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How to use _example_path method in prospector

Best Python code snippet using prospector_python

__init__.py

Source:__init__.py

...28        self._zoom = zoom29    @property30    def zoom(self):31        return self._zoom32def _example_path(extension):33    return os.path.join(os.path.dirname(__file__),'files',extension)34def _icon_path(suffix):35    return os.path.join(os.path.dirname(__file__), 'icons', suffix)36_grid_obstacle_dict = dict({'ego-xvar-module' : 'robot',37                      'ego-xvar-name' : 'ax',38                      'ego-yvar-module' : 'robot',39                      'ego-yvar-name': 'ay',40                      'xmax-constant': 'axMAX',41                      'ymax-constant': 'ayMAX',42                      'target-label': 'goal',43                      'traps-label': 'traps'44                      })45def obstacle(N, full_observable = False):46    if full_observable:47        return Model(_example_path("obstacle_full_observable.nm"), ProgramAnnotation(_grid_obstacle_dict),48                     ["Pmax=? [ \"notbad\" U \"goal\"]"], constants=f"N={N}")49    return Model(_example_path("obstacle.nm"), ProgramAnnotation(_grid_obstacle_dict),50                 ["Pmax=? [ \"notbad\" U \"goal\"]"], constants=f"N={N}")51_drone_dict = dict({'ego-xvar-module' : 'drone',52                      'ego-xvar-name' : 'dx',53                      'ego-yvar-module' : 'drone',54                      'ego-yvar-name': 'dy',55                      'adv-xvar-module': 'agent',56                        'adv-xvar-name': 'ax',57                        'adv-yvar-module': 'agent',58                        'adv-yvar-name': 'ay',59                        'xmax-constant': 'xMAX',60                      'ymax-constant': 'yMAX',61                      'target-label': 'goal',62                       'ego-radius-constant' : "RADIUS",63                       'scan-action': 'scan',64                        'adv-area': ['a'],65                      'traps-label': None66                      })67def evade(N,RADIUS):68    return Model(_example_path("evade.nm"), ProgramAnnotation(_drone_dict),69                 ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N},RADIUS={RADIUS}")70_intercept_dict = dict({'ego-xvar-module' : 'drone',71                      'ego-xvar-name' : 'dx',72                      'ego-yvar-module' : 'drone',73                      'ego-yvar-name': 'dy',74                      'adv-xvar-module': 'agent',75                        'adv-xvar-name': 'ax',76                        'adv-yvar-module': 'agent',77                        'adv-yvar-name': 'ay',78                        'xmax-constant': 'dxMAX',79                      'ymax-constant': 'dyMAX',80                      'traps-label': None,81                        'ego-radius-constant': "RADIUS",82                        'camera': ['CAMERA'],83                        'adv-goals-label': 'exits'84                      })85def intercept(N,RADIUS):86    return Model(_example_path("intercept.nm"), ProgramAnnotation(_intercept_dict),87                 ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N},RADIUS={RADIUS}")88_surveillance_dict = dict({'ego-xvar-module' : 'drone',89                      'ego-xvar-name' : 'dx',90                      'ego-yvar-module' : 'drone',91                      'ego-yvar-name': 'dy',92                      'adv-xvar-module': ['agent','agent2'],93                       'adv-xvar-name': ['ax','ax2'],94                       'adv-yvar-module': ['agent','agent2'],95                        'adv-yvar-name': ['ay','ay2'],96                       'adv-dirvar-module': ['agent', 'agent2'],97                           'adv-dirvar-name': ['dir', 'dir2'],98                           'xmax-constant': 'xMAX',99                      'ymax-constant': 'yMAX',100                      'target-label': 'goal',101                      'traps-label': None,102                      'adv-dirvalue-mapping': {1: Direction.WEST, 0: Direction.EAST},103                       'adv-radius-constant' : "ARADIUS",104                       'ego-radius-constant' : "RADIUS"105                      })106def surveillance(N,RADIUS=2):107    return Model(_example_path("avoid.nm"), ProgramAnnotation(_surveillance_dict), ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N},RADIUS={RADIUS}")108_grid_refuel = dict({'ego-xvar-module' : 'rover',109                      'ego-xvar-name' : 'ax',110                      'ego-yvar-module' : 'rover',111                      'ego-yvar-name': 'ay',112                      'xmax-constant': 'axMAX',113                      'ymax-constant': 'ayMAX',114                      'target-label': 'goal',115                      'traps-label': 'traps',116                      'landmarks': 'stationvisit',117                      'resource-module': 'tank',118                      'resource-variable': 'fuel',119                      'resource-name': 'fuel',120                       'resource-maximum-constant': 'fuelCAP'121                      })122def refuel(N, ENERGY):123    return Model(_example_path("refuel.nm"), ProgramAnnotation(_grid_refuel), ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N},ENERGY={ENERGY}")124_grid_rocks = dict({'ego-xvar-module' : 'robot',125                      'ego-xvar-name' : 'x',126                      'ego-yvar-module' : 'robot',127                      'ego-yvar-name': 'y',128                      'xmax-constant': 'xMAX',129                      'ymax-constant': 'yMAX',130                      'target-label': 'goal',131                      'traps-label' : None,132                      'interactive-landmarks-x': ['r1x', 'r2x'],133                      'interactive-landmarks-y': ['r1y', 'r2y'],134                      'il-statusvar-module': ['rock1', 'rock2'],135                      'il-statusvar-name': ['r1qual', 'r2qual'],136                      'il-clearancevar-module': ['rock1', 'rock2'],137                      'il-clearancevar-name': ['r1taken', 'r2taken'],138                       'goal-action' : True139                    })140def rocks(N, K=2):141    K = int(K)142    if K == 2:143        return Model(_example_path("rocks2.nm"), ProgramAnnotation(_grid_rocks), ["Pmax=? [\"notbad\" U \"goal\"]"], constants=f"N={N}")144    else:...

tf_io_pipline_tools.py

Source:tf_io_pipline_tools.py

1#!/usr/bin/env python32# -*- coding: utf-8 -*-3# @Time    : 19-2-15 ä¸å2:134# @Author  : MaybeShewill-CV5# @Site    : https://github.com/MaybeShewill-CV/CRNN_Tensorflow6# @File    : tf_io_pipline_tools.py7# @IDE: PyCharm8"""9Some tensorflow records io tools10"""11import os12import os.path as ops13import cv214import tensorflow as tf15import glog as log16from config import global_config17CFG = global_config.cfg18_R_MEAN = 123.6819_G_MEAN = 116.7820_B_MEAN = 103.9421_CHANNEL_MEANS = [_B_MEAN, _G_MEAN, _R_MEAN]22def int64_feature(value):23    """24    :return:25    """26    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))27def bytes_feature(value):28    """29    :param value:30    :return:31    """32    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))33def write_example_tfrecords(example_paths, example_labels, tfrecords_path):34    """35    write tfrecords36    :param example_paths:37    :param example_labels:38    :param tfrecords_path:39    :return:40    """41    _tfrecords_dir = ops.split(tfrecords_path)[0]42    os.makedirs(_tfrecords_dir, exist_ok=True)43    log.info('Writing {:s}....'.format(tfrecords_path))44    with tf.python_io.TFRecordWriter(tfrecords_path) as _writer:45        for _index, _example_path in enumerate(example_paths):46            with open(_example_path, 'rb') as f:47                check_chars = f.read()[-2:]48            if check_chars != b'\xff\xd9':49                log.error('Image file {:s} is not complete'.format(_example_path))50                continue51            else:52                _example_image = cv2.imread(_example_path, cv2.IMREAD_COLOR)53                _example_image = cv2.resize(_example_image,54                                            dsize=(CFG.TRAIN.IMG_WIDTH, CFG.TRAIN.IMG_HEIGHT),55                                            interpolation=cv2.INTER_CUBIC)56                _example_image_raw = _example_image.tostring()57                _example = tf.train.Example(58                    features=tf.train.Features(59                        feature={60                            'height': int64_feature(CFG.TRAIN.IMG_HEIGHT),61                            'width': int64_feature(CFG.TRAIN.IMG_WIDTH),62                            'depth': int64_feature(3),63                            'label': int64_feature(example_labels[_index]),64                            'image_raw': bytes_feature(_example_image_raw)65                        }))66                _writer.write(_example.SerializeToString())67    log.info('Writing {:s} complete'.format(tfrecords_path))68    return69def decode(serialized_example):70    """71    Parses an image and label from the given `serialized_example`72    :param serialized_example:73    :return:74    """75    features = tf.parse_single_example(76        serialized_example,77        # Defaults are not specified since both keys are required.78        features={79            'image_raw': tf.FixedLenFeature([], tf.string),80            'label': tf.FixedLenFeature([], tf.int64),81            'height': tf.FixedLenFeature([], tf.int64),82            'width': tf.FixedLenFeature([], tf.int64),83            'depth': tf.FixedLenFeature([], tf.int64)84        })85    # decode image86    image = tf.decode_raw(features['image_raw'], tf.uint8)87    image_shape = tf.stack([CFG.TRAIN.IMG_HEIGHT, CFG.TRAIN.IMG_WIDTH, 3])88    image = tf.reshape(image, image_shape)89    # Convert label from a scalar int64 tensor to an int32 scalar.90    label = tf.cast(features['label'], tf.int32)91    return image, label92def augment_for_train(image, label):93    """94    :param image:95    :param label:96    :return:97    """98    # first apply random crop99    image = tf.image.random_crop(value=image,100                                 size=[CFG.TRAIN.CROP_IMG_HEIGHT, CFG.TRAIN.CROP_IMG_WIDTH, 3],101                                 seed=tf.set_random_seed(1234),102                                 name='crop_image')103    # apply random flip104    image = tf.image.random_flip_left_right(image=image, seed=tf.set_random_seed(1234))105    return image, label106def augment_for_validation(image, label):107    """108    :param image:109    :param label:110    :return:111    """112    assert CFG.TRAIN.IMG_HEIGHT == CFG.TRAIN.IMG_WIDTH113    assert CFG.TRAIN.CROP_IMG_HEIGHT == CFG.TRAIN.CROP_IMG_WIDTH114    # apply central crop115    central_fraction = CFG.TRAIN.CROP_IMG_HEIGHT / CFG.TRAIN.IMG_HEIGHT116    image = tf.image.central_crop(image=image, central_fraction=central_fraction)117    return image, label118def normalize(image, label):119    """120    Normalize the image data by substracting the imagenet mean value121    :param image:122    :param label:123    :return:124    """125    if image.get_shape().ndims != 3:126        raise ValueError('Input must be of size [height, width, C>0]')127    image_fp = tf.cast(image, dtype=tf.float32)128    means = tf.expand_dims(tf.expand_dims(_CHANNEL_MEANS, 0), 0)...

utils.py

Source:utils.py

1import re2class DataFiles:3    _code_path = ""4    _input_path = ""5    _example_path = ""6    input = ""7    inputEX = ""8    raw_input = ""9    raw_inputEX = ""10    def __init__(self, path):11        self._code_path = path12        self._input_path = path.replace("code", "input").replace("Day", "").replace(".py", ".txt")13        self._example_path = re.sub(r"(\w+)(.txt$)", r"Example\g<2>", self._input_path)14        self._inputFile = open(self._input_path)15        self._inputEXFile = open(self._example_path)16        self.input = self.get_input()17        self.inputEX = self.get_inputEX()18    def __del__(self):19        self._inputFile.close()20        self._inputEXFile.close()21    def print_paths(self):22        print(self._code_path)23        print(self._input_path)24        print(self._example_path)25    def get_input(self):26        return self._inputFile.read().splitlines()27    def get_inputEX(self):28        return self._inputEXFile.read().splitlines()29    def get_input_raw(self):30        return self._inputFile.read()31    def get_inputEX_raw(self):...

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