85 lines
2.1 KiB
Python
85 lines
2.1 KiB
Python
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import os
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import random
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import sys
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from shutil import copyfile
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import datetime
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import torch
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import logging
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logger = logging.getLogger()
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import numpy as np
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def set_global_gpu_env(opt):
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os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
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os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpu)
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torch.cuda.set_device(opt.gpu)
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def copy_source(file, output_dir):
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copyfile(file, os.path.join(output_dir, os.path.basename(file)))
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def setup_logging(output_dir):
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log_format = logging.Formatter("%(asctime)s : %(message)s")
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logger = logging.getLogger()
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logger.handlers = []
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output_file = os.path.join(output_dir, 'output.log')
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file_handler = logging.FileHandler(output_file)
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file_handler.setFormatter(log_format)
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logger.addHandler(file_handler)
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console_handler = logging.StreamHandler(sys.stdout)
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console_handler.setFormatter(log_format)
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err_handler = logging.StreamHandler(sys.stderr)
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err_handler.setFormatter(log_format)
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logger.addHandler(err_handler)
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logger.setLevel(logging.INFO)
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return logger
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def get_output_dir(prefix, exp_id):
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t = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
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output_dir = os.path.join(prefix, 'output/' + exp_id, t)
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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return output_dir
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def set_seed(opt):
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if opt.manualSeed is None:
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opt.manualSeed = random.randint(1, 10000)
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print("Random Seed: ", opt.manualSeed)
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random.seed(opt.manualSeed)
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torch.manual_seed(opt.manualSeed)
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np.random.seed(opt.manualSeed)
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if opt.gpu is not None and torch.cuda.is_available():
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torch.cuda.manual_seed_all(opt.manualSeed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = True
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def setup_output_subdirs(output_dir, *subfolders):
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output_subdirs = output_dir
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try:
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os.makedirs(output_subdirs)
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except OSError:
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pass
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subfolder_list = []
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for sf in subfolders:
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curr_subf = os.path.join(output_subdirs, sf)
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try:
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os.makedirs(curr_subf)
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except OSError:
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pass
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subfolder_list.append(curr_subf)
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return subfolder_list
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