minor

Author: AlekseySh

docs/source/contents/datasets.rst

@@ -7,28 +7,28 @@ Datasets
 .. contents::
    :local:
 
-ImagesBaseDataset
+ImageBaseDataset
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: oml.datasets.images.BaseDataset
+.. autoclass:: oml.datasets.images.ImageBaseDataset
     :undoc-members:
     :show-inheritance:
 
     .. automethod:: __init__
     .. automethod:: visualize
 
-ImagesDatasetLabeled
+ImageDatasetLabeled
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: oml.datasets.images.ImagesDatasetLabeled
+.. autoclass:: oml.datasets.images.ImageDatasetLabeled
     :undoc-members:
     :show-inheritance:
 
     .. automethod:: __init__
     .. automethod:: __getitem__
     .. automethod:: get_labels
 
-ImagesDatasetQueryGalleryLabeled
+ImageDatasetQueryGalleryLabeled
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: oml.datasets.images.DatasetQueryGallery
+.. autoclass:: oml.datasets.images.ImageDatasetQueryGalleryLabeled
     :undoc-members:
     :show-inheritance:
 

oml/datasets/base.py

@@ -1,13 +1,13 @@
-from oml.datasets.images import ImagesDatasetLabeled, ImagesDatasetQueryGalleryLabeled
+from oml.datasets.images import ImageDatasetLabeled, ImageDatasetQueryGalleryLabeled
 
 
-class DatasetWithLabels(ImagesDatasetLabeled):
-    # this class allows to have back compatibility
+class DatasetWithLabels(ImageDatasetLabeled):
+    # this class allows to have backward compatibility
     pass
 
 
-class DatasetQueryGallery(ImagesDatasetQueryGalleryLabeled):
-    # this class allows to have back compatibility
+class DatasetQueryGallery(ImageDatasetQueryGalleryLabeled):
+    # this class allows to have backward compatibility
     pass
 
 

oml/datasets/images.py

@@ -75,7 +75,7 @@ def parse_bboxes(df: pd.DataFrame) -> Optional[TBBoxes]:
     return bboxes
 
 
-class ImagesBaseDataset(IBaseDataset, IVisualizableDataset):
+class ImageBaseDataset(IBaseDataset, IVisualizableDataset):
     """
     The base class that handles image specific logic.
 
@@ -218,7 +218,7 @@ def bboxes_keys(self) -> Tuple[str, ...]:
         return self.x1_key, self.y1_key, self.x2_key, self.y2_key
 
 
-class ImagesDatasetLabeled(ImagesBaseDataset, IDatasetLabeled):
+class ImageDatasetLabeled(ImageBaseDataset, IDatasetLabeled):
     """
     The dataset of images having their ground truth labels.
 
@@ -298,7 +298,7 @@ def get_label2category(self) -> Optional[Dict[int, Union[str, int]]]:
         return label2category
 
 
-class ImagesDatasetQueryGalleryLabeled(ImagesDatasetLabeled, IDatasetQueryGalleryLabeled):
+class ImageDatasetQueryGalleryLabeled(ImageDatasetLabeled, IDatasetQueryGalleryLabeled):
     """
     The dataset of images having `query`/`gallery` split.
 
@@ -399,7 +399,7 @@ def get_retrieval_images_datasets(
     df_train = df[df[SPLIT_COLUMN] == "train"].reset_index(drop=True)
     df_train[LABELS_COLUMN] = df_train[LABELS_COLUMN].map(mapper)
 
-    train_dataset = ImagesDatasetLabeled(
+    train_dataset = ImageDatasetLabeled(
         df=df_train,
         dataset_root=dataset_root,
         transform=transforms_train,
@@ -409,7 +409,7 @@ def get_retrieval_images_datasets(
 
     # val (query + gallery)
     df_query_gallery = df[df[SPLIT_COLUMN] == "validation"].reset_index(drop=True)
-    valid_dataset = ImagesDatasetQueryGalleryLabeled(
+    valid_dataset = ImageDatasetQueryGalleryLabeled(
         df=df_query_gallery,
         dataset_root=dataset_root,
         transform=transforms_val,
@@ -421,8 +421,8 @@ def get_retrieval_images_datasets(
 
 
 __all__ = [
-    "ImagesBaseDataset",
-    "ImagesDatasetLabeled",
-    "ImagesDatasetQueryGalleryLabeled",
+    "ImageBaseDataset",
+    "ImageDatasetLabeled",
+    "ImageDatasetQueryGalleryLabeled",
     "get_retrieval_images_datasets",
 ]

oml/datasets/pairs.py

@@ -4,7 +4,7 @@
 from torch import Tensor
 
 from oml.const import INDEX_KEY, PAIR_1ST_KEY, PAIR_2ND_KEY, TBBoxes
-from oml.datasets.images import ImagesBaseDataset
+from oml.datasets.images import ImageBaseDataset
 from oml.interfaces.datasets import IPairsDataset
 from oml.transforms.images.torchvision import get_normalisation_torch
 from oml.transforms.images.utils import TTransforms
@@ -98,8 +98,8 @@ def __init__(
 
         cache_size = cache_size // 2 if cache_size else None
         dataset_args = {"transform": transform, "f_imread": f_imread, "cache_size": cache_size}
-        self.dataset1 = ImagesBaseDataset(paths=paths1, bboxes=bboxes1, **dataset_args)
-        self.dataset2 = ImagesBaseDataset(paths=paths2, bboxes=bboxes2, **dataset_args)
+        self.dataset1 = ImageBaseDataset(paths=paths1, bboxes=bboxes1, **dataset_args)
+        self.dataset2 = ImageBaseDataset(paths=paths2, bboxes=bboxes2, **dataset_args)
 
         self.pair_1st_key = pair_1st_key
         self.pair_2nd_key = pair_2nd_key

oml/inference/flat.py

@@ -7,7 +7,7 @@
 from torch import Tensor, nn
 
 from oml.const import PATHS_COLUMN, SPLIT_COLUMN
-from oml.datasets.images import ImagesBaseDataset
+from oml.datasets.images import ImageBaseDataset
 from oml.inference.abstract import _inference
 from oml.interfaces.models import IExtractor
 from oml.transforms.images.utils import TTransforms
@@ -28,7 +28,7 @@ def inference_on_images(
     use_fp16: bool = False,
     accumulate_on_cpu: bool = True,
 ) -> Tensor:
-    dataset = ImagesBaseDataset(paths=paths, bboxes=None, transform=transform, f_imread=f_imread, cache_size=0)
+    dataset = ImageBaseDataset(paths=paths, bboxes=None, transform=transform, f_imread=f_imread, cache_size=0)
     device = get_device(model)
 
     def _apply(model_: nn.Module, batch_: Dict[str, Any]) -> Tensor:

oml/interfaces/datasets.py

@@ -74,7 +74,7 @@ def get_gallery_ids(self) -> LongTensor:
 
 class IDatasetQueryGalleryLabeled(IDatasetQueryGallery, IDatasetLabeled, ABC):
     """
-    This class is similar to "IDatasetQueryGalleryPrediction", but we also have ground truth labels.
+    This interface is similar to `IDatasetQueryGallery`, but there are ground truth labels.
     """
 
 

oml/lightning/pipelines/predict.py

@@ -7,7 +7,7 @@
 from torch.utils.data import DataLoader
 
 from oml.const import IMAGE_EXTENSIONS, TCfg
-from oml.datasets.images import ImagesBaseDataset
+from oml.datasets.images import ImageBaseDataset
 from oml.ddp.utils import get_world_size_safe, is_main_process, sync_dicts_ddp
 from oml.lightning.modules.extractor import ExtractorModule
 from oml.lightning.pipelines.parser import parse_engine_params_from_config
@@ -41,7 +41,7 @@ def extractor_prediction_pipeline(cfg: TCfg) -> None:
     if broken_images:
         raise ValueError(f"There are images that cannot be open:\n {broken_images}.")
 
-    dataset = ImagesBaseDataset(paths=filenames, transform=transforms, f_imread=f_imread)
+    dataset = ImageBaseDataset(paths=filenames, transform=transforms, f_imread=f_imread)
 
     loader = DataLoader(
         dataset=dataset, batch_size=cfg["bs"], num_workers=cfg["num_workers"], shuffle=False, drop_last=False

oml/lightning/pipelines/train_postprocessor.py

@@ -11,7 +11,7 @@
 from torch.utils.data import DataLoader
 
 from oml.const import BBOXES_COLUMNS, EMBEDDINGS_KEY, TCfg
-from oml.datasets.base import ImagesDatasetLabeled, ImagesDatasetQueryGalleryLabeled
+from oml.datasets.base import ImageDatasetLabeled, ImageDatasetQueryGalleryLabeled
 from oml.inference.flat import inference_on_dataframe
 from oml.interfaces.models import IPairwiseModel
 from oml.lightning.callbacks.metric import MetricValCallback, MetricValCallbackDDP
@@ -81,13 +81,13 @@ def get_loaders_with_embeddings(cfg: TCfg) -> Tuple[DataLoader, DataLoader]:
         use_fp16=int(cfg.get("precision", 32)) == 16,
     )
 
-    train_dataset = ImagesDatasetLabeled(
+    train_dataset = ImageDatasetLabeled(
         df=df_train,
         transform=get_transforms_by_cfg(cfg["transforms_train"]),
         extra_data={EMBEDDINGS_KEY: emb_train},
     )
 
-    valid_dataset = ImagesDatasetQueryGalleryLabeled(
+    valid_dataset = ImageDatasetQueryGalleryLabeled(
         df=df_val,
         # we don't care about transforms, since the only goal of this dataset is to deliver embeddings
         transform=get_normalisation_resize_torch(im_size=8),

tests/test_oml/test_transforms/test_image_augs.py

@@ -5,7 +5,7 @@
 from omegaconf import OmegaConf
 
 from oml.const import CONFIGS_PATH, MOCK_DATASET_PATH
-from oml.datasets.images import ImagesDatasetLabeled
+from oml.datasets.images import ImageDatasetLabeled
 from oml.registry.transforms import TRANSFORMS_REGISTRY, get_transforms_by_cfg
 
 
@@ -14,7 +14,7 @@ def test_transforms(aug_name: Optional[str]) -> None:
     df = pd.read_csv(MOCK_DATASET_PATH / "df.csv")
     transforms = get_transforms_by_cfg(OmegaConf.load(CONFIGS_PATH / "transforms" / f"{aug_name}.yaml"))
 
-    dataset = ImagesDatasetLabeled(df=df, dataset_root=MOCK_DATASET_PATH, transform=transforms)
+    dataset = ImageDatasetLabeled(df=df, dataset_root=MOCK_DATASET_PATH, transform=transforms)
 
     _ = dataset[0]
 
@@ -23,6 +23,6 @@ def test_transforms(aug_name: Optional[str]) -> None:
 
 def test_default_transforms() -> None:
     df = pd.read_csv(MOCK_DATASET_PATH / "df.csv")
-    dataset = ImagesDatasetLabeled(df=df, dataset_root=MOCK_DATASET_PATH, transform=None)
+    dataset = ImageDatasetLabeled(df=df, dataset_root=MOCK_DATASET_PATH, transform=None)
     _ = dataset[0]
     assert True