Python collections.OrderedDict() Examples

def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
    """Compute the union of the current variables and checkpoint variables."""
    assignment_map = {}
    initialized_variable_names = {}

    name_to_variable = collections.OrderedDict()
    for var in tvars:
        name = var.name
        m = re.match("^(.*):\\d+$", name)
        if m is not None:
            name = m.group(1)
        name_to_variable[name] = var

    init_vars = tf.train.list_variables(init_checkpoint)

    assignment_map = collections.OrderedDict()
    for x in init_vars:
        (name, var) = (x[0], x[1])
        if name not in name_to_variable:
            continue
        assignment_map[name] = name
        initialized_variable_names[name] = 1
        initialized_variable_names[name + ":0"] = 1

    return (assignment_map, initialized_variable_names) 

def __init__(self):
        self.args = None
        self.alignDistance = 0
        self.samples = collections.OrderedDict()
        self.genome = None
        self.sources = {}
        self.annotationSets = collections.OrderedDict()

        # for storing axes, annotations, etc, by allele
        self.alleleTracks = collections.defaultdict(collections.OrderedDict)
        self.trackCompositor = None

        self.dotplots = {}
        self.info = {}

        self.reset() 

def __init__(self, chromPartsCollection, pixelWidth, dividerSize=25):
        # length is in genomic coordinates, starts is in pixels
        self.dividerSize = dividerSize
        self.partsToLengths = collections.OrderedDict()
        self.partsToStartPixels = collections.OrderedDict()
        self.chromPartsCollection = chromPartsCollection

        for part in chromPartsCollection:
            self.partsToLengths[part.id] = len(part)

        self.pixelWidth = pixelWidth

        totalLength = sum(self.partsToLengths.values()) + (len(self.partsToLengths)-1)*dividerSize
        self.basesPerPixel = totalLength / float(pixelWidth)

        curStart = 0
        for regionID in self.partsToLengths:
            self.partsToStartPixels[regionID] = curStart
            curStart += (self.partsToLengths[regionID]+dividerSize) / self.basesPerPixel 

def compute_f1(answer_stats, prefix=''):
  """Computes F1, precision, recall for a list of answer scores.

  Args:
    answer_stats: List of per-example scores.
    prefix (''): Prefix to prepend to score dictionary.

  Returns:
    Dictionary mapping string names to scores.
  """

  has_gold, has_pred, is_correct, _ = list(zip(*answer_stats))
  precision = safe_divide(sum(is_correct), sum(has_pred))
  recall = safe_divide(sum(is_correct), sum(has_gold))
  f1 = safe_divide(2 * precision * recall, precision + recall)

  return OrderedDict({
      prefix + 'n': len(answer_stats),
      prefix + 'f1': f1,
      prefix + 'precision': precision,
      prefix + 'recall': recall
  }) 

def get_metrics_with_answer_stats(long_answer_stats, short_answer_stats):
  """Generate metrics dict using long and short answer stats."""

  def _get_metric_dict(answer_stats, prefix=''):
    """Compute all metrics for a set of answer statistics."""
    opt_result, pr_table = compute_pr_curves(
        answer_stats, targets=[0.5, 0.75, 0.9])
    f1, precision, recall, threshold = opt_result
    metrics = OrderedDict({
        'best-threshold-f1': f1,
        'best-threshold-precision': precision,
        'best-threshold-recall': recall,
        'best-threshold': threshold,
    })
    for target, recall, precision, _ in pr_table:
      metrics['recall-at-precision>={:.2}'.format(target)] = recall
      metrics['precision-at-precision>={:.2}'.format(target)] = precision

    # Add prefix before returning.
    return dict([(prefix + k, v) for k, v in six.iteritems(metrics)])

  metrics = _get_metric_dict(long_answer_stats, 'long-')
  metrics.update(_get_metric_dict(short_answer_stats, 'short-'))
  return metrics 

def _init_fields(self):
        self.name               = None          # User-specified name, defaults to build func name if None.
        self.scope              = None          # Unique TF graph scope, derived from the user-specified name.
        self.static_kwargs      = dict()        # Arguments passed to the user-supplied build func.
        self.num_inputs         = 0             # Number of input tensors.
        self.num_outputs        = 0             # Number of output tensors.
        self.input_shapes       = [[]]          # Input tensor shapes (NC or NCHW), including minibatch dimension.
        self.output_shapes      = [[]]          # Output tensor shapes (NC or NCHW), including minibatch dimension.
        self.input_shape        = []            # Short-hand for input_shapes[0].
        self.output_shape       = []            # Short-hand for output_shapes[0].
        self.input_templates    = []            # Input placeholders in the template graph.
        self.output_templates   = []            # Output tensors in the template graph.
        self.input_names        = []            # Name string for each input.
        self.output_names       = []            # Name string for each output.
        self.vars               = OrderedDict() # All variables (localname => var).
        self.trainables         = OrderedDict() # Trainable variables (localname => var).
        self._build_func        = None          # User-supplied build function that constructs the network.
        self._build_func_name   = None          # Name of the build function.
        self._build_module_src  = None          # Full source code of the module containing the build function.
        self._run_cache         = dict()        # Cached graph data for Network.run(). 

def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
    if bucket_size_mb > 0:
        bucket_size_bytes = bucket_size_mb * 1024 * 1024
        buckets = _take_tensors(tensors, bucket_size_bytes)
    else:
        buckets = OrderedDict()
        for tensor in tensors:
            tp = tensor.type()
            if tp not in buckets:
                buckets[tp] = []
            buckets[tp].append(tensor)
        buckets = buckets.values()

    for bucket in buckets:
        flat_tensors = _flatten_dense_tensors(bucket)
        dist.all_reduce(flat_tensors)
        flat_tensors.div_(world_size)
        for tensor, synced in zip(
                bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
            tensor.copy_(synced) 

def test_max_pool_2d():
    test_cases = OrderedDict([('in_w', [10, 20]), ('in_h', [10, 20]),
                              ('in_channel', [1, 3]), ('out_channel', [1, 3]),
                              ('kernel_size', [3, 5]), ('stride', [1, 2]),
                              ('padding', [0, 1]), ('dilation', [1, 2])])

    for in_h, in_w, in_cha, out_cha, k, s, p, d in product(
            *list(test_cases.values())):
        # wrapper op with 0-dim input
        x_empty = torch.randn(0, in_cha, in_h, in_w, requires_grad=True)
        wrapper = MaxPool2d(k, stride=s, padding=p, dilation=d)
        wrapper_out = wrapper(x_empty)

        # torch op with 3-dim input as shape reference
        x_normal = torch.randn(3, in_cha, in_h, in_w)
        ref = nn.MaxPool2d(k, stride=s, padding=p, dilation=d)
        ref_out = ref(x_normal)

        assert wrapper_out.shape[0] == 0
        assert wrapper_out.shape[1:] == ref_out.shape[1:]

        assert torch.equal(wrapper(x_normal), ref_out) 

def convert(src, dst):
    """Convert keys in pycls pretrained RegNet models to mmdet style."""
    # load caffe model
    regnet_model = torch.load(src)
    blobs = regnet_model['model_state']
    # convert to pytorch style
    state_dict = OrderedDict()
    converted_names = set()
    for key, weight in blobs.items():
        if 'stem' in key:
            convert_stem(key, weight, state_dict, converted_names)
        elif 'head' in key:
            convert_head(key, weight, state_dict, converted_names)
        elif key.startswith('s'):
            convert_reslayer(key, weight, state_dict, converted_names)

    # check if all layers are converted
    for key in blobs:
        if key not in converted_names:
            print(f'not converted: {key}')
    # save checkpoint
    checkpoint = dict()
    checkpoint['state_dict'] = state_dict
    torch.save(checkpoint, dst) 

def __init__(self, cmd_root, cli_args, folders=None, files=None):
        """Main tool runner."""
        # Run options
        self.cmd_root = cmd_root  # Folder on which the command was executed
        self.config = load_config(cmd_root, cli_args)
        self.file_manager = FileManager(folders=folders, files=files)
        self.folders = folders
        self.files = files
        self.all_results = OrderedDict()
        self.all_tools = {}
        self.test_results = None
        self.failed_checks = set()

        self.check = self.config.get_value('check')
        self.enforce = self.config.get_value('enforce')
        self.diff_mode = self.config.get_value('diff_mode')
        self.file_mode = self.config.get_value('file_mode')
        self.branch = self.config.get_value('branch')
        self.disable_formatters = cli_args.disable_formatters
        self.disable_linters = cli_args.disable_linters
        self.disable_tests = cli_args.disable_tests 

def parse_coverage(self):
        """Parse .coverage json report generated by coverage."""
        coverage_string = ("!coverage.py: This is a private format, don't "
                           "read it directly!")
        coverage_path = os.path.join(self.cmd_root, '.coverage')

        covered_lines = {}
        if os.path.isfile(coverage_path):
            with open(coverage_path, 'r') as file_obj:
                data = file_obj.read()
                data = data.replace(coverage_string, '')

            cov = json.loads(data)
            covered_lines = OrderedDict()
            lines = cov['lines']
            for path in sorted(lines):
                covered_lines[path] = lines[path]
        return covered_lines 

def fprop(self, x):

        output = OrderedDict()
        # first convolutional layer
        h_conv1 = tf.nn.relu(self._conv2d(x, self.W_conv1) + self.b_conv1)
        h_pool1 = self._max_pool_2x2(h_conv1)

        # second convolutional layer
        h_conv2 = tf.nn.relu(
            self._conv2d(h_pool1, self.W_conv2) + self.b_conv2)
        h_pool2 = self._max_pool_2x2(h_conv2)

        # first fully connected layer

        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, self.W_fc1) + self.b_fc1)

        # output layer
        logits = tf.matmul(h_fc1, self.W_fc2) + self.b_fc2

        output = deterministic_dict(locals())
        del output["self"]
        output[self.O_PROBS] = tf.nn.softmax(logits=logits)

        return output 

def read_cpg_profiles(filenames, log=None, *args, **kwargs):
    """Read methylation profiles.

    Input files can be gzip compressed.

    Returns
    -------
    dict
        `dict (key, value)`, where `key` is the output name and `value` the CpG
        table.
    """

    cpg_profiles = OrderedDict()
    for filename in filenames:
        if log:
            log(filename)
        #cpg_file = dat.GzipFile(filename, 'r')
        cpg_file = get_fh(filename, 'r')
        output_name = split_ext(filename)
        cpg_profile = dat.read_cpg_profile(cpg_file, sort=True, *args, **kwargs)
        cpg_profiles[output_name] = cpg_profile
        cpg_file.close()
    return cpg_profiles 

def map_cpg_tables(cpg_tables, chromo, chromo_pos):
    """Maps values from cpg_tables to `chromo_pos`.

    Positions in `cpg_tables` for `chromo`  must be a subset of `chromo_pos`.
    Inserts `dat.CPG_NAN` for uncovered positions.
    """
    chromo_pos.sort()
    mapped_tables = OrderedDict()
    for name, cpg_table in six.iteritems(cpg_tables):
        cpg_table = cpg_table.loc[cpg_table.chromo == chromo]
        cpg_table = cpg_table.sort_values('pos')
        mapped_table = map_values(cpg_table.value.values,
                                  cpg_table.pos.values,
                                  chromo_pos)
        assert len(mapped_table) == len(chromo_pos)
        mapped_tables[name] = mapped_table
    return mapped_tables 

def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
        # 初始化权重
        self.params = {}
        # 用高斯分布初始化
        self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
        self.params['b1'] = np.zeros(hidden_size)
        self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
        self.params['b2'] = np.zeros(output_size)

        # 生成层
        self.layers = OrderedDict()
        self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
        self.layers['Relu1'] = Relu()
        self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])

        self.lastLayer = SoftmaxWithLoss() 

def __init__(self, input_size, hidden_size_list, output_size,
                 activation='relu', weight_init_std='relu', weight_decay_lambda=0):
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_size_list = hidden_size_list
        self.hidden_layer_num = len(hidden_size_list)
        self.weight_decay_lambda = weight_decay_lambda
        self.params = {}

        # 初始化权重
        self.__init_weight(weight_init_std)

        # 生成层
        activation_layer = {'sigmoid': Sigmoid, 'relu': Relu}
        self.layers = OrderedDict()
        for idx in range(1, self.hidden_layer_num+1):
            self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)],
                                                      self.params['b' + str(idx)])
            self.layers['Activation_function' + str(idx)] = activation_layer[activation]()

        idx = self.hidden_layer_num + 1
        self.layers['Affine' + str(idx)] = Affine(self.params['W' + str(idx)],
            self.params['b' + str(idx)])

        self.last_layer = SoftmaxWithLoss() 

def escape_wikitext(wikitext):
    """Escape wikitext for use in file description."""
    rep = OrderedDict([
        ('{|', '{{(}}|'),
        ('|}', '|{{)}}'),
        ('||', '||'),
        ('|', '|'),
        ('[[', '{{!((}}'),
        (']]', '{{))!}}'),
        ('{{', '{{((}}'),
        ('}}', '{{))}}'),
        ('{', '{{(}}'),
        ('}', '{{)}}'),
    ])
    rep = dict((re.escape(k), v) for k, v in rep.iteritems())
    pattern = re.compile("|".join(rep.keys()))
    return pattern.sub(lambda m: rep[re.escape(m.group(0))], wikitext)


# Source: mediawiki.Title.js@9df363d 

def load_vocab(vocab_file):
    """Loads a vocabulary file into a dictionary."""
    vocab = collections.OrderedDict()
    index = 0
    with tf.gfile.GFile(vocab_file, "r") as reader:
        while True:
            token = convert_to_unicode(reader.readline())
            if not token:
                break
            token = token.strip()
            vocab[token] = index
            index += 1
    return vocab 

def file_based_convert_examples_to_features(
        examples, label_list, max_seq_length, tokenizer, output_file):
    """Convert a set of `InputExample`s to a TFRecord file."""

    writer = tf.python_io.TFRecordWriter(output_file)
    
    label_map = {}
    for (i, label) in enumerate(sorted(label_list)):
        label_map[label] = i
        
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

        feature = convert_single_example(ex_index, example, label_map,
                                                    max_seq_length, tokenizer)

        def create_int_feature(values):
            f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
            return f

        features = collections.OrderedDict()
        features["input_ids"] = create_int_feature(feature.input_ids)
        features["input_mask"] = create_int_feature(feature.input_mask)
        features["segment_ids"] = create_int_feature(feature.segment_ids)
        features["label_ids"] = create_int_feature([feature.label_id])

        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(tf_example.SerializeToString())
    return label_map 

def __init__(self, name, attrs=None, members=None,
                 duration=INF, action=None, member_creator=None,
                 num_members=None, serial_obj=None,
                 reg=True, **kwargs):

        self.num_members_ever = 0
        self.members = OrderedDict()
        super().__init__(name, attrs=attrs, duration=duration,
                         action=action, serial_obj=serial_obj,
                         reg=False)

        self.type = type(self).__name__

        if serial_obj is not None:
            self.restore(serial_obj)
        else:
            if members is not None:
                for member in members:
                    join(self, member)
            if num_members is None:
                num_members = 1  # A default if they forgot to pass this.
            self.num_members_ever = num_members
            self.member_creator = None
            if member_creator is not None:
                self.member_creator = member_creator
                # If we have a member creator function, call it
                # `num_members` times to create group members.
                for i in range(num_members):
                    join(self, member_creator(self.name, i, **kwargs))
        if reg:
            add_group(self.name, self) 

def subset(self, predicate, *args, name=None):  # noqa E999
        new_dict = OrderedDict()
        for mbr in self:
            if predicate(self[mbr], *args):
                new_dict[mbr] = self[mbr]
        return grp_from_nm_dict(name, new_dict) 

def __init__(self, name, user, level=0):
        super().__init__(name)
        self.user = user
        self.menu_items = OrderedDict()
        self.def_act = None
        self.level = level 

def __init__(self, name):
        super().__init__(name)
        self.vars = OrderedDict()
        self.graph = nx.Graph() 

def __init__(self, methodName):
        super().__init__(methodName=methodName)
        # A dictionary for different types of dummy agents in order to test agent pop class
        # First we will fill this dict with various dummy agents,
        # then we will fill the Agent Pop class, using dict_for_reference as source
        # The OrderedDict in Agent Pop should be the same as dic_for_reference. That will test if append works
        # If append works and we have the OrderedDict in Agent Pop,
        # we will test rest of the functions if they return correct result, with reference of dict_for_reference
        # format: {var:{"agents": [], "pop_data": 0,"pop_hist": [],"my_periods": 0,"disp_color": None}}
        self.dic_for_reference = OrderedDict()
        self.agentpop = ap.AgentPop("test")
        # add 3 varieties
        for i in range(3):
            self.dic_for_reference[str(i)] = {AGENTS: [], POP_DATA: 0,POP_HIST: [],MY_PERIODS: 0,DISP_COLOR: None}
        for i in range(3):
            # add color to each variety
            self.dic_for_reference[str(i)][DISP_COLOR] = "#" + str(i)
            # add agents; agent type is Node
            for r in range(random.randint(1,7)):
                self.dic_for_reference[str(i)][AGENTS].append(node.Node("agent"+str(r)))
        for var in self.dic_for_reference:
            for a in self.dic_for_reference[var][AGENTS]:
                a.ntype = var
                #assign ntype for each agent; each dic_for_reference's variety should be the same as its agents' ntype!
    #     finish building dic_for_reference,
    #     dic_for_reference should look like: {
    #       '0':{"agents": [1-7 agents of ntype('0')], "pop_data": 0,"pop_hist": [],"my_periods": 0,"disp_color": '#0'}
    #       '1':{"agents": [1-7 agents of ntype('1')], "pop_data": 0,"pop_hist": [],"my_periods": 0,"disp_color": '#1'}
    #       '2':{"agents": [1-7 agents of ntype('2')], "pop_data": 0,"pop_hist": [],"my_periods": 0,"disp_color": '#2'}
    # }
    # Note: each individual test function has its own unique self.dic_for_reference and self.agent_pop. Change of self.dic_for_reference
    # in one function won't affect the use of it in other functions. (Two functions will have different self.dic_for_reference) 

def __init__(self, parts=None):
        self.parts = collections.OrderedDict()
        if parts is not None:
            for part in parts:
                self.parts[part.id] = part 

def getCounts(self):
        if self._counts is None:
            self._counts = collections.OrderedDict()
            for name, sample in self.samples.items():
                self._counts[name] = collections.Counter([alnCollection.choice for alnCollection in sample.alnCollections])
            self._counts["Total"] = dict((allele, sum(self._counts[name][allele] for name in self.samples)) 
                                          for allele in ["alt", "ref", "amb"])

        return self._counts 

def reset(self):
        self.reads = []
        self.alnCollections = []
        self.tracks = collections.OrderedDict() 

def addTrackSVG(self, section, name, tracksvg, viewbox=None, height=100):
        if not section in self.sections:
            self.sections[section] = collections.OrderedDict()
        self.sections[section][name] = {"svg":tracksvg,
                                        "viewbox":viewbox,
                                        "height": height
                                       } 

def _initialize_members(self,date,limit,logger): 

        # get logger if exists. if not, create new instance.
        self._logger = logging.getLogger('OA.Flow') if logger else Util.get_logger('OA.Flow',create_file=False)

        # initialize required parameters.
        self._scrtip_path = os.path.dirname(os.path.abspath(__file__))
        self._date = date
        self._table_name = "flow"
        self._flow_results = []
        self._limit = limit
        self._data_path = None
        self._ipynb_path = None
        self._ingest_summary_path = None
        self._flow_scores = []
        self._results_delimiter = '\t'
        

        # get app configuration.
        self._spot_conf = Util.get_spot_conf()

        # # get scores fields conf
        conf_file = "{0}/flow_conf.json".format(self._scrtip_path)
        self._conf = json.loads(open (conf_file).read(),object_pairs_hook=OrderedDict)

        # initialize data engine
        self._db = self._spot_conf.get('conf', 'DBNAME').replace("'", "").replace('"', '') 

def _initialize_members(self,date,limit,logger):

        # get logger if exists. if not, create new instance.
        self._logger = logging.getLogger('OA.PROXY') if logger else Util.get_logger('OA.PROXY',create_file=False)

        # initialize required parameters.
        self._scrtip_path = os.path.dirname(os.path.abspath(__file__))
        self._date = date
        self._table_name = "proxy"
        self._proxy_results = []
        self._limit = limit
        self._data_path = None
        self._ipynb_path = None
        self._ingest_summary_path = None
        self._proxy_scores = []
        self._proxy_scores_headers = []
        self._proxy_extra_columns = []
        self._results_delimiter = '\t'

        # get app configuration.
        self._spot_conf = Util.get_spot_conf()

        # get scores fields conf
        conf_file = "{0}/proxy_conf.json".format(self._scrtip_path)
        self._conf = json.loads(open (conf_file).read(),object_pairs_hook=OrderedDict)

        # initialize data engine
        self._db = self._spot_conf.get('conf', 'DBNAME').replace("'", "").replace('"', '')