Core Compatibility Report: humorous-fox/v0¤
| 🟡 | bioimageio format validation |
|---|---|
| status | valid-format |
| source | https://hypha.aicell.io/bioimage-io/artifacts/humorous-fox/files/rdf.yaml?version=v0 |
| id | humorous-fox |
| version | 0.1.0 |
| applied format | model 0.5.9 |
| bioimageio.spec | 0.5.9.1 |
| Location | Details | |
|---|---|---|
| ✔️ | Successfully created `ModelDescr` instance. | |
| ✔️ | bioimageio.spec format validation model 0.5.9 | |
| ⚠ | inputs.0.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ | outputs.0.sample_tensor |
Needs to be filled for FAIR compliance |
| ✔️ | weights.pytorch_state_dict |
Found existing conda environment 'a4a69c7b0241722dbb721e78ee9fbbe7c18c681497e88237e37bc450ab1b0458' |
| ❌ | weights.pytorch_state_dict |
Reproduce test outputs from test inputs (pytorch_state_dict) |
| ❌ | weights.pytorch_state_dict |
No module named 'careamics' |
| See Traceback 1. | ||
weights.pytorch_state_dict |
recommended conda environment (Reproduce test outputs from test inputs (pytorch_state_dict)) |
|
%YAML 1.2
---
name: careamics
channels:
- conda-forge
- nodefaults
dependencies:
- conda-forge::bioimageio.core>=0.9.4
- pip
- python=3.12
- pip:
- careamics==0.0.16
- torch==2.7.1+cu126
- torchvision==0.22.1+cu126
|
||
weights.pytorch_state_dict |
conda compare (Reproduce test outputs from test inputs (pytorch_state_dict)) |
|
careamics not found torch not found torchvision not found |
||
| ❌ | weights.pytorch_state_dict |
Run pytorch_state_dict inference for parametrized inputs |
| ❌ | weights.pytorch_state_dict |
No module named 'careamics' |
| See Traceback 2. |
Traceback 1
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/_resource_tests.py:880 in _test │
│ │
│ 877 │ │ test_input = get_test_input_sample(model) │
│ 878 │ │ expected = get_test_output_sample(model) │
│ 879 │ │ │
│ ❱ 880 │ │ with create_prediction_pipeline( │
│ 881 │ │ │ bioimageio_model=model, devices=devices, weight_format=weight_format │
│ 882 │ │ ) as prediction_pipeline: │
│ 883 │ │ │ prediction_pipeline.apply_preprocessing(test_input) │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/_prediction_pipeline.py:407 in │
│ │
│ 404 │ │ │ f"deprecated create_prediction_pipeline kwargs: {set(deprecated_kwargs)}" │
│ 405 │ │ ) │
│ 406 │ │
│ ❱ 407 │ model_adapter = model_adapter or create_model_adapter( │
│ 408 │ │ model_description=bioimageio_model, │
│ 409 │ │ devices=devices, │
│ 410 │ │ weight_format_priority_order=weights_format and (weights_format,), │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/_model_adapter.py:185 │
│ │
│ 182 │ │ assert errors │
│ 183 │ │ if len(weight_format_priority_order) == 1: │
│ 184 │ │ │ assert len(errors) == 1 │
│ ❱ 185 │ │ │ raise errors[0] │
│ 186 │ │ │
│ 187 │ │ else: │
│ 188 │ │ │ msg = ( │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/_model_adapter.py:115 │
│ │
│ 112 │ │ │ │ try: │
│ 113 │ │ │ │ │ from .pytorch_backend import PytorchModelAdapter │
│ 114 │ │ │ │ │ │
│ ❱ 115 │ │ │ │ │ return PytorchModelAdapter( │
│ 116 │ │ │ │ │ │ model_description=model_description, devices=devices │
│ 117 │ │ │ │ │ ) │
│ 118 │ │ │ │ except Exception as e: │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:38 │
│ │
│ 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": │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:103 │
│ │
│ 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 │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/digest_spec.py:72 in import_cal │
│ │
│ 69 │ │ module = importlib.import_module(node.module_name) │
│ 70 │ │ c = getattr(module, str(node.callable_name)) │
│ 71 │ elif isinstance(node, ArchitectureFromLibraryDescr): │
│ ❱ 72 │ │ module = importlib.import_module(node.import_from) │
│ 73 │ │ c = getattr(module, str(node.callable)) │
│ 74 │ elif isinstance(node, CallableFromFile): │
│ 75 │ │ c = _import_from_file_impl(node.source_file, str(node.callable_name), **kwargs) │
│ │
│ /usr/share/miniconda/envs/a4a69c7b0241722dbb721e78ee9fbbe7c18c681497e88237e37bc450ab1b0458/lib/p │
│ │
│ 87 │ │ │ if character != '.': │
│ 88 │ │ │ │ break │
│ 89 │ │ │ level += 1 │
│ ❱ 90 │ return _bootstrap._gcd_import(name[level:], package, level) │
│ 91 │
│ 92 │
│ 93 _RELOADING = {} │
│ in _gcd_import:1387 │
│ in _find_and_load:1360 │
│ in _find_and_load_unlocked:1310 │
│ in _call_with_frames_removed:488 │
│ in _gcd_import:1387 │
│ in _find_and_load:1360 │
│ in _find_and_load_unlocked:1310 │
│ in _call_with_frames_removed:488 │
│ in _gcd_import:1387 │
│ in _find_and_load:1360 │
│ in _find_and_load_unlocked:1324 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'careamics'
Traceback 2
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/_resource_tests.py:1123 in _tes │
│ │
│ 1120 │ try: │
│ 1121 │ │ test_input = get_test_input_sample(model) │
│ 1122 │ │ │
│ ❱ 1123 │ │ with create_prediction_pipeline( │
│ 1124 │ │ │ bioimageio_model=model, devices=devices, weight_format=weight_format │
│ 1125 │ │ ) as prediction_pipeline: │
│ 1126 │ │ │ for n, batch_size, inputs, exptected_output_shape in generate_test_cases(): │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/_prediction_pipeline.py:407 in │
│ │
│ 404 │ │ │ f"deprecated create_prediction_pipeline kwargs: {set(deprecated_kwargs)}" │
│ 405 │ │ ) │
│ 406 │ │
│ ❱ 407 │ model_adapter = model_adapter or create_model_adapter( │
│ 408 │ │ model_description=bioimageio_model, │
│ 409 │ │ devices=devices, │
│ 410 │ │ weight_format_priority_order=weights_format and (weights_format,), │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/_model_adapter.py:185 │
│ │
│ 182 │ │ assert errors │
│ 183 │ │ if len(weight_format_priority_order) == 1: │
│ 184 │ │ │ assert len(errors) == 1 │
│ ❱ 185 │ │ │ raise errors[0] │
│ 186 │ │ │
│ 187 │ │ else: │
│ 188 │ │ │ msg = ( │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/_model_adapter.py:115 │
│ │
│ 112 │ │ │ │ try: │
│ 113 │ │ │ │ │ from .pytorch_backend import PytorchModelAdapter │
│ 114 │ │ │ │ │ │
│ ❱ 115 │ │ │ │ │ return PytorchModelAdapter( │
│ 116 │ │ │ │ │ │ model_description=model_description, devices=devices │
│ 117 │ │ │ │ │ ) │
│ 118 │ │ │ │ except Exception as e: │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:38 │
│ │
│ 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": │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:103 │
│ │
│ 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 │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/digest_spec.py:72 in import_cal │
│ │
│ 69 │ │ module = importlib.import_module(node.module_name) │
│ 70 │ │ c = getattr(module, str(node.callable_name)) │
│ 71 │ elif isinstance(node, ArchitectureFromLibraryDescr): │
│ ❱ 72 │ │ module = importlib.import_module(node.import_from) │
│ 73 │ │ c = getattr(module, str(node.callable)) │
│ 74 │ elif isinstance(node, CallableFromFile): │
│ 75 │ │ c = _import_from_file_impl(node.source_file, str(node.callable_name), **kwargs) │
│ │
│ /usr/share/miniconda/envs/a4a69c7b0241722dbb721e78ee9fbbe7c18c681497e88237e37bc450ab1b0458/lib/p │
│ │
│ 87 │ │ │ if character != '.': │
│ 88 │ │ │ │ break │
│ 89 │ │ │ level += 1 │
│ ❱ 90 │ return _bootstrap._gcd_import(name[level:], package, level) │
│ 91 │
│ 92 │
│ 93 _RELOADING = {} │
│ in _gcd_import:1387 │
│ in _find_and_load:1360 │
│ in _find_and_load_unlocked:1310 │
│ in _call_with_frames_removed:488 │
│ in _gcd_import:1387 │
│ in _find_and_load:1360 │
│ in _find_and_load_unlocked:1310 │
│ in _call_with_frames_removed:488 │
│ in _gcd_import:1387 │
│ in _find_and_load:1360 │
│ in _find_and_load_unlocked:1324 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'careamics'