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Core Compatibility Report: philosophical-panda/v0¤

✔️ bioimageio format validation
status passed
source https://hypha.aicell.io/bioimage-io/artifacts/philosophical-panda/files/rdf.yaml?version=v0
id philosophical-panda
version 0.0.11
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
⚠ 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)
ℹ weights.pytorch_state_dict 
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 (= \|-1.8250423e-09 - -1.8250423e-09\|) at {'z': np.int64(0), 'channel': np.int64(0), 'y': np.int64(56), 'x': np.int64(20)}
ℹ weights.pytorch_state_dict 
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 (= \|-4.1718099e-06 - -4.1718099e-06\|) at {'z': np.int64(8), 'channel': np.int64(100)}
ℹ weights.pytorch_state_dict 
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 (= \|2.3283064e-09 - 2.3283064e-09\|) at {'z': np.int64(28), 'channel': np.int64(21), 'y': np.int64(55), 'x': np.int64(25)}
ℹ weights.pytorch_state_dict 
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 (= \|6.0535967e-09 - 6.0535967e-09\|) at {'z': np.int64(26), 'channel': np.int64(60), 'y': np.int64(3), 'x': np.int64(2)}
ℹ weights.pytorch_state_dict 
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 (= \|-4.1909516e-09 - -4.1909516e-09\|) at {'z': np.int64(74), 'channel': np.int64(113), 'y': np.int64(8), 'x': np.int64(5)}
ℹ weights.pytorch_state_dict 
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 (= \|1.7229468e-08 - 1.7229468e-08\|) at {'z': np.int64(67), 'channel': np.int64(128), 'y': np.int64(6), 'x': np.int64(4)}
weights.pytorch_state_dict 
recommended conda environment (Reproduce test outputs from test inputs (pytorch_state_dict))
 
%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.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 inputs with batch_size: 1 and size parameter n: 0
✔️ weights.pytorch_state_dict Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n: 1
✔️ weights.pytorch_state_dict Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n: 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 (= \|-1.8250423e-09 - -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 (= \|-4.1718099e-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 (= \|2.3283064e-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 (= \|6.0535967e-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 (= \|-4.1909516e-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 (= \|1.7229468e-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))
 
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