Python humanize.naturalsize方法代碼示例

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def __init__(self, args, config):
        self.args = args
        self.config = config
        self.cache_dir = utils.get_cache_dir(config)
        self.model_dir = utils.get_model_dir(config)
        self.category = utils.get_category(config, self.cache_dir if os.path.exists(self.cache_dir) else None)
        self.draw_bbox = utils.visualize.DrawBBox(self.category, colors=args.colors, thickness=args.thickness)
        self.anchors = torch.from_numpy(utils.get_anchors(config)).contiguous()
        self.height, self.width = tuple(map(int, config.get('image', 'size').split()))
        self.path, self.step, self.epoch = utils.train.load_model(self.model_dir)
        state_dict = torch.load(self.path, map_location=lambda storage, loc: storage)
        self.dnn = utils.parse_attr(config.get('model', 'dnn'))(model.ConfigChannels(config, state_dict), self.anchors, len(self.category))
        self.dnn.load_state_dict(state_dict)
        self.inference = model.Inference(config, self.dnn, self.anchors)
        self.inference.eval()
        if torch.cuda.is_available():
            self.inference.cuda()
        logging.info(humanize.naturalsize(sum(var.cpu().numpy().nbytes for var in self.inference.state_dict().values())))
        self.cap = self.create_cap()
        self.keys = set(args.keys)
        self.resize = transform.parse_transform(config, config.get('transform', 'resize_test'))
        self.transform_image = transform.get_transform(config, config.get('transform', 'image_test').split())
        self.transform_tensor = transform.get_transform(config, config.get('transform', 'tensor').split()) 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def main():
    args = make_args()
    config = configparser.ConfigParser()
    utils.load_config(config, args.config)
    for cmd in args.modify:
        utils.modify_config(config, cmd)
    with open(os.path.expanduser(os.path.expandvars(args.logging)), 'r') as f:
        logging.config.dictConfig(yaml.load(f))
    height, width = tuple(map(int, config.get('image', 'size').split()))
    cache_dir = utils.get_cache_dir(config)
    model_dir = utils.get_model_dir(config)
    category = utils.get_category(config, cache_dir if os.path.exists(cache_dir) else None)
    anchors = utils.get_anchors(config)
    anchors = torch.from_numpy(anchors).contiguous()
    path, step, epoch = utils.train.load_model(model_dir)
    state_dict = torch.load(path, map_location=lambda storage, loc: storage)
    dnn = utils.parse_attr(config.get('model', 'dnn'))(model.ConfigChannels(config, state_dict), anchors, len(category))
    inference = model.Inference(config, dnn, anchors)
    inference.eval()
    logging.info(humanize.naturalsize(sum(var.cpu().numpy().nbytes for var in inference.state_dict().values())))
    dnn.load_state_dict(state_dict)
    image = torch.autograd.Variable(torch.randn(args.batch_size, 3, height, width), volatile=True)
    path = model_dir + '.onnx'
    logging.info('save ' + path)
    torch.onnx.export(dnn, image, path, export_params=True, verbose=args.verbose) # PyTorch's bug 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def __init__(self, args, config):
        self.args = args
        self.config = config
        self.model_dir = utils.get_model_dir(config)
        self.category = utils.get_category(config)
        self.anchors = torch.from_numpy(utils.get_anchors(config)).contiguous()
        self.dnn = utils.parse_attr(config.get('model', 'dnn'))(model.ConfigChannels(config), self.anchors, len(self.category))
        self.dnn.eval()
        logging.info(humanize.naturalsize(sum(var.cpu().numpy().nbytes for var in self.dnn.state_dict().values())))
        if torch.cuda.is_available():
            self.dnn.cuda()
        self.height, self.width = tuple(map(int, config.get('image', 'size').split()))
        output = self.dnn(torch.autograd.Variable(utils.ensure_device(torch.zeros(1, 3, self.height, self.width)), volatile=True))
        _, _, self.rows, self.cols = output.size()
        self.i, self.j = self.rows // 2, self.cols // 2
        self.output = output[:, :, self.i, self.j]
        dataset = Dataset(self.height, self.width)
        try:
            workers = self.config.getint('data', 'workers')
        except configparser.NoOptionError:
            workers = multiprocessing.cpu_count()
        self.loader = torch.utils.data.DataLoader(dataset, batch_size=self.args.batch_size, num_workers=workers) 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def __str__(self):
        """Format model description as a string."""
        try:
            dump = self.dump()
        except NotImplementedError:
            dump = ""
        except AttributeError:
            return repr(self)
        if dump:
            dump = "\n" + dump
        meta = deepcopy(self.meta)
        meta["created_at"] = format_datetime(meta["created_at"])
        meta["size"] = humanize.naturalsize(self.size)
        try:
            meta["environment"]["packages"] = \
                " ".join("%s==%s" % tuple(p) for p in self.environment["packages"])
        except KeyError:
            pass
        return "%s%s" % (pformat(meta, width=1024), dump) 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def extract_model_meta(base_meta: dict, extra_meta: dict, model_url: str) -> dict:
    """
    Merge the metadata from the backend and the extra metadata into a dict which is suitable for \
    `index.json`.

    :param base_meta: tree["meta"] :class:`dict` containing data from the backend.
    :param extra_meta: dict containing data from the user, similar to `meta.json`.
    :param model_url: public URL of the model.
    :return: converted dict.
    """
    meta = {"default": {"default": base_meta["uuid"],
                        "description": base_meta["description"],
                        "code": extra_meta["code"]}}
    del base_meta["model"]
    del base_meta["uuid"]
    meta["model"] = base_meta
    meta["model"].update({k: extra_meta[k] for k in ("code", "datasets", "references", "tags",
                                                     "extra")})
    response = requests.get(model_url, stream=True)
    meta["model"]["size"] = humanize.naturalsize(int(response.headers["content-length"]))
    meta["model"]["url"] = model_url
    meta["model"]["created_at"] = format_datetime(meta["model"]["created_at"])
    return meta 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def summarise_usage():
    wall_time = humanize.naturaldelta(time.time() - __before)
    user_time = humanize.naturaldelta(os.times().user)
    sys_time = os.times().system
    if resource is None:
        # Don't report max memory on Windows. We could do this using the psutil lib, via
        # psutil.Process(os.getpid()).get_ext_memory_info().peak_wset if demand exists
        maxmem_str = ""
    else:
        max_mem = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
        if sys.platform != "darwin":
            max_mem *= 1024  # Linux and other OSs (e.g. freeBSD) report maxrss in kb
        maxmem_str = "; max memory={}".format(
            humanize.naturalsize(max_mem, binary=True)
        )
    logger.info("wall time = {}".format(wall_time))
    logger.info("rusage: user={}; sys={:.2f}s".format(user_time, sys_time) + maxmem_str) 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def add_rom(codename, link, info):
    update = {}
    file_size = naturalsize(int(get(link, stream=True).headers['Content-Length']))
    file = link.split('/')[-1]
    version = link.split('/')[3]
    android = link.split('_')[-2]
    update.update({"android": android})
    update.update({"codename": codename})
    update.update({"device": info['name']})
    update.update({"download": link})
    update.update({"filename": file})
    update.update({"size": file_size})
    update.update({"md5": "null"})
    update.update({"version": version})
    DATA.append(update)
    with open(f'stable_fastboot/{codename}.yml', 'w', newline='\n') as output:
        yaml.dump(update, output, Dumper=yaml.CDumper) 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def _handle_file(self, them_d):
        file_data = them_d["file"]
        self.abs_destname = self._decide_destname("file",
                                                  file_data["filename"])
        self.xfersize = file_data["filesize"]
        free = estimate_free_space(self.abs_destname)
        if free is not None and free < self.xfersize:
            self._msg(u"Error: insufficient free space (%sB) for file (%sB)" %
                      (free, self.xfersize))
            raise TransferRejectedError()

        self._msg(u"Receiving file (%s) into: %s" %
                  (naturalsize(self.xfersize),
                   os.path.basename(self.abs_destname)))
        self._ask_permission()
        tmp_destname = self.abs_destname + ".tmp"
        return open(tmp_destname, "wb") 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def about(self, context):
		"""Tells you information about the bot itself."""
		# this command is based off of code provided by Rapptz under the MIT license
		# https://github.com/Rapptz/RoboDanny/blob/f6638d520ea0f559cb2ae28b862c733e1f165970/cogs/stats.py
		# Copyright © 2015 Rapptz

		embed = discord.Embed(description=self.bot.config['description'])

		embed.add_field(name='Latest changes', value=await self._latest_changes(), inline=False)

		owner = self.bot.get_user(self.bot.config.get('primary_owner', self.bot.owner_id))
		embed.set_author(name=str(owner), icon_url=owner.avatar_url)

		embed.add_field(name='Servers', value=await self.bot.cogs['Stats'].guild_count())

		cpu_usage = self.process.cpu_percent() / psutil.cpu_count()
		mem_usage = humanize.naturalsize(self.process.memory_full_info().uss)
		embed.add_field(name='Process', value=f'{mem_usage}\n{cpu_usage:.2f}% CPU')

		embed.add_field(name='Uptime', value=self.bot.cogs['BotBinMisc'].uptime(brief=True))
		embed.set_footer(text='Made with discord.py', icon_url='https://i.imgur.com/5BFecvA.png')

		await context.send(embed=embed) 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def __init__(self, api, add_time, file_id, info_hash, last_update,
                 left_time, move, name, peers, percent_done, rate_download,
                 size, status, cid, pid, url, *args, **kwargs):
        self.api = api
        self.cid = cid
        self.name = name
        self.add_time = add_time
        self.file_id = file_id
        self.info_hash = info_hash
        self.last_update = last_update
        self.left_time = left_time
        self.move = move
        self.peers = peers
        self.percent_done = percent_done
        self.rate_download = rate_download
        self.size = size
        self.size_human = humanize.naturalsize(size, binary=True)
        self.status = status
        self.url = url
        self._directory = None
        self._deleted = False
        self._count = -1 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def mega_dl(url: str) -> str:
    """ MEGA.nz direct links generator
    Using https://github.com/tonikelope/megadown"""
    reply = ''
    try:
        link = re.findall(r'\bhttps?://.*mega.*\.nz\S+', url)[0]
    except IndexError:
        reply = "`No MEGA.nz links found`\n"
        return reply
    command = f'megadown -q -m {link}'
    result = popen(command).read()
    try:
        data = json.loads(result)
        print(data)
    except json.JSONDecodeError:
        reply += "`Error: Can't extract the link`\n"
        return reply
    dl_url = data['url']
    name = data['name']
    size = naturalsize(int(data['file_size']))
    reply += f'[{name} ({size})]({dl_url})\n'
    return reply 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def cm_ru(url: str) -> str:
    """ cloud.mail.ru direct links generator
    Using https://github.com/JrMasterModelBuilder/cmrudl.py"""
    reply = ''
    try:
        link = re.findall(r'\bhttps?://.*cloud\.mail\.ru\S+', url)[0]
    except IndexError:
        reply = "`No cloud.mail.ru links found`\n"
        return reply
    command = f'cmrudl -s {link}'
    result = popen(command).read()
    result = result.splitlines()[-1]
    try:
        data = json.loads(result)
    except json.decoder.JSONDecodeError:
        reply += "`Error: Can't extract the link`\n"
        return reply
    dl_url = data['download']
    name = data['file_name']
    size = naturalsize(int(data['file_size']))
    reply += f'[{name} ({size})]({dl_url})\n'
    return reply 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def info(self, ctx):
        """Retrieve various Node/Server/Player information."""
        player = self.bot.wavelink.get_player(ctx.guild.id)
        node = player.node

        used = humanize.naturalsize(node.stats.memory_used)
        total = humanize.naturalsize(node.stats.memory_allocated)
        free = humanize.naturalsize(node.stats.memory_free)
        cpu = node.stats.cpu_cores

        fmt = f'**WaveLink:** `{wavelink.__version__}`\n\n' \
              f'Connected to `{len(self.bot.wavelink.nodes)}` nodes.\n' \
              f'Best available Node `{self.bot.wavelink.get_best_node().__repr__()}`\n' \
              f'`{len(self.bot.wavelink.players)}` players are distributed on nodes.\n' \
              f'`{node.stats.players}` players are distributed on server.\n' \
              f'`{node.stats.playing_players}` players are playing on server.\n\n' \
              f'Server Memory: `{used}/{total}` | `({free} free)`\n' \
              f'Server CPU: `{cpu}`\n\n' \
              f'Server Uptime: `{datetime.timedelta(milliseconds=node.stats.uptime)}`'
        await ctx.send(fmt) 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def stream_closed(self, stream, **kw):
        # print("closed", stream, self._active)
        if stream.id not in self._active:
            print(
                "Previously unknown stream to {stream.target_host} died".format(
                    stream=stream,
                )
            )
        else:
            bw = self._active[stream.id]
            print(
                "Stream {stream.id} to {stream.target_host}: {read} read, {written} written in {duration:.1f}s ({read_rate})".format(
                    stream=stream,
                    read=util.colors.green(humanize.naturalsize(bw.bytes_read())),
                    written=util.colors.red(humanize.naturalsize(bw.bytes_written())),
                    read_rate=humanize.naturalsize(sum(bw.rate())) + '/s',
                    duration=bw.duration(),
                )
            ) 

# 需要導入模塊: import humanize [as 別名]
# 或者: from humanize import naturalsize [as 別名]
def _draw_node(self, node, edge):
        if hasattr(node, 'variable'):
            name = self.var_name[node.variable.data._cdata]
            tensor = self.state_dict[name]
            label = '\n'.join(map(str, filter(lambda x: x is not None, [
                '%d: %s' % (self.index, name),
                type(self)._pretty_size(tensor.size(), edge),
                humanize.naturalsize(tensor.numpy().nbytes),
            ])))
            self.dot.node(str(id(node)), label, shape='note')
        else:
            name = type(node).__name__
            label = '%d: %s' % (self.index, name)
            self.dot.node(str(id(node)), label, fillcolor='white')