| 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, eps=1e-3) : |
| |
| 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, eps=eps) |
| |
| 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 |
| |
