def test_D2L():
# set options
opt = Option()
opt.root_dir = root + '/dataset/test'
opt.checkpoints_dir = root + '/checkpoints/D2L'
opt.result_dir = opt.root_dir
opt.gpu_ids = [0]
opt.batch_size = 16
opt.coarse = False
opt.pool_size = 0
opt.no_lsgan = True
opt.is_train = False
opt.fine_tune_sidewalk = False
# load data
root_dir_train = opt.root_dir
dataset_train = D2LDataLoader(root_dir=root_dir_train,
train=opt.is_train,
coarse=opt.coarse,
fine_tune_sidewalk=opt.fine_tune_sidewalk)
data_loader_test = DataLoader(dataset_train,
batch_size=opt.batch_size,
shuffle=opt.shuffle,
num_workers=opt.num_workers,
pin_memory=opt.pin_memory)
# load model
model = D2LModel()
model.initialize(opt)
model.load_networks(50)
# do testung
for idx_batch, data_batch in enumerate(data_loader_test):
print(idx_batch)
model.set_input(data_batch, 0)
model.forward()
fake_S = model.fake_S.detach().cpu()
n, c, h, w = fake_S.size()
for i in range(0, n):
sem = fake_S[i, :, :, :] * 0.5 + 0.5
img_id = data_batch['img_id'][i]
# save image
path_sem = root_dir_train + '/' + img_id + '_pred_sem_wo_mask.png'
#torchvision.utils.save_image(depth.float(), path_depth)
torchvision.utils.save_image(sem.float(), path_sem)
def train_D2L():
# set options
opt = Option()
opt.root_dir = root + '/dataset/D2L'
opt.checkpoints_dir = root + '/checkpoints/D2L'
opt.gpu_ids = [0]
opt.batch_size = 16
opt.coarse = False
opt.pool_size = 0
opt.no_lsgan = True
opt.fine_tune_sidewalk = False
# load data
root_dir_train = opt.root_dir + '/train'
dataset_train = D2LDataLoader(root_dir=root_dir_train,
train=True,
coarse=opt.coarse,
fine_tune_sidewalk=opt.fine_tune_sidewalk)
data_loader_train = DataLoader(dataset_train,
batch_size=opt.batch_size,
shuffle=opt.shuffle,
num_workers=opt.num_workers,
pin_memory=opt.pin_memory)
# load model
model = D2LModel()
model.initialize(opt)
model.load_networks(-1)
# do training
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
file = open(opt.root_dir + '/logs.txt', 'a')
for idx_batch, data_batch in enumerate(data_loader_train):
print(idx_batch)
model.set_input(data_batch, epoch)
model.optimize_parameters()
print('epoch: ' + str(epoch) + ', train loss_G_Loss: ' +
str(model.loss_G.data))
file.write('epoch: ' + str(epoch) + ', train loss_G_Loss: ' +
str(model.loss_G.data) + '\n')
file.close()
# save
if epoch % 5 == 0:
model.save_networks(epoch)
