Core Compatibility Report: humorous-crab/v0¤
| 🟡 | bioimageio format validation |
|---|---|
| status | valid-format |
| source | https://hypha.aicell.io/bioimage-io/artifacts/humorous-crab/files/rdf.yaml?version=v0 |
| id | humorous-crab |
| version | 1 |
| applied format | model 0.5.10 |
| bioimageio.spec | 0.5.10.2 |
| Location | Details | |
|---|---|---|
| ✔️ | Successfully created `ModelDescr` instance. | |
| ✔️ | bioimageio.spec format validation model 0.5.10 | |
| ⚠ | inputs.0.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ | inputs.1.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ | inputs.2.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ | inputs.3.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ | inputs.4.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ | inputs.5.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ | outputs.0.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ | outputs.1.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ | outputs.2.sample_tensor |
Needs to be filled for FAIR compliance |
| ✔️ | weights.pytorch_state_dict |
Found existing conda environment '08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5' |
| ❌ | weights.pytorch_state_dict |
Reproduce test outputs from test inputs (pytorch_state_dict) |
| ❌ | weights.pytorch_state_dict |
PredictorAdaptor.load_state_dict() got an unexpected keyword argument 'strict' |
| See Traceback 1. | ||
weights.pytorch_state_dict |
recommended conda environment (Reproduce test outputs from test inputs (pytorch_state_dict)) |
|
%YAML 1.2
---
name: sam
channels:
- pytorch
- conda-forge
- nodefaults
dependencies:
- conda-forge::bioimageio.core>=0.9.4
- pip
- segment-anything
|
||
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. |
||
| ❌ | weights.pytorch_state_dict |
Run pytorch_state_dict inference for parametrized inputs |
| ❌ | weights.pytorch_state_dict |
PredictorAdaptor.load_state_dict() got an unexpected keyword argument 'strict' |
| See Traceback 2. |
Traceback 1
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 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) │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 468 │ │ │ f"deprecated create_prediction_pipeline kwargs: {set(deprecated_kwargs)}" │
│ 469 │ │ ) │
│ 470 │ │
│ ❱ 471 │ model_adapter = model_adapter or create_model_adapter( │
│ 472 │ │ model_description=bioimageio_model, │
│ 473 │ │ devices=devices, │
│ 474 │ │ weight_format_priority_order=weights_format and (weights_format,), │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 183 │ │ assert errors │
│ 184 │ │ if len(weight_format_priority_order) == 1: │
│ 185 │ │ │ assert len(errors) == 1 │
│ ❱ 186 │ │ │ raise errors[0] │
│ 187 │ │ │
│ 188 │ │ else: │
│ 189 │ │ │ msg = ( │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 113 │ │ │ │ try: │
│ 114 │ │ │ │ │ from .pytorch_backend import PytorchModelAdapter │
│ 115 │ │ │ │ │ │
│ ❱ 116 │ │ │ │ │ return PytorchModelAdapter( │
│ 117 │ │ │ │ │ │ model_description=model_description, devices=devices │
│ 118 │ │ │ │ │ ) │
│ 119 │ │ │ │ except Exception as e: │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 62 │ │ │ raise ValueError("No `pytorch_state_dict` weights found") │
│ 63 │ │ │
│ 64 │ │ devices = get_devices(devices) │
│ ❱ 65 │ │ self._model = load_torch_model(weights, load_state=True, devices=devices) │
│ 66 │ │ if mode == "eval": │
│ 67 │ │ │ self._model = self._model.eval() │
│ 68 │ │ elif mode == "train": │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 157 │ │ use_devices = get_devices(devices) │
│ 158 │ │ torch_model = torch_model.to(use_devices[0]) │
│ 159 │ │ if load_state: │
│ ❱ 160 │ │ │ torch_model = load_torch_state_dict( │
│ 161 │ │ │ │ torch_model, │
│ 162 │ │ │ │ path=download(weight_spec), │
│ 163 │ │ │ │ devices=use_devices, │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 204 │ │ │ │ ) │
│ 205 │ │ │ │ raise ValueError(msg) from e │
│ 206 │ │
│ ❱ 207 │ incompatible = model.load_state_dict(state, strict=strict) │
│ 208 │ if ( │
│ 209 │ │ isinstance(incompatible, tuple) │
│ 210 │ │ and hasattr(incompatible, "missing_keys") │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: PredictorAdaptor.load_state_dict() got an unexpected keyword argument 'strict'
Traceback 2
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 1124 │ try: │
│ 1125 │ │ test_input = get_test_input_sample(model) │
│ 1126 │ │ │
│ ❱ 1127 │ │ with create_prediction_pipeline( │
│ 1128 │ │ │ bioimageio_model=model, devices=devices, weight_format=weight_format │
│ 1129 │ │ ) as prediction_pipeline: │
│ 1130 │ │ │ for n, batch_size, inputs, exptected_output_shape in generate_test_cases(): │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 468 │ │ │ f"deprecated create_prediction_pipeline kwargs: {set(deprecated_kwargs)}" │
│ 469 │ │ ) │
│ 470 │ │
│ ❱ 471 │ model_adapter = model_adapter or create_model_adapter( │
│ 472 │ │ model_description=bioimageio_model, │
│ 473 │ │ devices=devices, │
│ 474 │ │ weight_format_priority_order=weights_format and (weights_format,), │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 183 │ │ assert errors │
│ 184 │ │ if len(weight_format_priority_order) == 1: │
│ 185 │ │ │ assert len(errors) == 1 │
│ ❱ 186 │ │ │ raise errors[0] │
│ 187 │ │ │
│ 188 │ │ else: │
│ 189 │ │ │ msg = ( │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 113 │ │ │ │ try: │
│ 114 │ │ │ │ │ from .pytorch_backend import PytorchModelAdapter │
│ 115 │ │ │ │ │ │
│ ❱ 116 │ │ │ │ │ return PytorchModelAdapter( │
│ 117 │ │ │ │ │ │ model_description=model_description, devices=devices │
│ 118 │ │ │ │ │ ) │
│ 119 │ │ │ │ except Exception as e: │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 62 │ │ │ raise ValueError("No `pytorch_state_dict` weights found") │
│ 63 │ │ │
│ 64 │ │ devices = get_devices(devices) │
│ ❱ 65 │ │ self._model = load_torch_model(weights, load_state=True, devices=devices) │
│ 66 │ │ if mode == "eval": │
│ 67 │ │ │ self._model = self._model.eval() │
│ 68 │ │ elif mode == "train": │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 157 │ │ use_devices = get_devices(devices) │
│ 158 │ │ torch_model = torch_model.to(use_devices[0]) │
│ 159 │ │ if load_state: │
│ ❱ 160 │ │ │ torch_model = load_torch_state_dict( │
│ 161 │ │ │ │ torch_model, │
│ 162 │ │ │ │ path=download(weight_spec), │
│ 163 │ │ │ │ devices=use_devices, │
│ │
│ /usr/share/miniconda/envs/08f8ffa27c39c9aa97fede9155e0b8428e8e934a25bbb625c5d1dd8c912d76f5/lib/p │
│ │
│ 204 │ │ │ │ ) │
│ 205 │ │ │ │ raise ValueError(msg) from e │
│ 206 │ │
│ ❱ 207 │ incompatible = model.load_state_dict(state, strict=strict) │
│ 208 │ if ( │
│ 209 │ │ isinstance(incompatible, tuple) │
│ 210 │ │ and hasattr(incompatible, "missing_keys") │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: PredictorAdaptor.load_state_dict() got an unexpected keyword argument 'strict'