SDK: add a ProjectVisionDataset class

It's the analog of `TaskVisionDataset` for projects.

CHANGELOG.md

@@ -8,6 +8,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## \[2.4.0] - Unreleased
 ### Added
 - Filename pattern to simplify uploading cloud storage data for a task (<https://github.com/opencv/cvat/pull/5498>)
+- SDK: class to represent a project as a PyTorch dataset
+  (<https://github.com/opencv/cvat/pull/5523>)
 
 ### Changed
 - TDB

cvat-sdk/cvat_sdk/pytorch/__init__.py

@@ -25,14 +25,15 @@
 import attrs.validators
 import PIL.Image
 import torch
+import torch.utils.data
 import torchvision.datasets
 from typing_extensions import TypedDict
 
 import cvat_sdk.core
 import cvat_sdk.core.exceptions
+import cvat_sdk.models as models
 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")
 
@@ -50,8 +51,8 @@ class FrameAnnotations:
     Contains annotations that pertain to a single frame.
     """
 
-    tags: List[LabeledImage] = attrs.Factory(list)
-    shapes: List[LabeledShape] = attrs.Factory(list)
+    tags: List[models.LabeledImage] = attrs.Factory(list)
+    shapes: List[models.LabeledShape] = attrs.Factory(list)
 
 
 @attrs.frozen
@@ -70,6 +71,12 @@ class Target:
     """
 
 
+def _get_server_dir(client: cvat_sdk.core.Client) -> Path:
+    # 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()
+    return _CACHE_DIR / f"servers/{server_dir_name}"
+
+
 class TaskVisionDataset(torchvision.datasets.VisionDataset):
     """
     Represents a task on a CVAT server as a PyTorch Dataset.
@@ -135,13 +142,7 @@ def __init__(
                 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._task_dir = _get_server_dir(client) / f"tasks/{self._task.id}"
         self._initialize_task_dir()
 
         super().__init__(
@@ -152,7 +153,7 @@ def __init__(
         )
 
         data_meta = self._ensure_model(
-            "data_meta.json", DataMetaRead, self._task.get_meta, "data metadata"
+            "data_meta.json", models.DataMetaRead, self._task.get_meta, "data metadata"
         )
         self._active_frame_indexes = sorted(
             set(range(self._task.size)) - set(data_meta.deleted_frames)
@@ -189,7 +190,7 @@ def __init__(
             )
 
         annotations = self._ensure_model(
-            "annotations.json", LabeledData, self._task.get_annotations, "annotations"
+            "annotations.json", models.LabeledData, self._task.get_annotations, "annotations"
         )
 
         self._frame_annotations: Dict[int, FrameAnnotations] = collections.defaultdict(
@@ -209,7 +210,7 @@ def _initialize_task_dir(self) -> None:
 
         try:
             with open(task_json_path, "rb") as task_json_file:
-                saved_task = TaskRead._new_from_openapi_data(**json.load(task_json_file))
+                saved_task = models.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")
 
@@ -298,6 +299,94 @@ def __len__(self) -> int:
         return len(self._active_frame_indexes)
 
 
+class ProjectVisionDataset(torchvision.datasets.VisionDataset):
+    """
+    Represents a project on a CVAT server as a PyTorch Dataset.
+
+    The dataset contains one sample for each frame of each task in the project
+    (except for tasks that are filtered out - see the description of `task_filter`
+    in the constructor). The sequence of samples is formed by concatening sequences
+    of samples from all included tasks in an arbitrary order that's consistent
+    between executions. Each task's sequence of samples corresponds to the sequence
+    of frames on the server.
+
+    See `TaskVisionDataset` for information on sample format, caching, and
+    current limitations.
+    """
+
+    def __init__(
+        self,
+        client: cvat_sdk.core.Client,
+        project_id: int,
+        *,
+        transforms: Optional[Callable] = None,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        label_name_to_index: Mapping[str, int] = None,
+        task_filter: Optional[Callable[[models.ITaskRead], bool]] = None,
+    ) -> None:
+        """
+        Creates a dataset corresponding to the project with ID `project_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.
+
+        See `TaskVisionDataset.__init__` for information on `label_name_to_index`.
+
+        `task_filter`, if set to a non-`None` value, determines which of the project's
+        tasks will be included in the dataset. The filter will be called for each task,
+        and only tasks for which it returns a true value will be included.
+
+        If `task_filter` is set to None. then all of the project's tasks will be included.
+        """
+
+        self._logger = client.logger
+
+        self._logger.info(f"Fetching project {project_id}...")
+        project = client.projects.retrieve(project_id)
+
+        # We don't actually need to save anything to this directory (yet),
+        # but VisionDataset.__init__ requires a root, so make one.
+        # It could be useful in the future to store the project data for
+        # offline-only mode.
+        project_dir = _get_server_dir(client) / f"projects/{project_id}"
+        project_dir.mkdir(parents=True, exist_ok=True)
+
+        super().__init__(
+            os.fspath(project_dir),
+            transforms=transforms,
+            transform=transform,
+            target_transform=target_transform,
+        )
+
+        self._logger.info("Fetching project tasks...")
+        tasks = project.get_tasks()
+        if task_filter:
+            tasks = list(filter(task_filter, tasks))
+        tasks.sort(key=lambda t: t.id)  # ensure consistent order between executions
+
+        self._underlying = torch.utils.data.ConcatDataset(
+            [
+                TaskVisionDataset(client, task.id, label_name_to_index=label_name_to_index)
+                for task in tasks
+            ]
+        )
+
+    def __getitem__(self, sample_index: int):
+        sample_image, sample_target = self._underlying[sample_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._underlying)
+
+
 @attrs.frozen
 class ExtractSingleLabelIndex:
     """

tests/python/sdk/test_pytorch.py

@@ -3,6 +3,7 @@
 # SPDX-License-Identifier: MIT
 
 import io
+import itertools
 import os
 from logging import Logger
 from pathlib import Path
@@ -25,37 +26,36 @@
 from shared.utils.helpers import generate_image_files
 
 
+@pytest.fixture(autouse=True)
+def _common_setup(
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+    fxt_login: Tuple[Client, str],
+    fxt_logger: Tuple[Logger, io.StringIO],
+):
+    logger = fxt_logger[0]
+    client = fxt_login[0]
+    client.logger = logger
+
+    api_client = client.api_client
+    for k in api_client.configuration.logger:
+        api_client.configuration.logger[k] = logger
+
+    monkeypatch.setattr(cvatpt, "_CACHE_DIR", tmp_path / "cache")
+
+
 @pytest.mark.skipif(cvatpt is None, reason="PyTorch dependencies are not installed")
 class TestTaskVisionDataset:
     @pytest.fixture(autouse=True)
     def setup(
         self,
-        monkeypatch: pytest.MonkeyPatch,
         tmp_path: Path,
         fxt_login: Tuple[Client, str],
-        fxt_logger: Tuple[Logger, io.StringIO],
-        fxt_stdout: io.StringIO,
     ):
-        self.tmp_path = tmp_path
-        logger, self.logger_stream = fxt_logger
-        self.stdout = fxt_stdout
-        self.client, self.user = fxt_login
-        self.client.logger = logger
-
-        api_client = self.client.api_client
-        for k in api_client.configuration.logger:
-            api_client.configuration.logger[k] = logger
-
-        monkeypatch.setattr(cvatpt, "_CACHE_DIR", self.tmp_path / "cache")
-
-        self._create_task()
-
-        yield
-
-    def _create_task(self):
+        self.client = fxt_login[0]
         self.images = generate_image_files(10)
 
-        image_dir = self.tmp_path / "images"
+        image_dir = tmp_path / "images"
         image_dir.mkdir()
 
         image_paths = []
@@ -224,3 +224,129 @@ def test_custom_label_mapping(self):
         _, target = dataset[5]
         assert target.label_id_to_index[label_name_to_id["person"]] == 123
         assert target.label_id_to_index[label_name_to_id["car"]] == 456
+
+
+@pytest.mark.skipif(cvatpt is None, reason="PyTorch dependencies are not installed")
+class TestProjectVisionDataset:
+    @pytest.fixture(autouse=True)
+    def setup(
+        self,
+        tmp_path: Path,
+        fxt_login: Tuple[Client, str],
+    ):
+        self.client = fxt_login[0]
+
+        self.project = self.client.projects.create(
+            models.ProjectWriteRequest(
+                "PyTorch integration test project",
+                labels=[
+                    models.PatchedLabelRequest(name="person"),
+                    models.PatchedLabelRequest(name="car"),
+                ],
+            )
+        )
+        self.label_ids = sorted(l.id for l in self.project.labels)
+
+        all_images = generate_image_files(9)
+        self.images_per_task = [all_images[i * 3 : i * 3 + 3] for i in range(3)]
+
+        image_dir = tmp_path / "images"
+        image_dir.mkdir()
+
+        image_paths_per_task = []
+        for images in self.images_per_task:
+            image_paths = []
+            for image in images:
+                image_path = image_dir / image.name
+                image_path.write_bytes(image.getbuffer())
+                image_paths.append(image_path)
+            image_paths_per_task.append(image_paths)
+
+        self.tasks = [
+            self.client.tasks.create_from_data(
+                models.TaskWriteRequest(
+                    "PyTorch integration test task",
+                    project_id=self.project.id,
+                    subset=subset,
+                ),
+                ResourceType.LOCAL,
+                image_paths,
+                data_params={"image_quality": 70},
+            )
+            for subset, image_paths in zip(["Train", "Test", "Val"], image_paths_per_task)
+        ]
+
+        # sort both self.tasks and self.images_per_task in the order that ProjectVisionDataset uses
+        self.tasks, self.images_per_task = zip(
+            *sorted(zip(self.tasks, self.images_per_task), key=lambda t: t[0].id)
+        )
+
+        for task_id, label_index in ((0, 0), (1, 1), (2, 0)):
+            self.tasks[task_id].update_annotations(
+                models.PatchedLabeledDataRequest(
+                    tags=[
+                        models.LabeledImageRequest(
+                            frame=task_id, label_id=self.label_ids[label_index]
+                        ),
+                    ],
+                )
+            )
+
+    def test_basic(self):
+        dataset = cvatpt.ProjectVisionDataset(self.client, self.project.id)
+
+        assert len(dataset) == sum(task.size for task in self.tasks)
+
+        for sample, image in zip(dataset, itertools.chain.from_iterable(self.images_per_task)):
+            assert torch.equal(TF.pil_to_tensor(sample[0]), TF.pil_to_tensor(PIL.Image.open(image)))
+
+        assert dataset[0][1].annotations.tags[0].label_id == self.label_ids[0]
+        assert dataset[4][1].annotations.tags[0].label_id == self.label_ids[1]
+        assert dataset[8][1].annotations.tags[0].label_id == self.label_ids[0]
+
+    def test_task_filter(self):
+        dataset = cvatpt.ProjectVisionDataset(
+            self.client, self.project.id, task_filter=lambda t: t.subset != self.tasks[0].subset
+        )
+
+        assert len(dataset) == sum(task.size for task in self.tasks[1:])
+
+        for sample, image in zip(dataset, itertools.chain.from_iterable(self.images_per_task[1:])):
+            assert torch.equal(TF.pil_to_tensor(sample[0]), TF.pil_to_tensor(PIL.Image.open(image)))
+
+        assert dataset[1][1].annotations.tags[0].label_id == self.label_ids[1]
+        assert dataset[5][1].annotations.tags[0].label_id == self.label_ids[0]
+
+    def test_custom_label_mapping(self):
+        label_name_to_id = {label.name: label.id for label in self.project.labels}
+
+        dataset = cvatpt.ProjectVisionDataset(
+            self.client, self.project.id, label_name_to_index={"person": 123, "car": 456}
+        )
+
+        _, target = dataset[5]
+        assert target.label_id_to_index[label_name_to_id["person"]] == 123
+        assert target.label_id_to_index[label_name_to_id["car"]] == 456
+
+    def test_separate_transforms(self):
+        dataset = cvatpt.ProjectVisionDataset(
+            self.client,
+            self.project.id,
+            transform=torchvision.transforms.ToTensor(),
+            target_transform=cvatpt.ExtractSingleLabelIndex(),
+        )
+
+        assert torch.equal(
+            dataset[0][0], TF.pil_to_tensor(PIL.Image.open(self.images_per_task[0][0]))
+        )
+        assert torch.equal(dataset[0][1], torch.tensor(0))
+
+    def test_combined_transforms(self):
+        dataset = cvatpt.ProjectVisionDataset(
+            self.client,
+            self.project.id,
+            transforms=lambda x, y: (y, x),
+        )
+
+        assert isinstance(dataset[0][0], cvatpt.Target)
+        assert isinstance(dataset[0][1], PIL.Image.Image)