Arulkumar03/Wheat_HEAD_Detection_Counting_ComputerVision_Model

1# -*- coding: utf-8 -*-2# Copyright (c) Facebook, Inc. and its affiliates.3 4"""5Note:6For your custom dataset, there is no need to hard-code metadata anywhere in the code.7For example, for COCO-format dataset, metadata will be obtained automatically8when calling `load_coco_json`. For other dataset, metadata may also be obtained in other ways9during loading.10 11However, we hard-coded metadata for a few common dataset here.12The only goal is to allow users who don't have these dataset to use pre-trained models.13Users don't have to download a COCO json (which contains metadata), in order to visualize a14COCO model (with correct class names and colors).15"""16 17 18# All coco categories, together with their nice-looking visualization colors19# It's from https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json20COCO_CATEGORIES = [21    {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"},22    {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"},23    {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"},24    {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"},25    {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"},26    {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"},27    {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"},28    {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"},29    {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"},30    {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"},31    {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"},32    {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"},33    {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"},34    {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"},35    {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"},36    {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"},37    {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"},38    {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"},39    {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"},40    {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"},41    {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"},42    {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"},43    {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"},44    {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"},45    {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"},46    {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"},47    {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"},48    {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"},49    {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"},50    {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"},51    {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"},52    {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"},53    {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"},54    {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"},55    {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"},56    {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"},57    {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"},58    {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"},59    {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"},60    {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"},61    {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"},62    {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"},63    {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"},64    {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"},65    {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"},66    {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"},67    {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"},68    {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"},69    {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"},70    {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"},71    {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"},72    {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"},73    {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"},74    {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"},75    {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"},76    {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"},77    {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"},78    {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"},79    {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"},80    {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"},81    {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"},82    {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"},83    {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"},84    {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"},85    {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"},86    {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"},87    {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"},88    {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"},89    {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"},90    {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"},91    {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"},92    {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"},93    {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"},94    {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"},95    {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"},96    {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"},97    {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"},98    {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"},99    {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"},100    {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"},101    {"color": [255, 255, 128], "isthing": 0, "id": 92, "name": "banner"},102    {"color": [147, 211, 203], "isthing": 0, "id": 93, "name": "blanket"},103    {"color": [150, 100, 100], "isthing": 0, "id": 95, "name": "bridge"},104    {"color": [168, 171, 172], "isthing": 0, "id": 100, "name": "cardboard"},105    {"color": [146, 112, 198], "isthing": 0, "id": 107, "name": "counter"},106    {"color": [210, 170, 100], "isthing": 0, "id": 109, "name": "curtain"},107    {"color": [92, 136, 89], "isthing": 0, "id": 112, "name": "door-stuff"},108    {"color": [218, 88, 184], "isthing": 0, "id": 118, "name": "floor-wood"},109    {"color": [241, 129, 0], "isthing": 0, "id": 119, "name": "flower"},110    {"color": [217, 17, 255], "isthing": 0, "id": 122, "name": "fruit"},111    {"color": [124, 74, 181], "isthing": 0, "id": 125, "name": "gravel"},112    {"color": [70, 70, 70], "isthing": 0, "id": 128, "name": "house"},113    {"color": [255, 228, 255], "isthing": 0, "id": 130, "name": "light"},114    {"color": [154, 208, 0], "isthing": 0, "id": 133, "name": "mirror-stuff"},115    {"color": [193, 0, 92], "isthing": 0, "id": 138, "name": "net"},116    {"color": [76, 91, 113], "isthing": 0, "id": 141, "name": "pillow"},117    {"color": [255, 180, 195], "isthing": 0, "id": 144, "name": "platform"},118    {"color": [106, 154, 176], "isthing": 0, "id": 145, "name": "playingfield"},119    {"color": [230, 150, 140], "isthing": 0, "id": 147, "name": "railroad"},120    {"color": [60, 143, 255], "isthing": 0, "id": 148, "name": "river"},121    {"color": [128, 64, 128], "isthing": 0, "id": 149, "name": "road"},122    {"color": [92, 82, 55], "isthing": 0, "id": 151, "name": "roof"},123    {"color": [254, 212, 124], "isthing": 0, "id": 154, "name": "sand"},124    {"color": [73, 77, 174], "isthing": 0, "id": 155, "name": "sea"},125    {"color": [255, 160, 98], "isthing": 0, "id": 156, "name": "shelf"},126    {"color": [255, 255, 255], "isthing": 0, "id": 159, "name": "snow"},127    {"color": [104, 84, 109], "isthing": 0, "id": 161, "name": "stairs"},128    {"color": [169, 164, 131], "isthing": 0, "id": 166, "name": "tent"},129    {"color": [225, 199, 255], "isthing": 0, "id": 168, "name": "towel"},130    {"color": [137, 54, 74], "isthing": 0, "id": 171, "name": "wall-brick"},131    {"color": [135, 158, 223], "isthing": 0, "id": 175, "name": "wall-stone"},132    {"color": [7, 246, 231], "isthing": 0, "id": 176, "name": "wall-tile"},133    {"color": [107, 255, 200], "isthing": 0, "id": 177, "name": "wall-wood"},134    {"color": [58, 41, 149], "isthing": 0, "id": 178, "name": "water-other"},135    {"color": [183, 121, 142], "isthing": 0, "id": 180, "name": "window-blind"},136    {"color": [255, 73, 97], "isthing": 0, "id": 181, "name": "window-other"},137    {"color": [107, 142, 35], "isthing": 0, "id": 184, "name": "tree-merged"},138    {"color": [190, 153, 153], "isthing": 0, "id": 185, "name": "fence-merged"},139    {"color": [146, 139, 141], "isthing": 0, "id": 186, "name": "ceiling-merged"},140    {"color": [70, 130, 180], "isthing": 0, "id": 187, "name": "sky-other-merged"},141    {"color": [134, 199, 156], "isthing": 0, "id": 188, "name": "cabinet-merged"},142    {"color": [209, 226, 140], "isthing": 0, "id": 189, "name": "table-merged"},143    {"color": [96, 36, 108], "isthing": 0, "id": 190, "name": "floor-other-merged"},144    {"color": [96, 96, 96], "isthing": 0, "id": 191, "name": "pavement-merged"},145    {"color": [64, 170, 64], "isthing": 0, "id": 192, "name": "mountain-merged"},146    {"color": [152, 251, 152], "isthing": 0, "id": 193, "name": "grass-merged"},147    {"color": [208, 229, 228], "isthing": 0, "id": 194, "name": "dirt-merged"},148    {"color": [206, 186, 171], "isthing": 0, "id": 195, "name": "paper-merged"},149    {"color": [152, 161, 64], "isthing": 0, "id": 196, "name": "food-other-merged"},150    {"color": [116, 112, 0], "isthing": 0, "id": 197, "name": "building-other-merged"},151    {"color": [0, 114, 143], "isthing": 0, "id": 198, "name": "rock-merged"},152    {"color": [102, 102, 156], "isthing": 0, "id": 199, "name": "wall-other-merged"},153    {"color": [250, 141, 255], "isthing": 0, "id": 200, "name": "rug-merged"},154]155 156# fmt: off157COCO_PERSON_KEYPOINT_NAMES = (158    "nose",159    "left_eye", "right_eye",160    "left_ear", "right_ear",161    "left_shoulder", "right_shoulder",162    "left_elbow", "right_elbow",163    "left_wrist", "right_wrist",164    "left_hip", "right_hip",165    "left_knee", "right_knee",166    "left_ankle", "right_ankle",167)168# fmt: on169 170# Pairs of keypoints that should be exchanged under horizontal flipping171COCO_PERSON_KEYPOINT_FLIP_MAP = (172    ("left_eye", "right_eye"),173    ("left_ear", "right_ear"),174    ("left_shoulder", "right_shoulder"),175    ("left_elbow", "right_elbow"),176    ("left_wrist", "right_wrist"),177    ("left_hip", "right_hip"),178    ("left_knee", "right_knee"),179    ("left_ankle", "right_ankle"),180)181 182# rules for pairs of keypoints to draw a line between, and the line color to use.183KEYPOINT_CONNECTION_RULES = [184    # face185    ("left_ear", "left_eye", (102, 204, 255)),186    ("right_ear", "right_eye", (51, 153, 255)),187    ("left_eye", "nose", (102, 0, 204)),188    ("nose", "right_eye", (51, 102, 255)),189    # upper-body190    ("left_shoulder", "right_shoulder", (255, 128, 0)),191    ("left_shoulder", "left_elbow", (153, 255, 204)),192    ("right_shoulder", "right_elbow", (128, 229, 255)),193    ("left_elbow", "left_wrist", (153, 255, 153)),194    ("right_elbow", "right_wrist", (102, 255, 224)),195    # lower-body196    ("left_hip", "right_hip", (255, 102, 0)),197    ("left_hip", "left_knee", (255, 255, 77)),198    ("right_hip", "right_knee", (153, 255, 204)),199    ("left_knee", "left_ankle", (191, 255, 128)),200    ("right_knee", "right_ankle", (255, 195, 77)),201]202 203# All Cityscapes categories, together with their nice-looking visualization colors204# It's from https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py  # noqa205CITYSCAPES_CATEGORIES = [206    {"color": (128, 64, 128), "isthing": 0, "id": 7, "trainId": 0, "name": "road"},207    {"color": (244, 35, 232), "isthing": 0, "id": 8, "trainId": 1, "name": "sidewalk"},208    {"color": (70, 70, 70), "isthing": 0, "id": 11, "trainId": 2, "name": "building"},209    {"color": (102, 102, 156), "isthing": 0, "id": 12, "trainId": 3, "name": "wall"},210    {"color": (190, 153, 153), "isthing": 0, "id": 13, "trainId": 4, "name": "fence"},211    {"color": (153, 153, 153), "isthing": 0, "id": 17, "trainId": 5, "name": "pole"},212    {"color": (250, 170, 30), "isthing": 0, "id": 19, "trainId": 6, "name": "traffic light"},213    {"color": (220, 220, 0), "isthing": 0, "id": 20, "trainId": 7, "name": "traffic sign"},214    {"color": (107, 142, 35), "isthing": 0, "id": 21, "trainId": 8, "name": "vegetation"},215    {"color": (152, 251, 152), "isthing": 0, "id": 22, "trainId": 9, "name": "terrain"},216    {"color": (70, 130, 180), "isthing": 0, "id": 23, "trainId": 10, "name": "sky"},217    {"color": (220, 20, 60), "isthing": 1, "id": 24, "trainId": 11, "name": "person"},218    {"color": (255, 0, 0), "isthing": 1, "id": 25, "trainId": 12, "name": "rider"},219    {"color": (0, 0, 142), "isthing": 1, "id": 26, "trainId": 13, "name": "car"},220    {"color": (0, 0, 70), "isthing": 1, "id": 27, "trainId": 14, "name": "truck"},221    {"color": (0, 60, 100), "isthing": 1, "id": 28, "trainId": 15, "name": "bus"},222    {"color": (0, 80, 100), "isthing": 1, "id": 31, "trainId": 16, "name": "train"},223    {"color": (0, 0, 230), "isthing": 1, "id": 32, "trainId": 17, "name": "motorcycle"},224    {"color": (119, 11, 32), "isthing": 1, "id": 33, "trainId": 18, "name": "bicycle"},225]226 227# fmt: off228ADE20K_SEM_SEG_CATEGORIES = [229    "wall", "building", "sky", "floor", "tree", "ceiling", "road, route", "bed", "window ", "grass", "cabinet", "sidewalk, pavement", "person", "earth, ground", "door", "table", "mountain, mount", "plant", "curtain", "chair", "car", "water", "painting, picture", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock, stone", "wardrobe, closet, press", "lamp", "tub", "rail", "cushion", "base, pedestal, stand", "box", "column, pillar", "signboard, sign", "chest of drawers, chest, bureau, dresser", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator, icebox", "grandstand, covered stand", "path", "stairs", "runway", "case, display case, showcase, vitrine", "pool table, billiard table, snooker table", "pillow", "screen door, screen", "stairway, staircase", "river", "bridge, span", "bookcase", "blind, screen", "coffee table", "toilet, can, commode, crapper, pot, potty, stool, throne", "flower", "book", "hill", "bench", "countertop", "stove", "palm, palm tree", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel, hut, hutch, shack, shanty", "bus", "towel", "light", "truck", "tower", "chandelier", "awning, sunshade, sunblind", "street lamp", "booth", "tv", "plane", "dirt track", "clothes", "pole", "land, ground, soil", "bannister, banister, balustrade, balusters, handrail", "escalator, moving staircase, moving stairway", "ottoman, pouf, pouffe, puff, hassock", "bottle", "buffet, counter, sideboard", "poster, posting, placard, notice, bill, card", "stage", "van", "ship", "fountain", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "canopy", "washer, automatic washer, washing machine", "plaything, toy", "pool", "stool", "barrel, cask", "basket, handbasket", "falls", "tent", "bag", "minibike, motorbike", "cradle", "oven", "ball", "food, solid food", "step, stair", "tank, storage tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket, cover", "sculpture", "hood, exhaust hood", "sconce", "vase", "traffic light", "tray", "trash can", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass, drinking glass", "clock", "flag", # noqa230]231# After processed by `prepare_ade20k_sem_seg.py`, id 255 means ignore232# fmt: on233 234 235def _get_coco_instances_meta():236    thing_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 1]237    thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]238    assert len(thing_ids) == 80, len(thing_ids)239    # Mapping from the incontiguous COCO category id to an id in [0, 79]240    thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}241    thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]242    ret = {243        "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,244        "thing_classes": thing_classes,245        "thing_colors": thing_colors,246    }247    return ret248 249 250def _get_coco_panoptic_separated_meta():251    """252    Returns metadata for "separated" version of the panoptic segmentation dataset.253    """254    stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0]255    assert len(stuff_ids) == 53, len(stuff_ids)256 257    # For semantic segmentation, this mapping maps from contiguous stuff id258    # (in [0, 53], used in models) to ids in the dataset (used for processing results)259    # The id 0 is mapped to an extra category "thing".260    stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)}261    # When converting COCO panoptic annotations to semantic annotations262    # We label the "thing" category to 0263    stuff_dataset_id_to_contiguous_id[0] = 0264 265    # 54 names for COCO stuff categories (including "things")266    stuff_classes = ["things"] + [267        k["name"].replace("-other", "").replace("-merged", "")268        for k in COCO_CATEGORIES269        if k["isthing"] == 0270    ]271 272    # NOTE: I randomly picked a color for things273    stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0]274    ret = {275        "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,276        "stuff_classes": stuff_classes,277        "stuff_colors": stuff_colors,278    }279    ret.update(_get_coco_instances_meta())280    return ret281 282 283def _get_builtin_metadata(dataset_name):284    if dataset_name == "coco":285        return _get_coco_instances_meta()286    if dataset_name == "coco_panoptic_separated":287        return _get_coco_panoptic_separated_meta()288    elif dataset_name == "coco_panoptic_standard":289        meta = {}290        # The following metadata maps contiguous id from [0, #thing categories +291        # #stuff categories) to their names and colors. We have to replica of the292        # same name and color under "thing_*" and "stuff_*" because the current293        # visualization function in D2 handles thing and class classes differently294        # due to some heuristic used in Panoptic FPN. We keep the same naming to295        # enable reusing existing visualization functions.296        thing_classes = [k["name"] for k in COCO_CATEGORIES]297        thing_colors = [k["color"] for k in COCO_CATEGORIES]298        stuff_classes = [k["name"] for k in COCO_CATEGORIES]299        stuff_colors = [k["color"] for k in COCO_CATEGORIES]300 301        meta["thing_classes"] = thing_classes302        meta["thing_colors"] = thing_colors303        meta["stuff_classes"] = stuff_classes304        meta["stuff_colors"] = stuff_colors305 306        # Convert category id for training:307        #   category id: like semantic segmentation, it is the class id for each308        #   pixel. Since there are some classes not used in evaluation, the category309        #   id is not always contiguous and thus we have two set of category ids:310        #       - original category id: category id in the original dataset, mainly311        #           used for evaluation.312        #       - contiguous category id: [0, #classes), in order to train the linear313        #           softmax classifier.314        thing_dataset_id_to_contiguous_id = {}315        stuff_dataset_id_to_contiguous_id = {}316 317        for i, cat in enumerate(COCO_CATEGORIES):318            if cat["isthing"]:319                thing_dataset_id_to_contiguous_id[cat["id"]] = i320            else:321                stuff_dataset_id_to_contiguous_id[cat["id"]] = i322 323        meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id324        meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id325 326        return meta327    elif dataset_name == "coco_person":328        return {329            "thing_classes": ["person"],330            "keypoint_names": COCO_PERSON_KEYPOINT_NAMES,331            "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP,332            "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES,333        }334    elif dataset_name == "cityscapes":335        # fmt: off336        CITYSCAPES_THING_CLASSES = [337            "person", "rider", "car", "truck",338            "bus", "train", "motorcycle", "bicycle",339        ]340        CITYSCAPES_STUFF_CLASSES = [341            "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light",342            "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car",343            "truck", "bus", "train", "motorcycle", "bicycle",344        ]345        # fmt: on346        return {347            "thing_classes": CITYSCAPES_THING_CLASSES,348            "stuff_classes": CITYSCAPES_STUFF_CLASSES,349        }350    raise KeyError("No built-in metadata for dataset {}".format(dataset_name))351