Core Compatibility Report: amiable-otter/v0¤
| 🟡 | bioimageio format validation |
|---|---|
| status | valid-format |
| source | https://hypha.aicell.io/bioimage-io/artifacts/amiable-otter/files/rdf.yaml?version=v0 |
| id | amiable-otter |
| format version | model 0.5.6 |
| bioimageio.spec | 0.5.6.0 |
| Location | Details | |
|---|---|---|
| ✔️ | Successfully created `ModelDescr` instance. | |
| ✔️ | bioimageio.spec format validation model 0.5.6 | |
| ⚠ | 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 |
Reproduce test outputs from test inputs (pytorch_state_dict) |
| ❌ | weights.pytorch_state_dict |
Failed to import bmz_model.py |
| 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 │
│ │
│ 142 │ │ │ sys.modules[module_name] = module # cache this module │
│ 143 │ │ │ │
│ 144 │ │ │ assert importlib_spec.loader is not None │
│ ❱ 145 │ │ │ importlib_spec.loader.exec_module(module) │
│ 146 │ │ │
│ 147 │ │ except Exception as e: │
│ 148 │ │ │ if module_name in sys.modules: │
│ in exec_module:883 │
│ in _call_with_frames_removed:241 │
│ │
│ /tmp/tmp6lp6631h/bmz_model_5541387ffb9eefaac2b9c2763b8b45119186c7ee1fe248d025990420cb979225.py:6 │
│ │
│ 3 from functools import partial │
│ 4 from inspect import isfunction │
│ 5 │
│ ❱ 6 import yaml │
│ 7 import torch │
│ 8 import torch.nn as nn │
│ 9 import numpy as np │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'yaml'
The above exception was the direct cause of the following exception:
╭─────────────────────────────── 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 │
│ │
│ 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/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/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/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/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/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/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/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/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/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/lib/p │
│ │
│ 73 │ elif isinstance(node, CallableFromFile): │
│ 74 │ │ c = _import_from_file_impl(node.source_file, str(node.callable_name), **kwargs) │
│ 75 │ elif isinstance(node, ArchitectureFromFileDescr): │
│ ❱ 76 │ │ c = _import_from_file_impl(node.source, str(node.callable), sha256=node.sha256) │
│ 77 │ else: │
│ 78 │ │ assert_never(node) │
│ 79 │
│ │
│ /usr/share/miniconda/envs/b67f0851ec76977c6bebe397fb842f1c9b3e02c3f4da6dba02caf69ff1f8a0ee/lib/p │
│ │
│ 148 │ │ │ if module_name in sys.modules: │
│ 149 │ │ │ │ del sys.modules[module_name] │
│ 150 │ │ │ │
│ ❱ 151 │ │ │ raise ImportError(f"Failed to import {source}") from e │
│ 152 │ │
│ 153 │ try: │
│ 154 │ │ callable_attr = getattr(module, callable_name) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ImportError: Failed to import bmz_model.py