def test(args):
# 1. get dataset
train_loader, val_loader, test_loader, class_list = return_dataset(args)
# 2. generator
if args.net == 'resnet50':
G = ResBase50()
inc = 2048
elif args.net == 'resnet101':
G = ResBase101()
inc = 2048
elif args.net == "alexnet":
G = AlexNetBase()
inc = 4096
elif args.net == "vgg":
G = VGGBase()
inc = 4096
elif args.net == "inception_v3":
G = models.inception_v3(pretrained=True)
inc = 1000
elif args.net == "googlenet":
G = models.googlenet(pretrained = True)
inc = 1000
elif args.net == "densenet":
G = models.densenet161(pretrained = True)
inc = 1000
elif args.net == "resnext":
G = models.resnext50_32x4d(pretrained = True)
inc = 1000
elif args.net == "squeezenet":
G = models.squeezenet1_0(pretrained = True)
inc = 1000
else:
raise ValueError('Model cannot be recognized.')
G.cuda()
G.train()
# 3. classifier
F = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
F.cuda()
F.train()
# 4. load pre-trained model
G.load_state_dict(torch.load(os.path.join(args.checkpath,
"G_net_{}_loss_{}.pth".format(args.net, args.loss))))
F.load_state_dict(torch.load(os.path.join(args.checkpath,
"F_net_{}_loss_{}.pth".format(args.net, args.loss))))
# 5. testing
acc_test = eval(test_loader, G, F, class_list)
print('Testing accuracy: {:.3f}\n'.format(acc_test))
return acc_test
def test_ensemble(args, alphas=None):
# 1. get dataset
#problem: inception_v3 crops dataset differently than all the others
args.net = args.ensemble[0] #test, might introduce problem
print("args .net when loading: ", args.net)
train_loader, val_loader, test_loader, class_list = return_dataset(args)
print("Loading in ")
# 2. generator
G_list = [] #use a list of models
F_list = [] #use a list of predictors, one for each classifier in args.ensemble
for classifier in args.ensemble:
print("classifier: ", classifier)
if classifier == 'resnet50':
G = ResBase50()
inc = 2048
elif classifier == 'resnet101':
G = ResBase101()
inc = 2048
elif classifier == "alexnet":
G = AlexNetBase()
inc = 4096
elif classifier == "vgg":
G = VGGBase()
inc = 4096
elif classifier == "inception_v3":
G = models.inception_v3(pretrained=True)
inc = 1000
elif classifier == "googlenet":
G = models.googlenet(pretrained = True)
inc = 1000
elif classifier == "densenet":
G = models.densenet161(pretrained = True)
inc = 1000
elif classifier == "resnext":
G = models.resnext50_32x4d(pretrained = True)
inc = 1000
elif classifier == "squeezenet":
G = models.squeezenet1_0(pretrained = True)
inc = 1000
else:
raise ValueError('Model cannot be recognized.')
G.cuda()
G.train()
# 3. classifier
F = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
F.cuda()
F.train()
# 4. load pre-trained model
G.load_state_dict(torch.load(os.path.join(args.checkpath,
"G_net_{}_loss_{}.pth".format(classifier, args.loss))))
F.load_state_dict(torch.load(os.path.join(args.checkpath,
"F_net_{}_loss_{}.pth".format(classifier, args.loss))))
G_list.append(G)
F_list.append(F)
# 5. testing
print("evaluating accuracy")
acc_test = eval_ensemble(args,test_loader, G_list, F_list, class_list,alphas)
print('Testing accuracy: {:.3f}\n'.format(acc_test))
return acc_test
def train(args,weights=None):
if os.path.exists(args.checkpath) == False:
os.mkdir(args.checkpath)
# 1. get dataset
train_loader, val_loader, test_loader, class_list = return_dataset(args)
# 2. generator
if args.net == 'resnet50':
G = ResBase50()
inc = 2048
elif args.net == 'resnet101':
G = ResBase101()
inc = 2048
elif args.net == "alexnet":
G = AlexNetBase()
inc = 4096
elif args.net == "vgg":
G = VGGBase()
inc = 4096
elif args.net == "inception_v3":
G = models.inception_v3(pretrained=True)
inc = 1000
elif args.net == "googlenet":
G = models.googlenet(pretrained = True)
inc = 1000
elif args.net == "densenet":
G = models.densenet161(pretrained = True)
inc = 1000
elif args.net == "resnext":
G = models.resnext50_32x4d(pretrained = True)
inc = 1000
elif args.net == "squeezenet":
G = models.squeezenet1_0(pretrained = True)
inc = 1000
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': args.multi,
'weight_decay': 0.0005}]
else:
params += [{'params': [value], 'lr': args.multi * 10,
'weight_decay': 0.0005}]
G.cuda()
G.train()
# 3. classifier
F = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
weights_init(F)
F.cuda()
F.train()
# 4. optimizer
optimizer_g = optim.SGD(params, momentum=0.9,
weight_decay=0.0005, nesterov=True)
optimizer_f = optim.SGD(list(F.parameters()), lr=1.0, momentum=0.9,
weight_decay=0.0005, nesterov=True)
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"])
# 5. training
data_iter_train = iter(train_loader)
len_train = len(train_loader)
best_acc = 0
for step in range(args.steps):
# update optimizer and lr
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']
if step % len_train == 0:
data_iter_train = iter(train_loader)
# forwarding
data = next(data_iter_train)
im_data = data[0].cuda()
gt_label = data[1].cuda()
feature = G(im_data)
if args.net == 'inception_v3': #its not a tensor output but some 'inceptionOutput' object
feature = feature.logits #get the tensor object
if args.loss=='CrossEntropy':
#call with weights if present
loss = crossentropy(F, feature, gt_label, None if (weights == None) else weights[step % len_train])
#although the weights might be defaulting to none
elif args.loss=='FocalLoss':
loss = focal_loss(F, feature, gt_label, None if (weights == None) else weights[step % len_train])
elif args.loss=='ASoftmaxLoss':
loss = asoftmax_loss(F, feature, gt_label, None if (weights == None) else weights[step % len_train])
elif args.loss=='SmoothCrossEntropy':
loss = smooth_crossentropy(F, feature, gt_label, None if (weights == None) else weights[step % len_train])
else:
print('To add new loss')
loss.backward()
# backpropagation
optimizer_g.step()
optimizer_f.step()
optimizer_g.zero_grad()
optimizer_f.zero_grad()
G.zero_grad()
F.zero_grad()
if step%args.log_interval==0:
log_train = 'Train iter: {} lr{} Loss Classification: {:.6f}\n'.format(step, lr, loss.data)
print(log_train)
if step and step%args.save_interval==0:
# evaluate and save
acc_val = eval(val_loader, G, F, class_list)
G.train()
F.train()
if args.save_check and acc_val >= best_acc:
best_acc = acc_val
print('saving model')
print('best_acc: '+str(best_acc) + ' acc_val: '+str(acc_val))
torch.save(G.state_dict(), os.path.join(args.checkpath,
"G_net_{}_loss_{}.pth".format(args.net, args.loss)))
torch.save(F.state_dict(), os.path.join(args.checkpath,
"F_net_{}_loss_{}.pth".format(args.net, args.loss)))
if (weights is not None):
print("computing error rate")
error_rate = eval_adaboost_error_rate(train_loader, G, F, class_list, weights)
model_importance = torch.log((1-error_rate)/error_rate)/2
#now update the weights
print("updating weights")
update_weights_adaboost(train_loader, G, F, class_list, weights, model_importance)
return error_rate, model_importance