training = Trainer(
cuda=cuda,
model_depth=model_depth,
model_baseline=model_baseline,
model_ladder=model_ladder,
optimizer_depth=optimizer_depth,
optimizer_baseline=optimizer_baseline,
optimizer_ladder=optimizer_ladder,
train_loader=train_loader,
max_iter=cfg[1]['max_iteration'],
snapshot=cfg[1]['spshot'],
outpath=args.snapshot_root,
sshow=cfg[1]['sshow']
)
training.epoch = start_epoch
training.iteration = start_iteration
training.train()
else:
res = []
for id, (data, depth, img_name, img_size) in enumerate(test_loader):
# print('testing bach %d' % id)
inputs = Variable(data).cuda()
depth = Variable(depth).cuda()
n, c, h, w = inputs.size()
# depth = torch.unsqueeze(depth, 1)
depth = depth.view(n, 1, h, w).repeat(1, c, 1, 1)
torch.cuda.synchronize()
start = time.time()
h2, h3, h4, h5 = model_baseline(inputs)
d2, d3, d4 = model_depth(depth)
