '_' +
str(main_dict['step']) + '.txt',
sep=" ",
header=None)
df = df[[3, 0]]
df = df.rename(columns={3: "img", 0: "weight"})
im_data_s = torch.FloatTensor(1)
im_data_t = torch.FloatTensor(1)
im_data_tu = torch.FloatTensor(1)
gt_labels_t = torch.LongTensor(1)
gt_labels_s = torch.LongTensor(1)
sample_labels_t = torch.LongTensor(1)
sample_labels_s = torch.LongTensor(1)
im_data_s = im_data_s.to(device)
im_data_t = im_data_t.to(device)
im_data_tu = im_data_tu.to(device)
gt_labels_s = gt_labels_s.to(device)
gt_labels_t = gt_labels_t.to(device)
sample_labels_t = sample_labels_t.to(device)
sample_labels_s = sample_labels_s.to(device)
im_data_s = Variable(im_data_s)
im_data_t = Variable(im_data_t)
im_data_tu = Variable(im_data_tu)
gt_labels_s = Variable(gt_labels_s)
gt_labels_t = Variable(gt_labels_t)
sample_labels_t = Variable(sample_labels_t)
sample_labels_s = Variable(sample_labels_s)
if os.path.exists(args.checkpath) == False:
os.mkdir(args.checkpath)
def train():
G.train()
F1.train()
optimizer_g = optim.SGD(params,
momentum=0.9,
weight_decay=0.0005,
nesterov=True)
optimizer_f = optim.SGD(list(F1.parameters()),
lr=1.0,
momentum=0.9,
weight_decay=0.0005,
nesterov=True)
# Loading the states of the two optmizers
optimizer_g.load_state_dict(main_dict['optimizer_g'])
optimizer_f.load_state_dict(main_dict['optimizer_f'])
print("Loaded optimizer states")
def zero_grad_all():
optimizer_g.zero_grad()
optimizer_f.zero_grad()
param_lr_g = []
for param_group in optimizer_g.param_groups:
param_lr_g.append(param_group["lr"])
param_lr_f = []
for param_group in optimizer_f.param_groups:
param_lr_f.append(param_group["lr"])
# Setting the loss function to be used for the classification loss
if args.loss == 'CE':
criterion = nn.CrossEntropyLoss().to(device)
if args.loss == 'FL':
criterion = FocalLoss(alpha=1, gamma=1).to(device)
if args.loss == 'CBFL':
# Calculating the list having the number of examples per class which is going to be used in the CB focal loss
beta = 0.99
effective_num = 1.0 - np.power(beta, class_num_list)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
per_cls_weights = per_cls_weights / np.sum(per_cls_weights) * len(
class_num_list)
per_cls_weights = torch.FloatTensor(per_cls_weights).to(device)
criterion = CBFocalLoss(weight=per_cls_weights,
gamma=0.5).to(device)
all_step = args.steps
data_iter_s = iter(source_loader)
data_iter_t = iter(target_loader)
data_iter_t_unl = iter(target_loader_unl)
len_train_source = len(source_loader)
len_train_target = len(target_loader)
len_train_target_semi = len(target_loader_unl)
best_acc = 0
counter = 0
for step in range(all_step):
optimizer_g = inv_lr_scheduler(param_lr_g,
optimizer_g,
step,
init_lr=args.lr)
optimizer_f = inv_lr_scheduler(param_lr_f,
optimizer_f,
step,
init_lr=args.lr)
lr = optimizer_f.param_groups[0]['lr']
# condition for restarting the iteration for each of the data loaders
if step % len_train_target == 0:
data_iter_t = iter(target_loader)
if step % len_train_target_semi == 0:
data_iter_t_unl = iter(target_loader_unl)
if step % len_train_source == 0:
data_iter_s = iter(source_loader)
data_t = next(data_iter_t)
data_t_unl = next(data_iter_t_unl)
data_s = next(data_iter_s)
with torch.no_grad():
im_data_s.resize_(data_s[0].size()).copy_(data_s[0])
gt_labels_s.resize_(data_s[1].size()).copy_(data_s[1])
im_data_t.resize_(data_t[0].size()).copy_(data_t[0])
gt_labels_t.resize_(data_t[1].size()).copy_(data_t[1])
im_data_tu.resize_(data_t_unl[0].size()).copy_(data_t_unl[0])
zero_grad_all()
data = torch.cat((im_data_s, im_data_t), 0)
target = torch.cat((gt_labels_s, gt_labels_t), 0)
output = G(data)
out1 = F1(output)
loss = criterion(out1, target)
loss.backward(retain_graph=True)
optimizer_g.step()
optimizer_f.step()
zero_grad_all()
# list of the weights and image paths in this batch
img_paths = list(data_t_unl[2])
df1 = df.loc[df['img'].isin(img_paths)]
df1 = df1['weight']
weight_list = list(df1)
if not args.method == 'S+T':
output = G(im_data_tu)
if args.method == 'ENT':
loss_t = entropy(F1, output, args.lamda)
loss_t.backward()
optimizer_f.step()
optimizer_g.step()
elif args.method == 'MME':
loss_t = adentropy(F1, output, args.lamda, weight_list)
loss_t.backward()
optimizer_f.step()
optimizer_g.step()
else:
raise ValueError('Method cannot be recognized.')
log_train = 'S {} T {} Train Ep: {} lr{} \t ' \
'Loss Classification: {:.6f} Loss T {:.6f} ' \
'Method {}\n'.format(args.source, args.target,
step, lr, loss.data,
-loss_t.data, args.method)
else:
log_train = 'S {} T {} Train Ep: {} lr{} \t ' \
'Loss Classification: {:.6f} Method {}\n'.\
format(args.source, args.target,
step, lr, loss.data,
args.method)
G.zero_grad()
F1.zero_grad()
zero_grad_all()
if step % args.log_interval == 0:
print(log_train)
if step % args.save_interval == 0 and step > 0:
loss_val, acc_val = test(target_loader_val)
loss_test, acc_test = test(target_loader_test)
G.train()
F1.train()
if acc_test >= best_acc:
best_acc = acc_test
best_acc_test = acc_test
counter = 0
else:
counter += 1
if args.early:
if counter > args.patience:
break
print('best acc test %f best acc val %f' %
(best_acc_test, acc_val))
print('record %s' % record_file)
with open(record_file, 'a') as f:
f.write('step %d best %f final %f \n' %
(step, best_acc_test, acc_val))
G.train()
F1.train()
#saving model as a checkpoint dict having many things
if args.save_check:
print('saving model')
is_best = True if counter == 0 else False
save_mymodel(
args, {
'step': step,
'arch': args.net,
'G_state_dict': G.state_dict(),
'F1_state_dict': F1.state_dict(),
'best_acc_test': best_acc_test,
'optimizer_g': optimizer_g.state_dict(),
'optimizer_f': optimizer_f.state_dict(),
}, is_best)
# defining the function for in training validation and testing
def test(loader):
G.eval()
F1.eval()
test_loss = 0
correct = 0
size = 0
num_class = len(class_list)
output_all = np.zeros((0, num_class))
# Setting the loss function to be used for the classification loss
if args.loss == 'CE':
criterion = nn.CrossEntropyLoss().to(device)
if args.loss == 'FL':
criterion = FocalLoss(alpha=1, gamma=1).to(device)
if args.loss == 'CBFL':
# Calculating the list having the number of examples per class which is going to be used in the CB focal loss
beta = 0.99
effective_num = 1.0 - np.power(beta, class_num_list)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
per_cls_weights = per_cls_weights / np.sum(per_cls_weights) * len(
class_num_list)
per_cls_weights = torch.FloatTensor(per_cls_weights).to(device)
criterion = CBFocalLoss(weight=per_cls_weights,
gamma=0.5).to(device)
confusion_matrix = torch.zeros(num_class, num_class)
with torch.no_grad():
for batch_idx, data_t in enumerate(loader):
im_data_t.data.resize_(data_t[0].size()).copy_(data_t[0])
gt_labels_t.data.resize_(data_t[1].size()).copy_(data_t[1])
feat = G(im_data_t)
output1 = F1(feat)
output_all = np.r_[output_all, output1.data.cpu().numpy()]
size += im_data_t.size(0)
pred1 = output1.data.max(1)[1]
for t, p in zip(gt_labels_t.view(-1), pred1.view(-1)):
confusion_matrix[t.long(), p.long()] += 1
correct += pred1.eq(gt_labels_t.data).cpu().sum()
test_loss += criterion(output1, gt_labels_t) / len(loader)
np.save("cf_target.npy", confusion_matrix)
#print(confusion_matrix)
print('\nTest set: Average loss: {:.4f}, '
'Accuracy: {}/{} F1 ({:.0f}%)\n'.format(test_loss, correct, size,
100. * correct / size))
return test_loss.data, 100. * float(correct) / size
print("Starting stage 2 training of the model ...")
train()
def main():
# Training settings
parser = argparse.ArgumentParser(description='SSDA Classification')
parser.add_argument('--steps',
type=int,
default=50000,
metavar='N',
help='maximum number of iterations '
'to train (default: 50000)')
parser.add_argument(
'--method',
type=str,
default='MME',
choices=['S+T', 'ENT', 'MME'],
help='MME is proposed method, ENT is entropy minimization,'
' S+T is training only on labeled examples')
parser.add_argument('--lr',
type=float,
default=0.01,
metavar='LR',
help='learning rate (default: 0.001)')
parser.add_argument('--multi',
type=float,
default=0.1,
metavar='MLT',
help='learning rate multiplication')
parser.add_argument('--T',
type=float,
default=0.05,
metavar='T',
help='temperature (default: 0.05)')
parser.add_argument('--lamda',
type=float,
default=0.1,
metavar='LAM',
help='value of lamda')
parser.add_argument('--save_check',
action='store_true',
default=False,
help='save checkpoint or not')
parser.add_argument('--checkpath',
type=str,
default='./save_model_ssda',
help='dir to save checkpoint')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval',
type=int,
default=100,
metavar='N',
help='how many batches to wait before logging '
'training status')
parser.add_argument('--save_interval',
type=int,
default=500,
metavar='N',
help='how many batches to wait before saving a model')
parser.add_argument('--net',
type=str,
default='al exnet',
help='which network to use')
parser.add_argument('--source',
type=str,
default='real',
help='source domain')
parser.add_argument('--target',
type=str,
default='sketch',
help='target domain')
parser.add_argument('--dataset',
type=str,
default='multi',
help='the name of dataset')
parser.add_argument('--num',
type=int,
default=3,
help='number of labeled examples in the target')
parser.add_argument('--patience',
type=int,
default=5,
metavar='S',
help='early stopping to wait for improvment '
'before terminating. (default: 5 (5000 iterations))')
parser.add_argument('--early',
action='store_false',
default=True,
help='early stopping on validation or not')
parser.add_argument('--loss',
type=str,
default='CE',
choices=['CE', 'FL', 'CBFL'],
help='classifier loss function')
parser.add_argument('--beta',
type=float,
default=0.99,
required=False,
help='beta value in CBFL loss')
parser.add_argument('--gamma',
type=float,
default=0.5,
required=False,
help='gamma value in CBFL or FL')
parser.add_argument('--reg',
type=float,
default=0.1,
required=False,
help='weight of semantic regularizer')
parser.add_argument('--attribute',
type=str,
default=None,
help='semantic attribute feature vector to be used')
parser.add_argument(
'--dim',
type=int,
default=50,
help=
'dimensionality of the feature vector - make sure this in sync with the dim of the semantic attribute vector'
)
parser.add_argument('--mode',
type=str,
default='train',
choices=['train', 'infer'],
help='mode of script train or infer')
# this argument is valid only if the mode is infer
parser.add_argument('--model_path',
type=str,
help='path to the checkpoint of the model')
parser.add_argument(
'--uda',
type=int,
default=0,
help='unsupervised domain adaptation or not - 0 for ssda and 1 for uda'
)
args = parser.parse_args()
print('Dataset %s Source %s Target %s Labeled num perclass %s Network %s' %
(args.dataset, args.source, args.target, args.num, args.net))
source_loader, target_loader, target_loader_unl, target_loader_val, \
target_loader_test, class_num_list, class_list = return_dataset(args) # class num list is returned for CBFL
use_gpu = torch.cuda.is_available()
record_dir = 'record/%s/%s' % (args.dataset, args.method)
if not os.path.exists(record_dir):
os.makedirs(record_dir)
record_file = os.path.join(
record_dir, '%s_net_%s_%s_to_%s_num_%s' %
(args.method, args.net, args.source, args.target, args.num))
if use_gpu:
device = 'cuda'
else:
device = 'cpu'
print("Device: %s Loss: %s Attributes: %s" %
(device, args.loss, args.attribute))
if use_gpu:
torch.cuda.manual_seed(args.seed)
else:
torch.manual_seed(args.seed)
if args.net == 'resnet34':
G = resnet34()
inc = 512
elif args.net == "alexnet":
G = AlexNetBase()
inc = 4096
elif args.net == "vgg":
G = VGGBase()
inc = 4096
else:
raise ValueError('Model cannot be recognized.')
params = []
for key, value in dict(G.named_parameters()).items():
if value.requires_grad:
if 'classifier' not in key:
params += [{
'params': [value],
'lr': 0.1,
'weight_decay': 0.0005
}]
else:
params += [{
'params': [value],
'lr': 1,
'weight_decay': 0.0005
}]
# Setting the predictor layer
if args.attribute is not None:
if args.net == 'resnet34':
F1 = Predictor_deep_attributes(num_class=len(class_list),
inc=inc,
feat_dim=args.dim)
print("Using: Predictor_deep_attributes")
else:
F1 = Predictor_attributes(num_class=len(class_list),
inc=inc,
feat_dim=args.dim)
print("Using: Predictor_attributes")
else:
if args.net == 'resnet34':
F1 = Predictor_deep(num_class=len(class_list), inc=inc)
print("Using: Predictor_deep")
else:
F1 = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
print("Using: Predictor")
# Initializing the weights of the prediction layer
weights_init(F1)
# Setting the prediction layer weights as the semantic attributes
if args.attribute is not None:
att = np.load('attributes/%s_%s.npy' % (args.dataset, args.attribute))
#att = np.load('attributes/multi_%s.npy'%(args.attribute))
if use_gpu:
att = nn.Parameter(torch.cuda.FloatTensor(att))
else:
att = nn.Parameter(torch.FloatTensor(att, device="cpu"))
if args.net == 'resnet34':
F1.fc3.weight = att
else:
F1.fc2.weight = att
print("attribute shape is: ", att.shape)
lr = args.lr
# If the mode is inference then load the pretrained network
if args.mode == 'infer':
# loading the model checkpoint
main_dict = torch.load(args.model_path)
G.load_state_dict(main_dict['G_state_dict'])
F1.load_state_dict(main_dict['F_state_dict'])
print("Loaded pretrained model weights")
G.to(device)
F1.to(device)
if args.uda == 1:
print("Using: Unsupervised domain adaptation")
im_data_s = torch.FloatTensor(1)
im_data_t = torch.FloatTensor(1)
im_data_tu = torch.FloatTensor(1)
gt_labels_t = torch.LongTensor(1)
gt_labels_s = torch.LongTensor(1)
sample_labels_t = torch.LongTensor(1)
sample_labels_s = torch.LongTensor(1)
im_data_s = im_data_s.to(device)
im_data_t = im_data_t.to(device)
im_data_tu = im_data_tu.to(device)
gt_labels_s = gt_labels_s.to(device)
gt_labels_t = gt_labels_t.to(device)
sample_labels_t = sample_labels_t.to(device)
sample_labels_s = sample_labels_s.to(device)
im_data_s = Variable(im_data_s)
im_data_t = Variable(im_data_t)
im_data_tu = Variable(im_data_tu)
gt_labels_s = Variable(gt_labels_s)
gt_labels_t = Variable(gt_labels_t)
sample_labels_t = Variable(sample_labels_t)
sample_labels_s = Variable(sample_labels_s)
if os.path.exists(args.checkpath) == False:
os.mkdir(args.checkpath)
time_stamp = datetime.now()
print(time_stamp)
def train(class_dist_threshold_list):
G.train()
F1.train()
optimizer_g = optim.SGD(params,
momentum=0.9,
weight_decay=0.0005,
nesterov=True)
optimizer_f = optim.SGD(list(F1.parameters()),
lr=1.0,
momentum=0.9,
weight_decay=0.0005,
nesterov=True)
def zero_grad_all():
optimizer_g.zero_grad()
optimizer_f.zero_grad()
param_lr_g = []
for param_group in optimizer_g.param_groups:
param_lr_g.append(param_group["lr"])
param_lr_f = []
for param_group in optimizer_f.param_groups:
param_lr_f.append(param_group["lr"])
# Setting the loss function to be used for the classification loss
if args.loss == 'CE':
criterion = nn.CrossEntropyLoss().to(device)
if args.loss == 'FL':
criterion = FocalLoss(alpha=1, gamma=args.gamma).to(device)
if args.loss == 'CBFL':
# Calculating the list having the number of examples per class which is going to be used in the CB focal loss
beta = args.beta
effective_num = 1.0 - np.power(beta, class_num_list)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
per_cls_weights = per_cls_weights / np.sum(per_cls_weights) * len(
class_num_list)
per_cls_weights = torch.FloatTensor(per_cls_weights).to(device)
criterion = CBFocalLoss(weight=per_cls_weights,
gamma=args.gamma).to(device)
all_step = args.steps
data_iter_s = iter(source_loader)
data_iter_t = iter(target_loader)
data_iter_t_unl = iter(target_loader_unl)
len_train_source = len(source_loader)
len_train_target = len(target_loader)
len_train_target_semi = len(target_loader_unl)
best_acc = 0
counter = 0
"""
x = torch.load("./freezed_models/alexnet_p2r.ckpt.best.pth.tar")
G.load_state_dict(x['G_state_dict'])
F1.load_state_dict(x['F1_state_dict'])
optimizer_f.load_state_dict(x['optimizer_f'])
optimizer_g.load_state_dict(x['optimizer_g'])
"""
reg_weight = args.reg
for step in range(all_step):
optimizer_g = inv_lr_scheduler(param_lr_g,
optimizer_g,
step,
init_lr=args.lr)
optimizer_f = inv_lr_scheduler(param_lr_f,
optimizer_f,
step,
init_lr=args.lr)
lr = optimizer_f.param_groups[0]['lr']
# condition for restarting the iteration for each of the data loaders
if step % len_train_target == 0:
data_iter_t = iter(target_loader)
if step % len_train_target_semi == 0:
data_iter_t_unl = iter(target_loader_unl)
if step % len_train_source == 0:
data_iter_s = iter(source_loader)
data_t = next(data_iter_t)
data_t_unl = next(data_iter_t_unl)
data_s = next(data_iter_s)
with torch.no_grad():
im_data_s.resize_(data_s[0].size()).copy_(data_s[0])
gt_labels_s.resize_(data_s[1].size()).copy_(data_s[1])
im_data_t.resize_(data_t[0].size()).copy_(data_t[0])
gt_labels_t.resize_(data_t[1].size()).copy_(data_t[1])
im_data_tu.resize_(data_t_unl[0].size()).copy_(data_t_unl[0])
zero_grad_all()
if args.uda == 1:
data = im_data_s
target = gt_labels_s
else:
data = torch.cat((im_data_s, im_data_t), 0)
target = torch.cat((gt_labels_s, gt_labels_t), 0)
#print(data.shape)
output = G(data)
out1 = F1(output)
if args.attribute is not None:
if args.net == 'resnet34':
reg_loss = regularizer(F1.fc3.weight, att)
loss = criterion(out1, target) + reg_weight * reg_loss
else:
reg_loss = regularizer(F1.fc2.weight, att)
loss = criterion(out1, target) + reg_weight * reg_loss
else:
reg_loss = torch.tensor(0)
loss = criterion(out1, target)
if args.attribute is not None:
if step % args.save_interval == 0 and step != 0:
reg_weight = 0.5 * reg_weight
print("Reduced Reg weight to: ", reg_weight)
loss.backward(retain_graph=True)
optimizer_g.step()
optimizer_f.step()
zero_grad_all()
if not args.method == 'S+T':
output = G(im_data_tu)
if args.method == 'ENT':
loss_t = entropy(F1, output, args.lamda)
#print(loss_t.cpu().data.item())
loss_t.backward()
optimizer_f.step()
optimizer_g.step()
elif args.method == 'MME':
loss_t = adentropy(F1, output, args.lamda,
class_dist_threshold_list)
loss_t.backward()
optimizer_f.step()
optimizer_g.step()
else:
raise ValueError('Method cannot be recognized.')
log_train = 'S {} T {} Train Ep: {} lr{} \t ' \
'Loss Classification: {:.6f} Reg: {:.6f} Loss T {:.6f} ' \
'Method {}\n'.format(args.source, args.target,
step, lr, loss.data, reg_weight*reg_loss.data,
-loss_t.data, args.method)
else:
log_train = 'S {} T {} Train Ep: {} lr{} \t ' \
'Loss Classification: {:.6f} Reg: {:.6f} Method {}\n'.\
format(args.source, args.target,
step, lr, loss.data, reg_weight * reg_loss.data,
args.method)
G.zero_grad()
F1.zero_grad()
zero_grad_all()
if step % args.log_interval == 0:
print(log_train)
if step % args.save_interval == 0 and step > 0:
loss_val, acc_val = test(target_loader_val)
loss_test, acc_test = test(target_loader_test)
G.train()
F1.train()
if acc_val >= best_acc:
best_acc = acc_val
best_acc_test = acc_test
counter = 0
else:
counter += 1
if args.early:
if counter > args.patience:
break
print('best acc test %f best acc val %f' %
(best_acc_test, acc_val))
print('record %s' % record_file)
with open(record_file, 'a') as f:
f.write('step %d best %f final %f \n' %
(step, best_acc_test, acc_val))
G.train()
F1.train()
#saving model as a checkpoint dict having many things
if args.save_check:
print('saving model')
is_best = True if counter == 0 else False
save_mymodel(
args, {
'step': step,
'arch': args.net,
'G_state_dict': G.state_dict(),
'F1_state_dict': F1.state_dict(),
'best_acc_test': best_acc_test,
'optimizer_g': optimizer_g.state_dict(),
'optimizer_f': optimizer_f.state_dict(),
}, is_best, time_stamp)
# defining the function for in training validation and testing
def test(loader):
G.eval()
F1.eval()
test_loss = 0
correct = 0
size = 0
num_class = len(class_list)
output_all = np.zeros((0, num_class))
# Setting the loss function to be used for the classification loss
if args.loss == 'CE':
criterion = nn.CrossEntropyLoss().to(device)
if args.loss == 'FL':
criterion = FocalLoss(alpha=1, gamma=args.gamma).to(device)
if args.loss == 'CBFL':
# Calculating the list having the number of examples per class which is going to be used in the CB focal loss
beta = args.beta
effective_num = 1.0 - np.power(beta, class_num_list)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
per_cls_weights = per_cls_weights / np.sum(per_cls_weights) * len(
class_num_list)
per_cls_weights = torch.FloatTensor(per_cls_weights).to(device)
criterion = CBFocalLoss(weight=per_cls_weights,
gamma=args.gamma).to(device)
confusion_matrix = torch.zeros(num_class, num_class)
with torch.no_grad():
for batch_idx, data_t in enumerate(loader):
im_data_t.data.resize_(data_t[0].size()).copy_(data_t[0])
gt_labels_t.data.resize_(data_t[1].size()).copy_(data_t[1])
feat = G(im_data_t)
output1 = F1(feat)
output_all = np.r_[output_all, output1.data.cpu().numpy()]
size += im_data_t.size(0)
pred1 = output1.data.max(1)[1]
for t, p in zip(gt_labels_t.view(-1), pred1.view(-1)):
confusion_matrix[t.long(), p.long()] += 1
correct += pred1.eq(gt_labels_t.data).cpu().sum()
test_loss += criterion(output1, gt_labels_t) / len(loader)
np.save("cf_target.npy", confusion_matrix)
#print(confusion_matrix)
print('\nTest set: Average loss: {:.4f}, '
'Accuracy: {}/{} F1 ({:.0f}%)\n'.format(test_loss, correct, size,
100. * correct / size))
return test_loss.data, 100. * float(correct) / size
# defining the function for inference which is similar to the testing function as above but with some additional functionality for calculating the distances between the class prototypes and the predicted testing samples
def infer(loader):
G.eval()
F1.eval()
test_loss = 0
correct = 0
size = 0
num_class = len(class_list)
output_all = np.zeros((0, num_class))
# Setting the loss function to be used for the classification loss
if args.loss == 'CE':
criterion = nn.CrossEntropyLoss().to(device)
if args.loss == 'FL':
criterion = FocalLoss(alpha=1, gamma=1).to(device)
if args.loss == 'CBFL':
# Calculating the list having the number of examples per class which is going to be used in the CB focal loss
beta = 0.99
effective_num = 1.0 - np.power(beta, class_num_list)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
per_cls_weights = per_cls_weights / np.sum(per_cls_weights) * len(
class_num_list)
per_cls_weights = torch.FloatTensor(per_cls_weights).to(device)
criterion = CBFocalLoss(weight=per_cls_weights,
gamma=0.5).to(device)
# defining a nested list to store the cosine similarity (or distances) of the vectors from the class prototypes
class_dist_list = []
for i in range(num_class):
empty_dists = []
class_dist_list.append(empty_dists)
confusion_matrix = torch.zeros(num_class, num_class)
# iterating through the elements of the batch in the dataloader
with torch.no_grad():
for batch_idx, data_t in enumerate(loader):
im_data_t.data.resize_(data_t[0].size()).copy_(data_t[0])
gt_labels_t.data.resize_(data_t[1].size()).copy_(data_t[1])
feat = G(im_data_t)
output1 = F1(feat)
output_all = np.r_[output_all, output1.data.cpu().numpy()]
size += im_data_t.size(0)
pred1 = output1.data.max(1)[1]
# filling the elements of the confusion matrix
for t, p in zip(gt_labels_t.view(-1), pred1.view(-1)):
confusion_matrix[t.long(), p.long()] += 1
correct += pred1.eq(gt_labels_t.data).cpu().sum()
test_loss += criterion(output1, gt_labels_t) / len(loader)
pred1 = pred1.cpu().numpy()
dists = output1.data.max(1)[0]
dists = dists.cpu().numpy()
# forming the lists of the distances of the predicted labels and the class prototype
for label, dist in zip(pred1, dists):
label = int(label)
class_dist_list[label].append(dist)
# sorting the distances in ascending order for each of the classes, also finding a threshold for similarity of each class
summ = 0
class_dist_threshold_list = []
for class_ in range(len(class_dist_list)):
class_dist_list[class_].sort()
l = len(class_dist_list[class_])
tenth = l / 10
idx_tenth = math.ceil(tenth)
class_dist_threshold_list.append(
class_dist_list[class_][idx_tenth])
print('\nTest set: Average loss: {:.4f}, '
'Accuracy: {}/{} F1 ({:.2f}%)\n'.format(test_loss, correct, size,
100. * correct / size))
return test_loss.data, 100. * float(
correct) / size, class_dist_threshold_list
# choosing the mode of the model - whether to be used for training or for inference
if args.mode == 'train':
print("Training the model...")
train(None)
if args.mode == 'infer':
print("Infering from the model...")
_, _, class_dist_threshold_list = infer(target_loader_test)
print(
"Starting model retraining using weights for entropy maximization..."
)
train(class_dist_threshold_list)
else:
params += [{
'params': [value],
'lr': args.multi * 10,
'weight_decay': 0.0005
}]
if "resnet" in args.net or "densenet" in args.net or "mobilenet" in args.net or "inception" in args.net:
F1 = Predictor_deep(num_class=len(class_list), inc=inc)
else:
F1 = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
weights_init(F1)
lr = args.lr
device = torch.device("cuda:" + args.device)
G.to(device)
F1.to(device)
im_data_s = torch.FloatTensor(1)
im_data_t = torch.FloatTensor(1)
im_data_tu = torch.FloatTensor(1)
gt_labels_s = torch.LongTensor(1)
gt_labels_t = torch.LongTensor(1)
sample_labels_t = torch.LongTensor(1)
sample_labels_s = torch.LongTensor(1)
im_data_s = im_data_s.to(device)
im_data_t = im_data_t.to(device)
im_data_tu = im_data_tu.to(device)
gt_labels_s = gt_labels_s.to(device)
gt_labels_t = gt_labels_t.to(device)