Method __init__

utils/misc.py:212–273

:val_len: num validation images :tensorboard: push summary to tensorboard :webpage: generate a summary html page :webpage_fn: name of webpage file :dump_all_images: dump all (validation) images, e.g. for video :dump_num: number of images to dump if no

(self, val_len, tensorboard=True, write_webpage=True,
                 webpage_fn='index.html', dump_all_images=False, dump_assets=False,
                 dump_err_prob=False, dump_num=10, dump_for_auto_labelling=False, 
                 dump_for_submission=False)

    writes the images out to disk.
    """
    def __init__(self, val_len, tensorboard=True, write_webpage=True,
                 webpage_fn='index.html', dump_all_images=False, dump_assets=False,
                 dump_err_prob=False, dump_num=10, dump_for_auto_labelling=False, 
                 dump_for_submission=False):
        """
        :val_len: num validation images
        :tensorboard: push summary to tensorboard
        :webpage: generate a summary html page
        :webpage_fn: name of webpage file
        :dump_all_images: dump all (validation) images, e.g. for video
        :dump_num: number of images to dump if not dumping all
        :dump_assets: dump attention maps
        """
        self.val_len = val_len
        self.tensorboard = tensorboard
        self.write_webpage = write_webpage
        self.webpage_fn = os.path.join(cfg.RESULT_DIR,
                                       'best_images', webpage_fn)
        self.dump_assets = dump_assets
        self.dump_for_auto_labelling = dump_for_auto_labelling
        self.dump_for_submission = dump_for_submission

        self.viz_frequency = max(1, val_len // dump_num)
        if dump_all_images:
            self.dump_frequency = 1
        else:
            self.dump_frequency = self.viz_frequency

        inv_mean = [-mean / std for mean, std in zip(cfg.DATASET.MEAN,
                                                   cfg.DATASET.STD)]
        inv_std = [1 / std for std in cfg.DATASET.STD]
        self.inv_normalize = standard_transforms.Normalize(
            mean=inv_mean, std=inv_std
        )

        if self.dump_for_submission:
            self.save_dir = os.path.join(cfg.RESULT_DIR, 'submit')
        elif self.dump_for_auto_labelling:
            self.save_dir = os.path.join(cfg.RESULT_DIR)
        else:
            self.save_dir = os.path.join(cfg.RESULT_DIR, 'best_images')

        os.makedirs(self.save_dir, exist_ok=True)

        self.imgs_to_tensorboard = []
        self.imgs_to_webpage = []

        if cfg.DATASET.NAME == 'cityscapes':
            # If all images of a dataset are identical, as in cityscapes,
            # there's no need to crop the images before tiling them into a
            # grid for displaying in tensorboard. Otherwise, need to center
            # crop the images
            self.visualize = standard_transforms.Compose([
                standard_transforms.Resize(384),
                standard_transforms.ToTensor()
            ])
        else:
            self.visualize = standard_transforms.Compose([