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How to use shake method in robotframework-appiumlibrary

Best Python code snippet using robotframework-appiumlibrary_python

shake_shake.py

Source:shake_shake.py

1# Copyright 2018 The TensorFlow Authors All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Builds the Shake-Shake Model."""16from __future__ import absolute_import17from __future__ import division18from __future__ import print_function19import custom_ops as ops20import tensorflow as tf21def _shake_shake_skip_connection(x, output_filters, stride):22  """Adds a residual connection to the filter x for the shake-shake model."""23  curr_filters = int(x.shape[3])24  if curr_filters == output_filters:25    return x26  stride_spec = ops.stride_arr(stride, stride)27  # Skip path 128  path1 = tf.nn.avg_pool(29      x, [1, 1, 1, 1], stride_spec, 'VALID', data_format='NHWC')30  path1 = ops.conv2d(path1, int(output_filters / 2), 1, scope='path1_conv')31  # Skip path 232  # First pad with 0's then crop33  pad_arr = [[0, 0], [0, 1], [0, 1], [0, 0]]34  path2 = tf.pad(x, pad_arr)[:, 1:, 1:, :]35  concat_axis = 336  path2 = tf.nn.avg_pool(37      path2, [1, 1, 1, 1], stride_spec, 'VALID', data_format='NHWC')38  path2 = ops.conv2d(path2, int(output_filters / 2), 1, scope='path2_conv')39  # Concat and apply BN40  final_path = tf.concat(values=[path1, path2], axis=concat_axis)41  final_path = ops.batch_norm(final_path, scope='final_path_bn')42  return final_path43def _shake_shake_branch(x, output_filters, stride, rand_forward, rand_backward,44                        is_training):45  """Building a 2 branching convnet."""46  x = tf.nn.relu(x)47  x = ops.conv2d(x, output_filters, 3, stride=stride, scope='conv1')48  x = ops.batch_norm(x, scope='bn1')49  x = tf.nn.relu(x)50  x = ops.conv2d(x, output_filters, 3, scope='conv2')51  x = ops.batch_norm(x, scope='bn2')52  if is_training:53    x = x * rand_backward + tf.stop_gradient(x * rand_forward -54                                             x * rand_backward)55  else:56    x *= 1.0 / 257  return x58def _shake_shake_block(x, output_filters, stride, is_training):59  """Builds a full shake-shake sub layer."""60  batch_size = tf.shape(x)[0]61  # Generate random numbers for scaling the branches62  rand_forward = [63      tf.random_uniform(64          [batch_size, 1, 1, 1], minval=0, maxval=1, dtype=tf.float32)65      for _ in range(2)66  ]67  rand_backward = [68      tf.random_uniform(69          [batch_size, 1, 1, 1], minval=0, maxval=1, dtype=tf.float32)70      for _ in range(2)71  ]72  # Normalize so that all sum to 173  total_forward = tf.add_n(rand_forward)74  total_backward = tf.add_n(rand_backward)75  rand_forward = [samp / total_forward for samp in rand_forward]76  rand_backward = [samp / total_backward for samp in rand_backward]77  zipped_rand = zip(rand_forward, rand_backward)78  branches = []79  for branch, (r_forward, r_backward) in enumerate(zipped_rand):80    with tf.variable_scope('branch_{}'.format(branch)):81      b = _shake_shake_branch(x, output_filters, stride, r_forward, r_backward,82                              is_training)83      branches.append(b)84  res = _shake_shake_skip_connection(x, output_filters, stride)85  return res + tf.add_n(branches)86def _shake_shake_layer(x, output_filters, num_blocks, stride,87                       is_training):88  """Builds many sub layers into one full layer."""89  for block_num in range(num_blocks):90    curr_stride = stride if (block_num == 0) else 191    with tf.variable_scope('layer_{}'.format(block_num)):92      x = _shake_shake_block(x, output_filters, curr_stride,93                             is_training)94  return x95def build_shake_shake_model(images, num_classes, hparams, is_training):96  """Builds the Shake-Shake model.97  Build the Shake-Shake model from https://arxiv.org/abs/1705.07485.98  Args:99    images: Tensor of images that will be fed into the Wide ResNet Model.100    num_classes: Number of classed that the model needs to predict.101    hparams: tf.HParams object that contains additional hparams needed to102      construct the model. In this case it is the `shake_shake_widen_factor`103      that is used to determine how many filters the model has.104    is_training: Is the model training or not.105  Returns:106    The logits of the Shake-Shake model.107  """108  depth = 26109  k = hparams.shake_shake_widen_factor  # The widen factor110  n = int((depth - 2) / 6)111  x = images112  x = ops.conv2d(x, 16, 3, scope='init_conv')113  x = ops.batch_norm(x, scope='init_bn')114  with tf.variable_scope('L1'):115    x = _shake_shake_layer(x, 16 * k, n, 1, is_training)116  with tf.variable_scope('L2'):117    x = _shake_shake_layer(x, 32 * k, n, 2, is_training)118  with tf.variable_scope('L3'):119    x = _shake_shake_layer(x, 64 * k, n, 2, is_training)120  x = tf.nn.relu(x)121  x = ops.global_avg_pool(x)122  # Fully connected123  logits = ops.fc(x, num_classes)...

shake.js

Source:shake.js

1/*globals define*/2define([3  'utils'4], function( Utils ) {5  'use strict';6  function Shake() {7    this.magnitude = 0;8    this.duration = 0;9    this.time = 0;10    // Variables pertaining to the current shake cycle.11    // Change shake angle at frequency (20 fps).12    this.shakePeriod = 1 / 20;13    this.shakeTime = 0;14    this.shakeMagnitude = 0;15    this.shakeAngle = 0;16  }17  Shake.prototype.applyTransform = function( ctx ) {18    if ( !this.magnitude ) {19      return;20    }21    ctx.translate(22      Math.cos( this.shakeAngle ) * this.shakeMagnitude,23      Math.sin( this.shakeAngle ) * this.shakeMagnitude24    );25  };26  Shake.prototype.update = function( dt ) {27    if ( !this.magnitude ) {28      return;29    }30    this.time += dt;31    if ( this.time > this.duration ) {32      this.magnitude = 0;33      this.time = 0;34      return;35    }36    this.shakeTime += dt;37    // A new oscillation!38    if ( this.shakeTime > this.shakePeriod ) {39      this.shakeTime = 0;40      this.shakeAngle = Math.random() * Utils.PI2;41      // Lerp scale magnitude down to zero.42      var scale = ( this.duration - this.time ) / this.duration;43      this.shakeMagnitude = this.magnitude * scale;44    }45  };46  Shake.prototype.shake = function( magnitude, duration ) {47    this.magnitude = magnitude || 0;48    this.duration  = duration || 0;49    this.update(0);50  };51  Object.defineProperty( Shake.prototype, 'frequency', {52    get: function() {53      return 1 / this.shakePeriod;54    },55    set: function( frequency ) {56      this.shakePeriod = 1 / frequency;57    }58  });59  return Shake;...

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