123 lines
3.8 KiB
Python
123 lines
3.8 KiB
Python
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
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import time
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import os
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import numpy as np
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from loguru import logger
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from math import isnan
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from calmsize import size as calmsize
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def parse_cfg_str(cfg_str):
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""" parse a string into a dict
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string format: k1=v1,k2=v2,k3=v3
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"""
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cfg_list = cfg_str.split('-')
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cfg_list = [c for c in cfg_list if len(c) > 0]
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cfg_expand_list = []
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for c in cfg_list:
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k, v = c.split('=')
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cfg_expand_list.append(k)
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cfg_expand_list.append(v)
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return cfg_expand_list
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##cfg_dict = {}
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# if cfg_str == '':
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# return cfg_dict
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##cfg_str_list = cfg_str.split(',')
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# for p in cfg_str_list:
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## kvs = p.split('=')
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## assert(len(kvs) == 2), f'wrong format, expect k1=v1 for {p}'
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## k, v = kvs
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## cfg_dict[k] = v
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# return cfg_dict
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def readable_size(num_bytes: int) -> str:
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return '' if isnan(num_bytes) else '{:.1f}'.format(calmsize(num_bytes))
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class ExpTimer(object):
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def __init__(self, num_epoch, start_epoch=0):
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self.cur_epoch = start_epoch
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self.num_epoch = num_epoch
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self.time_list = []
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def tic(self):
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self.last_tic = time.time()
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def toc(self):
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self.time_list.append(time.time() - self.last_tic)
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self.cur_epoch += 1
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def hours_left(self):
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if len(self.time_list) == 0:
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return 0
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num_epoch_left = self.num_epoch - self.cur_epoch
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mean_epoch_time = np.array(self.time_list).mean()
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hours_left = (mean_epoch_time * num_epoch_left) / 3600.0 # hours
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return hours_left
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def print(self):
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logger.info('est: {:.1}h', self.hours_left)
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def format_e(n):
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if n == 0:
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return '0'
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a = '%E' % n
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return a.split('E')[0].rstrip('0').rstrip('.') + 'E' + a.split('E-0')[1]
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def get_evalname(config):
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# generate tag for the generated samples
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tag = ''
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if config.ddpm.model_var_type != 'fixedlarge':
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tag += config.ddpm.model_var_type
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if not config.ddpm.ema:
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tag += 'noema'
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tag += f"s{config.trainer.seed}"
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if config.data.te_max_sample_points != 2048:
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tag += 'N%d' % config.data.te_max_sample_points
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if config.eval_ddim_step > 0:
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tag += 'ddim%d_%s%.1f' % (
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config.eval_ddim_step,
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config.sde.ddim_skip_type,
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config.sde.ddim_kappa)
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githash = os.popen('git rev-parse HEAD').read().strip()[:5]
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logger.info('git hash: {}', githash)
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tag += f"H{githash}"
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return tag
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def get_expname(config):
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if config.exp_name == '' or config.exp_name == 'none':
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cate = config.data.cates if type(
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config.data.cates) is str else config.data.cates[0]
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cfg_file_name = ''
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if config.data.type == 'datasets.neuralspline_datasets':
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cfg_file_name += 'ns'
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cfg_file_name += '%s/' % cate
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if len(config.hash):
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cfg_file_name += '%s_' % config.hash
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cfg_file_name += f"{config.trainer.type.split('.')[-1].split('_')[0]}_"
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if len(config.cmt):
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cfg_file_name += config.cmt + '_'
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cfg_file_name += 'B%d' % config.data.batch_size
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if config.data.tr_max_sample_points != 2048:
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cfg_file_name += 'N%d' % config.data.tr_max_sample_points
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run_time = time.strftime('%m%d')
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cfg_file_name = run_time + '/' + cfg_file_name
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else:
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cfg_file_name = config.exp_name
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return cfg_file_name
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