Python glob.glob() Examples

def get_cifar10(data_dir):
    if not os.path.isdir(data_dir):
        os.system("mkdir " + data_dir)
    cwd = os.path.abspath(os.getcwd())
    os.chdir(data_dir)
    if (not os.path.exists('train.rec')) or \
       (not os.path.exists('test.rec')) :
        import urllib, zipfile, glob
        dirname = os.getcwd()
        zippath = os.path.join(dirname, "cifar10.zip")
        urllib.urlretrieve("http://data.mxnet.io/mxnet/data/cifar10.zip", zippath)
        zf = zipfile.ZipFile(zippath, "r")
        zf.extractall()
        zf.close()
        os.remove(zippath)
        for f in glob.glob(os.path.join(dirname, "cifar", "*")):
            name = f.split(os.path.sep)[-1]
            os.rename(f, os.path.join(dirname, name))
        os.rmdir(os.path.join(dirname, "cifar"))
    os.chdir(cwd)

# data 

def locate_result_subdir(run_id_or_result_subdir):
    if isinstance(run_id_or_result_subdir, str) and os.path.isdir(run_id_or_result_subdir):
        return run_id_or_result_subdir

    searchdirs = []
    searchdirs += ['']
    searchdirs += ['results']
    searchdirs += ['networks']

    for searchdir in searchdirs:
        dir = config.result_dir if searchdir == '' else os.path.join(config.result_dir, searchdir)
        dir = os.path.join(dir, str(run_id_or_result_subdir))
        if os.path.isdir(dir):
            return dir
        prefix = '%03d' % run_id_or_result_subdir if isinstance(run_id_or_result_subdir, int) else str(run_id_or_result_subdir)
        dirs = sorted(glob.glob(os.path.join(config.result_dir, searchdir, prefix + '-*')))
        dirs = [dir for dir in dirs if os.path.isdir(dir)]
        if len(dirs) == 1:
            return dirs[0]
    raise IOError('Cannot locate result subdir for run', run_id_or_result_subdir) 

def test_completeness(self):
        """
        Make sure that every tutorial that isn't in the whitelist is considered for testing by this
        file. Exceptions should be added to the whitelist.
        N.B. If the test is commented out, then that will be viewed as an intentional disabling of the
        test.
        """
        # Open up this test file.
        with open(__file__, 'r') as f:
            notebook_test_text = '\n'.join(f.readlines())

        notebooks_path = os.path.join(os.path.dirname(__file__), 'straight_dope_book')
        notebooks = glob.glob(os.path.join(notebooks_path, '**', '*.ipynb'))

        # Compile a list of notebooks that are tested
        tested_notebooks = set(re.findall(r"assert _test_notebook\('(.*)'\)", notebook_test_text))

       # Ensure each notebook in the straight dope book directory is on the whitelist or is tested.
        for notebook in notebooks:
            friendly_name = '/'.join(notebook.split('/')[-2:]).split('.')[0]
            if friendly_name not in tested_notebooks and friendly_name not in NOTEBOOKS_WHITELIST:
                assert False, friendly_name + " has not been added to the nightly/tests/straight_" + \
                              "dope/test_notebooks_single_gpu.py test_suite. Consider also adding " + \
                              "it to nightly/tests/straight_dope/test_notebooks_multi_gpu.py as " + \
                              "well if the notebooks makes use of multiple GPUs." 

def test_tutorial_downloadable():
    """
    Make sure that every tutorial that isn't in the whitelist has the placeholder
    that enables notebook download
    """
    download_button_string = '<!-- INSERT SOURCE DOWNLOAD BUTTONS -->'

    tutorial_path = os.path.join(os.path.dirname(__file__), '..', '..', 'docs', 'tutorials')
    tutorials = glob.glob(os.path.join(tutorial_path, '**', '*.md'))

    for tutorial in tutorials:
        with open(tutorial, 'r') as file:
            lines= file.readlines()
        last = lines[-1]
        second_last = lines[-2]
        downloadable = download_button_string in last or download_button_string in second_last
        friendly_name = '/'.join(tutorial.split('/')[-2:])
        if not downloadable and friendly_name  not in whitelist_set:
            print(last, second_last)
            assert False, "{} is missing <!-- INSERT SOURCE DOWNLOAD BUTTONS --> as its last line".format(friendly_name) 

def test_multiprocessing_download_successful():
    """ test download with multiprocessing """
    tmp = tempfile.mkdtemp()
    tmpfile = os.path.join(tmp, 'README.md')
    process_list = []
    # test it with 10 processes
    for i in range(10):
        process_list.append(mp.Process(
            target=_download_successful, args=(tmpfile,)))
        process_list[i].start()
    for i in range(10):
        process_list[i].join()
    assert os.path.getsize(tmpfile) > 100, os.path.getsize(tmpfile)
    # check only one file we want left
    pattern = os.path.join(tmp, 'README.md*')
    assert len(glob.glob(pattern)) == 1, glob.glob(pattern)
    # delete temp dir
    shutil.rmtree(tmp) 

def convert_images2bmp():
    # cv2.imread() jpg at 230 img/s, *.bmp at 400 img/s
    for path in ['../coco/images/val2014/', '../coco/images/train2014/']:
        folder = os.sep + Path(path).name
        output = path.replace(folder, folder + 'bmp')
        if os.path.exists(output):
            shutil.rmtree(output)  # delete output folder
        os.makedirs(output)  # make new output folder

        for f in tqdm(glob.glob('%s*.jpg' % path)):
            save_name = f.replace('.jpg', '.bmp').replace(folder, folder + 'bmp')
            cv2.imwrite(save_name, cv2.imread(f))

    for label_path in ['../coco/trainvalno5k.txt', '../coco/5k.txt']:
        with open(label_path, 'r') as file:
            lines = file.read()
        lines = lines.replace('2014/', '2014bmp/').replace('.jpg', '.bmp').replace(
            '/Users/glennjocher/PycharmProjects/', '../')
        with open(label_path.replace('5k', '5k_bmp'), 'w') as file:
            file.write(lines) 

def main(_):
  """Runs `text_utils.simplify_nq_example` over all shards of a split.

  Prints simplified examples to a single gzipped file in the same directory
  as the input shards.
  """
  split = os.path.basename(FLAGS.data_dir)
  outpath = os.path.join(FLAGS.data_dir,
                         "simplified-nq-{}.jsonl.gz".format(split))
  with gzip.open(outpath, "wb") as fout:
    num_processed = 0
    start = time.time()
    for inpath in glob.glob(os.path.join(FLAGS.data_dir, "nq-*-??.jsonl.gz")):
      print("Processing {}".format(inpath))
      with gzip.open(inpath, "rb") as fin:
        for l in fin:
          utf8_in = l.decode("utf8", "strict")
          utf8_out = json.dumps(
              text_utils.simplify_nq_example(json.loads(utf8_in))) + u"\n"
          fout.write(utf8_out.encode("utf8"))
          num_processed += 1
          if not num_processed % 100:
            print("Processed {} examples in {}.".format(num_processed,
                                                        time.time() - start)) 

def find_previous(self):
    sfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.pth')
    sfiles = glob.glob(sfiles)
    sfiles.sort(key=os.path.getmtime)
    # Get the snapshot name in pytorch
    redfiles = []
    for stepsize in cfg.TRAIN.STEPSIZE:
      redfiles.append(os.path.join(self.output_dir, 
                      cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}.pth'.format(stepsize+1)))
    sfiles = [ss for ss in sfiles if ss not in redfiles]

    nfiles = os.path.join(self.output_dir, cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_*.pkl')
    nfiles = glob.glob(nfiles)
    nfiles.sort(key=os.path.getmtime)
    redfiles = [redfile.replace('.pth', '.pkl') for redfile in redfiles]
    nfiles = [nn for nn in nfiles if nn not in redfiles]

    lsf = len(sfiles)
    assert len(nfiles) == lsf

    return lsf, nfiles, sfiles 

def main():
  opt, logger, vis = utils.build(is_train=False)

  dloader = data.get_data_loader(opt)
  print('Val dataset: {}'.format(len(dloader.dataset)))
  model = models.get_model(opt)

  for epoch in opt.which_epochs:
    # Load checkpoint
    if epoch == -1:
      # Find the latest checkpoint
      checkpoints = glob.glob(os.path.join(opt.ckpt_path, 'net*.pth'))
      assert len(checkpoints) > 0
      epochs = [int(filename.split('_')[-1][:-4]) for filename in checkpoints]
      epoch = max(epochs)
    logger.print('Loading checkpoints from {}, epoch {}'.format(opt.ckpt_path, epoch))
    model.load(opt.ckpt_path, epoch)

    results = evaluate(opt, dloader, model)
    for metric in results:
      logger.print('{}: {}'.format(metric, results[metric])) 

def index(request):
    ctx = get_context(request)
    cname = os.environ["PORTAL_CNAME"]
    template_dir = get_app_template_dirs("templates/notebooks")[0]
    htmls = os.path.join(template_dir, cname, "*.html")
    ctx["notebooks"] = [
        p.split("/" + cname + "/")[-1].replace(".html", "") for p in glob(htmls)
    ]
    ctx["PORTAL_CNAME"] = cname
    ctx["landing_pages"] = []
    mask = ["project", "title", "authors", "is_public", "description", "urls"]
    client = Client(headers=get_consumer(request))  # sets/returns global variable
    entries = client.projects.get_entries(_fields=mask).result()["data"]
    for entry in entries:
        authors = entry["authors"].strip().split(",", 1)
        if len(authors) > 1:
            authors[1] = authors[1].strip()
        entry["authors"] = authors
        entry["description"] = entry["description"].split(".", 1)[0] + "."
        ctx["landing_pages"].append(
            entry
        )  # visibility governed by is_public flag and X-Consumer-Groups header
    return render(request, "home.html", ctx.flatten()) 

def create_celeba(tfrecord_dir, celeba_dir, cx=89, cy=121):
    print('Loading CelebA from "%s"' % celeba_dir)
    glob_pattern = os.path.join(celeba_dir, 'img_align_celeba_png', '*.png')
    image_filenames = sorted(glob.glob(glob_pattern))
    expected_images = 202599
    if len(image_filenames) != expected_images:
        error('Expected to find %d images' % expected_images)
    
    with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
        order = tfr.choose_shuffled_order()
        for idx in range(order.size):
            img = np.asarray(PIL.Image.open(image_filenames[order[idx]]))
            assert img.shape == (218, 178, 3)
            img = img[cy - 64 : cy + 64, cx - 64 : cx + 64]
            img = img.transpose(2, 0, 1) # HWC => CHW
            tfr.add_image(img)

#---------------------------------------------------------------------------- 

def _get_random_object(self, num_objects, test):
    """Randomly choose an object urdf from the random_urdfs directory.

    Args:
      num_objects:
        Number of graspable objects.

    Returns:
      A list of urdf filenames.
    """
    if test:
      urdf_pattern = os.path.join(self._urdfRoot, 'random_urdfs/*0/*.urdf')
    else:
      urdf_pattern = os.path.join(self._urdfRoot, 'random_urdfs/*[^0]/*.urdf')
    found_object_directories = glob.glob(urdf_pattern)
    total_num_objects = len(found_object_directories)
    selected_objects = np.random.choice(np.arange(total_num_objects),
                                        num_objects)
    selected_objects_filenames = []
    for object_index in selected_objects:
      selected_objects_filenames += [found_object_directories[object_index]]
    return selected_objects_filenames 

def main():
    quiet = len(sys.argv) >= 2 and sys.argv[1] == '-q'

    file_names = glob.glob('openfermion-cirq/**/*.py', recursive=True)
    failed, attempted = run_tests(file_names,
                                  include_modules=True,
                                  include_local=False,
                                  quiet=quiet)

    if failed != 0:
        print(
            shell_tools.highlight(
                f'Failed: {failed} failed, '
                '{attempted - failed} passed, {attempted} total',
                shell_tools.RED))
        sys.exit(1)
    else:
        print(shell_tools.highlight(f'Passed: {attempted}', shell_tools.GREEN))
        sys.exit(0) 

def copy_dependencies(f):
    config_path = '/etc/yangcatalog/yangcatalog.conf'
    config = ConfigParser.ConfigParser()
    config._interpolation = ConfigParser.ExtendedInterpolation()
    config.read(config_path)
    yang_models = config.get('Directory-Section', 'save-file-dir')
    tmp = config.get('Directory-Section', 'temp')
    out = f.getvalue()
    letters = string.ascii_letters
    suffix = ''.join(random.choice(letters) for i in range(8))
    dep_dir = '{}/yangvalidator-dependencies-{}'.format(tmp, suffix)
    os.mkdir(dep_dir)
    dependencies = out.split(':')[1].strip().split(' ')
    for dep in dependencies:
        for file in glob.glob(r'{}/{}*.yang'.format(yang_models, dep)):
            shutil.copy(file, dep_dir)
    return dep_dir 

def get_last_checkpoint(self):
        """
        Assumes that the last checpoint has a higher checkpoint id. Checkpoint will be saved in this exact format
        model_<checkpint_id>.ckpt Eg - model_100.ckpt

        :return:
        """
        '''

        '''
        self.present_checkpoints = glob.glob(self.get_checkpoint_location() + '/*.ckpt')
        if len(self.present_checkpoints) != 0:
            present_ids = [self.__get_id(ckpt) for ckpt in self.present_checkpoints]
            # sort the ID's and return the model for the last ID
            present_ids.sort()
            self.last_id = present_ids[-1]
            self.last_ckpt = self.get_checkpoint_location() + '/model_' +\
                str(self.last_id) + '.ckpt'

        return self.last_ckpt 

def delete_previous_checkpoints(self, num_previous=5):
        """
        Deletes all previous checkpoints that are <num_previous> before the present checkpoint.
        This is done to prevent blowing out of memory due to too many checkpoints
        
        :param num_previous:
        :return:
        """
        self.present_checkpoints = glob.glob(self.get_checkpoint_location() + '/*.ckpt')
        if len(self.present_checkpoints) > num_previous:
            present_ids = [self.__get_id(ckpt) for ckpt in self.present_checkpoints]
            present_ids.sort()
            ids_2_delete = present_ids[0:len(present_ids) - num_previous]
            for ckpt_id in ids_2_delete:
                ckpt_file_nm = self.get_checkpoint_location() + '/model_' + str(ckpt_id) + '.ckpt'
                os.remove(ckpt_file_nm) 

def __init__(self, path, img_size=416, half=False):
        path = str(Path(path))  # os-agnostic
        files = []
        if os.path.isdir(path):
            files = sorted(glob.glob(os.path.join(path, '*.*')))
        elif os.path.isfile(path):
            files = [path]

        images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
        videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
        nI, nV = len(images), len(videos)

        self.img_size = img_size
        self.files = images + videos
        self.nF = nI + nV  # number of files
        self.video_flag = [False] * nI + [True] * nV
        self.mode = 'images'
        self.half = half  # half precision fp16 images
        if any(videos):
            self.new_video(videos[0])  # new video
        else:
            self.cap = None
        assert self.nF > 0, 'No images or videos found in ' + path 

def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
    # Makes single-class coco datasets. from utils.utils import *; coco_single_class_labels()
    if os.path.exists('new/'):
        shutil.rmtree('new/')  # delete output folder
    os.makedirs('new/')  # make new output folder
    os.makedirs('new/labels/')
    os.makedirs('new/images/')
    for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
        with open(file, 'r') as f:
            labels = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
        i = labels[:, 0] == label_class
        if any(i):
            img_file = file.replace('labels', 'images').replace('txt', 'jpg')
            labels[:, 0] = 0  # reset class to 0
            with open('new/images.txt', 'a') as f:  # add image to dataset list
                f.write(img_file + '\n')
            with open('new/labels/' + Path(file).name, 'a') as f:  # write label
                for l in labels[i]:
                    f.write('%g %.6f %.6f %.6f %.6f\n' % tuple(l))
            shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg'))  # copy images 

def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results()
    # Plot training results files 'results*.txt'
    fig, ax = plt.subplots(2, 5, figsize=(14, 7))
    ax = ax.ravel()
    s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
         'val GIoU', 'val Objectness', 'val Classification', 'mAP', 'F1']
    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
        n = results.shape[1]  # number of rows
        x = range(start, min(stop, n) if stop else n)
        for i in range(10):
            y = results[i, x]
            if i in [0, 1, 2, 5, 6, 7]:
                y[y == 0] = np.nan  # dont show zero loss values
            ax[i].plot(x, y, marker='.', label=f.replace('.txt', ''))
            ax[i].set_title(s[i])
            if i in [5, 6, 7]:  # share train and val loss y axes
                ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])

    fig.tight_layout()
    ax[1].legend()
    fig.savefig('results.png', dpi=200) 

def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_results_overlay()
    # Plot training results files 'results*.txt', overlaying train and val losses
    s = ['train', 'train', 'train', 'Precision', 'mAP', 'val', 'val', 'val', 'Recall', 'F1']  # legends
    t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
        n = results.shape[1]  # number of rows
        x = range(start, min(stop, n) if stop else n)
        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5))
        ax = ax.ravel()
        for i in range(5):
            for j in [i, i + 5]:
                y = results[j, x]
                if i in [0, 1, 2]:
                    y[y == 0] = np.nan  # dont show zero loss values
                ax[i].plot(x, y, marker='.', label=s[j])
            ax[i].set_title(t[i])
            ax[i].legend()
            ax[i].set_ylabel(f) if i == 0 else None  # add filename
        fig.tight_layout()
        fig.savefig(f.replace('.txt', '.png'), dpi=200) 

def scan(self, path, exclude=[]) -> List[str]:
        """Scan path for matching files.

        :param path: the path to scan
        :param exclude: a list of directories to exclude

        :return: a list of sorted filenames
        """
        res = []
        path = path.rstrip("/").rstrip("\\")
        for pat in self.input_patterns:
            res.extend(glob.glob(path + os.sep + pat, recursive=True))

        res = list(filter(lambda p: os.path.isfile(p), res))

        if exclude:
            def excluded(path):
                for e in exclude:
                    if path.startswith(e):
                        return True
                return False

            res = list(filter(lambda p: not excluded(p), res))

        return sorted(res) 

def globs(pathnames, dirpath='.'):
    import glob
    files = []
    for pathname in pathnames:
        files.extend(glob.glob(os.path.join(dirpath, pathname)))
    return files 

def test_deploy_utils(self):
        deploy_utils_bindir = os.path.join(pkg_root, "aegea", "rootfs.skel", "usr", "bin")
        for script in glob.glob(deploy_utils_bindir + "/aegea*"):
            self.call([script, "--help"], expect=[dict(return_codes=[0, 1])])
        for script in "aegea-deploy-pilot", "aegea-git-ssh-helper":
            self.call(os.path.join(deploy_utils_bindir, script),
                      expect=[dict(return_codes=[2], stderr="(required|too few)")]) 

def read_annotation(path_name, n_threads=10):
  """Read annotations with real multiple processes."""
  input_paths = glob.glob(path_name)
  pool = multiprocessing.Pool(n_threads)
  try:
    dict_list = pool.map(read_annotation_from_one_split, input_paths)
  finally:
    pool.close()
    pool.join()

  final_dict = {}
  for single_dict in dict_list:
    final_dict.update(single_dict)

  return final_dict 

def run(mpfile):
    identifier = mpfile.ids[0]
    xcol, ycol = "V [V]", "J {}°C {} [mA/cm²]"
    full_df = None
    for fn in sorted(glob(os.path.join("Data", "Figure 4", "*_01_DIV.txt"))):
        with open(fn, "r") as f:
            name = os.path.splitext(os.path.basename(fn))[0]
            body = "\n".join(["\t".join([xcol, ycol]), f.read()])
            df = (
                read_csv(body, sep="\t")
                .apply(to_numeric, errors="coerce")
                .sort_values(by=[xcol])
            )
            if full_df is None:
                full_df = df[xcol].to_frame()

            offset = 0.0
            if "fwd_dB_p3" in name:
                offset = -6.70273000e-11
            elif "rev_dB_p3" in name:
                offset = 4.49694000e-10
            elif "fwd_dG_p6" in name:
                offset = -8.90037000e-11
            elif "rev_dG_p6" in name:
                offset = 8.42196000e-10

            temp = name[4:].split("CZnO", 1)[0]
            direction = "fwd" if "fwd" in name else "rev"
            col = ycol.format(temp, direction)
            full_df[col] = (df[ycol] + offset).abs() * 1000.0 / 0.045

    mpfile.add_data_table(identifier, full_df, "JV|dark") 

def extra_requirements(cls, glob_pattern):
        before, after = glob_pattern.split("*", 1)
        pattern = os.path.join(os.path.dirname(__file__), glob_pattern)
        requirements = {}
        for path in glob.glob(pattern):
            name = path[len(before) : -len(after)]
            requirements[name] = cls.requirements(path)
        return requirements 

def create_from_images(tfrecord_dir, image_dir, label_dir, shuffle):
    print('Loading images from "%s"' % image_dir)
    image_filenames = sorted(glob.glob(os.path.join(image_dir, '*')))
    if len(image_filenames) == 0:
        error('No input images found')
        
    img = np.asarray(PIL.Image.open(image_filenames[0]))
    resolution = img.shape[0]
    channels = img.shape[2] if img.ndim == 3 else 1
    if img.shape[1] != resolution:
        error('Input images must have the same width and height')
    if resolution != 2 ** int(np.floor(np.log2(resolution))):
        error('Input image resolution must be a power-of-two')
    if channels not in [1, 3]:
        error('Input images must be stored as RGB or grayscale')

    try:
        with open(label_dir, 'rb') as file:
            labels = pickle.load(file)
    except:
        error('Label file was not found')
    
    with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
        order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames))
        reordered_names = []
        for idx in range(order.size):
            image_filename = image_filenames[order[idx]]
            img = np.asarray(PIL.Image.open(image_filename))
            if channels == 1:
                img = img[np.newaxis, :, :] # HW => CHW
            else:
                img = img.transpose(2, 0, 1) # HWC => CHW
            tfr.add_image(img)
            reordered_names.append(os.path.basename(image_filename))
        reordered_labels = []
        for key in reordered_names:
            reordered_labels += [labels[key]]
        reordered_labels = np.stack(reordered_labels, 0)
        tfr.add_labels(reordered_labels)

#---------------------------------------------------------------------------- 

def create_result_subdir(result_dir, desc):

    # Select run ID and create subdir.
    while True:
        run_id = 0
        for fname in glob.glob(os.path.join(result_dir, '*')):
            try:
                fbase = os.path.basename(fname)
                ford = int(fbase[:fbase.find('-')])
                run_id = max(run_id, ford + 1)
            except ValueError:
                pass

        result_subdir = os.path.join(result_dir, '%03d-%s' % (run_id, desc))
        try:
            os.makedirs(result_subdir)
            break
        except OSError:
            if os.path.isdir(result_subdir):
                continue
            raise

    print("Saving results to", result_subdir)
    set_output_log_file(os.path.join(result_subdir, 'log.txt'))

    # Export config.
    try:
        with open(os.path.join(result_subdir, 'config.txt'), 'wt') as fout:
            for k, v in sorted(config.__dict__.items()):
                if not k.startswith('_'):
                    fout.write("%s = %s\n" % (k, str(v)))
    except:
        pass

    return result_subdir 

def list_network_pkls(run_id_or_result_subdir, include_final=True):
    result_subdir = locate_result_subdir(run_id_or_result_subdir)
    pkls = sorted(glob.glob(os.path.join(result_subdir, 'network-*.pkl')))
    if len(pkls) >= 1 and os.path.basename(pkls[0]) == 'network-final.pkl':
        if include_final:
            pkls.append(pkls[0])
        del pkls[0]
    return pkls 

def compute_victim(self, lfw_160_path, name):
        imgfolder = os.path.join(lfw_160_path, name)
        assert os.path.isdir(imgfolder), imgfolder
        images = glob.glob(os.path.join(imgfolder, '*.png')) + glob.glob(os.path.join(imgfolder, '*.jpg'))
        image_batch = [cv2.imread(f, cv2.IMREAD_COLOR)[:, :, ::-1] for f in images]
        for img in image_batch:
            assert img.shape[0] == 160 and img.shape[1] == 160, \
                "--data should only contain 160x160 images. Please read the README carefully."
        embeddings = self.eval_embeddings(image_batch)
        self.victim_embeddings = embeddings
        return embeddings