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5 MiB
JSON
1 line
5 MiB
JSON
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{"attribute_calls":["#list#.append()","#str#.format()","#str#.join()","os.path.join()","os.path.exists()","numpy.array()","#open#.write()","numpy.zeros()","#open#.read()","os.makedirs()","time.time()","os.path.dirname()","argparse.ArgumentParser()","time.sleep()","#list#.extend()","json.loads()","argparse.ArgumentParser.add_argument()","sys.exit()","os.path.isfile()","argparse.ArgumentParser.parse_args()","os.path.isdir()","json.load()","#set#.add()","os.path.basename()","os.listdir()","json.dumps()","torch.cat()","os.path.abspath()","#open#.close()","numpy.arange()","numpy.sum()","copy.deepcopy()","numpy.concatenate()","os.remove()","re.sub()","re.compile()","logging.getLogger()","re.search()","numpy.ones()","collections.defaultdict()","numpy.mean()","#dict#.items()","#dict#.update()","#dict#.get()","datetime.datetime.now()","numpy.sqrt()","#open#.readlines()","logging.getLogger.info()","os.path.splitext()","re.match()","subprocess.Popen()","random.randint()","collections.OrderedDict()","shutil.rmtree()","numpy.max()","numpy.asarray()","torch.zeros()","warnings.warn()","json.dump()","numpy.where()","#dict#.keys()","os.mkdir()","requests.get()","glob.glob()","math.sqrt()","torch.from_numpy()","pathlib.Path()","torch.load()","logging.getLogger.debug()","PIL.Image.open()","numpy.random.randint()","os.walk()","numpy.abs()","torch.save()","numpy.random.seed()","functools.partial()","#list#.sort()","re.compile.match()","os.environ.get()","numpy.exp()","torch.stack()","torch.sum()","datetime.timedelta()","torch.utils.data.DataLoader()","torch.tensor()","#str#.split()","random.choice()","threading.Thread()","numpy.dot()","numpy.argmax()","os.system()","logging.getLogger.warning()","#list#(#str#).append()","logging.getLogger.error()","#list#.pop()","numpy.clip()","os.getcwd()","tqdm.tqdm()","random.shuffle()","numpy.min()","numpy.random.choice()","re.search.group()","logging.info()","collections.namedtuple()","logging.getLogger.setLevel()","numpy.linspace()","os.path.split()","sys.stdout.write()","os.path.expanduser()","importlib.import_module()","re.findall()","numpy.expand_dims()","numpy.load()","matplotlib.pyplot.figure()","logging.StreamHandler()","random.random()","numpy.log()","cv2.resize()","os.path.realpath()","random.seed()","torch.max()","numpy.transpose()","torch.arange()","numpy.vstack()","subprocess.check_output()","numpy.reshape()","numpy.stack()","numpy.cos()","numpy.sin()","cv2.imread()","numpy.linalg.norm()","logging.Formatter()","subprocess.Popen.communicate()","torch.nn.functional.relu()","torch.ones()","logging.getLogger.addHandler()","numpy.maximum()","matplotlib.pyplot.show()","numpy.argsort()","#str#.lower()","numpy.empty()","torch.cuda.is_available()","#str#.replace()","numpy.random.uniform()","sys.stdout.flush()","copy.copy()","torch.mean()","#str#.encode()","torch.nn.Conv2d()","logging.basicConfig()","numpy.zeros_like()","element_of(#open#).strip()","io.BytesIO()","math.ceil()","torch.nn.functional.softmax()","#dict#(#str#,#NoneType#).update()","subprocess.call()","numpy.unique()","#dict#.values()","PIL.Image.fromarray()","torch.nn.Sequential()","requests.post()","numpy.random.rand()","numpy.hstack()","torch.autograd.Variable()","pickle.load()","struct.unpack()","torch.Tensor()","torch.optim.Adam()","#list#.insert()","re.compile.search()","collections.deque()","cv2.cvtColor()","torch.device()","torch.exp()","os.chdir()","datetime.datetime.strptime()","base64.b64encode()","torch.zeros_like()","time.strftime()","logging.StreamHandler.setFormatter()","os.getenv()","collections.Counter()","sys.stderr.write()","numpy.array.append()","numpy.eye()","numpy.tile()","threading.Thread.start()","#open#.readline()","torch.manual_seed()","#str#.startswith()","torch.log()","torch.FloatTensor()","re.match.group()","shutil.copyfile()","numpy.round()","numpy.ceil()","io.StringIO()","torchvision.transforms.Compose()","numpy.all()","hashlib.md5()","re.compile.match.group()","matplotlib.pyplot.savefig()","numpy.minimum()","tempfile.mkdtemp()","matplotlib.pyplot.plot()","math.log()","torch.nn.Linear()","torch.sigmoid()","nu
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