fixed issue with gitignore

.gitignore

@@ -1,7 +1,6 @@
 *.so
 *.egg-info/
 *.pyc
-data/
 build/
 libs/
-.idea/
+.idea/

src/dagr/data/ncaltech101_data.py

@@ -0,0 +1,82 @@
+import numpy as np
+import torch
+import hdf5plugin
+import h5py
+
+from pathlib import Path
+from typing import Optional, Callable
+from torch.utils.data import Dataset
+from torch_geometric.data import Data
+from dagr.data.augment import init_transforms
+from dagr.data.utils import to_data
+
+
+class NCaltech101(Dataset):
+
+    def __init__(self, root: Path, split, transform=Optional[Callable[[Data,], Data]], num_events: int=50000):
+        super().__init__()
+        self.load_dir = root / split
+        self.classes = sorted([d.name for d in self.load_dir.glob("*")])
+        self.num_classes = len(self.classes)
+        self.files = sorted(list(self.load_dir.rglob("*.h5")))
+        self.height = 180
+        self.width = 240
+        if transform is not None and hasattr(transform, "transforms"):
+            init_transforms(transform.transforms, self.height, self.width)
+        self.transform = transform
+        self.time_window = 1000000
+        self.num_events = num_events
+
+    def __len__(self):
+        return len(self.files)
+
+    def preprocess(self, data):
+        data.t -= (data.t[-1] - self.time_window + 1)
+        return data
+
+    def load_events(self, f_path):
+        return _load_events(f_path, self.num_events)
+
+    def __getitem__(self, idx):
+        f_path = self.files[idx]
+        target = self.classes.index(str(f_path.parent.name))
+
+        events = self.load_events(f_path)
+        data = to_data(**events,  bbox=self.load_bboxes(f_path, target),
+                       t0=events['t'][0], t1=events['t'], width=self.width, height=self.height,
+                       time_window=self.time_window)
+
+        data = self.preprocess(data)
+
+        data = self.transform(data) if self.transform is not None else data
+
+        if not hasattr(data, "t"):
+            data.t = data.pos[:, -1:]
+            data.pos = data.pos[:, :2].type(torch.int16)
+
+        return data
+
+    def load_bboxes(self, raw_file: Path, class_id):
+        rel_path = str(raw_file.relative_to(self.load_dir))
+        rel_path = rel_path.replace("image_", "annotation_").replace(".h5", ".bin")
+        annotation_file = self.load_dir / "../annotations" / rel_path
+        with annotation_file.open() as fh:
+            annotations = np.fromfile(fh, dtype=np.int16)
+            annotations = np.array(annotations[2:10])
+
+        return np.array([
+            annotations[0], annotations[1],  # upper-left corner
+            annotations[2] - annotations[0],  # width
+            annotations[5] - annotations[1],  # height
+            class_id,
+            1
+        ]).astype("float32").reshape((1,-1))
+
+def _load_events(f_path, num_events):
+    with h5py.File(str(f_path)) as fh:
+        fh = fh['events']
+        x = fh["x"][-num_events:]
+        y = fh["y"][-num_events:]
+        t = fh["t"][-num_events:]
+        p = fh["p"][-num_events:]
+    return dict(x=x, y=y, t=t, p=p)