Bioimageio updates

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 24.12.0
+current_version = 25.4.0
 parse = (?P<year>\d+)\.(?P<month>\d+)\.(?P<number>\d+)((?P<release>[a-z]+)?(?P<build>\d+)?)?
 serialize = 
 	{year}.{month}.{number}{release}

.github/workflows/ci.yml

@@ -24,6 +24,7 @@ jobs:
           auto-activate-base: false
           activate-environment: tiktorch-server-env
           environment-file: environment.yml
+          conda-remove-defaults: "true"
           channel-priority: strict
           miniforge-variant: Miniforge3
       - name: Get the latest commit hash and target ref
@@ -51,6 +52,7 @@ jobs:
           auto-activate-base: false
           activate-environment: tiktorch-server-env
           environment-file: environment.yml
+          conda-remove-defaults: "true"
           channel-priority: strict
           miniforge-variant: Miniforge3
       - name: conda diagnostics
@@ -89,6 +91,8 @@ jobs:
         with:
           auto-update-conda: true
           auto-activate-base: true
+          channels: conda-forge
+          conda-remove-defaults: "true"
           channel-priority: strict
           miniforge-variant: Miniforge3
       - name: install common conda dependencies
@@ -134,6 +138,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           channel-priority: strict
+          conda-remove-defaults: "true"
           miniforge-variant: Miniforge3
       - name: install common conda dependencies
         run: conda install -n base -c conda-forge conda-build setuptools_scm -y
@@ -150,14 +155,14 @@ jobs:
         shell: bash -l {0}
         run: |
           conda build --test --override-channels \
-            -c ./pkgs -c ilastik-forge -c pytorch -c conda-forge \
+            -c ./pkgs -c pytorch -c conda-forge \
             ./pkgs/noarch/${TIKTORCH_PACKAGE_NAME}
       - name: macOS Test
         if: matrix.os == 'macos-latest'
         shell: bash -l {0}
         run: |
           conda build --test --override-channels \
-            -c ./pkgs -c ilastik-forge -c pytorch -c conda-forge \
+            -c ./pkgs -c pytorch -c conda-forge \
             ./pkgs/noarch/${TIKTORCH_PACKAGE_NAME}
       - name: Windows Test
         if: matrix.os == 'windows-latest'
@@ -169,5 +174,5 @@ jobs:
         shell: cmd /C CALL {0}
         run: |
           conda build --test --override-channels ^
-            -c %CD%\pkgs -c ilastik-forge -c pytorch -c conda-forge ^
+            -c %CD%\pkgs -c pytorch -c conda-forge ^
             .\pkgs\noarch\%TIKTORCH_PACKAGE_NAME%

Makefile

@@ -12,7 +12,7 @@ version:
 
 devenv:
 	. $$(conda info --base)/etc/profile.d/conda.sh
-	mamba env create --file environment.yml --name $(TIKTORCH_ENV_NAME)
+	conda env create --file environment.yml --name $(TIKTORCH_ENV_NAME)
 	make install_submodules
 
 run_server:

conda-recipe/meta.yaml

@@ -62,7 +62,6 @@ test:
     - pytest-grpc
     - pytest-timeout
     - cpuonly
-    - ilastik-pytorch-version-helper-cpu
   imports:
     # client
     - tiktorch

environment.yml

@@ -1,9 +1,7 @@
 name: tiktorch-server-env
 channels:
   - pytorch
-  - ilastik-forge
   - conda-forge
-  - nodefaults
 dependencies:
   # - bioimage.spec via submodule
   # - bioimage.core via submodule

setup.py

@@ -11,7 +11,7 @@
 
 setup(
     name="tiktorch",
-    version="24.12.0",
+    version="25.4.0",
     description="Tiktorch client/server",
     long_description=long_description,
     long_description_content_type="text/markdown",
@@ -27,8 +27,8 @@
     ],
     packages=find_packages(exclude=["tests"]),  # Required
     install_requires=[
-        "bioimageio.spec==0.5.3.3",
-        "bioimageio.core==0.6.10",
+        "bioimageio.spec",
+        "bioimageio.core==0.8.0",
         "grpcio>=1.31",
         "numpy<2",  # pytorch 2.2.2-py3.9_0 for macos is compiled with numpy 1.*
         "protobuf",

tests/conftest.py

@@ -393,7 +393,9 @@ def _bioimage_model_siso_v4(
     model_descr = v0_4.ModelDescr(
         name="mocked v4 model",
         authors=[v0_4.Author(name="me")],
-        cite=[v0_4.CiteEntry(text="for model training see my paper", url=HttpUrl("https://doi.org/10.1234something"))],
+        cite=[
+            v0_4.CiteEntry(text="for model training see my paper", url=HttpUrl("https://example.com/10.1234something"))
+        ],
         description="",
         inputs=[input_tensor],
         outputs=[output_tensor],

tests/test_server/test_grpc/test_inference_servicer.py

@@ -274,7 +274,6 @@ def test_call_predict_invalid_tensor_ids(self, grpc_stub, bioimage_model_explici
             ("channel", "batch", "x", "y"),
             ("time", "channel", "x", "y"),
             ("batch", "channel", "z", "y"),
-            ("b", "c", "x", "y"),
         ],
     )
     def test_call_predict_invalid_axes(self, grpc_stub, axes, bioimage_model_explicit_add_one_siso_v5):

tests/test_server/test_grpc/test_training_servicer.py

@@ -508,10 +508,13 @@ def test_close_trainer_session_twice(self, grpc_stub):
         "dims, shape",
         [
             (
-                ("b", "c", "z", "y", "x"),
+                ("batch", "channel", "z", "y", "x"),
                 (5, 3, 1, 128, 128),
             ),
-            (("b", "z", "y", "x", "c"), (5, 1, 128, 128, 3)),  # order of input may be different than the one expected
+            (
+                ("batch", "z", "y", "x", "channel"),
+                (5, 1, 128, 128, 3),
+            ),  # order of input may be different than the one expected
         ],
     )
     def test_forward_while_running(self, grpc_stub, dims, shape):
@@ -534,7 +537,7 @@ def test_forward_while_running(self, grpc_stub, dims, shape):
         predicted_tensors = [pb_tensor_to_xarray(pb_tensor) for pb_tensor in response.tensors]
         assert len(predicted_tensors) == 1
         predicted_tensor = predicted_tensors[0]
-        assert predicted_tensor.dims == ("b", "c", "z", "y", "x")
+        assert predicted_tensor.dims == ("batch", "channel", "z", "y", "x")
         out_channels_unet2d = 2
         assert predicted_tensor.shape == (5, out_channels_unet2d, 1, 128, 128)
 
@@ -550,7 +553,7 @@ def test_forward_while_paused(self, grpc_stub):
         out_channels_unet2d = 2
         shape = (batch, in_channels_unet2d, 1, 128, 128)
         data = np.random.rand(*shape).astype(np.float32)
-        xarray_data = xr.DataArray(data, dims=("b", "c", "z", "y", "x"))
+        xarray_data = xr.DataArray(data, dims=("batch", "channel", "z", "y", "x"))
         pb_tensor = xarray_to_pb_tensor(tensor_id="input", array=xarray_data)
         predict_request = utils_pb2.PredictRequest(modelSessionId=training_session_id, tensors=[pb_tensor])
 
@@ -564,7 +567,7 @@ def test_forward_while_paused(self, grpc_stub):
         predicted_tensors = [pb_tensor_to_xarray(pb_tensor) for pb_tensor in response.tensors]
         assert len(predicted_tensors) == 1
         predicted_tensor = predicted_tensors[0]
-        assert predicted_tensor.dims == ("b", "c", "z", "y", "x")
+        assert predicted_tensor.dims == ("batch", "channel", "z", "y", "x")
         assert predicted_tensor.shape == (batch, out_channels_unet2d, 1, 128, 128)
 
     def test_forward_invalid_dims(self, grpc_stub):

tiktorch/trainer.py

@@ -433,13 +433,15 @@
 
     def _get_pytorch_tensor_from_bioimageio_tensor(self, bioimageio_tensor: bioimageio.core.Tensor) -> torch.Tensor:
         xr_array = bioimageio_tensor.data
-        expected_dims = {"b", "c", "z", "y", "x"}
+        expected_dims = {"batch", "channel", "z", "y", "x"}
         if set(xr_array.dims) != expected_dims:
             raise ValueError(f"Tensor dims should be {expected_dims}, but got {xr_array.dims}")
-        return torch.from_numpy(xr_array.transpose("b", "c", "z", "y", "x").values)
+        return torch.from_numpy(xr_array.transpose("batch", "channel", "z", "y", "x").values)
 
     def _get_bioimageio_tensor_from_pytorch_tensor(self, pytorch_tensor: torch.Tensor) -> bioimageio.core.Tensor:
-        return bioimageio.core.Tensor.from_xarray(xr.DataArray(pytorch_tensor.numpy(), dims=["b", "c", "z", "y", "x"]))
+        return bioimageio.core.Tensor.from_xarray(
+            xr.DataArray(pytorch_tensor.numpy(), dims=["batch", "channel", "z", "y", "x"])
+        )
 
     def should_stop(self) -> bool:
         """