Merge pull request #222 from thodkatz/bump-bioimageio

Bump bioimageio spec/0.5.3.3 core/0.6.10

Author: k-dominik

.github/workflows/ci.yml

@@ -24,7 +24,7 @@ jobs:
           auto-activate-base: false
           activate-environment: tiktorch-server-env
           environment-file: environment.yml
-          channel-priority: flexible
+          channel-priority: strict
           miniforge-variant: Miniforge3
       - name: Get the latest commit hash and target ref
         run: |
@@ -51,7 +51,7 @@ jobs:
           auto-activate-base: false
           activate-environment: tiktorch-server-env
           environment-file: environment.yml
-          channel-priority: flexible
+          channel-priority: strict
           miniforge-variant: Miniforge3
       - name: conda diagnostics
         run: |
@@ -89,7 +89,7 @@ jobs:
         with:
           auto-update-conda: true
           auto-activate-base: true
-          channel-priority: flexible
+          channel-priority: strict
           miniforge-variant: Miniforge3
       - name: install common conda dependencies
         run: conda install -n base -c conda-forge conda-build setuptools_scm -y
@@ -133,7 +133,7 @@ jobs:
         with:
           auto-update-conda: true
           auto-activate-base: true
-          channel-priority: flexible
+          channel-priority: strict
           miniforge-variant: Miniforge3
       - name: install common conda dependencies
         run: conda install -n base -c conda-forge conda-build setuptools_scm -y

.gitignore

@@ -8,3 +8,4 @@ tiktorch/__pycache/
 /#wrapper.py#
 /.#wrapper.py#
 .py~
+.vscode

Makefile

@@ -1,7 +1,7 @@
 SHELL=/bin/bash
 ROOT_DIR:=$(shell dirname $(realpath $(firstword $(MAKEFILE_LIST))))
 TIKTORCH_ENV_NAME ?= tiktorch-server-env
-SUBMODULES = ./vendor/core-bioimage-io-python ./vendor/spec-bioimage-io
+SUBMODULES = ./vendor/spec-bioimage-io ./vendor/core-bioimage-io-python
 
 protos:
 	python -m grpc_tools.protoc -I./proto --python_out=tiktorch/proto/ --grpc_python_out=tiktorch/proto/ ./proto/*.proto

environment.yml

@@ -1,21 +1,21 @@
 name: tiktorch-server-env
 channels:
-  - ilastik-forge
   - pytorch
+  - ilastik-forge
   - conda-forge
+  - nodefaults
 dependencies:
   # - bioimage.spec via submodule
   # - bioimage.core via submodule
   - python 3.9.*
-  - numpy
+  - numpy >=1.21,<2
   - grpcio=1.44 # protobuf 5 requires protoc version > 3.19.0 that requires grpcio >= 1.44
   - marshmallow-union
   - marshmallow=3.12.*
   - marshmallow-jsonschema
   - protobuf
   - pyyaml=5.3.*
   - requests
-  - ruamel.yaml
   - scikit-learn
   - scipy
   - typing-extensions
@@ -32,17 +32,32 @@ dependencies:
   - cpuonly
   # - cudatoolkit >=10.2
   # - cudnn
-  # - tochvision
+  - torchvision
 
   # tensorflow (1.14 is the latest 1.x version on cf)
   # so far we don't have any 2.x models in the model zoo
   # tensorflow skipped for now, as it conflicts with grpcio version 1.41
   # - tensorflow >=2.9,<3.0
 
-  # convenient to use bioiamgeio.core tools
-  - imageio
+  # bioimageio.spec / bioimageio.core dependencies:
+  - annotated-types >=0.5.0,<1
+  - email_validator
+  - h5py
+  - imageio >=2.10
+  - loguru
+  - packaging >=17.0
+  - pooch >=1.5,<2
+  - pydantic >=2.7.0,<2.10
+  - pydantic-core
+  - pydantic-settings >=2.5
+  - python-dateutil
+  - rich
+  - ruyaml
+  - tifffile
   - tqdm
   - typer
+  - zipp
+
 
   # dev stuff
   - pytest

setup.py

@@ -27,11 +27,12 @@
     ],
     packages=find_packages(exclude=["tests"]),  # Required
     install_requires=[
-        "bioimageio.spec==0.5.3.2",
-        "bioimageio.core==0.6.8",
+        "bioimageio.spec==0.5.3.3",
+        "bioimageio.core==0.6.10",
         "grpcio>=1.31",
         "numpy<2",  # pytorch 2.2.2-py3.9_0 for macos is compiled with numpy 1.*
         "protobuf",
+        "pydantic>=2.7.0,<2.10",
         "pyyaml",
         "xarray",
     ],

tests/test_converters.py

@@ -16,11 +16,11 @@
 from tiktorch.proto import inference_pb2
 
 
-def _numpy_to_pb_tensor(arr):
+def _numpy_to_pb_tensor(arr, tensor_id: str = "dummy_tensor_name"):
     """
     Makes sure that tensor was serialized/deserialized
     """
-    tensor = numpy_to_pb_tensor(arr)
+    tensor = numpy_to_pb_tensor(tensor_id, arr)
     parsed = inference_pb2.Tensor()
     parsed.ParseFromString(tensor.SerializeToString())
     return parsed

tiktorch/converters.py

@@ -34,12 +34,12 @@ def sample_to_pb_tensors(sample: Sample) -> List[inference_pb2.Tensor]:
     return [xarray_to_pb_tensor(tensor_id, res_tensor.data) for tensor_id, res_tensor in sample.members.items()]
 
 
-def numpy_to_pb_tensor(array: np.ndarray, axistags=None) -> inference_pb2.Tensor:
+def numpy_to_pb_tensor(tensor_id: str, array: np.ndarray, axistags=None) -> inference_pb2.Tensor:
     if axistags:
         shape = [inference_pb2.NamedInt(size=dim, name=name) for dim, name in zip(array.shape, axistags)]
     else:
         shape = [inference_pb2.NamedInt(size=dim) for dim in array.shape]
-    return inference_pb2.Tensor(dtype=str(array.dtype), shape=shape, buffer=bytes(array))
+    return inference_pb2.Tensor(tensorId=tensor_id, dtype=str(array.dtype), shape=shape, buffer=bytes(array))
 
 
 def xarray_to_pb_tensor(tensor_id: str, array: xr.DataArray) -> inference_pb2.Tensor:

tiktorch/server/session/process.py

@@ -9,7 +9,6 @@
 from bioimageio.core import PredictionPipeline, Tensor, create_prediction_pipeline
 from bioimageio.spec import InvalidDescr, load_description
 from bioimageio.spec.model import v0_5
-from bioimageio.spec.model.v0_5 import BatchAxis
 
 from tiktorch import log
 from tiktorch.rpc import Shutdown
@@ -37,9 +36,7 @@ def _check_shape(self, tensor_id: str, tensor: Tensor):
         for axis in spec.axes:
             source_axis_size = axis.size
             target_axis_size = tensor.sizes[axis.id]
-            if axis.id not in tensor.sizes:
-                ValueError(f"{axis.id} not found in {tensor.sizes}")
-            if isinstance(axis, BatchAxis) and axis.size is None:
+            if source_axis_size is None:
                 continue
             try:
                 self._validate_size(source_axis_size, target_axis_size)