def __init__(self, num_experts, lr=0, cam_centers=None, gating_capacity=1):
self.num_experts = num_experts
self.lr = lr # learning rate
if cam_centers is None:
cam_centers = torch.zeros(num_experts, 3)
cam_centers = cam_centers.cuda()
# setup gating network
self.model_g = Gating(num_experts, gating_capacity)
self.model_g = self.model_g.cuda()
self.model_g.train()
self.optimizer_g = optim.Adam(self.model_g.parameters(), lr=lr)
# setup expert networks
self.experts = []
self.expert_opts = []
for i in range(0, num_experts):
model_e = Expert(cam_centers[i])
model_e = model_e.cuda()
model_e.train()
optimizer_e = optim.Adam(model_e.parameters(), lr=lr)
self.experts.append(model_e)
self.expert_opts.append(optimizer_e)
trainset = ClusterDataset("training",
num_clusters=opt.clusters,
cluster=opt.expert)
trainset_loader = torch.utils.data.DataLoader(trainset,
shuffle=True,
num_workers=6)
model = Expert(torch.zeros((3, )))
model.load_state_dict(
torch.load('expert_e%d_%s.net' % (opt.expert, opt.session)))
print("Successfully loaded model.")
model.cuda()
model.train()
model_file = 'expert_e%d_%s_refined.net' % (opt.expert, opt.session)
optimizer = optim.Adam(model.parameters(), lr=opt.learningrate)
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=opt.lrssteps,
gamma=opt.lrsgamma)
iteration = 0
epochs = math.ceil(opt.iterations / len(trainset))
# keep track of training progress
train_log = open('log_refine_e%d_%s.txt' % (opt.expert, opt.session), 'w', 1)
# generate grid of target reprojection pixel positions