| 🟡 |
bioimageio format validation |
| status |
valid-format |
| source |
https://hypha.aicell.io/bioimage-io/artifacts/joyful-deer/files/rdf.yaml?version=v0 |
| id |
10.5281/zenodo.7274275/8123818 |
| format version |
model 0.4.10 |
| bioimageio.spec |
0.5.6.0 |
|
Location |
Details |
| ✔️ |
|
Successfully created `ModelDescr` instance. |
| ✔️ |
|
bioimageio.spec format validation model 0.4.10 |
| ❌ |
weights.pytorch_state_dict |
Reproduce test outputs from test inputs (pytorch_state_dict) |
| ❌ |
weights.pytorch_state_dict |
model.v0_4.ScaleLinearKwargs with axes not implemented, please consider updating the model to v0_5. |
|
|
See Traceback 1. |
|
weights.pytorch_state_dict |
recommended conda environment (Reproduce test outputs from test inputs (pytorch_state_dict)) |
|
|
%YAML 1.2
---
channels:
- pytorch
- conda-forge
- nodefaults
dependencies:
- conda-forge::bioimageio.core
- mkl ==2024.0.0
- numpy <2
- pip
- pytorch==1.13.1
- setuptools <70.0.0
- torchaudio==0.13.1
- torchvision==0.14.1
|
|
weights.pytorch_state_dict |
conda compare (Reproduce test outputs from test inputs (pytorch_state_dict)) |
|
|
Success. All the packages in the specification file are present in the environment with matching
version and build string. |
Traceback 1
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /usr/share/miniconda/envs/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/lib/p │
│ │
│ 781 │ │ test_input = get_test_input_sample(model) │
│ 782 │ │ expected = get_test_output_sample(model) │
│ 783 │ │ │
│ ❱ 784 │ │ with create_prediction_pipeline( │
│ 785 │ │ │ bioimageio_model=model, devices=devices, weight_format=weight_format │
│ 786 │ │ ) as prediction_pipeline: │
│ 787 │ │ │ results = prediction_pipeline.predict_sample_without_blocking(test_input) │
│ │
│ /usr/share/miniconda/envs/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/lib/p │
│ │
│ 384 │ │ │ else: │
│ 385 │ │ │ │ yield Sample(members=dict(zip(input_ids, x)), stat=common_stat, id=i) │
│ 386 │ │
│ ❱ 387 │ preprocessing, postprocessing = setup_pre_and_postprocessing( │
│ 388 │ │ bioimageio_model, │
│ 389 │ │ dataset(), │
│ 390 │ │ keep_updating_initial_dataset_stats=keep_updating_initial_dataset_statistics, │
│ │
│ /usr/share/miniconda/envs/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/lib/p │
│ │
│ 95 │ """ │
│ 96 │ Get pre- and postprocessing operators for a `model` description. │
│ 97 │ Used in `bioimageio.core.create_prediction_pipeline""" │
│ ❱ 98 │ prep, post, prep_meas, post_meas = _prepare_setup_pre_and_postprocessing(model) │
│ 99 │ │
│ 100 │ missing_dataset_stats = { │
│ 101 │ │ m │
│ │
│ /usr/share/miniconda/envs/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/lib/p │
│ │
│ 216 │
│ 217 def _prepare_setup_pre_and_postprocessing(model: AnyModelDescr) -> _SetupProcessing: │
│ 218 │ if isinstance(model, v0_4.ModelDescr): │
│ ❱ 219 │ │ pre = _prepare_procs(model.inputs) │
│ 220 │ │ post = _prepare_procs(model.outputs) │
│ 221 │ elif isinstance(model, v0_5.ModelDescr): │
│ 222 │ │ pre = _prepare_procs(model.inputs) │
│ │
│ /usr/share/miniconda/envs/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/lib/p │
│ │
│ 188 │ │ │
│ 189 │ │ if isinstance(t_descr, (v0_4.InputTensorDescr, v0_5.InputTensorDescr)): │
│ 190 │ │ │ for proc_d in t_descr.preprocessing: │
│ ❱ 191 │ │ │ │ procs.append(get_proc(proc_d, t_descr)) │
│ 192 │ │ elif isinstance(t_descr, (v0_4.OutputTensorDescr, v0_5.OutputTensorDescr)): │
│ 193 │ │ │ for proc_d in t_descr.postprocessing: │
│ 194 │ │ │ │ procs.append(get_proc(proc_d, t_descr)) │
│ │
│ /usr/share/miniconda/envs/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/lib/p │
│ │
│ 719 │ elif isinstance(proc_descr, v0_5.FixedZeroMeanUnitVarianceDescr): │
│ 720 │ │ return FixedZeroMeanUnitVariance.from_proc_descr(proc_descr, member_id) │
│ 721 │ elif isinstance(proc_descr, (v0_4.ScaleLinearDescr, v0_5.ScaleLinearDescr)): │
│ ❱ 722 │ │ return ScaleLinear.from_proc_descr(proc_descr, member_id) │
│ 723 │ elif isinstance( │
│ 724 │ │ proc_descr, (v0_4.ScaleMeanVarianceDescr, v0_5.ScaleMeanVarianceDescr) │
│ 725 │ ): │
│ │
│ /usr/share/miniconda/envs/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/lib/p │
│ │
│ 310 │ │ │ axis = kwargs.axis │
│ 311 │ │ elif isinstance(kwargs, v0_4.ScaleLinearKwargs): │
│ 312 │ │ │ if kwargs.axes is not None: │
│ ❱ 313 │ │ │ │ raise NotImplementedError( │
│ 314 │ │ │ │ │ "model.v0_4.ScaleLinearKwargs with axes not implemented, please consid │
│ 315 │ │ │ │ ) │
│ 316 │ │ │ axis = None │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
NotImplementedError: model.v0_4.ScaleLinearKwargs with axes not implemented, please consider updating the model to v0_5.