save_dir = os.path.join(save_dir_root, 'run_' + str(run_id))
if not os.path.exists(os.path.join(save_dir, 'models')):
os.makedirs(os.path.join(save_dir, 'models'))
# Network definition
modelName = 'dextr_pascal'
net = resnet.resnet101(1, pretrained=True, nInputChannels=nInputChannels, classifier=classifier)
if resume_epoch == 0:
print("Initializing from pretrained Deeplab-v2 model")
else:
print("Initializing weights from: {}".format(
os.path.join(save_dir, 'models', modelName + '_epoch-' + str(resume_epoch - 1) + '.pth')))
net.load_state_dict(
torch.load(os.path.join(save_dir, 'models', modelName + '_epoch-' + str(resume_epoch - 1) + '.pth'),
map_location=lambda storage, loc: storage))
train_params = [{'params': resnet.get_1x_lr_params(net), 'lr': p['lr']},
{'params': resnet.get_10x_lr_params(net), 'lr': p['lr'] * 10}]
net.to(device)
# Training the network
if resume_epoch != nEpochs:
# Logging into Tensorboard
log_dir = os.path.join(save_dir, 'models', datetime.now().strftime('%b%d_%H-%M-%S') + '_' + socket.gethostname())
# writer = SummaryWriter(log_dir=log_dir)
# Use the following optimizer
optimizer = optim.SGD(train_params, lr=p['lr'], momentum=p['momentum'], weight_decay=p['wd'])
p['optimizer'] = str(optimizer)
# Preparation of the data loaders
pretrained=True,
nInputChannels=nInputChannels,
classifier=classifier)
if resume_epoch == 0:
print("Initializing from pretrained Deeplab-v2 model")
else:
print("Initializing weights from: {}".format(
os.path.join(save_dir, 'models',
modelName + '_epoch-' + str(resume_epoch - 1) + '.pth')))
net.load_state_dict(
torch.load(os.path.join(
save_dir, 'models',
modelName + '_epoch-' + str(resume_epoch - 1) + '.pth'),
map_location=lambda storage, loc: storage))
train_params = [{
'params': resnet.get_1x_lr_params(net),
'lr': p['lr']
}, {
'params': resnet.get_10x_lr_params(net),
'lr': p['lr'] * 10
}]
# net.to(device)
net = torch.nn.DataParallel(net.cuda())
# net.to(device)
# Training the network
if resume_epoch != nEpochs:
# Logging into Tensorboard
log_dir = os.path.join(
save_dir, 'models',