def get_scheduler(optimizer, opt):
if opt.lr_policy == 'lambda':
def lambda_rule(epoch):
lr_l = 1.0 - max(0, epoch + 1 + opt.epoch_count -
opt.niter) / float(opt.niter_decay + 1)
return lr_l
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
elif opt.lr_policy == 'step':
scheduler = lr_scheduler.StepLR(optimizer,
step_size=opt.lr_decay_iters,
gamma=0.1)
elif opt.lr_policy == 'plateau':
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.2,
threshold=0.01,
patience=5)
else:
return NotImplementedError(
'learning rate policy [%s] is not implemented', opt.lr_policy)
return scheduler
else:
optim = Optim(config.optim,
config.learning_rate,
config.max_grad_norm,
lr_decay=config.learning_rate_decay,
start_decay_at=config.start_decay_at)
if config.use_center_loss:
optim.set_parameters(
list(model.parameters()) + list(center_loss.parameters()))
else:
optim.set_parameters(list(model.parameters()))
if config.schedule:
# scheduler = L.CosineAnnealingLR(optim.optimizer, T_max=config.epoch)
scheduler = L.StepLR(optim.optimizer, step_size=15, gamma=0.2)
# total number of parameters
param_count = 0
for param in model.parameters():
param_count += param.view(-1).size()[0]
# logging modeule
if not os.path.exists(config.log):
os.mkdir(config.log)
if opt.log == '':
log_path = config.log + utils.format_time(time.localtime()) + '/'
else:
log_path = config.log + opt.log + '/'
if not os.path.exists(log_path):
os.mkdir(log_path)
# Load model
model_ft = models.resnet18(pretrained=True)
# for param in model_ft.parameters():
# param.requires_grad = False
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft.load_state_dict(torch.load('savedInitialModel.pt'))
# Data mining until we are satisfied with our accuracy
accuracyHistory = []
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# optimizer_ft = optim.SGD(model_ft.fc.parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
acc = checkAccuracy(model_ft, criterion, optimizer_ft, exp_lr_scheduler)
accuracyHistory.append(acc)
cv2.waitKey(0)
dataMine = 1
while True:
try:
dataMine = int(raw_input("Continue data mining? (type 1) else (type 0)"))
except ValueError:
print("Please type either 1 to continue or 0 to exit.")
continue
else:
if dataMine != 0 and dataMine != 1:
print("Please type either 1 to continue or 0 to exit.")