|
|
import numpy as np
|
|
|
import torch
|
|
|
import torch.optim as optim
|
|
|
import logging
|
|
|
import os
|
|
|
import sys
|
|
|
|
|
|
def getCi(accLog):
|
|
|
|
|
|
mean = np.mean(accLog)
|
|
|
std = np.std(accLog)
|
|
|
ci95 = 1.96*std/np.sqrt(len(accLog))
|
|
|
|
|
|
return mean, ci95
|
|
|
|
|
|
def get_logger(out_dir):
|
|
|
logger = logging.getLogger('Exp')
|
|
|
logger.setLevel(logging.INFO)
|
|
|
formatter = logging.Formatter("%(asctime)s %(levelname)s %(message)s")
|
|
|
|
|
|
file_path = os.path.join(out_dir, "run.log")
|
|
|
file_hdlr = logging.FileHandler(file_path)
|
|
|
file_hdlr.setFormatter(formatter)
|
|
|
|
|
|
strm_hdlr = logging.StreamHandler(sys.stdout)
|
|
|
strm_hdlr.setFormatter(formatter)
|
|
|
|
|
|
logger.addHandler(file_hdlr)
|
|
|
logger.addHandler(strm_hdlr)
|
|
|
return logger
|
|
|
|
|
|
|
|
|
def initial_optim(decay_option, lr, weight_decay, net, optimizer) :
|
|
|
|
|
|
if optimizer == 'adamw' :
|
|
|
optimizer_adam_family = optim.AdamW
|
|
|
elif optimizer == 'adam' :
|
|
|
optimizer_adam_family = optim.Adam
|
|
|
if decay_option == 'all':
|
|
|
|
|
|
optimizer = optimizer_adam_family(net.parameters(), lr=lr, betas=(0.5, 0.9), weight_decay=weight_decay)
|
|
|
|
|
|
elif decay_option == 'noVQ':
|
|
|
all_params = set(net.parameters())
|
|
|
no_decay = set([net.vq_layer])
|
|
|
|
|
|
decay = all_params - no_decay
|
|
|
optimizer = optimizer_adam_family([
|
|
|
{'params': list(no_decay), 'weight_decay': 0},
|
|
|
{'params': list(decay), 'weight_decay' : weight_decay}], lr=lr)
|
|
|
|
|
|
return optimizer
|
|
|
|
|
|
|
|
|
def get_motion_with_trans(motion, velocity) :
|
|
|
'''
|
|
|
motion : torch.tensor, shape (batch_size, T, 72), with the global translation = 0
|
|
|
velocity : torch.tensor, shape (batch_size, T, 3), contain the information of velocity = 0
|
|
|
|
|
|
'''
|
|
|
trans = torch.cumsum(velocity, dim=1)
|
|
|
trans = trans - trans[:, :1]
|
|
|
trans = trans.repeat((1, 1, 21))
|
|
|
motion_with_trans = motion + trans
|
|
|
return motion_with_trans
|
|
|
|
