Core Compatibility Report: efficient-chipmunk/v0
| 🟡 |
bioimageio format validation |
| status |
valid-format |
| source |
https://hypha.aicell.io/bioimage-io/artifacts/efficient-chipmunk/files/rdf.yaml?version=v0 |
| id |
10.5281/zenodo.8401064/8429203 |
| 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.dependencies |
Custom dependencies (conda:dependencies.yaml) specified. Avoid this whenever possible to allow execution in a wider range of software environments. |
| ⚠ |
weights.pytorch_state_dict.pytorch_version |
missing. Please specify the PyTorch version these PyTorch state dict weights were created with. |
| ❌ |
weights.pytorch_state_dict |
Reproduce test outputs from test inputs (pytorch_state_dict) |
| ❌ |
weights.pytorch_state_dict |
No module named 'plantseg.models.model' |
|
|
See Traceback 1. |
|
weights.pytorch_state_dict |
recommended conda environment (Reproduce test outputs from test inputs (pytorch_state_dict)) |
|
|
%YAML 1.2
---
name: plant-seg
channels:
- pytorch
- nvidia
- conda-forge
- lcerrone
- nodefaults
dependencies:
- conda-forge::bioimageio.core
- pip
- plantseg
- pytorch-cuda=11.7
|
|
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/36b55098eb1d2219512c92f0ee4a7f969194a60a929a6e58f34d1b2b4928a73b/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/36b55098eb1d2219512c92f0ee4a7f969194a60a929a6e58f34d1b2b4928a73b/lib/p │
│ │
│ 368 │ │ │ f"deprecated create_prediction_pipeline kwargs: {set(deprecated_kwargs)}" │
│ 369 │ │ ) │
│ 370 │ │
│ ❱ 371 │ model_adapter = model_adapter or create_model_adapter( │
│ 372 │ │ model_description=bioimageio_model, │
│ 373 │ │ devices=devices, │
│ 374 │ │ weight_format_priority_order=weights_format and (weights_format,), │
│ │
│ /usr/share/miniconda/envs/36b55098eb1d2219512c92f0ee4a7f969194a60a929a6e58f34d1b2b4928a73b/lib/p │
│ │
│ 166 │ │ assert errors │
│ 167 │ │ if len(weight_format_priority_order) == 1: │
│ 168 │ │ │ assert len(errors) == 1 │
│ ❱ 169 │ │ │ raise errors[0] │
│ 170 │ │ │
│ 171 │ │ else: │
│ 172 │ │ │ msg = ( │
│ │
│ /usr/share/miniconda/envs/36b55098eb1d2219512c92f0ee4a7f969194a60a929a6e58f34d1b2b4928a73b/lib/p │
│ │
│ 109 │ │ │ │ try: │
│ 110 │ │ │ │ │ from .pytorch_backend import PytorchModelAdapter │
│ 111 │ │ │ │ │ │
│ ❱ 112 │ │ │ │ │ return PytorchModelAdapter( │
│ 113 │ │ │ │ │ │ model_description=model_description, devices=devices │
│ 114 │ │ │ │ │ ) │
│ 115 │ │ │ │ except Exception as e: │
│ │
│ /usr/share/miniconda/envs/36b55098eb1d2219512c92f0ee4a7f969194a60a929a6e58f34d1b2b4928a73b/lib/p │
│ │
│ 35 │ │ │ raise ValueError("No `pytorch_state_dict` weights found") │
│ 36 │ │ │
│ 37 │ │ devices = get_devices(devices) │
│ ❱ 38 │ │ self._model = load_torch_model(weights, load_state=True, devices=devices) │
│ 39 │ │ if mode == "eval": │
│ 40 │ │ │ self._model = self._model.eval() │
│ 41 │ │ elif mode == "train": │
│ │
│ /usr/share/miniconda/envs/36b55098eb1d2219512c92f0ee4a7f969194a60a929a6e58f34d1b2b4928a73b/lib/p │
│ │
│ 100 │ load_state: bool = True, │
│ 101 │ devices: Optional[Sequence[Union[str, torch.device]]] = None, │
│ 102 ) -> nn.Module: │
│ ❱ 103 │ custom_callable = import_callable( │
│ 104 │ │ weight_spec.architecture, │
│ 105 │ │ sha256=( │
│ 106 │ │ │ weight_spec.architecture_sha256 │
│ │
│ /usr/share/miniconda/envs/36b55098eb1d2219512c92f0ee4a7f969194a60a929a6e58f34d1b2b4928a73b/lib/p │
│ │
│ 65 ) -> Callable[..., Any]: │
│ 66 │ """import a callable (e.g. a torch.nn.Module) from a spec node describing it""" │
│ 67 │ if isinstance(node, CallableFromDepencency): │
│ ❱ 68 │ │ module = importlib.import_module(node.module_name) │
│ 69 │ │ c = getattr(module, str(node.callable_name)) │
│ 70 │ elif isinstance(node, ArchitectureFromLibraryDescr): │
│ 71 │ │ module = importlib.import_module(node.import_from) │
│ │
│ /usr/share/miniconda/envs/36b55098eb1d2219512c92f0ee4a7f969194a60a929a6e58f34d1b2b4928a73b/lib/p │
│ │
│ 85 │ │ │ if character != '.': │
│ 86 │ │ │ │ break │
│ 87 │ │ │ level += 1 │
│ ❱ 88 │ return _bootstrap._gcd_import(name[level:], package, level) │
│ 89 │
│ 90 │
│ 91 _RELOADING = {} │
│ in _gcd_import:1387 │
│ in _find_and_load:1360 │
│ in _find_and_load_unlocked:1324 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'plantseg.models.model'