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How to use _update_dict method in yandex-tank

Best Python code snippet using yandex-tank

functionality.py

Source:functionality.py

...66            coded_length = len(item)67            if coded_length in self.codes.keys():68                continue69            elif coded_length in self.easy_codes.keys():70                self._update_dict(item, self.easy_codes.get(coded_length))71            # Create inversely mapped codes72            self.inv_codes = {v: k for k, v in self.codes.items()}73    def _update_dict(self, item, number):74        update_dict = {frozenset(item): number}75        self.codes.update(update_dict)76    def decode_hard_codes(self):77        """78        Uses encoded wireing to deduce the wireing not easily found by 79        decode_easy_codes().80        the numbers nine, six and zero have 6 active wires:81            if all active wires are also in the four it must be a nine82            if thats false, but all active wires are also in a one it 83            it must be a 084            If thats not the case it must be a six.85        the numbers three, five and two have 5 active wires:86            if it has all the wires also found in a seven it's a three87            if it has one less wire than a four it's a 588            and if it has two less wires than a four it's a 289        """90        for item in self.input_encoded + self.output_encoded:91            coded_length = len(item)92            if coded_length in self.codes.keys():93                continue94            this_number = frozenset(item)95            if coded_length == 6:96                if self.inv_codes.get(4) - this_number == set():97                    self._update_dict(item, 9)98                elif self.inv_codes.get(1) - this_number != set():99                    self._update_dict(item, 6)100                else:101                    self._update_dict(item, 0)102            elif coded_length == 5:103                if self.inv_codes.get(7) - this_number == set():104                    self._update_dict(item, 3)105                elif len(self.inv_codes.get(4) - this_number) == 1:106                    self._update_dict(item, 5)107                elif len(self.inv_codes.get(4) - this_number) == 2:108                    self._update_dict(item, 2)109            # Update inversely mapped codes110            self.inv_codes = {v: k for k, v in self.codes.items()}111    def decode_output(self):112        """113        Decode the message with decoded codes.114        Returns115        -------116        int117            A four digit integer where each wireing combination from 118            the output is one digit.119        """120        decoded = ""121        for item in self.output_encoded:122            decoded += str(self.codes.get(frozenset(item)))...

layers.py

Source:layers.py

...7def normalize(layer):8  return layer/127.5 - 1.9def denormalize(layer):10  return (layer + 1.)/2.11def _update_dict(layer_dict, scope, layer):12  name = "{}/{}".format(tf.get_variable_scope().name, scope)13  layer_dict[name] = layer14def image_from_paths(paths, shape, is_grayscale=True, seed=None):15  filename_queue = tf.train.string_input_producer(list(paths), shuffle=False, seed=seed)16  reader = tf.WholeFileReader()17  filename, data = reader.read(filename_queue)18  image = tf.image.decode_png(data, channels=3, dtype=tf.uint8)19  if is_grayscale:20    image = tf.image.rgb_to_grayscale(image)21  image.set_shape(shape)22  return filename, tf.to_float(image)23@add_arg_scope24def resnet_block(25    inputs, scope, num_outputs=64, kernel_size=[3, 3],26    stride=[1, 1], padding="SAME", layer_dict={}):27  with tf.variable_scope(scope):28    layer = conv2d(29        inputs, num_outputs, kernel_size, stride,30        padding=padding, activation_fn=tf.nn.relu, scope="conv1")31    layer = conv2d(32        layer, num_outputs, kernel_size, stride,33        padding=padding, activation_fn=None, scope="conv2")34    outputs = tf.nn.relu(tf.add(inputs, layer))35  _update_dict(layer_dict, scope, outputs)36  return outputs37@add_arg_scope38def repeat(inputs, repetitions, layer, layer_dict={}, **kargv):39  outputs = slim.repeat(inputs, repetitions, layer, **kargv)40  _update_dict(layer_dict, kargv['scope'], outputs)41  return outputs42@add_arg_scope43def conv2d(inputs, num_outputs, kernel_size, stride,44           layer_dict={}, activation_fn=None,45           #weights_initializer=tf.random_normal_initializer(0, 0.001),46           weights_initializer=tf.contrib.layers.xavier_initializer(),47           scope=None, name="", **kargv):48  outputs = slim.conv2d(49      inputs, num_outputs, kernel_size,50      stride, activation_fn=activation_fn, 51      weights_initializer=weights_initializer,52      biases_initializer=tf.zeros_initializer(dtype=tf.float32), scope=scope, **kargv)53  if name:54    scope = "{}/{}".format(name, scope)55  _update_dict(layer_dict, scope, outputs)56  return outputs57@add_arg_scope58def max_pool2d(inputs, kernel_size=[3, 3], stride=[1, 1],59               layer_dict={}, scope=None, name="", **kargv):60  outputs = slim.max_pool2d(inputs, kernel_size, stride, **kargv)61  if name:62    scope = "{}/{}".format(name, scope)63  _update_dict(layer_dict, scope, outputs)64  return outputs65@add_arg_scope66def tanh(inputs, layer_dict={}, name=None, **kargv):67  outputs = tf.nn.tanh(inputs, name=name, **kargv)68  _update_dict(layer_dict, name, outputs)...

dir_dict.py

Source:dir_dict.py

1from collections.abc import MutableMapping2from functools import wraps3from typing import Iterator4import os5def _update_dict(func):6    @wraps(func)7    def wrapper(self, *args, **kwargs):8        self._temp_dic = {}9        try:10            for file_name in os.listdir(self._path):11                with open(self._path + os.path.sep + file_name) as f:12                    self._temp_dict[file_name] = f.readline()13        except OSError:14            print("can't open " + str(self._path))15        return func(self, *args, **kwargs)16    return wrapper17class DirDict(MutableMapping):18    def __init__(self, path):19        self._path = path...

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