torch is optional dep

bioimageio/core/weight_converter/torch/_onnx.py

@@ -4,7 +4,6 @@
 from typing import Any, List, Sequence, cast
 
 import numpy as np
-import torch
 from numpy.testing import assert_array_almost_equal
 
 from bioimageio.spec import load_description
@@ -14,6 +13,11 @@
 from ...digest_spec import get_member_id, get_test_inputs
 from ...weight_converter.torch._utils import load_torch_model
 
+try:
+    import torch
+except ImportError:
+    torch = None
+
 
 def add_onnx_weights(
     model_spec: "str | Path | v0_4.ModelDescr | v0_5.ModelDescr",
@@ -48,6 +52,7 @@ def add_onnx_weights(
             "The provided model does not have weights in the pytorch state dict format"
         )
 
+    assert torch is not None
     with torch.no_grad():
 
         sample = get_test_inputs(model_spec)

bioimageio/core/weight_converter/torch/_torchscript.py

@@ -3,7 +3,6 @@
 from typing import List, Sequence, Union
 
 import numpy as np
-import torch
 from numpy.testing import assert_array_almost_equal
 from typing_extensions import Any, assert_never
 
@@ -12,14 +11,21 @@
 
 from ._utils import load_torch_model
 
+try:
+    import torch
+except ImportError:
+    torch = None
+
 
 # FIXME: remove Any
 def _check_predictions(
     model: Any,
     scripted_model: Any,
     model_spec: "v0_4.ModelDescr | v0_5.ModelDescr",
-    input_data: Sequence[torch.Tensor],
+    input_data: Sequence["torch.Tensor"],
 ):
+    assert torch is not None
+
     def _check(input_: Sequence[torch.Tensor]) -> None:
         expected_tensors = model(*input_)
         if isinstance(expected_tensors, torch.Tensor):

bioimageio/core/weight_converter/torch/_utils.py

@@ -1,22 +1,24 @@
 from typing import Union
 
-import torch
-
 from bioimageio.core.model_adapters._pytorch_model_adapter import PytorchModelAdapter
 from bioimageio.spec.model import v0_4, v0_5
 from bioimageio.spec.utils import download
 
+try:
+    import torch
+except ImportError:
+    torch = None
+
 
 # additional convenience for pytorch state dict, eventually we want this in python-bioimageio too
 # and for each weight format
 def load_torch_model(  # pyright: ignore[reportUnknownParameterType]
     node: Union[v0_4.PytorchStateDictWeightsDescr, v0_5.PytorchStateDictWeightsDescr],
 ):
+    assert torch is not None
     model = (  # pyright: ignore[reportUnknownVariableType]
         PytorchModelAdapter.get_network(node)
     )
-    state = torch.load(  # pyright: ignore[reportUnknownVariableType]
-        download(node.source).path, map_location="cpu"
-    )
+    state = torch.load(download(node.source).path, map_location="cpu")
     model.load_state_dict(state)  # FIXME: check incompatible keys?
     return model.eval()  # pyright: ignore[reportUnknownVariableType]