Spaces:
Running
on
T4
Running
on
T4
File size: 2,108 Bytes
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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
## Optimizer
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=(0.9, 0.999), weight_decay=weight_decay)
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] ## the first root is initialized at 0 (just for visualization)
trans = trans.repeat((1, 1, 21))
motion_with_trans = motion + trans
return motion_with_trans
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