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celinelee
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python_fn_calls

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self._urlopener.open('http://icanhazip.com')
res.read()
decode('utf-8')
self.err(ex)
_get_local_ip(self)
socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 80)
s.getsockname()
self.err(ex)
License (MIT)
main()
ArgumentParser(add_help=False)
parser.add_argument('-c', '--config_path', type=str, default='./src/configs/CIFAR10/ContraGAN.json')
parser.add_argument('--checkpoint_folder', type=str, default=None)
parser.add_argument('-current', '--load_current', action='store_true', help='whether you load the current or best checkpoint')
parser.add_argument('--log_output_path', type=str, default=None)
parser.add_argument('-DDP', '--distributed_data_parallel', action='store_true')
parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N')
parser.add_argument('-nr', '--nr', default=0, type=int, help='ranking within the nodes')
parser.add_argument('--seed', type=int, default=-1, help='seed for generating random numbers')
parser.add_argument('--num_workers', type=int, default=8, help='')
parser.add_argument('-sync_bn', '--synchronized_bn', action='store_true', help='whether turn on synchronized batchnorm')
parser.add_argument('-mpc', '--mixed_precision', action='store_true', help='whether turn on mixed precision training')
parser.add_argument('-LARS', '--LARS_optimizer', action='store_true', help='whether turn on LARS optimizer')
parser.add_argument('-rm_API', '--disable_debugging_API', action='store_true', help='whether disable pytorch autograd debugging mode')
parser.add_argument('--reduce_train_dataset', type=float, default=1.0, help='control the number of train dataset')
parser.add_argument('--truncated_factor', type=float, default=-1.0, help='factor for truncation trick')
parser.add_argument('-stat_otf', '--bn_stat_OnTheFly', action='store_true', help='when evaluating, use the statistics of a batch')
parser.add_argument('-std_stat', '--standing_statistics', action='store_true')
parser.add_argument('--standing_step', type=int, default=-1, help='# of steps for accumulation batchnorm')
parser.add_argument('--freeze_layers', type=int, default=-1, help='# of layers for freezing discriminator')
parser.add_argument('-l', '--load_all_data_in_memory', action='store_true')
parser.add_argument('-t', '--train', action='store_true')
parser.add_argument('-e', '--eval', action='store_true')
parser.add_argument('-s', '--save_images', action='store_true')
parser.add_argument('-iv', '--image_visualization', action='store_true', help='select whether conduct image visualization')
parser.add_argument('-knn', '--k_nearest_neighbor', action='store_true', help='select whether conduct k-nearest neighbor analysis')
parser.add_argument('-itp', '--interpolation', action='store_true', help='whether conduct interpolation analysis')
parser.add_argument('-fa', '--frequency_analysis', action='store_true', help='whether conduct frequency analysis')
parser.add_argument('-tsne', '--tsne_analysis', action='store_true', help='whether conduct tsne analysis')
parser.add_argument('--nrow', type=int, default=10, help='number of rows to plot image canvas')
parser.add_argument('--ncol', type=int, default=8, help='number of cols to plot image canvas')
parser.add_argument('--print_every', type=int, default=100, help='control log interval')
parser.add_argument('--save_every', type=int, default=2000, help='control evaluation and save interval')
parser.add_argument('--eval_type', type=str, default='test', help='[train/valid/test]')
update_parser_defaults_from_yaml(parser=parser)
parser.parse_args()
parser.print_help(sys.stderr)
sys.exit(1)
open(args.config_path)
json.load(f)
vars(args)
make_hdf5(model_configs['data_processing'], train_configs, mode="train")
random.randint(1,4096)
fix_all_seed(train_configs['seed'])
torch.cuda.device_count()
torch.cuda.current_device()
warnings.warn('You have chosen a specific GPU. This will completely disable data parallelism.')
make_run_name(RUN_NAME_FORMAT, framework=train_configs['config_path'].split('/')
torch.autograd.set_detect_anomaly(False)
check_flags(train_configs, model_configs, world_size)
print("Train the models through DistributedDataParallel (DDP)
prepare_train_eval(rank, gpus_per_node, world_size, run_name, train_configs, model_configs, hdf5_path_train=hdf5_path_train)
main()
scan (i.e., for which it doesn't have a specific scanner in its dictionary)
TestSCons.TestSCons()
open(sys.argv[1], 'w')
open(infile, 'r')
process(infp, outfp, include_prefix=include_prefix)
infp.readlines()
len(include_prefix)
len(include_prefix)
open(file, 'r')
process(f, outfp)
outfp.write(line)
process(ifp, ofp)
sys.exit(0)
SConscript('SConscript')
re.compile(r'^include1\s+(\S+)
re.compile(r'^include2\s+(\S+)
re.compile(r'^include3\s+(\S+)
k1_scan(node, env, scanpaths, arg=None)
node.get_text_contents()
include1_re.findall(contents)
k2_scan(node, env, scanpaths, arg=None)
node.get_text_contents()
include2_re.findall(contents)
k3_scan(node, env, scanpaths, arg=None)
node.get_text_contents()
include3_re.findall(contents)
Scanner({'.k1' : Scanner(k1_scan)
Scanner(k2_scan)
Builder(action=r'%(_python_)
Environment(BUILDERS={'Build':b})
kscanner.add_scanner('.k3', Scanner(k3_scan)
env.Build('aaa', 'aaa.k1')
env.Build('bbb', 'bbb.k2')
env.Build('ccc', 'ccc.k3')
env.Build('ddd', ['ddd.k4', 'aaa.k1', 'bbb.k2', 'ccc.k3'])
locals()