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  • #4

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Open
Created Aug 31, 2021 by Narcon Nicolas@nicolas.narconDeveloper

Segmentation fault en cas de tagsets incorrects

A exécuter, dans le dossier otbtf/test :

otbcli_TensorflowModelServe -model.tagsets train -source1.il data/pxs_subset.tif -source1.rfieldx 16 -source1.rfieldy 16 -source1.placeholder x -model.dir models/model1/ -output.names prediction -out /mnt/mo-gpu/decloud/results/otbtf_test_unitaire_model1_avec_OTBTF2.tif 

ça fonctionne comme attendu. En ne spécifiant pas -model.tagsets train ou en spécifiant un tag-set qui existe mais qui est vide par contre ça segfault. Par exemple:

otbcli_TensorflowModelServe -model.tagsets serve -source1.il data/pxs_subset.tif -source1.rfieldx 16 -source1.rfieldy 16 -source1.placeholder x -model.dir models/model1/ -output.names prediction -out /mnt/mo-gpu/decloud/results/otbtf_test_unitaire_model1_avec_OTBTF2.tif 

le saved_model_cli de model2:

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

MetaGraphDef with tag-set: 'train' contains the following SignatureDefs:
signature_def['model']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['x1:0'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, -1, -1, 4)
        name: x1:0
    inputs['x2:0'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, -1, -1, 1)
        name: x2:0
    inputs['y:0'] tensor_info:
        dtype: DT_INT32
        shape: (-1, -1, -1, 1)
        name: y:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['features:0'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 128)
        name: features:0
    outputs['prediction:0'] tensor_info:
        dtype: DT_INT64
        shape: (-1)
        name: prediction:0
  Method name is: tensorflow/serving/predict
Edited Aug 31, 2021 by Narcon Nicolas
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