How to use get_colored method in Slash

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SemanticSegmentation.py

Source:SemanticSegmentation.py

1"""2Performs a semantic segmentation task with 19 classes taken from the Cityscapes dataset.3This method creates a function given a model. The function can then be used on data to perform semantic segmentation. 4"""5import torch.nn as nn6import torch7import numpy as np8colors_data = [ [0,  0,  0],9          [128, 64,128],10          [244, 35,232],11          [70, 70, 70],12          [102,102,156],13          [190,153,153],14          [153,153,153],15          [250,170, 30],16          [250,170, 30],17          [107,142, 35],18          [152,251,152],19          [ 70,130,180],20          [220, 20, 60],21          [255,  0,  0],22          [ 0,  0,142],23          [  0,  0, 70],24          [  0,  0, 70],25          [  0, 80,100],26          [  0,  0,230],27          [119, 11, 32] ]28def convert_to_RGB(y):29    """30    Identifies the class by taking the argmax across values.31    Replaces the class id by the corresponding color.32    33    Returns 34        the identified classes `idtfd` with each pixel assigned with a class i.e. shape of (N, C, H, W) with C the class id,35        the colored images `imgs` with each pixel assigned a color corresponding to the class i.e. shape (N, 3, H, W)36    """37    # n_classes = 1938    n_classes = y.shape[1]39    40    # Create color attribution for coloring41    colors = np.array(colors_data[:n_classes])42    print(f"Using {len(colors)} classes.")43    44    # Identify the class45    idtfd = y.argmax(dim=1).numpy()46    # Create the colored images47    imgs = colors[idtfd]48    imgs = imgs.transpose(0, 3, 1, 2)49    50    return idtfd, imgs51def get_semantic_RGB(model):52    """53    Creates a semantic segmentation task for a single model. 54    Returns a fonction that can be used on a list of RGB (3 channels) images.55    56    Note: the code makes use of the model as an implementation of the `torch.nn.Module`.57    58        semantic_task = get_semantic(UNet)59        preds = semantic_task(imgs)60        preds, colored_preds = semantic_task(imgs, get_colored=True)61        preds, classified_preds = semantic_task(imgs, get_classification=True)62    Parameters:63        model: torch.Tensor64            The model to use for the segmentation65    """66    67    if not isinstance(model, nn.Module):68        raise AttributeError("The passed model should be an implementation of the `torch.nn.Module` class.")69    70    def semantic_segmentation_RGB(imgs, get_colored=False, get_classification=False):71        """72        Performs the semantic segmentation task with the input model.73        Note: the code makes use of the model as an implementation of the `torch.nn.Module`.74    75            semantic_task = get_semantic(UNet)76            preds = semantic_task(imgs)77            preds, colored_preds = semantic_task(imgs, get_colored=True)78            preds, classified_preds = semantic_task(imgs, get_classification=True)79            80        Parameters: 81            imgs: torch.Tensor82                The list of RGB images to segment.83            get_colored: bool (default=False)84                Parameter indicating wether to return the colored images as well. One color per class.85            get_classification: bool (default=False)86                Parameter indicating wether to return the classified pixels. Most confident class per pixel.87                88        """89        if not isinstance(imgs, torch.Tensor):90            raise AttributeError("The input for the segmentation task should be a tensor of images.")91        if not imgs.shape[1] == 3 or len(imgs.shape)!=4:92            raise AttributeError("The input should have the following dimensons (N, C, H, W), respectively number of images, channels, height and width.")93        94        # Setting the model in eval mode95        model.eval()96                97        # running the images through the model98        results = model(imgs)99        100        # Identifying class and coloring.101        idtfd, colored = convert_to_RGB(results)102        103        output = [results]104        105        if get_colored:106            output.append(colored)107        if get_classification:108            output.append(idtfd)109        if len(output)==1:110            return output[0]111        return output112        ...

cfparser.py

Source:cfparser.py

...39    for s in sample_test_o:40        sto.append(s.get_text().replace('Output',''))41    tab = '    '42    paged = '\n'43    paged += get_colored(tab+title, "blue")+"\n"44    paged += get_colored(tab+"Time: ",'blue')+get_colored(time_limit, color='magenta')+"\n"45    paged += get_colored(tab+"Memory: ",'blue')+get_colored(mem_limit, color='magenta')+"\n\n\n"46    paged += get_colored(tab+"Problem Statement:\n", 'blue')+"\n"47    for s in state:48        paged += get_colored(tab+"".join(textwrap.wrap(s, 150))+"\n")+"\n"49    paged += get_colored(tab+"Input:\n", 'blue')+"\n"50    for s in input_s:51        paged += get_colored(tab+"".join(textwrap.wrap(s, 150))+"\n")+"\n"52    paged += get_colored(tab+"Output:\n", 'blue')+"\n"53    for s in output_s:54        paged += get_colored(tab+"\n\t".join(textwrap.wrap(s, 150))+"\n")+"\n"55    for i in range(len(sti)):56        paged += get_colored(tab+"Sample Input "+str(i)+":", 'blue')+"\n"57        paged += get_colored(tab+sti[i].replace('\n', '\n\t '), 'cyan')+"\n"58        paged += get_colored(tab+"Sample Output "+str(i)+":", 'blue')+"\n"59        paged += get_colored(tab+sto[i].replace('\n', '\n\t '), 'green')+"\n"60    # print(paged)61    print(LatexNodes2Text().latex_to_text(paged))62    # pydoc.pager(paged)...

color_string.py

Source:color_string.py

1import colorama2import functools3class ColorStringBase(object):4    def get_colored(self):5        raise NotImplementedError() # pragma: no cover6    def __repr__(self):7        return repr(str(self))8    def __add__(self, other):9        return ColorCompoundString(self, other)10    def __radd__(self, other):11        return ColorCompoundString(other, self)12class ColorString(ColorStringBase):13    def __init__(self, string, color):14        super(ColorString, self).__init__()15        self._string = string16        self._color = color17    def __len__(self):18        return len(self._string)19    def ljust(self, *args):20        return ColorString(self._string.ljust(*args), self._color)21    @classmethod22    def get_formatter(cls, color):23        return functools.partial(cls, color=color)24    def __mod__(self, values):25        return ColorString(self._string % values, self._color)26    def __str__(self):27        return str(self._string)28    def get_colored(self):29        return "{}{}{}".format(getattr(colorama.Fore, self._color.upper()), self._string, colorama.Fore.RESET) # pylint: disable=no-member30class ColorCompoundString(ColorStringBase):31    def __init__(self, *strings):32        super(ColorCompoundString, self).__init__()33        self._strings = strings34    def __str__(self):35        return ''.join(str(x) for x in  self._strings)36    def __len__(self):37        return sum(len(s) for s in self._strings)38    def ljust(self):39        raise NotImplementedError() # pragma: no cover40    def get_colored(self):...

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