Python json.dump方法代碼示例

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def _write_coco_results_file(self, all_boxes, res_file):
    # [{"image_id": 42,
    #   "category_id": 18,
    #   "bbox": [258.15,41.29,348.26,243.78],
    #   "score": 0.236}, ...]
    results = []
    for cls_ind, cls in enumerate(self.classes):
      if cls == '__background__':
        continue
      print('Collecting {} results ({:d}/{:d})'.format(cls, cls_ind,
                                                       self.num_classes - 1))
      coco_cat_id = self._class_to_coco_cat_id[cls]
      results.extend(self._coco_results_one_category(all_boxes[cls_ind],
                                                     coco_cat_id))
    print('Writing results json to {}'.format(res_file))
    with open(res_file, 'w') as fid:
      json.dump(results, fid) 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def save_eval_file(opt, stats, eval_type="losses", split="dev", ext="pickle"):
    if cfg.test_save:
        name = "{}/{}.{}".format(utils.make_name(
            opt, prefix="garbage/{}/".format(eval_type),
            is_dir=True, eval_=True), split, ext)
    else:
        name = "{}/{}.{}".format(utils.make_name(
            opt, prefix="results/{}/".format(eval_type),
            is_dir=True, eval_=True), split, ext)
    print("Saving {} {} to {}".format(split, eval_type, name))

    if ext == "pickle":
        with open(name, "wb") as f:
            pickle.dump(stats, f)
    elif ext == "txt":
        with open(name, "w") as f:
            f.write(stats)
    elif ext == "json":
        with open(name, "w") as f:
            json.dump(stats, f)
    else:
        raise 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def register(self, name, serializer):
        """Register ``serializer`` object under ``name``.

        Raises :class:`AttributeError` if ``serializer`` in invalid.

        .. note::

            ``name`` will be used as the file extension of the saved files.

        :param name: Name to register ``serializer`` under
        :type name: ``unicode`` or ``str``
        :param serializer: object with ``load()`` and ``dump()``
            methods

        """
        # Basic validation
        getattr(serializer, 'load')
        getattr(serializer, 'dump')

        self._serializers[name] = serializer 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def cache_data(self, name, data):
        """Save ``data`` to cache under ``name``.

        If ``data`` is ``None``, the corresponding cache file will be
        deleted.

        :param name: name of datastore
        :param data: data to store. This may be any object supported by
                the cache serializer

        """
        serializer = manager.serializer(self.cache_serializer)

        cache_path = self.cachefile('%s.%s' % (name, self.cache_serializer))

        if data is None:
            if os.path.exists(cache_path):
                os.unlink(cache_path)
                self.logger.debug('deleted cache file: %s', cache_path)
            return

        with atomic_writer(cache_path, 'wb') as file_obj:
            serializer.dump(data, file_obj)

        self.logger.debug('cached data: %s', cache_path) 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def gt_roidb(self):
    """
    Return the database of ground-truth regions of interest.
    This function loads/saves from/to a cache file to speed up future calls.
    """
    cache_file = osp.join(self.cache_path, self.name + '_gt_roidb.pkl')
    if osp.exists(cache_file):
      with open(cache_file, 'rb') as fid:
        roidb = pickle.load(fid)
      print('{} gt roidb loaded from {}'.format(self.name, cache_file))
      return roidb

    gt_roidb = [self._load_coco_annotation(index)
                for index in self._image_index]

    with open(cache_file, 'wb') as fid:
      pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
    print('wrote gt roidb to {}'.format(cache_file))
    return gt_roidb 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def savepb(self):
		"""
		Create a standalone const graph def that 
		C++	can load and run.
		"""
		darknet_pb = self.to_darknet()
		flags_pb = self.FLAGS
		flags_pb.verbalise = False
		
		flags_pb.train = False
		# rebuild another tfnet. all const.
		tfnet_pb = TFNet(flags_pb, darknet_pb)		
		tfnet_pb.sess = tf.Session(graph = tfnet_pb.graph)
		# tfnet_pb.predict() # uncomment for unit testing
		name = 'built_graph/{}.pb'.format(self.meta['name'])
		os.makedirs(os.path.dirname(name), exist_ok=True)
		#Save dump of everything in meta
		with open('built_graph/{}.meta'.format(self.meta['name']), 'w') as fp:
			json.dump(self.meta, fp)
		self.say('Saving const graph def to {}'.format(name))
		graph_def = tfnet_pb.sess.graph_def
		tf.train.write_graph(graph_def,'./', name, False) 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def make_model_yaml(template_yaml, model_json, output_yaml_path):
    #
    with open(template_yaml, 'r') as f:
        model_yaml = yaml.load(f)
    #
    # get the model config:
    json_file = open(model_json, 'r')
    loaded_model_json = json_file.read()
    json_file.close()
    loaded_model = keras.models.model_from_json(loaded_model_json)
    #
    model_yaml["schema"]["targets"] = []
    for oname, oshape in zip(loaded_model.output_names, loaded_model.output_shape):
        append_el ={"name":oname , "shape":str(oshape)#replace("None,", "")
        , "doc":"Methylation probability for %s"%oname}
        model_yaml["schema"]["targets"].append(append_el)
    #
    with open(output_yaml_path, 'w') as f:
        yaml.dump(model_yaml, f, default_flow_style=False) 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def make_secondary_dl_yaml(template_yaml, model_json, output_yaml_path):
    with open(template_yaml, 'r') as f:
        model_yaml = yaml.load(f)
    #
    # get the model config:
    json_file = open(model_json, 'r')
    loaded_model_json = json_file.read()
    json_file.close()
    loaded_model = keras.models.model_from_json(loaded_model_json)
    #
    model_yaml["output_schema"]["targets"] = []
    for oname, oshape in zip(loaded_model.output_names, loaded_model.output_shape):
        append_el ={"name":oname , "shape":str(oshape)#replace("None,", "")
        , "doc":"Methylation probability for %s"%oname}
        model_yaml["output_schema"]["targets"].append(append_el)
    #
    with open(output_yaml_path, 'w') as f:
        yaml.dump(model_yaml, f, default_flow_style=False) 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def saverc(filename, dat, overwrite=False):
    '''
    saverc(filename, d) saves the given configuration dictionary d to the given filename in JSON
      format. If d is not a dictionary or if filename already exists or cannot be created, an error
      is raised. This funciton does not create directories.

    The optional argument overwrite (default: False) may be passed as True to overwrite files that
    already exist.
    '''
    filename = os.path.expanduser(os.path.expandvars(filename))
    if not overwrite and os.path.isfile(filename):
        raise ValueError('Given filename %s already exists' % filename)
    if not pimms.is_map(dat):
        try: dat = dict(dat)
        except Exception: raise ValueError('Given config data must be a dictionary')
    with open(filename, 'w') as fl:
        json.dump(dat, fl, sort_keys=True)
    return filename

# the private class that handles all the details... 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def save_json(filename, obj, normalize=True):
    '''
    save_json(filename, obj) writes the given object to the given filename (or stream) in a
      normalized JSON format.

    The optional argument normalize (default True) may be set to False to prevent the object from
    being run through neuropythy's normalize system.
    '''
    from neuropythy.util import normalize as norm
    dat = norm(obj) if normalize else obj
    if pimms.is_str(filename):
        jsonstr = json.dumps(dat)
        if any(filename.endswith(s) for s in ('.gz', '.bz2', '.lzma')):
            with gzip.open(filename, 'wt') as fl: fl.write(jsonstr)
        else:
            with open(filename, 'wt') as fl: fl.write(jsonstr)
    else: json.dump(dat, filename)
    return filename 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def _write_coco_results(self, _coco, detections):
        """ example results
        [{"image_id": 42,
          "category_id": 18,
          "bbox": [258.15,41.29,348.26,243.78],
          "score": 0.236}, ...]
        """
        cats = [cat['name'] for cat in _coco.loadCats(_coco.getCatIds())]
        class_to_coco_ind = dict(zip(cats, _coco.getCatIds()))
        results = []
        for cls_ind, cls in enumerate(self.classes):
            if cls == '__background__':
                continue
            logger.info('collecting %s results (%d/%d)' % (cls, cls_ind, self.num_classes - 1))
            coco_cat_id = class_to_coco_ind[cls]
            results.extend(self._coco_results_one_category(detections[cls_ind], coco_cat_id))
        logger.info('writing results json to %s' % self._result_file)
        with open(self._result_file, 'w') as f:
            json.dump(results, f, sort_keys=True, indent=4) 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def loadProxies():
	proxiesList = []
	cprint("Loading proxies...","green")

	site2(proxiesList) # load proxies

	# proxiesList = ["13.85.80.251:443"]
	# proxiesList = ["13.85.80.251:443"]
	# proxiesList = ["144.217.16.78:3128"]
	proxiesList = proxiesList[::-1]
	proxiesList = proxiesList[:10]
	proxiesList = filterConnections(proxiesList) # filter for working connections

	# Write to file
	with open("proxies.txt", 'w') as outfile:
		json.dump(proxiesList, outfile)
	cprint("Proxies saved to proxies.txt!","magenta","on_grey", attrs=['bold']) 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def write_output(bucket_name, key_name, output_key_name, outstanding_requesters):
    logging.getLogger().debug('[write_output] Start')

    try:
        current_data = '/tmp/' + key_name.split('/')[-1] + '_LOCAL.json'
        with open(current_data, 'w') as outfile:
            json.dump(outstanding_requesters, outfile)

        s3 = boto3.client('s3')
        s3.upload_file(current_data, bucket_name, output_key_name, ExtraArgs={'ContentType': "application/json"})
        remove(current_data)

    except Exception as e:
        logging.getLogger().error("[write_output] \tError to write output file")
        logging.getLogger().error(e)

    logging.getLogger().debug('[write_output] End') 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def train(session, model, curr_dir, data_train, data_val):
    curr_dir = os.path.join(curr_dir, model.algorithm)
    bestmodel_dir = os.path.join(curr_dir, 'best_checkpoint')
    
    if not os.path.exists(curr_dir):
        os.makedirs(curr_dir)
    
    file_handler = logging.FileHandler(os.path.join(curr_dir, 'log.txt'))
    logging.getLogger().addHandler(file_handler)
    
    with open(os.path.join(curr_dir, FLAGS['save_name'] + '.json'), 'w') as f:
        json.dump(FLAGS, f)
    
    if not os.path.exists(bestmodel_dir):
        os.makedirs(bestmodel_dir)
    
    initialize_model(session, model, curr_dir, expect_exists=False)
    model.train(session, curr_dir, bestmodel_dir, data_train, data_val) 

# 需要導入模塊: import json [as 別名]
# 或者: from json import dump [as 別名]
def record_results(self, file_nm):
        json.dump(self, open(file_nm, 'w'), indent=4,
                  default=self.jsondump)