Core Compatibility Report: faithful-chicken/v0¤
| 🟡 | bioimageio format validation |
|---|---|
| status | valid-format |
| source | https://hypha.aicell.io/bioimage-io/artifacts/faithful-chicken/files/rdf.yaml?version=v0 |
| id | faithful-chicken |
| version | 1 |
| applied format | model 0.5.9 |
| bioimageio.spec | 0.5.9.1 |
| Location | Details | |
|---|---|---|
| ✔️ | Successfully created `ModelDescr` instance. | |
| ✔️ | bioimageio.spec format validation model 0.5.9 | |
| ⚠ | 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 |
Created conda environment '668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119' |
| ❌ | 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>=0.9.4
- 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 |
||
| ❌ | weights.pytorch_state_dict |
Run pytorch_state_dict inference for parametrized inputs |
| ❌ | weights.pytorch_state_dict |
'vit_t' |
| See Traceback 2. |
Traceback 1
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /usr/share/miniconda/envs/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/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/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/lib/p │
│ │
│ 404 │ │ │ f"deprecated create_prediction_pipeline kwargs: {set(deprecated_kwargs)}" │
│ 405 │ │ ) │
│ 406 │ │
│ ❱ 407 │ model_adapter = model_adapter or create_model_adapter( │
│ 408 │ │ model_description=bioimageio_model, │
│ 409 │ │ devices=devices, │
│ 410 │ │ weight_format_priority_order=weights_format and (weights_format,), │
│ │
│ /usr/share/miniconda/envs/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/lib/p │
│ │
│ 182 │ │ assert errors │
│ 183 │ │ if len(weight_format_priority_order) == 1: │
│ 184 │ │ │ assert len(errors) == 1 │
│ ❱ 185 │ │ │ raise errors[0] │
│ 186 │ │ │
│ 187 │ │ else: │
│ 188 │ │ │ msg = ( │
│ │
│ /usr/share/miniconda/envs/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/lib/p │
│ │
│ 112 │ │ │ │ try: │
│ 113 │ │ │ │ │ from .pytorch_backend import PytorchModelAdapter │
│ 114 │ │ │ │ │ │
│ ❱ 115 │ │ │ │ │ return PytorchModelAdapter( │
│ 116 │ │ │ │ │ │ model_description=model_description, devices=devices │
│ 117 │ │ │ │ │ ) │
│ 118 │ │ │ │ except Exception as e: │
│ │
│ /usr/share/miniconda/envs/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/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/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/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/tmp5vgn3562/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'
Traceback 2
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /usr/share/miniconda/envs/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/lib/p │
│ │
│ 1120 │ try: │
│ 1121 │ │ test_input = get_test_input_sample(model) │
│ 1122 │ │ │
│ ❱ 1123 │ │ with create_prediction_pipeline( │
│ 1124 │ │ │ bioimageio_model=model, devices=devices, weight_format=weight_format │
│ 1125 │ │ ) as prediction_pipeline: │
│ 1126 │ │ │ for n, batch_size, inputs, exptected_output_shape in generate_test_cases(): │
│ │
│ /usr/share/miniconda/envs/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/lib/p │
│ │
│ 404 │ │ │ f"deprecated create_prediction_pipeline kwargs: {set(deprecated_kwargs)}" │
│ 405 │ │ ) │
│ 406 │ │
│ ❱ 407 │ model_adapter = model_adapter or create_model_adapter( │
│ 408 │ │ model_description=bioimageio_model, │
│ 409 │ │ devices=devices, │
│ 410 │ │ weight_format_priority_order=weights_format and (weights_format,), │
│ │
│ /usr/share/miniconda/envs/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/lib/p │
│ │
│ 182 │ │ assert errors │
│ 183 │ │ if len(weight_format_priority_order) == 1: │
│ 184 │ │ │ assert len(errors) == 1 │
│ ❱ 185 │ │ │ raise errors[0] │
│ 186 │ │ │
│ 187 │ │ else: │
│ 188 │ │ │ msg = ( │
│ │
│ /usr/share/miniconda/envs/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/lib/p │
│ │
│ 112 │ │ │ │ try: │
│ 113 │ │ │ │ │ from .pytorch_backend import PytorchModelAdapter │
│ 114 │ │ │ │ │ │
│ ❱ 115 │ │ │ │ │ return PytorchModelAdapter( │
│ 116 │ │ │ │ │ │ model_description=model_description, devices=devices │
│ 117 │ │ │ │ │ ) │
│ 118 │ │ │ │ except Exception as e: │
│ │
│ /usr/share/miniconda/envs/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/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/668c740b436506ad2ddff14f48c19d8b1c5b1cad5a53e332986ec9aaabd10119/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/tmp5vgn3562/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'