Core Compatibility Report: greedy-whale/v0
| 🟡 |
bioimageio format validation |
| status |
valid-format |
| source |
https://hypha.aicell.io/bioimage-io/artifacts/greedy-whale/files/rdf.yaml?version=v0 |
| id |
greedy-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 |
| ⚠ |
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 |
Reproduce test outputs from test inputs (pytorch_state_dict) |
| ❌ |
weights.pytorch_state_dict |
'vit_t' |
|
|
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
- segment-anything
- pip:
- git+https://github.com/ChaoningZhang/MobileSAM.git
- pip
|
|
weights.pytorch_state_dict |
conda compare (Reproduce test outputs from test inputs (pytorch_state_dict)) |
|
|
//github.com/chaoningzhang/mobilesam.git not found |
Traceback 1
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /usr/share/miniconda/envs/46771e8d89d70cdd1f8a607c2df86121277e61f659413ee5ebf5f444a2574f83/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/46771e8d89d70cdd1f8a607c2df86121277e61f659413ee5ebf5f444a2574f83/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/46771e8d89d70cdd1f8a607c2df86121277e61f659413ee5ebf5f444a2574f83/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/46771e8d89d70cdd1f8a607c2df86121277e61f659413ee5ebf5f444a2574f83/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/46771e8d89d70cdd1f8a607c2df86121277e61f659413ee5ebf5f444a2574f83/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/46771e8d89d70cdd1f8a607c2df86121277e61f659413ee5ebf5f444a2574f83/lib/p │
│ │
│ 113 │ │ if isinstance(weight_spec, v0_4.PytorchStateDictWeightsDescr) │
│ 114 │ │ else weight_spec.architecture.kwargs │
│ 115 │ ) │
│ ❱ 116 │ torch_model = custom_callable(**model_kwargs) │
│ 117 │ │
│ 118 │ if not isinstance(torch_model, nn.Module): │
│ 119 │ │ if isinstance( │
│ │
│ /tmp/tmp2tl_qvfk/predictor_adaptor_c55a850510e70b6b815afbec1d19da92792de97c4cf4384ba95d0024157f5 │
│ │
│ 28 │ """ │
│ 29 │ def __init__(self, model_type: str) -> None: │
│ 30 │ │ super().__init__() │
│ ❱ 31 │ │ sam_model = sam_model_registry[model_type]() │
│ 32 │ │ self.sam = SamPredictor(sam_model) │
│ 33 │ │
│ 34 │ def load_state_dict(self, state): │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
KeyError: 'vit_t'