Implementation for cityscapes in proto datasets

test/builtin_dataset_mocks.py

@@ -1344,3 +1344,96 @@ def pcam(info, root, config):
             compressed_file.write(compressed_data)
 
     return num_images
+
+
+class CityScapesMockData:
+
+    _ARCHIVE_NAMES = {
+        ("Coarse", "train"): [("gtCoarse.zip", "gtCoarse"), ("leftImg8bit_trainvaltest.zip", "leftImg8bit")],
+        ("Coarse", "train_extra"): [("gtCoarse.zip", "gtCoarse"), ("leftImg8bit_trainextra.zip", "leftImg8bit")],
+        ("Coarse", "val"): [("gtCoarse.zip", "gtCoarse"), ("leftImg8bit_trainvaltest.zip", "leftImg8bit")],
+        ("Fine", "train"): [("gtFine_trainvaltest.zip", "gtFine"), ("leftImg8bit_trainvaltest.zip", "leftImg8bit")],
+        ("Fine", "test"): [("gtFine_trainvaltest.zip", "gtFine"), ("leftImg8bit_trainvaltest.zip", "leftImg8bit")],
+        ("Fine", "val"): [("gtFine_trainvaltest.zip", "gtFine"), ("leftImg8bit_trainvaltest.zip", "leftImg8bit")],
+    }
+
+    @classmethod
+    def generate(cls, root, config):
+
+        mode = config.mode.capitalize()
+        split = config.split
+
+        if split in ["train", "train_extra"]:
+            cities = ["bochum", "bremen"]
+            num_samples = 3
+        else:
+            cities = ["bochum"]
+            num_samples = 2
+
+        polygon_target = {
+            "imgHeight": 1024,
+            "imgWidth": 2048,
+            "objects": [
+                {
+                    "label": "sky",
+                    "polygon": [
+                        [1241, 0],
+                        [1234, 156],
+                        [1478, 197],
+                        [1611, 172],
+                        [1606, 0],
+                    ],
+                },
+                {
+                    "label": "road",
+                    "polygon": [
+                        [0, 448],
+                        [1331, 274],
+                        [1473, 265],
+                        [2047, 605],
+                        [2047, 1023],
+                        [0, 1023],
+                    ],
+                },
+            ],
+        }
+
+        gt_dir = root / f"gt{mode}"
+
+        for city in cities:
+
+            def make_image(name, size=10):
+                create_image_folder(
+                    root=gt_dir / split,
+                    name=city,
+                    file_name_fn=lambda idx: name.format(idx=idx),
+                    size=size,
+                    num_examples=num_samples,
+                )
+
+            make_image(f"{city}_000000_00000" + "{idx}" + f"_gt{mode}_instanceIds.png")
+            make_image(f"{city}_000000_00000" + "{idx}" + f"_gt{mode}_labelIds.png")
+            make_image(f"{city}_000000_00000" + "{idx}" + f"_gt{mode}_color.png", size=(4, 10, 10))
+
+            for idx in range(num_samples):
+                polygon_target_name = gt_dir / split / city / f"{city}_000000_00000{idx}_gt{mode}_polygons.json"
+                with open(polygon_target_name, "w") as outfile:
+                    json.dump(polygon_target, outfile)
+
+        # Create leftImg8bit folder
+        for city in cities:
+            create_image_folder(
+                root=root / "leftImg8bit" / split,
+                name=city,
+                file_name_fn=lambda idx: f"{city}_000000_00000{idx}_leftImg8bit.png",
+                num_examples=num_samples,
+            )
+
+        for zip_name, folder_name in cls._ARCHIVE_NAMES[(mode, split)]:
+            make_zip(root, zip_name, folder_name)
+        return len(cities) * num_samples
+
+
+@register_mock
+def cityscapes(info, root, config):
+    return CityScapesMockData.generate(root, config)

torchvision/prototype/datasets/_builtin/__init__.py

@@ -1,6 +1,7 @@
 from .caltech import Caltech101, Caltech256
 from .celeba import CelebA
 from .cifar import Cifar10, Cifar100
+from .cityscapes import Cityscapes
 from .clevr import CLEVR
 from .coco import Coco
 from .cub200 import CUB200

torchvision/prototype/datasets/_builtin/cityscapes.py

@@ -0,0 +1,201 @@
+import json
+from collections import namedtuple
+from functools import partial
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+from torchdata.datapipes.iter import Grouper, IterDataPipe, Mapper, Filter, IterKeyZipper
+from torchvision.prototype.datasets.utils import (
+    Dataset,
+    DatasetInfo,
+    DatasetConfig,
+    ManualDownloadResource,
+    OnlineResource,
+)
+from torchvision.prototype.datasets.utils._internal import INFINITE_BUFFER_SIZE, hint_sharding, hint_shuffling
+from torchvision.prototype.features import EncodedImage
+from torchvision.prototype.utils._internal import FrozenMapping
+
+
+class CityscapesDatasetInfo(DatasetInfo):
+    def __init__(self, *args: Any, **kwargs: Any):
+        super().__init__(*args, **kwargs)
+        self._configs = tuple(
+            config
+            for config in self._configs
+            if not (
+                (config.split == "test" and config.mode == "coarse")
+                or (config.split == "train_extra" and config.mode == "fine")
+            )
+        )
+
+    def make_config(self, **options: Any) -> DatasetConfig:
+        config = super().make_config(**options)
+        if config.split == "test" and config.mode == "coarse":
+            raise ValueError("`split='test'` is only available for `mode='fine'`")
+        if config.split == "train_extra" and config.mode == "fine":
+            raise ValueError("`split='train_extra'` is only available for `mode='coarse'`")
+
+        return config
+
+
+class CityscapesResource(ManualDownloadResource):
+    def __init__(self, **kwargs: Any) -> None:
+        super().__init__(
+            "Register on https://www.cityscapes-dataset.com/login/ and follow the instructions there.", **kwargs
+        )
+
+
+CityscapesClass = namedtuple(
+    "CityscapesClass",
+    ["name", "id", "train_id", "category", "category_id", "has_instances", "ignore_in_eval", "color"],
+)
+
+
+class Cityscapes(Dataset):
+
+    categories_to_details: FrozenMapping = FrozenMapping(
+        {
+            "unlabeled": CityscapesClass("unlabeled", 0, 255, "void", 0, False, True, (0, 0, 0)),
+            "ego vehicle": CityscapesClass("ego vehicle", 1, 255, "void", 0, False, True, (0, 0, 0)),
+            "rectification border": CityscapesClass("rectification border", 2, 255, "void", 0, False, True, (0, 0, 0)),
+            "out of roi": CityscapesClass("out of roi", 3, 255, "void", 0, False, True, (0, 0, 0)),
+            "static": CityscapesClass("static", 4, 255, "void", 0, False, True, (0, 0, 0)),
+            "dynamic": CityscapesClass("dynamic", 5, 255, "void", 0, False, True, (111, 74, 0)),
+            "ground": CityscapesClass("ground", 6, 255, "void", 0, False, True, (81, 0, 81)),
+            "road": CityscapesClass("road", 7, 0, "flat", 1, False, False, (128, 64, 128)),
+            "sidewalk": CityscapesClass("sidewalk", 8, 1, "flat", 1, False, False, (244, 35, 232)),
+            "parking": CityscapesClass("parking", 9, 255, "flat", 1, False, True, (250, 170, 160)),
+            "rail track": CityscapesClass("rail track", 10, 255, "flat", 1, False, True, (230, 150, 140)),
+            "building": CityscapesClass("building", 11, 2, "construction", 2, False, False, (70, 70, 70)),
+            "wall": CityscapesClass("wall", 12, 3, "construction", 2, False, False, (102, 102, 156)),
+            "fence": CityscapesClass("fence", 13, 4, "construction", 2, False, False, (190, 153, 153)),
+            "guard rail": CityscapesClass("guard rail", 14, 255, "construction", 2, False, True, (180, 165, 180)),
+            "bridge": CityscapesClass("bridge", 15, 255, "construction", 2, False, True, (150, 100, 100)),
+            "tunnel": CityscapesClass("tunnel", 16, 255, "construction", 2, False, True, (150, 120, 90)),
+            "pole": CityscapesClass("pole", 17, 5, "object", 3, False, False, (153, 153, 153)),
+            "polegroup": CityscapesClass("polegroup", 18, 255, "object", 3, False, True, (153, 153, 153)),
+            "traffic light": CityscapesClass("traffic light", 19, 6, "object", 3, False, False, (250, 170, 30)),
+            "traffic sign": CityscapesClass("traffic sign", 20, 7, "object", 3, False, False, (220, 220, 0)),
+            "vegetation": CityscapesClass("vegetation", 21, 8, "nature", 4, False, False, (107, 142, 35)),
+            "terrain": CityscapesClass("terrain", 22, 9, "nature", 4, False, False, (152, 251, 152)),
+            "sky": CityscapesClass("sky", 23, 10, "sky", 5, False, False, (70, 130, 180)),
+            "person": CityscapesClass("person", 24, 11, "human", 6, True, False, (220, 20, 60)),
+            "rider": CityscapesClass("rider", 25, 12, "human", 6, True, False, (255, 0, 0)),
+            "car": CityscapesClass("car", 26, 13, "vehicle", 7, True, False, (0, 0, 142)),
+            "truck": CityscapesClass("truck", 27, 14, "vehicle", 7, True, False, (0, 0, 70)),
+            "bus": CityscapesClass("bus", 28, 15, "vehicle", 7, True, False, (0, 60, 100)),
+            "caravan": CityscapesClass("caravan", 29, 255, "vehicle", 7, True, True, (0, 0, 90)),
+            "trailer": CityscapesClass("trailer", 30, 255, "vehicle", 7, True, True, (0, 0, 110)),
+            "train": CityscapesClass("train", 31, 16, "vehicle", 7, True, False, (0, 80, 100)),
+            "motorcycle": CityscapesClass("motorcycle", 32, 17, "vehicle", 7, True, False, (0, 0, 230)),
+            "bicycle": CityscapesClass("bicycle", 33, 18, "vehicle", 7, True, False, (119, 11, 32)),
+            "license plate": CityscapesClass("license plate", -1, -1, "vehicle", 7, False, True, (0, 0, 142)),
+        }
+    )
+
+    def _make_info(self) -> DatasetInfo:
+        name = "cityscapes"
+
+        return CityscapesDatasetInfo(
+            name,
+            categories=list(self.categories_to_details.keys()),
+            homepage="http://www.cityscapes-dataset.com/",
+            valid_options=dict(
+                split=("train", "val", "test", "train_extra"),
+                mode=("fine", "coarse"),
+            ),
+            extra=dict(categories_to_details=self.categories_to_details),
+        )
+
+    _FILES_CHECKSUMS = {
+        "gtCoarse.zip": "3555e09349ed49127053d940eaa66a87a79a175662b329c1a26a58d47e602b5b",
+        "gtFine_trainvaltest.zip": "40461a50097844f400fef147ecaf58b18fd99e14e4917fb7c3bf9c0d87d95884",
+        "leftImg8bit_trainextra.zip": "e41cc14c0c06aad051d52042465d9b8c22bacf6e4c93bb98de273ed7177b7133",
+        "leftImg8bit_trainvaltest.zip": "3ccff9ac1fa1d80a6a064407e589d747ed0657aac7dc495a4403ae1235a37525",
+    }
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+
+        if config.mode == "fine":
+            filenames = ("leftImg8bit_trainvaltest.zip", "gtFine_trainvaltest.zip")
+        else:
+            split_label = "trainextra" if config.split == "train_extra" else "trainvaltest"
+            filenames = (f"leftImg8bit_{split_label}.zip", "gtCoarse.zip")
+
+        resources: List[OnlineResource] = [
+            CityscapesResource(file_name=file_name, sha256=self._FILES_CHECKSUMS[file_name]) for file_name in filenames
+        ]
+        return resources
+
+    def _filter_split_images(self, data: Tuple[str, Any], *, req_split: str) -> bool:
+        path = Path(data[0])
+        split = path.parent.parts[-2]
+        return split == req_split and ".png" == path.suffix
+
+    def _filter_group_targets(self, data: Tuple[str, Any]) -> str:
+        path = Path(data[0])
+        # Taregt path looks like
+        # gtFine/val/frankfurt/frankfurt_000001_066574_gtFine_polygons.json
+        # and we produce the key: frankfurt_000001_066574
+        return path.name.rsplit("_", 1)[0]
+
+    def _prepare_sample(self, data: Tuple[Tuple[str, Any], Any]) -> Dict[str, Any]:
+        (img_path, img_data), target_data = data
+
+        output = dict(image_path=img_path, image=EncodedImage.from_file(img_data))
+        # reorder inside group of targets and setup output dictionary:
+        for path, data in target_data:
+            stem = Path(path).stem
+            for target_type in ["instance", "label", "polygon", "color"]:
+                if target_type in stem:
+                    if target_type == "polygon":
+                        enc_data = json.loads(data.read())
+                    else:
+                        enc_data = EncodedImage.from_file(data)
+                    output[target_type] = enc_data
+                    output[f"{target_type}_path"] = path
+                    break
+
+        return output
+
+    def _make_datapipe(
+        self,
+        resource_dps: List[IterDataPipe],
+        *,
+        config: DatasetConfig,
+    ) -> IterDataPipe[Dict[str, Any]]:
+        archive_images, archive_targets = resource_dps
+
+        images_dp = Filter(archive_images, filter_fn=partial(self._filter_split_images, req_split=config.split))
+
+        images_dp = hint_sharding(images_dp)
+        images_dp = hint_shuffling(images_dp)
+
+        targets_dp = Grouper(
+            archive_targets,
+            group_key_fn=self._filter_group_targets,
+            buffer_size=INFINITE_BUFFER_SIZE,
+            group_size=4,
+        )
+
+        def key_fn(data: Tuple[Any, Any]) -> str:
+            data0 = data[0]
+            if isinstance(data0, tuple):
+                data0 = data0[0]
+            path = Path(data0)
+            # The pathes for images and targets are
+            # - leftImg8bit/val/frankfurt/frankfurt_000001_066574_leftImg8bit.png
+            # - gtFine/val/frankfurt/frankfurt_000001_066574_gtFine_polygons.json
+            # - gtFine/val/frankfurt/frankfurt_000001_066574_gtFine_labelIds.png
+            # we transform them into "frankfurt_000001_066574"
+            return "_".join(path.name.split("_", 3)[:3])
+
+        samples = IterKeyZipper(
+            images_dp,
+            targets_dp,
+            key_fn=key_fn,
+            ref_key_fn=key_fn,
+            buffer_size=INFINITE_BUFFER_SIZE,
+        )
+        return Mapper(samples, fn=self._prepare_sample)