[build] NumPy 2.0 support (#1709)

.conda/meta.yaml

@@ -20,7 +20,7 @@ requirements:
     - setuptools
 
   run:
-    - numpy >=1.16.0, <2.0.0
+    - numpy >=1.16.0, <3.0.0
     - scipy >=1.4.0, <2.0.0
     - pillow >=9.2.0
     - h5py >=3.1.0, <4.0.0

doctr/transforms/functional/pytorch.py

@@ -89,7 +89,7 @@ def rotate_sample(
     rotated_geoms[..., 0] = rotated_geoms[..., 0] / rotated_img.shape[2]
     rotated_geoms[..., 1] = rotated_geoms[..., 1] / rotated_img.shape[1]
 
-    return rotated_img, np.clip(rotated_geoms, 0, 1)
+    return rotated_img, np.clip(np.around(rotated_geoms, decimals=15), 0, 1)
 
 
 def crop_detection(

doctr/utils/metrics.py

@@ -149,7 +149,7 @@ def box_iou(boxes_1: np.ndarray, boxes_2: np.ndarray) -> np.ndarray:
         right = np.minimum(r1, r2.T)
         bot = np.minimum(b1, b2.T)
 
-        intersection = np.clip(right - left, 0, np.Inf) * np.clip(bot - top, 0, np.Inf)
+        intersection = np.clip(right - left, 0, np.inf) * np.clip(bot - top, 0, np.inf)
         union = (r1 - l1) * (b1 - t1) + ((r2 - l2) * (b2 - t2)).T - intersection
         iou_mat = intersection / union
 

pyproject.toml

@@ -34,7 +34,7 @@ dynamic = ["version"]
 dependencies = [
     # For proper typing, mypy needs numpy>=1.20.0 (cf. https://github.com/numpy/numpy/pull/16515)
     # Additional typing support is brought by numpy>=1.22.4, but core build sticks to >=1.16.0
-    "numpy>=1.16.0,<2.0.0",
+    "numpy>=1.16.0,<3.0.0",
     "scipy>=1.4.0,<2.0.0",
     "h5py>=3.1.0,<4.0.0",
     "opencv-python>=4.5.0,<5.0.0",
@@ -75,7 +75,6 @@ contrib = [
 testing = [
     "pytest>=5.3.2",
     "coverage[toml]>=4.5.4",
-    "hdf5storage>=0.1.18",
     "onnxruntime>=1.11.0",
     "requests>=2.20.0",
     "psutil>=5.9.5"
@@ -112,7 +111,6 @@ dev = [
     # Testing
     "pytest>=5.3.2",
     "coverage[toml]>=4.5.4",
-    "hdf5storage>=0.1.18",
     "onnxruntime>=1.11.0",
     "requests>=2.20.0",
     "psutil>=5.9.5",

tests/conftest.py

@@ -4,7 +4,6 @@
 from io import BytesIO
 
 import cv2
-import hdf5storage
 import numpy as np
 import pytest
 import requests
@@ -257,24 +256,34 @@ def mock_imgur5k(tmpdir_factory, mock_image_stream):
 def mock_svhn_dataset(tmpdir_factory, mock_image_stream):
     root = tmpdir_factory.mktemp("datasets")
     svhn_root = root.mkdir("svhn")
+    train_root = svhn_root.mkdir("train")
     file = BytesIO(mock_image_stream)
+
+    # NOTE: hdf5storage seems not to be maintained anymore, ref.: https://github.com/frejanordsiek/hdf5storage/pull/134
+    # Instead we download the mocked data which was generated using the following code:
     # ascii image names
-    first = np.array([[49], [46], [112], [110], [103]], dtype=np.int16)  # 1.png
-    second = np.array([[50], [46], [112], [110], [103]], dtype=np.int16)  # 2.png
-    third = np.array([[51], [46], [112], [110], [103]], dtype=np.int16)  # 3.png
+    # first = np.array([[49], [46], [112], [110], [103]], dtype=np.int16)  # 1.png
+    # second = np.array([[50], [46], [112], [110], [103]], dtype=np.int16)  # 2.png
+    # third = np.array([[51], [46], [112], [110], [103]], dtype=np.int16)  # 3.png
     # labels: label is also ascii
-    label = {
-        "height": [35, 35, 35, 35],
-        "label": [1, 1, 3, 7],
-        "left": [116, 128, 137, 151],
-        "top": [27, 29, 29, 26],
-        "width": [15, 10, 17, 17],
-    }
-
-    matcontent = {"digitStruct": {"name": [first, second, third], "bbox": [label, label, label]}}
+    # label = {
+    #     "height": [35, 35, 35, 35],
+    #     "label": [1, 1, 3, 7],
+    #     "left": [116, 128, 137, 151],
+    #     "top": [27, 29, 29, 26],
+    #     "width": [15, 10, 17, 17],
+    # }
+
+    # matcontent = {"digitStruct": {"name": [first, second, third], "bbox": [label, label, label]}}
     # Mock train data
-    train_root = svhn_root.mkdir("train")
-    hdf5storage.write(matcontent, filename=train_root.join("digitStruct.mat"))
+    # hdf5storage.write(matcontent, filename=train_root.join("digitStruct.mat"))
+
+    # Downloading the mocked data
+    url = "https://github.com/mindee/doctr/releases/download/v0.9.0/digitStruct.mat"
+    response = requests.get(url)
+    with open(train_root.join("digitStruct.mat"), "wb") as f:
+        f.write(response.content)
+
     for i in range(3):
         fn = train_root.join(f"{i + 1}.png")
         with open(fn, "wb") as f: