import base64 import collections import json import os import shutil import types import zipfile from concurrent.futures import ThreadPoolExecutor from pathlib import Path from typing import ( Callable, Dict, FrozenSet, List, Mapping, Optional, Sequence, Tuple, Type, TypeVar, ) import appdirs import attrs import attrs.validators import PIL.Image import torch import torchvision.datasets from typing_extensions import TypedDict import cvat_sdk.core import cvat_sdk.core.exceptions from cvat_sdk.api_client.model_utils import to_json from cvat_sdk.core.utils import atomic_writer from cvat_sdk.models import DataMetaRead, LabeledData, LabeledImage, LabeledShape, TaskRead _ModelType = TypeVar("_ModelType") _CACHE_DIR = Path(appdirs.user_cache_dir("cvat-sdk", "CVAT.ai")) _NUM_DOWNLOAD_THREADS = 4 class UnsupportedDatasetError(cvat_sdk.core.exceptions.CvatSdkException): pass @attrs.frozen class FrameAnnotations: """ Contains annotations that pertain to a single frame. """ tags: List[LabeledImage] = attrs.Factory(list) shapes: List[LabeledShape] = attrs.Factory(list) @attrs.frozen class Target: """ Non-image data for a dataset sample. """ annotations: FrameAnnotations """Annotations for the frame corresponding to the sample.""" label_id_to_index: Mapping[int, int] """ A mapping from label_id values in `LabeledImage` and `LabeledShape` objects to an integer index. This mapping is consistent across all samples for a given task. """ class TaskVisionDataset(torchvision.datasets.VisionDataset): """ Represents a task on a CVAT server as a PyTorch Dataset. This dataset contains one sample for each frame in the task, in the same order as the frames are in the task. Deleted frames are omitted. Before transforms are applied, each sample is a tuple of (image, target), where: * image is a `PIL.Image.Image` object for the corresponding frame. * target is a `Target` object containing annotations for the frame. This class caches all data and annotations for the task on the local file system during construction. If the task is updated on the server, the cache is updated. Limitations: * Only tasks with image (not video) data are supported at the moment. * Track annotations are currently not accessible. """ def __init__( self, client: cvat_sdk.core.Client, task_id: int, *, transforms: Optional[Callable] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, label_name_to_index: Mapping[str, int] = None, ) -> None: """ Creates a dataset corresponding to the task with ID `task_id` on the server that `client` is connected to. `transforms`, `transform` and `target_transforms` are optional transformation functions; see the documentation for `torchvision.datasets.VisionDataset` for more information. `label_name_to_index` affects the `label_id_to_index` member in `Target` objects returned by the dataset. If it is specified, then it must contain an entry for each label name in the task. The `label_id_to_index` mapping will be constructed so that each label will be mapped to the index corresponding to the label's name in `label_name_to_index`. If `label_name_to_index` is unspecified or set to `None`, then `label_id_to_index` will map each label ID to a distinct integer in the range [0, `num_labels`), where `num_labels` is the number of labels defined in the task. This mapping will be generally unpredictable, but consistent for a given task. """ self._logger = client.logger self._logger.info(f"Fetching task {task_id}...") self._task = client.tasks.retrieve(task_id) if not self._task.size or not self._task.data_chunk_size: raise UnsupportedDatasetError("The task has no data") if self._task.data_original_chunk_type != "imageset": raise UnsupportedDatasetError( f"{self.__class__.__name__} only supports tasks with image chunks;" f" current chunk type is {self._task.data_original_chunk_type!r}" ) # Base64-encode the name to avoid FS-unsafe characters (like slashes) server_dir_name = ( base64.urlsafe_b64encode(client.api_map.host.encode()).rstrip(b"=").decode() ) server_dir = _CACHE_DIR / f"servers/{server_dir_name}" self._task_dir = server_dir / f"tasks/{self._task.id}" self._initialize_task_dir() super().__init__( os.fspath(self._task_dir), transforms=transforms, transform=transform, target_transform=target_transform, ) data_meta = self._ensure_model( "data_meta.json", DataMetaRead, self._task.get_meta, "data metadata" ) self._active_frame_indexes = sorted( set(range(self._task.size)) - set(data_meta.deleted_frames) ) self._logger.info("Downloading chunks...") self._chunk_dir = self._task_dir / "chunks" self._chunk_dir.mkdir(exist_ok=True, parents=True) needed_chunks = { index // self._task.data_chunk_size for index in self._active_frame_indexes } with ThreadPoolExecutor(_NUM_DOWNLOAD_THREADS) as pool: for _ in pool.map(self._ensure_chunk, sorted(needed_chunks)): # just need to loop through all results so that any exceptions are propagated pass self._logger.info("All chunks downloaded") if label_name_to_index is None: self._label_id_to_index = types.MappingProxyType( { label.id: label_index for label_index, label in enumerate( sorted(self._task.labels, key=lambda l: l.id) ) } ) else: self._label_id_to_index = types.MappingProxyType( {label.id: label_name_to_index[label.name] for label in self._task.labels} ) annotations = self._ensure_model( "annotations.json", LabeledData, self._task.get_annotations, "annotations" ) self._frame_annotations: Dict[int, FrameAnnotations] = collections.defaultdict( FrameAnnotations ) for tag in annotations.tags: self._frame_annotations[tag.frame].tags.append(tag) for shape in annotations.shapes: self._frame_annotations[shape.frame].shapes.append(shape) # TODO: tracks? def _initialize_task_dir(self) -> None: task_json_path = self._task_dir / "task.json" try: with open(task_json_path, "rb") as task_json_file: saved_task = TaskRead._new_from_openapi_data(**json.load(task_json_file)) except Exception: self._logger.info("Task is not yet cached or the cache is corrupted") # If the cache was corrupted, the directory might already be there; clear it. if self._task_dir.exists(): shutil.rmtree(self._task_dir) else: if saved_task.updated_date < self._task.updated_date: self._logger.info( "Task has been updated on the server since it was cached; purging the cache" ) shutil.rmtree(self._task_dir) self._task_dir.mkdir(exist_ok=True, parents=True) with atomic_writer(task_json_path, "w", encoding="UTF-8") as task_json_file: json.dump(to_json(self._task._model), task_json_file, indent=4) print(file=task_json_file) # add final newline def _ensure_chunk(self, chunk_index: int) -> None: chunk_path = self._chunk_dir / f"{chunk_index}.zip" if chunk_path.exists(): return # already downloaded previously self._logger.info(f"Downloading chunk #{chunk_index}...") with atomic_writer(chunk_path, "wb") as chunk_file: self._task.download_chunk(chunk_index, chunk_file, quality="original") def _ensure_model( self, filename: str, model_type: Type[_ModelType], download: Callable[[], _ModelType], model_description: str, ) -> _ModelType: path = self._task_dir / filename try: with open(path, "rb") as f: model = model_type._new_from_openapi_data(**json.load(f)) self._logger.info(f"Loaded {model_description} from cache") return model except FileNotFoundError: pass except Exception: self._logger.warning(f"Failed to load {model_description} from cache", exc_info=True) self._logger.info(f"Downloading {model_description}...") model = download() self._logger.info(f"Downloaded {model_description}") with atomic_writer(path, "w", encoding="UTF-8") as f: json.dump(to_json(model), f, indent=4) print(file=f) # add final newline return model def __getitem__(self, sample_index: int): """ Returns the sample with index `sample_index`. `sample_index` must satisfy the condition `0 <= sample_index < len(self)`. """ frame_index = self._active_frame_indexes[sample_index] chunk_index = frame_index // self._task.data_chunk_size member_index = frame_index % self._task.data_chunk_size with zipfile.ZipFile(self._chunk_dir / f"{chunk_index}.zip", "r") as chunk_zip: with chunk_zip.open(chunk_zip.infolist()[member_index]) as chunk_member: sample_image = PIL.Image.open(chunk_member) sample_image.load() sample_target = Target( annotations=self._frame_annotations[frame_index], label_id_to_index=self._label_id_to_index, ) if self.transforms: sample_image, sample_target = self.transforms(sample_image, sample_target) return sample_image, sample_target def __len__(self) -> int: """Returns the number of samples in the dataset.""" return len(self._active_frame_indexes) @attrs.frozen class ExtractSingleLabelIndex: """ A target transform that takes a `Target` object and produces a single label index based on the tag in that object, as a 0-dimensional tensor. This makes the dataset samples compatible with the image classification networks in torchvision. If the annotations contain no tags, or multiple tags, raises a `ValueError`. """ def __call__(self, target: Target) -> int: tags = target.annotations.tags if not tags: raise ValueError("sample has no tags") if len(tags) > 1: raise ValueError("sample has multiple tags") return torch.tensor(target.label_id_to_index[tags[0].label_id], dtype=torch.long) class LabeledBoxes(TypedDict): boxes: torch.Tensor labels: torch.Tensor _SUPPORTED_SHAPE_TYPES = frozenset(["rectangle", "polygon", "polyline", "points", "ellipse"]) @attrs.frozen class ExtractBoundingBoxes: """ A target transform that takes a `Target` object and returns a dictionary compatible with the object detection networks in torchvision. The dictionary contains the following entries: "boxes": a tensor with shape [N, 4], where each row represents a bounding box of a shape in the annotations in the (xmin, ymin, xmax, ymax) format. "labels": a tensor with shape [N] containing corresponding label indices. Limitations: * Only the following shape types are supported: rectangle, polygon, polyline, points, ellipse. * Rotated shapes are not supported. Unsupported shapes will cause a `UnsupportedDatasetError` exception to be raised unless they are filtered out by `include_shape_types`. """ include_shape_types: FrozenSet[str] = attrs.field( converter=frozenset, validator=attrs.validators.deep_iterable(attrs.validators.in_(_SUPPORTED_SHAPE_TYPES)), kw_only=True, ) """Shapes whose type is not in this set will be ignored.""" def __call__(self, target: Target) -> LabeledBoxes: boxes = [] labels = [] for shape in target.annotations.shapes: if shape.type.value not in self.include_shape_types: continue if shape.rotation != 0: raise UnsupportedDatasetError("Rotated shapes are not supported") x_coords = shape.points[0::2] y_coords = shape.points[1::2] boxes.append((min(x_coords), min(y_coords), max(x_coords), max(y_coords))) labels.append(target.label_id_to_index[shape.label_id]) return LabeledBoxes( boxes=torch.tensor(boxes, dtype=torch.float), labels=torch.tensor(labels, dtype=torch.long), )