Core Compatibility Report: helpful-whale/v0
| 🟡 |
bioimageio format validation |
| status |
valid-format |
| source |
https://hypha.aicell.io/bioimage-io/artifacts/helpful-whale/files/rdf.yaml?version=v0 |
| id |
helpful-whale |
| 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 |
| ⚠ |
documentation |
No '# Validation' (sub)section found in doc.md. |
| ⚠ |
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 |
Input image(s) must be 4-dimensional (batch, channel, height, width), got shape torch.Size([3, 512, 512]) |
|
|
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
- numpy >=2,<3
- pip
- pytorch==2.3.0
- torchaudio==2.3.0
- torchvision==0.18.0
|
|
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) ────────────────────────────────╮
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/_resource_tests.py:787 in _test │
│ │
│ 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) │
│ 788 │ │ │
│ 789 │ │ if len(results.members) != len(expected.members): │
│ 790 │ │ │ add_error_entry( │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/_prediction_pipeline.py:160 in │
│ │
│ 157 │ │ if not skip_preprocessing: │
│ 158 │ │ │ self.apply_preprocessing(sample) │
│ 159 │ │ │
│ ❱ 160 │ │ output = self._adapter.forward(sample) │
│ 161 │ │ if not skip_postprocessing: │
│ 162 │ │ │ self.apply_postprocessing(output) │
│ 163 │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/_model_adapter.py:202 │
│ │
│ 199 │ │ │ ) │
│ 200 │ │ │ for in_id, in_order in zip(self._input_ids, self._input_axes) │
│ 201 │ │ ] │
│ ❱ 202 │ │ output_arrays = self._forward_impl(input_arrays) │
│ 203 │ │ assert len(output_arrays) <= len(self._output_ids) │
│ 204 │ │ output_tensors = [ │
│ 205 │ │ │ None if a is None else Tensor(a, dims=d) │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/bioimageio/core/backends/pytorch_backend.py:65 │
│ │
│ 62 │ │ │ assert_never(self._mode) │
│ 63 │ │ │
│ 64 │ │ with ctxt(): │
│ ❱ 65 │ │ │ model_out = self._model(*tensors) │
│ 66 │ │ │
│ 67 │ │ if is_tuple(model_out) or is_list(model_out): │
│ 68 │ │ │ model_out_seq = model_out │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/torch/nn/modules/module.py:1775 in _wrapped_cal │
│ │
│ 1772 │ │ if self._compiled_call_impl is not None: │
│ 1773 │ │ │ return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] │
│ 1774 │ │ else: │
│ ❱ 1775 │ │ │ return self._call_impl(*args, **kwargs) │
│ 1776 │ │
│ 1777 │ # torchrec tests the code consistency with the following code │
│ 1778 │ # fmt: off │
│ │
│ /home/runner/.local/lib/python3.12/site-packages/torch/nn/modules/module.py:1786 in _call_impl │
│ │
│ 1783 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks o │
│ 1784 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │
│ 1785 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1786 │ │ │ return forward_call(*args, **kwargs) │
│ 1787 │ │ │
│ 1788 │ │ result = None │
│ 1789 │ │ called_always_called_hooks = set() │
│ │
│ /tmp/tmp_28ef4ju/model_47bd0e745eb83d5ed5543fc4db677f10d24a7e5aecd5dd1db62a90ce0c4e9494.py:139 i │
│ │
│ 136 │ │ │ ValueError: If input dimensions are invalid │
│ 137 │ │ """ │
│ 138 │ │ if len(x.shape) != 4: │
│ ❱ 139 │ │ │ raise ValueError( │
│ 140 │ │ │ │ f"Input image(s) must be 4-dimensional (batch, channel, height, width), " │
│ 141 │ │ │ │ f"got shape {x.shape}" │
│ 142 │ │ │ ) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ValueError: Input image(s) must be 4-dimensional (batch, channel, height, width), got shape torch.Size([3, 512, 512])