migrate coco prototype (#5473)

* migrate coco prototype

* revert unrelated change

* add kwargs to super constructor call

* remove unneeded changes

* fix docstring position

* make kwargs explicit

* add dependencies to docstring

* fix missing dependency message

Author: pmeier

test/builtin_dataset_mocks.py

@@ -600,9 +600,15 @@ def generate(
         return num_samples
 
 
-# @register_mock
-def coco(info, root, config):
-    return CocoMockData.generate(root, year=config.year, num_samples=5)
+@register_mock(
+    configs=combinations_grid(
+        split=("train", "val"),
+        year=("2017", "2014"),
+        annotations=("instances", "captions", None),
+    )
+)
+def coco(root, config):
+    return CocoMockData.generate(root, year=config["year"], num_samples=5)
 
 
 class SBDMockData:

torchvision/prototype/datasets/_builtin/coco.py

@@ -1,8 +1,8 @@
-import functools
 import pathlib
 import re
 from collections import OrderedDict
-from typing import Any, Dict, List, Optional, Tuple, cast, BinaryIO
+from collections import defaultdict
+from typing import Any, Dict, List, Optional, Tuple, cast, BinaryIO, Union
 
 import torch
 from torchdata.datapipes.iter import (
@@ -16,11 +16,10 @@
     UnBatcher,
 )
 from torchvision.prototype.datasets.utils import (
-    Dataset,
-    DatasetConfig,
     DatasetInfo,
     HttpResource,
     OnlineResource,
+    Dataset2,
 )
 from torchvision.prototype.datasets.utils._internal import (
     MappingIterator,
@@ -32,27 +31,51 @@
     hint_shuffling,
 )
 from torchvision.prototype.features import BoundingBox, Label, _Feature, EncodedImage
-from torchvision.prototype.utils._internal import FrozenMapping
-
-
-class Coco(Dataset):
-    def _make_info(self) -> DatasetInfo:
-        name = "coco"
-        categories, super_categories = zip(*DatasetInfo.read_categories_file(BUILTIN_DIR / f"{name}.categories"))
-
-        return DatasetInfo(
-            name,
-            dependencies=("pycocotools",),
-            categories=categories,
-            homepage="https://cocodataset.org/",
-            valid_options=dict(
-                split=("train", "val"),
-                year=("2017", "2014"),
-                annotations=(*self._ANN_DECODERS.keys(), None),
-            ),
-            extra=dict(category_to_super_category=FrozenMapping(zip(categories, super_categories))),
+
+from .._api import register_dataset, register_info
+
+
+NAME = "coco"
+
+
+@register_info(NAME)
+def _info() -> Dict[str, Any]:
+    categories, super_categories = zip(*DatasetInfo.read_categories_file(BUILTIN_DIR / f"{NAME}.categories"))
+    return dict(categories=categories, super_categories=super_categories)
+
+
+@register_dataset(NAME)
+class Coco(Dataset2):
+    """
+    - **homepage**: https://cocodataset.org/
+    - **dependencies**:
+        - <pycocotools `https://github.com/cocodataset/cocoapi`>_
+    """
+
+    def __init__(
+        self,
+        root: Union[str, pathlib.Path],
+        *,
+        split: str = "train",
+        year: str = "2017",
+        annotations: Optional[str] = "instances",
+        skip_integrity_check: bool = False,
+    ) -> None:
+        self._split = self._verify_str_arg(split, "split", {"train", "val"})
+        self._year = self._verify_str_arg(year, "year", {"2017", "2014"})
+        self._annotations = (
+            self._verify_str_arg(annotations, "annotations", self._ANN_DECODERS.keys())
+            if annotations is not None
+            else None
         )
 
+        info = _info()
+        categories, super_categories = info["categories"], info["super_categories"]
+        self._categories = categories
+        self._category_to_super_category = dict(zip(categories, super_categories))
+
+        super().__init__(root, dependencies=("pycocotools",), skip_integrity_check=skip_integrity_check)
+
     _IMAGE_URL_BASE = "http://images.cocodataset.org/zips"
 
     _IMAGES_CHECKSUMS = {
@@ -69,14 +92,14 @@ def _make_info(self) -> DatasetInfo:
         "2017": "113a836d90195ee1f884e704da6304dfaaecff1f023f49b6ca93c4aaae470268",
     }
 
-    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+    def _resources(self) -> List[OnlineResource]:
         images = HttpResource(
-            f"{self._IMAGE_URL_BASE}/{config.split}{config.year}.zip",
-            sha256=self._IMAGES_CHECKSUMS[(config.year, config.split)],
+            f"{self._IMAGE_URL_BASE}/{self._split}{self._year}.zip",
+            sha256=self._IMAGES_CHECKSUMS[(self._year, self._split)],
         )
         meta = HttpResource(
-            f"{self._META_URL_BASE}/annotations_trainval{config.year}.zip",
-            sha256=self._META_CHECKSUMS[config.year],
+            f"{self._META_URL_BASE}/annotations_trainval{self._year}.zip",
+            sha256=self._META_CHECKSUMS[self._year],
         )
         return [images, meta]
 
@@ -110,10 +133,8 @@ def _decode_instances_anns(self, anns: List[Dict[str, Any]], image_meta: Dict[st
                 format="xywh",
                 image_size=image_size,
             ),
-            labels=Label(labels, categories=self.categories),
-            super_categories=[
-                self.info.extra.category_to_super_category[self.info.categories[label]] for label in labels
-            ],
+            labels=Label(labels, categories=self._categories),
+            super_categories=[self._category_to_super_category[self._categories[label]] for label in labels],
             ann_ids=[ann["id"] for ann in anns],
         )
 
@@ -134,9 +155,14 @@ def _decode_captions_ann(self, anns: List[Dict[str, Any]], image_meta: Dict[str,
         fr"(?P<annotations>({'|'.join(_ANN_DECODERS.keys())}))_(?P<split>[a-zA-Z]+)(?P<year>\d+)[.]json"
     )
 
-    def _filter_meta_files(self, data: Tuple[str, Any], *, split: str, year: str, annotations: str) -> bool:
+    def _filter_meta_files(self, data: Tuple[str, Any]) -> bool:
         match = self._META_FILE_PATTERN.match(pathlib.Path(data[0]).name)
-        return bool(match and match["split"] == split and match["year"] == year and match["annotations"] == annotations)
+        return bool(
+            match
+            and match["split"] == self._split
+            and match["year"] == self._year
+            and match["annotations"] == self._annotations
+        )
 
     def _classify_meta(self, data: Tuple[str, Any]) -> Optional[int]:
         key, _ = data
@@ -157,38 +183,26 @@ def _prepare_image(self, data: Tuple[str, BinaryIO]) -> Dict[str, Any]:
     def _prepare_sample(
         self,
         data: Tuple[Tuple[List[Dict[str, Any]], Dict[str, Any]], Tuple[str, BinaryIO]],
-        *,
-        annotations: str,
     ) -> Dict[str, Any]:
         ann_data, image_data = data
         anns, image_meta = ann_data
 
         sample = self._prepare_image(image_data)
+        # this method is only called if we have annotations
+        annotations = cast(str, self._annotations)
         sample.update(self._ANN_DECODERS[annotations](self, anns, image_meta))
         return sample
 
-    def _make_datapipe(
-        self,
-        resource_dps: List[IterDataPipe],
-        *,
-        config: DatasetConfig,
-    ) -> IterDataPipe[Dict[str, Any]]:
+    def _datapipe(self, resource_dps: List[IterDataPipe]) -> IterDataPipe[Dict[str, Any]]:
         images_dp, meta_dp = resource_dps
 
-        if config.annotations is None:
+        if self._annotations is None:
             dp = hint_shuffling(images_dp)
             dp = hint_sharding(dp)
+            dp = hint_shuffling(dp)
             return Mapper(dp, self._prepare_image)
 
-        meta_dp = Filter(
-            meta_dp,
-            functools.partial(
-                self._filter_meta_files,
-                split=config.split,
-                year=config.year,
-                annotations=config.annotations,
-            ),
-        )
+        meta_dp = Filter(meta_dp, self._filter_meta_files)
         meta_dp = JsonParser(meta_dp)
         meta_dp = Mapper(meta_dp, getitem(1))
         meta_dp: IterDataPipe[Dict[str, Dict[str, Any]]] = MappingIterator(meta_dp)
@@ -216,26 +230,31 @@ def _make_datapipe(
             ref_key_fn=getitem("id"),
             buffer_size=INFINITE_BUFFER_SIZE,
         )
-
         dp = IterKeyZipper(
             anns_dp,
             images_dp,
             key_fn=getitem(1, "file_name"),
             ref_key_fn=path_accessor("name"),
             buffer_size=INFINITE_BUFFER_SIZE,
         )
+        return Mapper(dp, self._prepare_sample)
+
+    def __len__(self) -> int:
+        return {
+            ("train", "2017"): defaultdict(lambda: 118_287, instances=117_266),
+            ("train", "2014"): defaultdict(lambda: 82_783, instances=82_081),
+            ("val", "2017"): defaultdict(lambda: 5_000, instances=4_952),
+            ("val", "2014"): defaultdict(lambda: 40_504, instances=40_137),
+        }[(self._split, self._year)][
+            self._annotations  # type: ignore[index]
+        ]
 
-        return Mapper(dp, functools.partial(self._prepare_sample, annotations=config.annotations))
-
-    def _generate_categories(self, root: pathlib.Path) -> Tuple[Tuple[str, str]]:
-        config = self.default_config
-        resources = self.resources(config)
+    def _generate_categories(self) -> Tuple[Tuple[str, str]]:
+        self._annotations = "instances"
+        resources = self._resources()
 
-        dp = resources[1].load(root)
-        dp = Filter(
-            dp,
-            functools.partial(self._filter_meta_files, split=config.split, year=config.year, annotations="instances"),
-        )
+        dp = resources[1].load(self._root)
+        dp = Filter(dp, self._filter_meta_files)
         dp = JsonParser(dp)
 
         _, meta = next(iter(dp))

torchvision/prototype/datasets/_builtin/imagenet.py

@@ -54,7 +54,17 @@ class ImageNetDemux(enum.IntEnum):
 
 @register_dataset(NAME)
 class ImageNet(Dataset2):
-    def __init__(self, root: Union[str, pathlib.Path], *, split: str = "train") -> None:
+    """
+    - **homepage**: https://www.image-net.org/
+    """
+
+    def __init__(
+        self,
+        root: Union[str, pathlib.Path],
+        *,
+        split: str = "train",
+        skip_integrity_check: bool = False,
+    ) -> None:
         self._split = self._verify_str_arg(split, "split", {"train", "val", "test"})
 
         info = _info()
@@ -63,7 +73,7 @@ def __init__(self, root: Union[str, pathlib.Path], *, split: str = "train") -> N
         self._wnids = wnids
         self._wnid_to_category = dict(zip(wnids, categories))
 
-        super().__init__(root)
+        super().__init__(root, skip_integrity_check=skip_integrity_check)
 
     _IMAGES_CHECKSUMS = {
         "train": "b08200a27a8e34218a0e58fde36b0fe8f73bc377f4acea2d91602057c3ca45bb",

torchvision/prototype/datasets/_builtin/voc.py

@@ -50,7 +50,7 @@ def __init__(
         split: str = "train",
         year: str = "2012",
         task: str = "detection",
-        **kwargs: Any,
+        skip_integrity_check: bool = False,
     ) -> None:
         self._year = self._verify_str_arg(year, "year", ("2007", "2008", "2009", "2010", "2011", "2012"))
         if split == "test" and year != "2007":
@@ -64,7 +64,7 @@ def __init__(
 
         self._categories = _info()["categories"]
 
-        super().__init__(root, **kwargs)
+        super().__init__(root, skip_integrity_check=skip_integrity_check)
 
     _TRAIN_VAL_ARCHIVES = {
         "2007": ("VOCtrainval_06-Nov-2007.tar", "7d8cd951101b0957ddfd7a530bdc8a94f06121cfc1e511bb5937e973020c7508"),

torchvision/prototype/datasets/generate_category_files.py

@@ -51,7 +51,7 @@ def parse_args(argv=None):
 
 
 if __name__ == "__main__":
-    args = parse_args(["-f", "imagenet"])
+    args = parse_args()
 
     try:
         main(*args.names, force=args.force)

torchvision/prototype/datasets/utils/_dataset.py

@@ -196,7 +196,18 @@ def _verify_str_arg(
     ) -> str:
         return verify_str_arg(value, arg, valid_values, custom_msg=custom_msg)
 
-    def __init__(self, root: Union[str, pathlib.Path], *, skip_integrity_check: bool = False) -> None:
+    def __init__(
+        self, root: Union[str, pathlib.Path], *, skip_integrity_check: bool = False, dependencies: Collection[str] = ()
+    ) -> None:
+        for dependency in dependencies:
+            try:
+                importlib.import_module(dependency)
+            except ModuleNotFoundError:
+                raise ModuleNotFoundError(
+                    f"{type(self).__name__}() depends on the third-party package '{dependency}'. "
+                    f"Please install it, for example with `pip install {dependency}`."
+                ) from None
+
         self._root = pathlib.Path(root).expanduser().resolve()
         resources = [
             resource.load(self._root, skip_integrity_check=skip_integrity_check) for resource in self._resources()