|
Location |
Details |
| ✔️ |
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Successfully created `ModelDescr` instance. |
| ✔️ |
|
bioimageio.spec format validation model 0.5.10 |
| ⚠ |
inputs.0.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 |
| ⚠ |
outputs.3.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ |
outputs.4.sample_tensor |
Needs to be filled for FAIR compliance |
| ⚠ |
outputs.5.sample_tensor |
Needs to be filled for FAIR compliance |
| ✔️ |
weights.pytorch_state_dict |
Created conda environment '6cb978b242bca9861aed0ef0a3037223eb3e4f844c977039e2e140246450642e' |
| ❌ |
weights.pytorch_state_dict |
Reproduce test outputs from test inputs (pytorch_state_dict) |
| ❌ |
weights.pytorch_state_dict |
CPnet.load_state_dict() got an unexpected keyword argument 'strict' |
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See Traceback 1. |
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weights.pytorch_state_dict |
recommended conda environment (Reproduce test outputs from test inputs (pytorch_state_dict)) |
|
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%YAML 1.2
---
channels:
- conda-forge
- nodefaults
dependencies:
- conda-forge::bioimageio.core>=0.9.4
- pip
- pytorch==2.3.1
- torchvision==0.18.1
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weights.pytorch_state_dict |
conda compare (Reproduce test outputs from test inputs (pytorch_state_dict)) |
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/usr/share/miniconda/lib/python3.13/site-packages/conda/env/env.py:288: FutureWarning: The
environment file is not fully CEP 24 compliant is deprecated and will be removed in 26.9. In the
future, this configuration will be rejected. Please fix the following errors in order to make
the configuration valid: - Invalid type for 'name', expected a str deprecated.topic(
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 |
CPnet.load_state_dict() got an unexpected keyword argument 'strict' |
|
|
See Traceback 2. |
| ✔️ |
weights.torchscript |
Found existing conda environment '6cb978b242bca9861aed0ef0a3037223eb3e4f844c977039e2e140246450642e' |
| ✔️ |
weights.torchscript |
Reproduce test outputs from test inputs (torchscript) |
| ℹ |
weights.torchscript |
Output `flow`: all elements agree with expected values.
Max relative difference not accounted for by absolute tolerance (1.00e-03):
0.00e+00 (= \|-7.47e-02 - -7.47e-02\|/\|-7.47e-02 + 1e-6\|) at {'z': np.int64(0), 'channel': np.int64(0), 'y': np.int64(0), 'x': np.int64(0)}
Max absolute difference not accounted for by relative tolerance (1.00e-03):
0.00e+00 (= \|6.5230182e-08 - -1.8250423e-09\|) at {'z': np.int64(0), 'channel': np.int64(0), 'y': np.int64(56), 'x': np.int64(20)} |
| ℹ |
weights.torchscript |
Output `style`: all elements agree with expected values.
Max relative difference not accounted for by absolute tolerance (1.00e-03):
0.00e+00 (= \|-6.08e-02 - -6.08e-02\|/\|-6.08e-02 + 1e-6\|) at {'z': np.int64(0), 'channel': np.int64(0)}
Max absolute difference not accounted for by relative tolerance (1.00e-03):
0.00e+00 (= \|-4.1674111e-06 - -4.1718099e-06\|) at {'z': np.int64(8), 'channel': np.int64(100)} |
| ℹ |
weights.torchscript |
Output `downsampled_0`: all elements agree with expected values.
Max relative difference not accounted for by absolute tolerance (1.00e-03):
0.00e+00 (= \|8.76e-03 - 8.76e-03\|/\|8.76e-03 + 1e-6\|) at {'z': np.int64(0), 'channel': np.int64(0), 'y': np.int64(0), 'x': np.int64(0)}
Max absolute difference not accounted for by relative tolerance (1.00e-03):
0.00e+00 (= \|2.7939677e-09 - 2.3283064e-09\|) at {'z': np.int64(28), 'channel': np.int64(21), 'y': np.int64(55), 'x': np.int64(25)} |
| ℹ |
weights.torchscript |
Output `downsampled_1`: all elements agree with expected values.
Max relative difference not accounted for by absolute tolerance (1.00e-03):
0.00e+00 (= \|-2.46e-02 - -2.46e-02\|/\|-2.46e-02 + 1e-6\|) at {'z': np.int64(0), 'channel': np.int64(0), 'y': np.int64(0), 'x': np.int64(0)}
Max absolute difference not accounted for by relative tolerance (1.00e-03):
0.00e+00 (= \|9.5460564e-09 - 6.0535967e-09\|) at {'z': np.int64(26), 'channel': np.int64(60), 'y': np.int64(3), 'x': np.int64(2)} |
| ℹ |
weights.torchscript |
Output `downsampled_2`: all elements agree with expected values.
Max relative difference not accounted for by absolute tolerance (1.00e-03):
0.00e+00 (= \|1.41e-02 - 1.41e-02\|/\|1.41e-02 + 1e-6\|) at {'z': np.int64(0), 'channel': np.int64(0), 'y': np.int64(0), 'x': np.int64(0)}
Max absolute difference not accounted for by relative tolerance (1.00e-03):
0.00e+00 (= \|-9.3132257e-09 - -4.1909516e-09\|) at {'z': np.int64(74), 'channel': np.int64(113), 'y': np.int64(8), 'x': np.int64(5)} |
| ℹ |
weights.torchscript |
Output `downsampled_3`: all elements agree with expected values.
Max relative difference not accounted for by absolute tolerance (1.00e-03):
0.00e+00 (= \|2.14e-02 - 2.14e-02\|/\|2.14e-02 + 1e-6\|) at {'z': np.int64(0), 'channel': np.int64(0), 'y': np.int64(0), 'x': np.int64(0)}
Max absolute difference not accounted for by relative tolerance (1.00e-03):
0.00e+00 (= \|2.0489097e-08 - 1.7229468e-08\|) at {'z': np.int64(67), 'channel': np.int64(128), 'y': np.int64(6), 'x': np.int64(4)} |
|
weights.torchscript |
recommended conda environment (Reproduce test outputs from test inputs (torchscript)) |
|
|
%YAML 1.2
---
channels:
- conda-forge
- nodefaults
dependencies:
- conda-forge::bioimageio.core>=0.9.4
- pip
- pytorch==2.3.1
- torchvision==0.18.1
|
|
weights.torchscript |
conda compare (Reproduce test outputs from test inputs (torchscript)) |
|
|
/usr/share/miniconda/lib/python3.13/site-packages/conda/env/env.py:288: FutureWarning: The
environment file is not fully CEP 24 compliant is deprecated and will be removed in 26.9. In the
future, this configuration will be rejected. Please fix the following errors in order to make
the configuration valid: - Invalid type for 'name', expected a str deprecated.topic(
Success. All the packages in the specification file are present in the environment with matching
version and build string. |
| ✔️ |
weights.torchscript |
Run torchscript inference for inputs with batch_size: 1 and size parameter n: 0 |
| ✔️ |
weights.torchscript |
Run torchscript inference for inputs with batch_size: 1 and size parameter n: 1 |
| ✔️ |
weights.torchscript |
Run torchscript inference for inputs with batch_size: 1 and size parameter n: 2 |
╭─────────────────────────────── 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:471 in │
│ │
│ 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,), │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/_model_adapter.py:186 │
│ │
│ 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 = ( │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/_model_adapter.py:116 │
│ │
│ 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: │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:65 │
│ │
│ 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": │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:160 │
│ │
│ 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, │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:207 │
│ │
│ 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: CPnet.load_state_dict() got an unexpected keyword argument 'strict'
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/_resource_tests.py:1127 in _tes │
│ │
│ 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(): │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/_prediction_pipeline.py:471 in │
│ │
│ 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,), │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/_model_adapter.py:186 │
│ │
│ 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 = ( │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/_model_adapter.py:116 │
│ │
│ 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: │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:65 │
│ │
│ 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": │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:160 │
│ │
│ 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, │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:207 │
│ │
│ 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: CPnet.load_state_dict() got an unexpected keyword argument 'strict'