Python functools.partial() Examples

The following are 30 code examples for showing how to use functools.partial(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: wafw00f   Author: EnableSecurity   File: manager.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def load_plugins():
    here = os.path.abspath(os.path.dirname(__file__))
    get_path = partial(os.path.join, here)
    plugin_dir = get_path('plugins')

    plugin_base = PluginBase(
        package='wafw00f.plugins', searchpath=[plugin_dir]
    )
    plugin_source = plugin_base.make_plugin_source(
        searchpath=[plugin_dir], persist=True
    )

    plugin_dict = {}
    for plugin_name in plugin_source.list_plugins():
        plugin_dict[plugin_name] = plugin_source.load_plugin(plugin_name)

    return plugin_dict 
Example 2
Project: aegea   Author: kislyuk   File: ecs.py    License: Apache License 2.0 6 votes vote down vote up
def tasks(args):
    list_clusters = clients.ecs.get_paginator("list_clusters")
    list_tasks = clients.ecs.get_paginator("list_tasks")

    def list_tasks_worker(worker_args):
        cluster, status = worker_args
        return cluster, status, list(paginate(list_tasks, cluster=cluster, desiredStatus=status))

    def describe_tasks_worker(t, cluster=None):
        return clients.ecs.describe_tasks(cluster=cluster, tasks=t)["tasks"] if t else []

    task_descs = []
    if args.clusters is None:
        args.clusters = [__name__.replace(".", "_")] if args.tasks else list(paginate(list_clusters))
    if args.tasks:
        task_descs = describe_tasks_worker(args.tasks, cluster=args.clusters[0])
    else:
        with ThreadPoolExecutor() as executor:
            for cluster, status, tasks in executor.map(list_tasks_worker, product(args.clusters, args.desired_status)):
                worker = partial(describe_tasks_worker, cluster=cluster)
                descs = executor.map(worker, (tasks[pos:pos + 100] for pos in range(0, len(tasks), 100)))
                task_descs += sum(descs, [])
    page_output(tabulate(task_descs, args)) 
Example 3
Project: gql   Author: graphql-python   File: dsl.py    License: MIT License 6 votes vote down vote up
def get_arg_serializer(arg_type):
    if isinstance(arg_type, GraphQLNonNull):
        return get_arg_serializer(arg_type.of_type)
    if isinstance(arg_type, GraphQLInputField):
        return get_arg_serializer(arg_type.type)
    if isinstance(arg_type, GraphQLInputObjectType):
        serializers = {k: get_arg_serializer(v) for k, v in arg_type.fields.items()}
        return lambda value: ObjectValueNode(
            fields=FrozenList(
                ObjectFieldNode(name=NameNode(value=k), value=serializers[k](v))
                for k, v in value.items()
            )
        )
    if isinstance(arg_type, GraphQLList):
        inner_serializer = get_arg_serializer(arg_type.of_type)
        return partial(serialize_list, inner_serializer)
    if isinstance(arg_type, GraphQLEnumType):
        return lambda value: EnumValueNode(value=arg_type.serialize(value))
    return lambda value: ast_from_value(arg_type.serialize(value), arg_type) 
Example 4
Project: cherrypy   Author: cherrypy   File: wspbus.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def subscribe(self, channel, callback=None, priority=None):
        """Add the given callback at the given channel (if not present).

        If callback is None, return a partial suitable for decorating
        the callback.
        """
        if callback is None:
            return functools.partial(
                self.subscribe,
                channel,
                priority=priority,
            )

        ch_listeners = self.listeners.setdefault(channel, set())
        ch_listeners.add(callback)

        if priority is None:
            priority = getattr(callback, 'priority', 50)
        self._priorities[(channel, callback)] = priority 
Example 5
Project: mmdetection   Author: open-mmlab   File: misc.py    License: Apache License 2.0 6 votes vote down vote up
def multi_apply(func, *args, **kwargs):
    """Apply function to a list of arguments.

    Note:
        This function applies the ``func`` to multiple inputs and
            map the multiple outputs of the ``func`` into different
            list. Each list contains the same type of outputs corresponding
            to different inputs.

    Args:
        func (Function): A function that will be applied to a list of
            arguments

    Returns:
        tuple(list): A tuple containing multiple list, each list contains
            a kind of returned results by the function
    """
    pfunc = partial(func, **kwargs) if kwargs else func
    map_results = map(pfunc, *args)
    return tuple(map(list, zip(*map_results))) 
Example 6
Project: neural-fingerprinting   Author: StephanZheng   File: model.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def fprop(self, x, **kwargs):
        del kwargs
        my_conv = functools.partial(tf.layers.conv2d,
                                    kernel_size=3,
                                    strides=2,
                                    padding='valid',
                                    activation=tf.nn.relu,
                                    kernel_initializer=HeReLuNormalInitializer)
        my_dense = functools.partial(
            tf.layers.dense, kernel_initializer=HeReLuNormalInitializer)

        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
            for depth in [96, 256, 384, 384, 256]:
                x = my_conv(x, depth)
            y = tf.layers.flatten(x)
            y = my_dense(y, 4096, tf.nn.relu)
            y = fc7 = my_dense(y, 4096, tf.nn.relu)
            y = my_dense(y, 1000)
            return {'fc7': fc7,
                    self.O_LOGITS: y,
                    self.O_PROBS: tf.nn.softmax(logits=y)} 
Example 7
Project: multibootusb   Author: mbusb   File: monitor.py    License: GNU General Public License v2.0 6 votes vote down vote up
def run(self):
        self.monitor.start()
        notifier = poll.Poll.for_events(
            (self.monitor, 'r'), (self._stop_event.source, 'r'))
        while True:
            for file_descriptor, event in eintr_retry_call(notifier.poll):
                if file_descriptor == self._stop_event.source.fileno():
                    # in case of a stop event, close our pipe side, and
                    # return from the thread
                    self._stop_event.source.close()
                    return
                elif file_descriptor == self.monitor.fileno() and event == 'r':
                    read_device = partial(eintr_retry_call, self.monitor.poll, timeout=0)
                    for device in iter(read_device, None):
                        self._callback(device)
                else:
                    raise EnvironmentError('Observed monitor hung up') 
Example 8
Project: spleeter   Author: deezer   File: train.py    License: MIT License 6 votes vote down vote up
def _create_evaluation_spec(params, audio_adapter, audio_path):
    """ Setup eval spec evaluating ever n seconds

    :param params: TF params to build spec from.
    :returns: Built evaluation spec.
    """
    input_fn = partial(
        get_validation_dataset,
        params,
        audio_adapter,
        audio_path)
    evaluation_spec = tf.estimator.EvalSpec(
        input_fn=input_fn,
        steps=None,
        throttle_secs=params['throttle_secs'])
    return evaluation_spec 
Example 9
Project: deep-learning-note   Author: wdxtub   File: 2_simple_mnist.py    License: MIT License 6 votes vote down vote up
def __init__(self, learning_rate, max_iteration_steps, seed=None):
        """Initializes a `Generator` that builds `SimpleCNNs`.

        Args:
          learning_rate: The float learning rate to use.
          max_iteration_steps: The number of steps per iteration.
          seed: The random seed.

        Returns:
          An instance of `Generator`.
        """
        self._seed = seed
        self._cnn_builder_fn = functools.partial(
            SimpleCNNBuilder,
            learning_rate=learning_rate,
            max_iteration_steps=max_iteration_steps) 
Example 10
Project: dynamic-training-with-apache-mxnet-on-aws   Author: awslabs   File: rnn_cell.py    License: Apache License 2.0 6 votes vote down vote up
def __init__(self, input_shape, num_hidden,
                 h2h_kernel=(3, 3), h2h_dilate=(1, 1),
                 i2h_kernel=(3, 3), i2h_stride=(1, 1),
                 i2h_pad=(1, 1), i2h_dilate=(1, 1),
                 i2h_weight_initializer=None, h2h_weight_initializer=None,
                 i2h_bias_initializer='zeros', h2h_bias_initializer='zeros',
                 activation=functools.partial(symbol.LeakyReLU, act_type='leaky', slope=0.2),
                 prefix='ConvRNN_', params=None, conv_layout='NCHW'):
        super(ConvRNNCell, self).__init__(input_shape=input_shape, num_hidden=num_hidden,
                                          h2h_kernel=h2h_kernel, h2h_dilate=h2h_dilate,
                                          i2h_kernel=i2h_kernel, i2h_stride=i2h_stride,
                                          i2h_pad=i2h_pad, i2h_dilate=i2h_dilate,
                                          i2h_weight_initializer=i2h_weight_initializer,
                                          h2h_weight_initializer=h2h_weight_initializer,
                                          i2h_bias_initializer=i2h_bias_initializer,
                                          h2h_bias_initializer=h2h_bias_initializer,
                                          activation=activation, prefix=prefix,
                                          params=params, conv_layout=conv_layout) 
Example 11
Project: dynamic-training-with-apache-mxnet-on-aws   Author: awslabs   File: rnn_cell.py    License: Apache License 2.0 6 votes vote down vote up
def __init__(self, input_shape, num_hidden,
                 h2h_kernel=(3, 3), h2h_dilate=(1, 1),
                 i2h_kernel=(3, 3), i2h_stride=(1, 1),
                 i2h_pad=(1, 1), i2h_dilate=(1, 1),
                 i2h_weight_initializer=None, h2h_weight_initializer=None,
                 i2h_bias_initializer='zeros', h2h_bias_initializer='zeros',
                 activation=functools.partial(symbol.LeakyReLU, act_type='leaky', slope=0.2),
                 prefix='ConvLSTM_', params=None,
                 conv_layout='NCHW'):
        super(ConvLSTMCell, self).__init__(input_shape=input_shape, num_hidden=num_hidden,
                                           h2h_kernel=h2h_kernel, h2h_dilate=h2h_dilate,
                                           i2h_kernel=i2h_kernel, i2h_stride=i2h_stride,
                                           i2h_pad=i2h_pad, i2h_dilate=i2h_dilate,
                                           i2h_weight_initializer=i2h_weight_initializer,
                                           h2h_weight_initializer=h2h_weight_initializer,
                                           i2h_bias_initializer=i2h_bias_initializer,
                                           h2h_bias_initializer=h2h_bias_initializer,
                                           activation=activation, prefix=prefix,
                                           params=params, conv_layout=conv_layout) 
Example 12
Project: dynamic-training-with-apache-mxnet-on-aws   Author: awslabs   File: rnn_cell.py    License: Apache License 2.0 6 votes vote down vote up
def __init__(self, input_shape, num_hidden,
                 h2h_kernel=(3, 3), h2h_dilate=(1, 1),
                 i2h_kernel=(3, 3), i2h_stride=(1, 1),
                 i2h_pad=(1, 1), i2h_dilate=(1, 1),
                 i2h_weight_initializer=None, h2h_weight_initializer=None,
                 i2h_bias_initializer='zeros', h2h_bias_initializer='zeros',
                 activation=functools.partial(symbol.LeakyReLU, act_type='leaky', slope=0.2),
                 prefix='ConvGRU_', params=None, conv_layout='NCHW'):
        super(ConvGRUCell, self).__init__(input_shape=input_shape, num_hidden=num_hidden,
                                          h2h_kernel=h2h_kernel, h2h_dilate=h2h_dilate,
                                          i2h_kernel=i2h_kernel, i2h_stride=i2h_stride,
                                          i2h_pad=i2h_pad, i2h_dilate=i2h_dilate,
                                          i2h_weight_initializer=i2h_weight_initializer,
                                          h2h_weight_initializer=h2h_weight_initializer,
                                          i2h_bias_initializer=i2h_bias_initializer,
                                          h2h_bias_initializer=h2h_bias_initializer,
                                          activation=activation, prefix=prefix,
                                          params=params, conv_layout=conv_layout) 
Example 13
Project: DOTA_models   Author: ringringyi   File: eval.py    License: Apache License 2.0 6 votes vote down vote up
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir) 
Example 14
Project: soccer-matlab   Author: utra-robosoccer   File: minitaur_env_randomizer_from_config.py    License: BSD 2-Clause "Simplified" License 6 votes vote down vote up
def _build_randomization_function_dict(self, env):
    func_dict = {}
    func_dict["mass"] = functools.partial(
        self._randomize_masses, minitaur=env.minitaur)
    func_dict["inertia"] = functools.partial(
        self._randomize_inertia, minitaur=env.minitaur)
    func_dict["latency"] = functools.partial(
        self._randomize_latency, minitaur=env.minitaur)
    func_dict["joint friction"] = functools.partial(
        self._randomize_joint_friction, minitaur=env.minitaur)
    func_dict["motor friction"] = functools.partial(
        self._randomize_motor_friction, minitaur=env.minitaur)
    func_dict["restitution"] = functools.partial(
        self._randomize_contact_restitution, minitaur=env.minitaur)
    func_dict["lateral friction"] = functools.partial(
        self._randomize_contact_friction, minitaur=env.minitaur)
    func_dict["battery"] = functools.partial(
        self._randomize_battery_level, minitaur=env.minitaur)
    func_dict["motor strength"] = functools.partial(
        self._randomize_motor_strength, minitaur=env.minitaur)
    # Settinmg control step needs access to the environment.
    func_dict["control step"] = functools.partial(
        self._randomize_control_step, env=env)
    return func_dict 
Example 15
Project: soccer-matlab   Author: utra-robosoccer   File: wrappers.py    License: BSD 2-Clause "Simplified" License 6 votes vote down vote up
def step(self, action, blocking=True):
    """Step the environment.

    Args:
      action: The action to apply to the environment.
      blocking: Whether to wait for the result.

    Returns:
      Transition tuple when blocking, otherwise callable that returns the
      transition tuple.
    """
    self._conn.send((self._ACTION, action))
    if blocking:
      return self._receive(self._TRANSITION)
    else:
      return functools.partial(self._receive, self._TRANSITION) 
Example 16
Project: End-to-end-ASR-Pytorch   Author: Alexander-H-Liu   File: data.py    License: MIT License 6 votes vote down vote up
def load_textset(n_jobs, use_gpu, pin_memory, corpus, text):

    # Text tokenizer
    tokenizer = load_text_encoder(**text)
    # Dataset
    tr_set, dv_set, tr_loader_bs, dv_loader_bs, data_msg = create_textset(
        tokenizer, **corpus)
    collect_tr = partial(collect_text_batch, mode='train')
    collect_dv = partial(collect_text_batch, mode='dev')
    # Dataloader (Text data stored in RAM, no need num_workers)
    tr_set = DataLoader(tr_set, batch_size=tr_loader_bs, shuffle=True, drop_last=True, collate_fn=collect_tr,
                        num_workers=0, pin_memory=use_gpu)
    dv_set = DataLoader(dv_set, batch_size=dv_loader_bs, shuffle=False, drop_last=False, collate_fn=collect_dv,
                        num_workers=0, pin_memory=pin_memory)

    # Messages to show
    data_msg.append('I/O spec.  | Token type = {}\t| Vocab size = {}'
                    .format(tokenizer.token_type, tokenizer.vocab_size))

    return tr_set, dv_set, tokenizer.vocab_size, tokenizer, data_msg 
Example 17
Project: facebook-wda   Author: openatx   File: __init__.py    License: MIT License 6 votes vote down vote up
def set_alert_callback(self, callback):
        """
        Args:
            callback (func): called when alert popup
        
        Example of callback:

            def callback(session):
                session.alert.accept()
        """
        if callable(callback):
            self.http.alert_callback = functools.partial(callback, self)
        else:
            self.http.alert_callback = None

    #Not working
    #def get_clipboard(self):
    #    return self.http.post("/wda/getPasteboard").value

    # Not working
    #def siri_activate(self, text):
    #    self.http.post("/wda/siri/activate", {"text": text}) 
Example 18
Project: aegea   Author: kislyuk   File: logs.py    License: Apache License 2.0 5 votes vote down vote up
def grep(args):
    if args.context:
        args.before_context = args.after_context = args.context
    if not args.end_time:
        args.end_time = Timestamp("-0s")
    query = clients.logs.start_query(logGroupName=args.log_group,
                                     startTime=int(timestamp(args.start_time) * 1000),
                                     endTime=int(timestamp(args.end_time) * 1000),
                                     queryString=args.query)
    seen_results = {}
    print_with_context = partial(print_log_event_with_context, before=args.before_context, after=args.after_context)
    try:
        with ThreadPoolExecutor() as executor:
            while True:
                res = clients.logs.get_query_results(queryId=query["queryId"])
                log_record_pointers = []
                for record in res["results"]:
                    event = {r["field"]: r["value"] for r in record}
                    event_hash = hashlib.sha256(json.dumps(event, sort_keys=True).encode()).hexdigest()[:32]
                    if event_hash in seen_results:
                        continue
                    if "@ptr" in event and (args.before_context or args.after_context):
                        log_record_pointers.append(event["@ptr"])
                    else:
                        print_log_event(event)
                    seen_results[event_hash] = event
                if log_record_pointers:
                    executor.map(print_with_context, log_record_pointers)
                if res["status"] == "Complete":
                    break
                elif res["status"] in {"Failed", "Cancelled"}:
                    raise AegeaException("Query status: {}".format(res["status"]))
                time.sleep(1)
    finally:
        try:
            clients.logs.stop_query(queryId=query["queryId"])
        except clients.logs.exceptions.InvalidParameterException:
            pass
    logger.debug("Query %s: %s", query["queryId"], res["statistics"])
    return SystemExit(os.EX_OK if seen_results else os.EX_DATAERR) 
Example 19
Project: aegea   Author: kislyuk   File: __init__.py    License: Apache License 2.0 5 votes vote down vote up
def add_time_bound_args(p, snap=0):
    t = partial(Timestamp, snap=snap)
    p.add_argument("--start-time", type=t, default=Timestamp("-7d", snap=snap), help=Timestamp.__doc__, metavar="START")
    p.add_argument("--end-time", type=t, help=Timestamp.__doc__, metavar="END") 
Example 20
Project: BASS   Author: Cisco-Talos   File: util.py    License: GNU General Public License v2.0 5 votes vote down vote up
def file_sha256(path):
    """Get the hex digest of the given file"""
    sha256 = hashlib.sha256()
    with open(path, "rb") as f:
        map(sha256.update, iter(partial(f.read, BLOCK_SIZE), ""))
    return sha256.hexdigest() 
Example 21
Project: BASS   Author: Cisco-Talos   File: util.py    License: GNU General Public License v2.0 5 votes vote down vote up
def file_sha256(path):
    """Get the hex digest of the given file"""
    sha256 = hashlib.sha256()
    with open(path, "rb") as f:
        map(sha256.update, iter(partial(f.read, BLOCK_SIZE), ""))
    return sha256.hexdigest() 
Example 22
Project: Learning-Concurrency-in-Python   Author: PacktPublishing   File: asyncioEvent.py    License: MIT License 5 votes vote down vote up
def main(loop):
    # Create a shared event
    event = asyncio.Event()
    print('event start state: {}'.format(event.is_set()))

    loop.call_later(
        0.1, functools.partial(set_event, event)
    )

    await asyncio.wait([coro1(event), coro2(event)])
    print('event end state: {}'.format(event.is_set())) 
Example 23
Project: LPHK   Author: nimaid   File: window.py    License: GNU General Public License v3.0 5 votes vote down vote up
def popup_choice(self, window, title, image, text, choices):
        popup = tk.Toplevel(window)
        popup.resizable(False, False)
        if MAIN_ICON != None:
            if os.path.splitext(MAIN_ICON)[1].lower() == ".gif":
                dummy = None
                #popup.call('wm', 'iconphoto', popup._w, tk.PhotoImage(file=MAIN_ICON))
            else:
                popup.iconbitmap(MAIN_ICON)
        popup.wm_title(title)
        popup.tkraise(window)
        
        def run_end(func):
            popup.destroy()
            if func != None:
                func()

        picture_label = tk.Label(popup, image=image)
        picture_label.photo = image
        picture_label.grid(column=0, row=0, rowspan=2, padx=10, pady=10)
        tk.Label(popup, text=text, justify=tk.CENTER).grid(column=1, row=0, columnspan=len(choices), padx=10, pady=10)
        for idx, choice in enumerate(choices):
            run_end_func = partial(run_end, choice[1])
            tk.Button(popup, text=choice[0], command=run_end_func).grid(column=1 + idx, row=1, padx=10, pady=10)
        popup.wait_visibility()
        popup.grab_set()
        popup.wait_window() 
Example 24
Project: neural-fingerprinting   Author: StephanZheng   File: test_attacks.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def setUp(self):
        super(TestFastFeatureAdversaries, self).setUp()

        def make_imagenet_cnn(input_shape=(None, 224, 224, 3)):
            """
            Similar CNN to AlexNet.
            """

            class ModelImageNetCNN(Model):
                def __init__(self, scope, nb_classes=1000, **kwargs):
                    del kwargs
                    Model.__init__(self, scope, nb_classes, locals())

                def fprop(self, x, **kwargs):
                    del kwargs
                    my_conv = functools.partial(tf.layers.conv2d,
                                                kernel_size=3,
                                                strides=2,
                                                padding='valid',
                                                activation=tf.nn.relu,
                                                kernel_initializer=HeReLuNormalInitializer)
                    my_dense = functools.partial(tf.layers.dense,
                                                 kernel_initializer=HeReLuNormalInitializer)
                    with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
                        for depth in [96, 256, 384, 384, 256]:
                            x = my_conv(x, depth)
                        y = tf.layers.flatten(x)
                        y = my_dense(y, 4096, tf.nn.relu)
                        y = fc7 = my_dense(y, 4096, tf.nn.relu)
                        y = my_dense(y, 1000)
                        return {'fc7': fc7,
                                self.O_LOGITS: y,
                                self.O_PROBS: tf.nn.softmax(logits=y)}

            return ModelImageNetCNN('imagenet')

        self.input_shape = [10, 224, 224, 3]
        self.sess = tf.Session()
        self.model = make_imagenet_cnn(self.input_shape)
        self.attack = FastFeatureAdversaries(self.model) 
Example 25
def fprop(self, x, **kwargs):
        del kwargs
        my_dense = functools.partial(
            tf.layers.dense, kernel_initializer=HeReLuNormalInitializer)
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
            y = tf.layers.flatten(x)
            y = my_dense(y, self.nb_filters, activation=tf.nn.relu)
            y = my_dense(y, self.nb_filters, activation=tf.nn.relu)
            logits = my_dense(y, self.nb_classes)
            return {self.O_LOGITS: logits,
                    self.O_PROBS: tf.nn.softmax(logits=logits)} 
Example 26
Project: neural-fingerprinting   Author: StephanZheng   File: mnist_blackbox.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def fprop(self, x, **kwargs):
        del kwargs
        my_dense = functools.partial(
            tf.layers.dense, kernel_initializer=HeReLuNormalInitializer)
        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
            y = tf.layers.flatten(x)
            y = my_dense(y, self.nb_filters, activation=tf.nn.relu)
            y = my_dense(y, self.nb_filters, activation=tf.nn.relu)
            logits = my_dense(y, self.nb_classes)
            return {self.O_LOGITS: logits,
                    self.O_PROBS: tf.nn.softmax(logits=logits)} 
Example 27
Project: multibootusb   Author: mbusb   File: pipe.py    License: GNU General Public License v2.0 5 votes vote down vote up
def _get_pipe2_implementation():
    """Find the appropriate implementation for ``pipe2``.

Return a function implementing ``pipe2``."""
    if hasattr(os, 'pipe2'):
        return os.pipe2 # pylint: disable=no-member
    else:
        try:
            libc = load_ctypes_library("libc", SIGNATURES, ERROR_CHECKERS)
            return (partial(_pipe2_ctypes, libc)
                    if hasattr(libc, 'pipe2') else
                    _pipe2_by_pipe)
        except ImportError:
            return _pipe2_by_pipe 
Example 28
Project: multibootusb   Author: mbusb   File: param_rewrite.py    License: GNU General Public License v2.0 5 votes vote down vote up
def add_tokens(*tokens):
    return partial(op_add_tokens, tokens) 
Example 29
Project: multibootusb   Author: mbusb   File: param_rewrite.py    License: GNU General Public License v2.0 5 votes vote down vote up
def remove_tokens(*tokens):
    return partial(op_remove_tokens, tokens) 
Example 30
Project: multibootusb   Author: mbusb   File: param_rewrite.py    License: GNU General Public License v2.0 5 votes vote down vote up
def add_or_replace_kv(key, value):
    assert key.endswith('=')
    return partial(op_add_or_replace_kv, key, value)