Add VHR10 datamodule

tests/conf/vhr10.yaml

@@ -0,0 +1,17 @@
+model:
+  class_path: ObjectDetectionTask
+  init_args:
+    model: "faster-rcnn"
+    backbone: "resnet50"
+    num_classes: 11
+    lr: 2.5e-5
+    patience: 10
+data:
+  class_path: VHR10DataModule
+  init_args:
+    batch_size: 1
+    num_workers: 0
+    patch_size: 4
+  dict_kwargs:
+    root: "tests/data/vhr10"
+    download: true

tests/data/vhr10/annotations.json

@@ -1 +1 @@
-{"images": [{"file_name": "001.jpg", "height": 8, "width": 8, "id": 0}, {"file_name": "002.jpg", "height": 8, "width": 8, "id": 1}, {"file_name": "003.jpg", "height": 8, "width": 8, "id": 2}, {"file_name": "004.jpg", "height": 8, "width": 8, "id": 3}, {"file_name": "005.jpg", "height": 8, "width": 8, "id": 4}], "annotations": [{"id": 0, "image_id": 0, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "iscrowd": 0}, {"id": 1, "image_id": 1, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]], "iscrowd": 0}, {"id": 2, "image_id": 2, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]], "iscrowd": 0}, {"id": 3, "image_id": 3, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]], "iscrowd": 0}, {"id": 4, "image_id": 4, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]], "iscrowd": 0}]}
+{"images": [{"file_name": "001.jpg", "height": 8, "width": 8, "id": 0}, {"file_name": "002.jpg", "height": 8, "width": 8, "id": 1}, {"file_name": "003.jpg", "height": 8, "width": 8, "id": 2}, {"file_name": "004.jpg", "height": 8, "width": 8, "id": 3}, {"file_name": "005.jpg", "height": 8, "width": 8, "id": 4}], "annotations": [{"id": 0, "image_id": 0, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]], "iscrowd": 0}, {"id": 1, "image_id": 1, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]], "iscrowd": 0}, {"id": 2, "image_id": 2, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]], "iscrowd": 0}, {"id": 3, "image_id": 3, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]], "iscrowd": 0}, {"id": 4, "image_id": 4, "category_id": 1, "area": 4.0, "bbox": [4, 4, 2, 2], "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]], "iscrowd": 0}]}

tests/data/vhr10/data.py

@@ -5,7 +5,6 @@
 import os
 import shutil
 import subprocess
-from copy import deepcopy
 
 import numpy as np
 from PIL import Image
@@ -47,7 +46,7 @@ def generate_test_data(root: str, n_imgs: int = 3) -> str:
         )
 
     ann = 0
-    for i, img in enumerate(ANNOTATION_FILE["images"]):
+    for _, img in enumerate(ANNOTATION_FILE["images"]):
         annot = {
             "id": ann,
             "image_id": img["id"],
@@ -57,12 +56,7 @@ def generate_test_data(root: str, n_imgs: int = 3) -> str:
             "segmentation": [[1, 1, 2, 2, 3, 3, 4, 5, 5]],
             "iscrowd": 0,
         }
-        if i != 0:
-            ANNOTATION_FILE["annotations"].append(annot)
-        else:
-            noseg_annot = deepcopy(annot)
-            del noseg_annot["segmentation"]
-            ANNOTATION_FILE["annotations"].append(noseg_annot)
+        ANNOTATION_FILE["annotations"].append(annot)
         ann += 1
 
     with open(ann_file, "w") as j:

tests/datasets/test_vhr10.py

@@ -35,11 +35,11 @@ def dataset(
         monkeypatch.setattr(torchgeo.datasets.utils, "download_url", download_url)
         url = os.path.join("tests", "data", "vhr10", "NWPU VHR-10 dataset.rar")
         monkeypatch.setitem(VHR10.image_meta, "url", url)
-        md5 = "5fddb0dfd56a80638831df9f90cbf37a"
+        md5 = "92769845cae6a4e8c74bfa1a0d1d4a80"
         monkeypatch.setitem(VHR10.image_meta, "md5", md5)
         url = os.path.join("tests", "data", "vhr10", "annotations.json")
         monkeypatch.setitem(VHR10.target_meta, "url", url)
-        md5 = "833899cce369168e0d4ee420dac326dc"
+        md5 = "567c4cd8c12624864ff04865de504c58"
         monkeypatch.setitem(VHR10.target_meta, "md5", md5)
         root = str(tmp_path)
         split = request.param

tests/trainers/test_detection.py

@@ -67,7 +67,7 @@ def plot(*args: Any, **kwargs: Any) -> None:
 
 
 class TestObjectDetectionTask:
-    @pytest.mark.parametrize("name", ["nasa_marine_debris"])
+    @pytest.mark.parametrize("name", ["nasa_marine_debris", "vhr10"])
     @pytest.mark.parametrize("model_name", ["faster-rcnn", "fcos", "retinanet"])
     def test_trainer(
         self, monkeypatch: MonkeyPatch, name: str, model_name: str, fast_dev_run: bool

tests/transforms/test_transforms.py

@@ -23,7 +23,7 @@ def batch_gray() -> dict[str, Tensor]:
     return {
         "image": torch.tensor([[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]], dtype=torch.float),
         "mask": torch.tensor([[[0, 0, 1], [0, 1, 1], [1, 1, 1]]], dtype=torch.long),
-        "boxes": torch.tensor([[[0, 1], [1, 1], [1, 0], [0, 0]]], dtype=torch.float),
+        "boxes": torch.tensor([[0.0, 0.0, 2.0, 2.0]], dtype=torch.float),
         "labels": torch.tensor([[0, 1]]),
     }
 
@@ -42,7 +42,7 @@ def batch_rgb() -> dict[str, Tensor]:
             dtype=torch.float,
         ),
         "mask": torch.tensor([[[0, 0, 1], [0, 1, 1], [1, 1, 1]]], dtype=torch.long),
-        "boxes": torch.tensor([[[0, 1], [1, 1], [1, 0], [0, 0]]], dtype=torch.float),
+        "boxes": torch.tensor([[0.0, 0.0, 2.0, 2.0]], dtype=torch.float),
         "labels": torch.tensor([[0, 1]]),
     }
 
@@ -63,7 +63,7 @@ def batch_multispectral() -> dict[str, Tensor]:
             dtype=torch.float,
         ),
         "mask": torch.tensor([[[0, 0, 1], [0, 1, 1], [1, 1, 1]]], dtype=torch.long),
-        "boxes": torch.tensor([[[0, 1], [1, 1], [1, 0], [0, 0]]], dtype=torch.float),
+        "boxes": torch.tensor([[0.0, 0.0, 2.0, 2.0]], dtype=torch.float),
         "labels": torch.tensor([[0, 1]]),
     }
 
@@ -79,7 +79,7 @@ def test_augmentation_sequential_gray(batch_gray: dict[str, Tensor]) -> None:
     expected = {
         "image": torch.tensor([[[[3, 2, 1], [6, 5, 4], [9, 8, 7]]]], dtype=torch.float),
         "mask": torch.tensor([[[1, 0, 0], [1, 1, 0], [1, 1, 1]]], dtype=torch.long),
-        "boxes": torch.tensor([[[1, 0], [2, 0], [2, 1], [1, 1]]], dtype=torch.float),
+        "boxes": torch.tensor([[1.0, 0.0, 3.0, 2.0]], dtype=torch.float),
         "labels": torch.tensor([[0, 1]]),
     }
     augs = transforms.AugmentationSequential(
@@ -102,7 +102,7 @@ def test_augmentation_sequential_rgb(batch_rgb: dict[str, Tensor]) -> None:
             dtype=torch.float,
         ),
         "mask": torch.tensor([[[1, 0, 0], [1, 1, 0], [1, 1, 1]]], dtype=torch.long),
-        "boxes": torch.tensor([[[1, 0], [2, 0], [2, 1], [1, 1]]], dtype=torch.float),
+        "boxes": torch.tensor([[1.0, 0.0, 3.0, 2.0]], dtype=torch.float),
         "labels": torch.tensor([[0, 1]]),
     }
     augs = transforms.AugmentationSequential(
@@ -129,7 +129,7 @@ def test_augmentation_sequential_multispectral(
             dtype=torch.float,
         ),
         "mask": torch.tensor([[[1, 0, 0], [1, 1, 0], [1, 1, 1]]], dtype=torch.long),
-        "boxes": torch.tensor([[[1, 0], [2, 0], [2, 1], [1, 1]]], dtype=torch.float),
+        "boxes": torch.tensor([[1.0, 0.0, 3.0, 2.0]], dtype=torch.float),
         "labels": torch.tensor([[0, 1]]),
     }
     augs = transforms.AugmentationSequential(
@@ -156,7 +156,7 @@ def test_augmentation_sequential_image_only(
             dtype=torch.float,
         ),
         "mask": torch.tensor([[[0, 0, 1], [0, 1, 1], [1, 1, 1]]], dtype=torch.long),
-        "boxes": torch.tensor([[[0, 1], [1, 1], [1, 0], [0, 0]]], dtype=torch.float),
+        "boxes": torch.tensor([[0.0, 0.0, 2.0, 2.0]], dtype=torch.float),
         "labels": torch.tensor([[0, 1]]),
     }
     augs = transforms.AugmentationSequential(
@@ -188,7 +188,7 @@ def test_sequential_transforms_augmentations(
             dtype=torch.float,
         ),
         "mask": torch.tensor([[[1, 0, 0], [1, 1, 0], [1, 1, 1]]], dtype=torch.long),
-        "boxes": torch.tensor([[[1, 0], [2, 0], [2, 1], [1, 1]]], dtype=torch.float),
+        "boxes": torch.tensor([[1.0, 0.0, 3.0, 2.0]], dtype=torch.float),
         "labels": torch.tensor([[0, 1]]),
     }
     train_transforms = transforms.AugmentationSequential(

torchgeo/datamodules/__init__.py

@@ -38,6 +38,7 @@
 from .usavars import USAVarsDataModule
 from .utils import MisconfigurationException
 from .vaihingen import Vaihingen2DDataModule
+from .vhr10 import VHR10DataModule
 from .xview import XView2DataModule
 
 __all__ = (
@@ -79,6 +80,7 @@
     "UCMercedDataModule",
     "USAVarsDataModule",
     "Vaihingen2DDataModule",
+    "VHR10DataModule",
     "XView2DataModule",
     # Base classes
     "BaseDataModule",

torchgeo/datamodules/nasa_marine_debris.py

@@ -5,28 +5,13 @@
 
 from typing import Any
 
+import kornia.augmentation as K
 import torch
-from torch import Tensor
 
 from ..datasets import NASAMarineDebris
+from ..transforms import AugmentationSequential
 from .geo import NonGeoDataModule
-from .utils import dataset_split
-
-
-def collate_fn(batch: list[dict[str, Tensor]]) -> dict[str, Any]:
-    """Custom object detection collate fn to handle variable boxes.
-
-    Args:
-        batch: list of sample dicts return by dataset
-
-    Returns:
-        batch dict output
-    """
-    output: dict[str, Any] = {}
-    output["image"] = torch.stack([sample["image"] for sample in batch])
-    output["boxes"] = [sample["boxes"] for sample in batch]
-    output["labels"] = [torch.tensor([1] * len(sample["boxes"])) for sample in batch]
-    return output
+from .utils import AugPipe, collate_fn_detection, dataset_split
 
 
 class NASAMarineDebrisDataModule(NonGeoDataModule):
@@ -35,6 +20,8 @@ class NASAMarineDebrisDataModule(NonGeoDataModule):
     .. versionadded:: 0.2
     """
 
+    std = torch.tensor(255)
+
     def __init__(
         self,
         batch_size: int = 64,
@@ -58,7 +45,14 @@ def __init__(
         self.val_split_pct = val_split_pct
         self.test_split_pct = test_split_pct
 
-        self.collate_fn = collate_fn
+        self.aug = AugPipe(
+            AugmentationSequential(
+                K.Normalize(mean=self.mean, std=self.std), data_keys=["image", "boxes"]
+            ),
+            batch_size,
+        )
+
+        self.collate_fn = collate_fn_detection
 
     def setup(self, stage: str) -> None:
         """Set up datasets.

torchgeo/datamodules/utils.py

@@ -5,10 +5,13 @@
 
 import math
 from collections.abc import Iterable
-from typing import Any, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 import numpy as np
-from torch import Generator
+import torch
+from einops import rearrange
+from torch import Generator, Tensor
+from torch.nn import Module
 from torch.utils.data import Subset, TensorDataset, random_split
 
 from ..datasets import NonGeoDataset
@@ -19,6 +22,86 @@ class MisconfigurationException(Exception):
     """Exception used to inform users of misuse with Lightning."""
 
 
+class AugPipe(Module):
+    """Pipeline for applying augmentations sequentially on select data keys.
+
+    .. versionadded:: 0.6
+    """
+
+    def __init__(
+        self, augs: Callable[[dict[str, Any]], dict[str, Any]], batch_size: int
+    ) -> None:
+        """Initialize a new AugPipe instance.
+
+        Args:
+            augs: Augmentations to apply.
+            batch_size: Batch size
+        """
+        super().__init__()
+        self.augs = augs
+        self.batch_size = batch_size
+
+    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+        """Apply the augmentation.
+
+        Args:
+            batch: Input batch.
+
+        Returns:
+            Augmented batch.
+        """
+        batch_len = len(batch["image"])
+        for bs in range(batch_len):
+            batch_dict = {
+                "image": batch["image"][bs],
+                "labels": batch["labels"][bs],
+                "boxes": batch["boxes"][bs],
+            }
+
+            if "masks" in batch:
+                batch_dict["masks"] = batch["masks"][bs]
+
+            batch_dict = self.augs(batch_dict)
+
+            batch["image"][bs] = batch_dict["image"]
+            batch["labels"][bs] = batch_dict["labels"]
+            batch["boxes"][bs] = batch_dict["boxes"]
+
+            if "masks" in batch:
+                batch["masks"][bs] = batch_dict["masks"]
+
+        # Stack images
+        batch["image"] = rearrange(batch["image"], "b () c h w -> b c h w")
+
+        return batch
+
+
+def collate_fn_detection(batch: list[dict[str, Tensor]]) -> dict[str, Any]:
+    """Custom collate fn for object detection and instance segmentation.
+
+    Args:
+        batch: list of sample dicts return by dataset
+
+    Returns:
+        batch dict output
+
+    .. versionadded:: 0.6
+    """
+    output: dict[str, Any] = {}
+    output["image"] = [sample["image"] for sample in batch]
+    output["boxes"] = [sample["boxes"].float() for sample in batch]
+    if "labels" in batch[0]:
+        output["labels"] = [sample["labels"] for sample in batch]
+    else:
+        output["labels"] = [
+            torch.tensor([1] * len(sample["boxes"])) for sample in batch
+        ]
+
+    if "masks" in batch[0]:
+        output["masks"] = [sample["masks"] for sample in batch]
+    return output
+
+
 def dataset_split(
     dataset: Union[TensorDataset, NonGeoDataset],
     val_pct: float,