Python json5.load方法代码示例

# 需要导入模块: import json5 [as 别名]
# 或者: from json5 import load [as 别名]
def test_sample_file(self):
        path = os.path.join(os.path.dirname(__file__), '..', 'sample.json5')
        with open(path) as fp:
            obj = json5.load(fp)
        self.assertEqual({
            u'oh': [
                u"we shouldn't forget",
                u"arrays can have",
                u"trailing commas too",
            ],
            u"this": u"is a multi-line string",
            u"delta": 10,
            u"hex": 3735928559,
            u"finally": "a trailing comma",
            u"here": "is another",
            u"to": float("inf"),
            u"while": True,
            u"half": 0.5,
            u"foo": u"bar"
            }, obj) 

# 需要导入模块: import json5 [as 别名]
# 或者: from json5 import load [as 别名]
def _load_param(root, file: str):
    file = os.path.join(root, file)
    if not file.endswith('.json5'):
        file += '.json5'
    with open(file) as f:
        config = json5.load(f)
        return config 

# 需要导入模块: import json5 [as 别名]
# 或者: from json5 import load [as 别名]
def main():
    site_pins = json5.load(sys.stdin)

    output_site_pins = {}
    output_site_pins["tile_type"] = site_pins["tile_type"]
    output_site_pins["sites"] = copy.deepcopy(site_pins["sites"])

    site_pin_to_wires = create_site_pin_to_wire_maps(
        site_pins['tile_name'], site_pins['nodes'])
    min_x_coord, min_y_coord = find_origin_coordinate(site_pins['sites'])

    for site in output_site_pins['sites']:
        orig_site_name = site['name']
        coordinate = SITE_COORDINATE_PATTERN.match(orig_site_name)

        x_coord = int(coordinate.group(2))
        y_coord = int(coordinate.group(3))
        site['name'] = 'X{}Y{}'.format(
            x_coord - min_x_coord, y_coord - min_y_coord)
        site['prefix'] = coordinate.group(1)
        site['x_coord'] = x_coord - min_x_coord
        site['y_coord'] = y_coord - min_y_coord

        for site_pin in site['site_pins']:
            assert site_pin['name'].startswith(orig_site_name + '/')
            if site_pin['name'] in site_pin_to_wires:
                site_pin['wire'] = site_pin_to_wires[site_pin['name']]
            else:
                print(
                    (
                        '***WARNING***: Site pin {} for tile type {} is not connected, '
                        'make sure all instaces of this tile type has this site_pin '
                        'disconnected.').format(
                            site_pin['name'], site_pins['tile_type']),
                    file=sys.stderr)

            site_pin['name'] = site_pin['name'][len(orig_site_name) + 1:]

    json.dumps(output_site_pins, indent=2, sort_keys=True) 

# 需要导入模块: import json5 [as 别名]
# 或者: from json5 import load [as 别名]
def _post_process(args: Object):
    if not args.output_dir.startswith('models'):
        args.output_dir = os.path.join('models', args.output_dir)
    os.makedirs(args.output_dir, exist_ok=True)
    if not args.name:
        args.name = str(datetime.now())
    args.summary_dir = os.path.join(args.output_dir, args.name)
    if os.path.exists(args.summary_dir):
        shutil.rmtree(args.summary_dir)
    os.makedirs(args.summary_dir)
    data_config_file = os.path.join(args.output_dir, 'data_config.json5')
    if os.path.exists(data_config_file):
        with open(data_config_file) as f:
            config = json5.load(f)
            for k, v in config.items():
                if not hasattr(args, k) or getattr(args, k) != v:
                    print('ERROR: Data configurations are different. Please use another output_dir or '
                          'remove the older one manually.')
                    exit()
    else:
        with open(data_config_file, 'w') as f:
            keys = ['data_dir', 'min_df', 'max_vocab', 'max_len', 'min_len', 'lower_case',
                    'pretrained_embeddings', 'embedding_mode']
            json5.dump({k: getattr(args, k) for k in keys}, f)
    args.metric = args.metric.lower()
    args.watch_metrics = [m.lower() for m in args.watch_metrics]
    if args.metric not in args.watch_metrics:
        args.watch_metrics.append(args.metric)
    args.cuda = args.cuda and torch.cuda.is_available()
    args.fix_embeddings = args.pretrained_embeddings and args.fix_embeddings

    def samples2steps(n):
        return int(math.ceil(n / args.batch_size))

    if not hasattr(args, 'log_per_updates'):
        args.log_per_updates = samples2steps(args.log_per_samples)
    if not hasattr(args, 'eval_per_updates'):
        args.eval_per_updates = samples2steps(args.eval_per_samples)
    if not hasattr(args, 'eval_per_updates_warmup'):
        args.eval_per_updates_warmup = samples2steps(args.eval_per_samples_warmup)
    if not hasattr(args, 'eval_warmup_steps'):
        args.eval_warmup_steps = samples2steps(args.eval_warmup_samples)
    if not hasattr(args, 'min_steps'):
        args.min_steps = samples2steps(args.min_samples)
    if not hasattr(args, 'early_stopping'):
        args.early_stopping = samples2steps(args.tolerance_samples)
    if not hasattr(args, 'lr_warmup_steps'):
        args.lr_warmup_steps = samples2steps(args.lr_warmup_samples)
    if not hasattr(args, 'lr_decay_steps'):
        args.lr_decay_steps = samples2steps(args.lr_decay_samples)
    if not hasattr(args, 'summary_per_updates'):
        args.summary_per_updates = args.summary_per_logs * args.log_per_updates
    assert args.lr >= args.min_lr, 'initial learning rate must be larger than min_lr' 

# 需要导入模块: import json5 [as 别名]
# 或者: from json5 import load [as 别名]
def _post_process(args: Object):
    if not args.output_dir.startswith('models'):
        args.output_dir = os.path.join('models', args.output_dir)
    os.makedirs(args.output_dir, exist_ok=True)
    if not args.name:
        args.name = str(datetime.now())
    args.summary_dir = os.path.join(args.output_dir, args.name)
    if os.path.exists(args.summary_dir):
        shutil.rmtree(args.summary_dir)
    os.makedirs(args.summary_dir)
    data_config_file = os.path.join(args.output_dir, 'data_config.json5')
    if os.path.exists(data_config_file):
        with open(data_config_file) as f:
            config = json5.load(f)
            for k, v in config.items():
                if not hasattr(args, k) or getattr(args, k) != v:
                    print('ERROR: Data configurations are different. Please use another output_dir or '
                          'remove the older one manually.')
                    exit()
    else:
        with open(data_config_file, 'w') as f:
            keys = ['data_dir', 'min_df', 'max_vocab', 'max_len', 'min_len', 'lower_case',
                    'pretrained_embeddings', 'embedding_mode']
            json5.dump({k: getattr(args, k) for k in keys}, f)
    args.metric = args.metric.lower()
    args.watch_metrics = [m.lower() for m in args.watch_metrics]
    if args.metric not in args.watch_metrics:
        args.watch_metrics.append(args.metric)
    assert args.pretrained_embeddings, 'pretrained embeddings must be provided.'

    def samples2steps(n):
        return int(math.ceil(n / args.batch_size))

    if not hasattr(args, 'log_per_updates'):
        args.log_per_updates = samples2steps(args.log_per_samples)
    if not hasattr(args, 'eval_per_updates'):
        args.eval_per_updates = samples2steps(args.eval_per_samples)
    if not hasattr(args, 'eval_per_updates_warmup'):
        args.eval_per_updates_warmup = samples2steps(args.eval_per_samples_warmup)
    if not hasattr(args, 'eval_warmup_steps'):
        args.eval_warmup_steps = samples2steps(args.eval_warmup_samples)
    if not hasattr(args, 'min_steps'):
        args.min_steps = samples2steps(args.min_samples)
    if not hasattr(args, 'early_stopping'):
        args.early_stopping = samples2steps(args.tolerance_samples)
    if not hasattr(args, 'lr_warmup_steps'):
        args.lr_warmup_steps = samples2steps(args.lr_warmup_samples)
    if not hasattr(args, 'lr_decay_steps'):
        args.lr_decay_steps = samples2steps(args.lr_decay_samples)