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How to use kill_branch method in hypothesis

Best Python code snippet using hypothesis

disassemble.py

Source:disassemble.py

...256def abolsute_branch_uncondtionional(N, **kwargs):257    return [N]258def absolute_call(next_pc, N, **kwargs):259    return [next_pc, N]260def kill_branch(**kwargs):261    return []262class B(I):263    def __init__(self, pattern, name, newpc):264        super().__init__(pattern, "")265        self.newpc = newpc266        self.name = name267    def to_string(self, dict):268        if self.newpc == kill_branch:269            return self.name.format(**dict)270        if self.newpc == relative_branch or self.newpc == relative_branch_unconditional:271            dest = dict["next_pc"] + dict["S"]272        elif self.newpc == relative_call:273            dest = dict["next_pc"] + dict["S"]274        else:...

utils_lib.py

Source:utils_lib.py

1# Copyright 2021 Google LLC2#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"""Utility library for optical flow network."""15from typing import Union, Tuple, Sequence16import tensorflow as tf17def coords_grid(18    batch_size: Union[tf.Tensor, int],19    height: Union[tf.Tensor, int],20    width: Union[tf.Tensor, int],21) -> tf.Tensor:22  """Creates a coordinate grid.23  Args:24    batch_size: A 0-D Tensor (scalar), batch size of coordinate grids.25    height: A 0-D Tensor (scalar), height of coordinate grids.26    width: A 0-D Tensor (scalar), width of coordinate grids.27  Returns:28    Coordinate grid: A Tensor of size B x H x W x 2 where B is batch size, H x W29      is the size of coordinate grid.30  """31  coords = tf.meshgrid(tf.range(height), tf.range(width), indexing='ij')32  coords = tf.cast(tf.stack(coords[::-1], axis=-1), dtype=tf.float32)33  coords = tf.expand_dims(coords, axis=0)34  coords = tf.repeat(coords, batch_size, axis=0)35  return coords36def initialize_flow(image: tf.Tensor,37                    division: int = 8) -> Tuple[tf.Tensor, tf.Tensor]:38  """Creates initial coodinates of flow field before and after update.39  Args:40    image: A Tensor of size B x H x W x C, where B is batch size, H x W is the41      image size, and C is the number of channels.42    division: A integer specifying the division factor between input image and43      flow field.44  Returns:45    Coordinate of flow field before update: A Tensor of size B x H/division x46      W/division x 2, where B is batch size, H/division x W/division is the47      flow field size.48    Coordinate of flow field after update: A Tensor of size B x H/division x49      W/division x 2, where B is batch size, H/division x W/division is the50      flow field size.51  """52  batch, height, width, _ = tf.unstack(tf.shape(image))53  pre_coords = coords_grid(batch, height // division, width // division)54  post_coords = coords_grid(batch, height // division, width // division)55  return pre_coords, post_coords56def compute_upsample_flow(flow: tf.Tensor,57                          size: Union[tf.Tensor, Tuple[int, int]]) -> tf.Tensor:58  """Resizes 'flow' to 'size' while rescaling flow value."""59  # For the boundary pixels, bilinear sampling will introduce incorrect value.60  # For example, the arms occluding the torso may have different flow value.61  flow_size = tf.unstack(flow.shape)62  if len(flow_size) == 3:63    flow_height = flow_size[0]64    flow_width = flow_size[1]65  else:66    flow_height = flow_size[1]67    flow_width = flow_size[2]68  upsampled_flow = tf.image.resize(flow, size)69  upsampled_x = upsampled_flow[..., 0] * tf.cast(70      size[1], dtype=tf.float32) / tf.cast(71          flow_width, dtype=tf.float32)72  upsampled_y = upsampled_flow[..., 1] * tf.cast(73      size[0], dtype=tf.float32) / tf.cast(74          flow_height, dtype=tf.float32)75  return tf.stack((upsampled_x, upsampled_y), axis=-1)76def create_path_drop_masks(p_flow: float,77                           p_surface: float) -> Tuple[tf.Tensor, tf.Tensor]:78  """Determines global path drop decision based on given probabilities.79  Args:80    p_flow: A scalar of float32, probability of keeping flow feature branch81    p_surface: A scalar of float32, probability of keeping surface feature82      branch83  Returns:84    final_flow_mask: A constant tensor mask containing either one or zero85      depending on the final coin flip probability.86    final_surface_mask: A constant tensor mask containing either one or87      zero depending on the final coin flip probability.88  """89  # The logic works as follows:90  # We have flipped 3 coins, first determines the chance of keeping91  # the flow branch, second determines keeping surface branch, the third92  # makes the final decision in the case where both branches were killed93  # off, otherwise the initial flow and surface chances are kept.94  random_values = tf.random.uniform(shape=[3], minval=0.0, maxval=1.0)95  keep_branch = tf.constant(1.0)96  kill_branch = tf.constant(0.0)97  flow_chances = tf.case(98      [(tf.math.less_equal(random_values[0], p_flow), lambda: keep_branch)],99      default=lambda: kill_branch)100  surface_chances = tf.case(101      [(tf.math.less_equal(random_values[1], p_surface), lambda: keep_branch)],102      default=lambda: kill_branch)103  # Decision to determine whether both branches were killed off104  third_flip = tf.math.logical_or(105      tf.cast(flow_chances, dtype=tf.bool),106      tf.cast(surface_chances, dtype=tf.bool))107  third_flip = tf.cast(third_flip, dtype=tf.float32)108  # Make a second choice, for the third case109  # Here we use a 50/50 chance to keep either flow or surface110  # If its greater than 0.5, keep the image111  flow_second_flip = tf.case(112      [(tf.math.greater(random_values[2], 0.5), lambda: keep_branch)],113      default=lambda: kill_branch)114  # If its less than or equal to 0.5, keep surface115  surface_second_flip = tf.case(116      [(tf.math.less_equal(random_values[2], 0.5), lambda: keep_branch)],117      default=lambda: kill_branch)118  final_flow_mask = tf.case([(tf.equal(third_flip, 1), lambda: flow_chances)],119                            default=lambda: flow_second_flip)120  final_surface_mask = tf.case(121      [(tf.equal(third_flip, 1), lambda: surface_chances)],122      default=lambda: surface_second_flip)123  return final_flow_mask, final_surface_mask124def build_pyramid(image: tf.Tensor,125                  num_levels: int = 7,126                  resize_method: str = 'bilinear',127                  rescale_flow: bool = False) -> Sequence[tf.Tensor]:128  """Return list of downscaled images (level 0 = original)."""129  pyramid = [image]130  size = tf.shape(image)[-3:-1]131  for _ in range(1, num_levels):132    size = size // 2133    image = tf.image.resize(pyramid[-1], size, method=resize_method)134    if rescale_flow:135      image /= 2  # half-resolution => half-size flow136    pyramid.append(image)...

models.py

Source:models.py

...64        return json_object65        66    def __unicode__(self):67        return str(self.short_desc)68    def kill_branch(self):69        offspring = Page.objects.all().filter(parent=self)70        for page in offspring:71            page.kill_branch()72        self.delete()73class Properties(models.Model):74    user = models.OneToOneField(User)75    avatar = models.ImageField(upload_to='images/user_avatars/%Y/%m/%d') 76 77    def __unicode__(self):78        return str(self.user.username)79    def getPages(self):80        return Page.objects.all().filter(author=self.user)...

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