3350 Remove PyTorch 1.5.x related logic and mark versions for all new APIs

monai/config/deviceconfig.py

@@ -200,8 +200,7 @@ def get_gpu_info() -> OrderedDict:
 
     if num_gpus > 0:
         _dict_append(output, "Current device", torch.cuda.current_device)
-        if hasattr(torch.cuda, "get_arch_list"):  # get_arch_list is new in torch 1.7.1
-            _dict_append(output, "Library compiled for CUDA architectures", torch.cuda.get_arch_list)
+        _dict_append(output, "Library compiled for CUDA architectures", torch.cuda.get_arch_list)
 
     for gpu in range(num_gpus):
         gpu_info = torch.cuda.get_device_properties(gpu)

monai/transforms/post/array.py

@@ -405,7 +405,7 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
             raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.")
 
         if isinstance(img, torch.Tensor):
-            if hasattr(torch, "isin"):
+            if hasattr(torch, "isin"):  # `isin` is new in torch 1.10.0
                 appl_lbls = torch.as_tensor(self.applied_labels, device=img.device)
                 return torch.where(torch.isin(img, appl_lbls), img, torch.tensor(0.0).to(img))
             else:

monai/transforms/utils.py

@@ -1306,7 +1306,7 @@ def shift_fourier(x: NdarrayOrTensor, spatial_dims: int, n_dims: Optional[int] =
         dims = tuple(range(-spatial_dims, 0))
         k: NdarrayOrTensor
         if isinstance(x, torch.Tensor):
-            if hasattr(torch.fft, "fftshift"):
+            if hasattr(torch.fft, "fftshift"):  # `fftshift` is new in torch 1.8.0
                 k = torch.fft.fftshift(torch.fft.fftn(x, dim=dims), dim=dims)
             else:
                 # if using old PyTorch, will convert to numpy array and return
@@ -1339,7 +1339,7 @@ def inv_shift_fourier(k: NdarrayOrTensor, spatial_dims: int, n_dims: Optional[in
         dims = tuple(range(-spatial_dims, 0))
         out: NdarrayOrTensor
         if isinstance(k, torch.Tensor):
-            if hasattr(torch.fft, "ifftshift"):
+            if hasattr(torch.fft, "ifftshift"):  # `ifftshift` is new in torch 1.8.0
                 out = torch.fft.ifftn(torch.fft.ifftshift(k, dim=dims), dim=dims, norm="backward").real
             else:
                 # if using old PyTorch, will convert to numpy array and return

monai/transforms/utils_pytorch_numpy_unification.py

@@ -42,7 +42,7 @@
 def moveaxis(x: NdarrayOrTensor, src: int, dst: int) -> NdarrayOrTensor:
     """`moveaxis` for pytorch and numpy, using `permute` for pytorch ver < 1.8"""
     if isinstance(x, torch.Tensor):
-        if hasattr(torch, "moveaxis"):
+        if hasattr(torch, "moveaxis"):  # `moveaxis` is new in torch 1.8.0
             return torch.moveaxis(x, src, dst)
         return _moveaxis_with_permute(x, src, dst)  # type: ignore
     if isinstance(x, np.ndarray):
@@ -110,7 +110,7 @@ def percentile(x: NdarrayOrTensor, q, dim: Optional[int] = None) -> Union[Ndarra
         result = np.percentile(x, q, axis=dim)
     else:
         q = torch.tensor(q, device=x.device)
-        if hasattr(torch, "quantile"):
+        if hasattr(torch, "quantile"):  # `quantile` is new in torch 1.7.0
             result = torch.quantile(x, q / 100.0, dim=dim)
         else:
             # Note that ``kthvalue()`` works one-based, i.e., the first sorted value
@@ -222,7 +222,7 @@ def ravel(x: NdarrayOrTensor):
         Return a contiguous flattened array/tensor.
     """
     if isinstance(x, torch.Tensor):
-        if hasattr(torch, "ravel"):
+        if hasattr(torch, "ravel"):  # `ravel` is new in torch 1.8.0
             return x.ravel()
         return x.flatten().contiguous()
     return np.ravel(x)
@@ -268,7 +268,7 @@ def maximum(a: NdarrayOrTensor, b: NdarrayOrTensor) -> NdarrayOrTensor:
     """
     if isinstance(a, torch.Tensor) and isinstance(b, torch.Tensor):
         # is torch and has torch.maximum (pt>1.6)
-        if hasattr(torch, "maximum"):
+        if hasattr(torch, "maximum"):  # `maximum` is new in torch 1.7.0
             return torch.maximum(a, b)
         return torch.stack((a, b)).max(dim=0)[0]
     return np.maximum(a, b)

monai/utils/misc.py

@@ -258,9 +258,9 @@ def set_determinism(
         torch.backends.cudnn.deterministic = _flag_deterministic
         torch.backends.cudnn.benchmark = _flag_cudnn_benchmark
     if use_deterministic_algorithms is not None:
-        if hasattr(torch, "use_deterministic_algorithms"):
+        if hasattr(torch, "use_deterministic_algorithms"):  # `use_deterministic_algorithms` is new in torch 1.8.0
             torch.use_deterministic_algorithms(use_deterministic_algorithms)
-        elif hasattr(torch, "set_deterministic"):
+        elif hasattr(torch, "set_deterministic"):  # `set_deterministic` is new in torch 1.7.0
             torch.set_deterministic(use_deterministic_algorithms)  # type: ignore
         else:
             warnings.warn("use_deterministic_algorithms=True, but PyTorch version is too old to set the mode.")

tests/test_mmar_download.py

@@ -22,7 +22,7 @@
 from monai.apps import RemoteMMARKeys, download_mmar, get_model_spec, load_from_mmar
 from monai.apps.mmars import MODEL_DESC
 from monai.apps.mmars.mmars import _get_val
-from tests.utils import SkipIfBeforePyTorchVersion, skip_if_quick
+from tests.utils import skip_if_quick
 
 TEST_CASES = [["clara_pt_prostate_mri_segmentation_1"], ["clara_pt_covid19_ct_lesion_segmentation_1"]]
 TEST_EXTRACT_CASES = [
@@ -104,7 +104,6 @@
 class TestMMMARDownload(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
     @skip_if_quick
-    @SkipIfBeforePyTorchVersion((1, 6))
     def test_download(self, idx):
         try:
             # test model specification