Updating to match Numpy 2.0 requirements

environment-dev.yml

@@ -5,10 +5,10 @@ channels:
  - nvidia
  - conda-forge
 dependencies:
- - numpy>=1.20
+ - numpy>=1.25,<2.0
  - pytorch>=1.9
  - torchvision
- - pytorch-cuda=11.6
+ - pytorch-cuda>=11.6
  - pip
  - pip:
  - -r requirements-dev.txt

monai/data/utils.py

@@ -927,7 +927,7 @@ def compute_shape_offset(
  corners = in_affine_ @ corners
  all_dist = corners_out[:-1].copy()
  corners_out = corners_out[:-1] / corners_out[-1]
- out_shape = np.round(corners_out.ptp(axis=1)) if scale_extent else np.round(corners_out.ptp(axis=1) + 1.0)
+ out_shape = np.round(np.ptp(corners_out, axis=1)) if scale_extent else np.round(np.ptp(corners_out, axis=1) + 1.0)
  offset = None
  for i in range(corners.shape[1]):
  min_corner = np.min(all_dist - all_dist[:, i : i + 1], 1)

monai/transforms/io/array.py

@@ -86,7 +86,7 @@ def switch_endianness(data, new="<"):
  if new not in ("<", ">"):
  raise NotImplementedError(f"Not implemented option new={new}.")
  if current_ != new:
- data = data.byteswap().newbyteorder(new)
+ data = data.byteswap().view(data.dtype.newbyteorder(new))
  elif isinstance(data, tuple):
  data = tuple(switch_endianness(x, new) for x in data)
  elif isinstance(data, list):

monai/transforms/spatial/functional.py

@@ -373,7 +373,7 @@ def rotate(img, angle, output_shape, mode, padding_mode, align_corners, dtype, l
  if output_shape is None:
  corners = np.asarray(np.meshgrid(*[(0, dim) for dim in im_shape], indexing="ij")).reshape((len(im_shape), -1))
  corners = transform[:-1, :-1] @ corners # type: ignore
- output_shape = np.asarray(corners.ptp(axis=1) + 0.5, dtype=int)
+ output_shape = np.asarray(np.ptp(corners, axis=1) + 0.5, dtype=int)
  else:
  output_shape = np.asarray(output_shape, dtype=int)
  shift = create_translate(input_ndim, ((np.array(im_shape) - 1) / 2).tolist())

monai/transforms/transform.py

@@ -203,8 +203,8 @@ def set_random_state(self, seed: int | None = None, state: np.random.RandomState
 
  """
  if seed is not None:
- _seed = id(seed) if not isinstance(seed, (int, np.integer)) else seed
- _seed = _seed % MAX_SEED
+ _seed = np.int64(id(seed) if not isinstance(seed, (int, np.integer)) else seed)
+ _seed = _seed % MAX_SEED # need to account for Numpy2.0 which doesn't silently convert to int64
  self.R = np.random.RandomState(_seed)
  return self
 

requirements.txt

@@ -1,2 +1,2 @@
 torch>=1.9
-numpy>=1.20,<=1.26.0
+numpy>=1.25,<2.0

setup.cfg

@@ -42,7 +42,7 @@ setup_requires =
  ninja
 install_requires =
  torch>=1.9
- numpy>=1.20
+ numpy>=1.25,<2.0
 
 [options.extras_require]
 all =

tests/test_meta_tensor.py

@@ -448,7 +448,7 @@ def test_shape(self):
 
  def test_astype(self):
  t = MetaTensor([1.0], affine=torch.tensor(1), meta={"fname": "filename"})
- for np_types in ("float32", "np.float32", "numpy.float32", np.float32, float, "int", np.compat.long, np.uint16):
+ for np_types in ("float32", "np.float32", "numpy.float32", np.float32, float, "int", np.uint16):
  self.assertIsInstance(t.astype(np_types), np.ndarray)
  for pt_types in ("torch.float", torch.float, "torch.float64"):
  self.assertIsInstance(t.astype(pt_types), torch.Tensor)

tests/test_nifti_endianness.py

@@ -82,7 +82,7 @@ def test_switch(self): # verify data types
  after = switch_endianness(before)
  np.testing.assert_allclose(after.astype(float), expected_float)
 
- before = np.array(["1.12", "-9.2", "42"], dtype=np.string_)
+ before = np.array(["1.12", "-9.2", "42"], dtype=np.bytes_)
  after = switch_endianness(before)
  np.testing.assert_array_equal(before, after)
 

tests/test_signal_fillempty.py

@@ -30,7 +30,7 @@ class TestSignalFillEmptyNumpy(unittest.TestCase):
  def test_correct_parameters_multi_channels(self):
  self.assertIsInstance(SignalFillEmpty(replacement=0.0), SignalFillEmpty)
  sig = np.load(TEST_SIGNAL)
- sig[:, 123] = np.NAN
+ sig[:, 123] = np.nan
  fillempty = SignalFillEmpty(replacement=0.0)
  fillemptysignal = fillempty(sig)
  self.assertTrue(not np.isnan(fillemptysignal).any())
@@ -42,7 +42,7 @@ class TestSignalFillEmptyTorch(unittest.TestCase):
  def test_correct_parameters_multi_channels(self):
  self.assertIsInstance(SignalFillEmpty(replacement=0.0), SignalFillEmpty)
  sig = convert_to_tensor(np.load(TEST_SIGNAL))
- sig[:, 123] = convert_to_tensor(np.NAN)
+ sig[:, 123] = convert_to_tensor(np.nan)
  fillempty = SignalFillEmpty(replacement=0.0)
  fillemptysignal = fillempty(sig)
  self.assertTrue(not torch.isnan(fillemptysignal).any())

tests/test_signal_fillemptyd.py

@@ -30,7 +30,7 @@ class TestSignalFillEmptyNumpy(unittest.TestCase):
  def test_correct_parameters_multi_channels(self):
  self.assertIsInstance(SignalFillEmptyd(replacement=0.0), SignalFillEmptyd)
  sig = np.load(TEST_SIGNAL)
- sig[:, 123] = np.NAN
+ sig[:, 123] = np.nan
  data = {}
  data["signal"] = sig
  fillempty = SignalFillEmptyd(keys=("signal",), replacement=0.0)
@@ -46,7 +46,7 @@ class TestSignalFillEmptyTorch(unittest.TestCase):
  def test_correct_parameters_multi_channels(self):
  self.assertIsInstance(SignalFillEmptyd(replacement=0.0), SignalFillEmptyd)
  sig = convert_to_tensor(np.load(TEST_SIGNAL))
- sig[:, 123] = convert_to_tensor(np.NAN)
+ sig[:, 123] = convert_to_tensor(np.nan)
  data = {}
  data["signal"] = sig
  fillempty = SignalFillEmptyd(keys=("signal",), replacement=0.0)