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