def init_ip_config(self):
self.ip_train_loader = get_data_loader(EXP_NAME, data_type="train", batch_size=CLIENT_BATCH_SIZE, shuffle=True,
num_workers=8, user_list=[0], pin_memory=True)
self.ip_test_loader = get_data_loader(EXP_NAME, data_type="test", num_workers=8, pin_memory=True)
ip_optimizer = SGD(self.model.parameters(), lr=INIT_LR)
self.ip_optimizer_wrapper = OptimizerWrapper(self.model, ip_optimizer)
self.ip_control = ControlModule(model=self.model, config=config)
def init_optimizer(self):
self.optimizer = SGD(self.model.parameters(),
lr=INIT_LR,
momentum=MOMENTUM,
weight_decay=WEIGHT_DECAY)
self.optimizer_scheduler = lr_scheduler.StepLR(
self.optimizer,
step_size=STEP_SIZE,
gamma=0.5**(STEP_SIZE / LR_HALF_LIFE))
self.optimizer_wrapper = OptimizerWrapper(self.model, self.optimizer,
self.optimizer_scheduler)
def init_optimizer(self):
self.optimizer = SGD(self.model.parameters(), lr=INIT_LR)
self.optimizer_scheduler = lr_scheduler.StepLR(
self.optimizer, step_size=1, gamma=0.5**(1 / LR_HALF_LIFE))
self.optimizer_wrapper = OptimizerWrapper(self.model, self.optimizer,
self.optimizer_scheduler)
def init_optimizer(self):
self.optimizer = SGD(self.model.parameters(), lr=INIT_LR)
self.optimizer_wrapper = OptimizerWrapper(self.model, self.optimizer)
server_pruning_rounds, num_pre_batch * config.CLIENT_BATCH_SIZE,
server_adjust_interval))
server_loader = get_data_loader(config.EXP_NAME,
data_type="train",
batch_size=config.CLIENT_BATCH_SIZE,
shuffle=True,
num_workers=8,
user_list=[0],
pin_memory=True)
server_inputs, server_outputs = [], []
for _ in range(num_pre_batch):
inp, out = server_loader.get_next_batch()
server_inputs.append(inp)
server_outputs.append(out)
server_optimizer = SGD(model.parameters(), lr=config.INIT_LR)
server_optimizer_wrapper = OptimizerWrapper(model, server_optimizer)
server_control = ControlModule(model=model, config=config)
prev_density, prev_num, prev_ind = None, 5, []
for server_i in range(1, server_pruning_rounds + 1):
for server_inp, server_out in zip(server_inputs, server_outputs):
list_grad = server_optimizer_wrapper.step(server_inp, server_out)
for (key, param), g in zip(model.named_parameters(), list_grad):
assert param.size() == g.size()
server_control.accumulate(key, g**2)
if server_i % server_adjust_interval == 0:
server_control.adjust(config.MAX_DEC_DIFF)
cur_density = disp_num_params(model)