def main():
# parse command line input
opt = utils.parse_arg()
# Set GPU
opt.cuda = opt.gpuid >= 0
if opt.cuda:
torch.cuda.set_device(opt.gpuid)
else:
utils.time_str("GPU acceleration is disabled.")
# prepare data
db = data_prepare.prepare_db(opt)
imagenet = data_prepare.ImageNetSmallData(opt, type='centres')
# imagenet = None
#
# # add imagenet dataset
db.update({'imagenet': imagenet})
# initialize the model
pre_trained_model = model.prepare_model(opt)
# prepare M_0(x) model, which is a fixed pre-trained model
opt.num_output = 1000
fixed_model = model.prepare_model(opt)
# prepare centres
if not os.path.exists('../datasets/imagenet/train_centres.txt'):
imagenet = data_prepare.ImageNetSmallData(opt, type='all')
trainer.prepare_centres(fixed_model, imagenet, opt)
# configurate the optimizer
optim, sche = optimizer.prepare_optim(pre_trained_model, opt)
# train the model
trainer.train(pre_trained_model, optim, sche, db, opt, model_0=fixed_model)
# trainer.train(pre_trained_model, optim, sche, db, opt, model_0 = None)
# save the trained model
if opt.save:
utils.save_model(pre_trained_model, opt)
def main():
# parse command line input
opt = utils.parse_arg()
# Set GPU
opt.cuda = opt.gpuid >= 0
if opt.cuda:
torch.cuda.set_device(opt.gpuid)
else:
utils.time_str("GPU acceleration is disabled.")
# prepare data
db = data_prepare.prepare_db(opt)
# initializa the model
pre_trained_model = model.prepare_model(opt)
# configurate the optimizer
optim, sche = optimizer.prepare_optim(pre_trained_model, opt)
# train the model
trainer.train(pre_trained_model, optim, sche, db, opt)
# save the trained model
utils.save_model(pre_trained_model, opt)
def main():
# logging configuration
logging.basicConfig(level=logging.INFO,
format="[%(asctime)s]: %(message)s")
# parse command line input
opt = utils.parse_arg()
# Set GPU
opt.cuda = opt.gpuid >= 0
if opt.cuda:
torch.cuda.set_device(opt.gpuid)
else:
# please use GPU for training, CPU version is not supported for now.
raise NotImplementedError
#logging.info("GPU acceleration is disabled.")
# prepare training and validation dataset
db = data_prepare.prepare_db(opt)
# sanity check for FG-NET dataset, not used for now
# assertion: the total images in the eval set lists should be 1002
total_eval_imgs = sum([len(db['eval'][i]) for i in range(len(db['eval']))])
print(total_eval_imgs)
if db['train'][0].name == 'FGNET':
assert total_eval_imgs == 1002, 'The preparation of the evalset is incorrect.'
# training
if opt.train:
best_MAEs = []
last_MAEs = []
# record the current time
opt.save_dir += time.asctime(time.localtime(time.time()))
# for FG-NET, do training multiple times for leave-one-out validation
# for CACD, do training just once
for exp_id in range(len(db['train'])):
# initialize the model
model_train = model.prepare_model(opt)
#print("model shape:")
# print(db['train'].head)
#print( np.array(db['eval']).shape)
# configurate the optimizer and learning rate scheduler
optim, sche = optimizer.prepare_optim(model_train, opt)
# train the model and record mean average error (MAE)
model_train, MAE, last_MAE = trainer.train(model_train, optim,
sche, db, opt, exp_id)
best_MAEs += MAE
last_MAEs.append(last_MAE.data.item())
# remove the trained model for leave-one-out validation
if exp_id != len(db['train']) - 1:
del model_train
#np.save('./MAE.npy', np.array(best_MAEs))
#np.save('./Last_MAE.npy', np.array(last_MAEs))
# save the final trained model
#utils.save_model(model_train, opt)
# testing a pre-trained model
elif opt.evaluate:
# path to the pre-trained model
save_dir = opt.test_model_path
#example: save_dir = '../model/CACD_MAE_4.59.pth'
model_loaded = torch.load(save_dir)
# test the model on the evaluation set
# the last subject is the test set (compatible with FG-NET)
trainer.evaluate(model_loaded, db['eval'][-1], opt)
return
if opt.dataset_name == 'Morph':
# Sorry that the MORPH dataset is currently not freely available.
# For now I can not release my pre-processed dataset without permission.
raise ValueError
elif opt.dataset_name == 'CACD':
model_path = "../pre-trained/CACD_MAE_4.59.pth"
else:
raise NotImplementedError
# load model
model = torch.load(model_path)
model.cuda()
# prepare dataset
db = prepare_db(opt)
dataset = db['eval'][0]
# compute saliency map
# pick a tree within the forest
tree_idx = 0
depth = model.forest.trees[0].depth
# pick a splitting node index (optional)
#node_idx = 0 # 0 - 510 for 511 splitting nodes
# get saliency maps for a specified node for different input tensors
# vis_utils.get_node_saliency_map(dataset, model, tree_idx, node_idx, name=opt.dataset)
# get the computational paths for the input
sample, labels, paths, class_pred = vis_utils.get_paths(dataset,
model,