@@ -313,17 +313,10 @@ def load_saved_model(model, saved_model_tags, input_tensor_names, output_tensor_
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def _get_graph_from_saved_model_v2 (saved_model_dir , input_tensor_names , output_tensor_names ):
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from tensorflow .python .saved_model import signature_constants , tag_constants
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- from neural_compressor .adaptor .tf_utils .util import parse_saved_model
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-
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saved_model_exported_names = [signature_constants .DEFAULT_SERVING_SIGNATURE_DEF_KEY ]
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saved_model_tags = set ([tag_constants .SERVING ])
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- try :
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- graph_def , _saved_model , _ , _ , input_names , output_names = parse_saved_model (
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- saved_model_dir , True , input_tensor_names , output_tensor_names
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- )
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- except :
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- return load_saved_model (saved_model_dir , saved_model_tags , input_tensor_names , output_tensor_names )
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- return graph_def , input_names , output_names
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+
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+ return load_saved_model (saved_model_dir , saved_model_tags , input_tensor_names , output_tensor_names )
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def _get_graph_from_original_keras_v2 (model , output_dir ):
@@ -467,6 +460,15 @@ def keras_session(model, input_tensor_names, output_tensor_names, **kwargs):
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try :
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tf .keras .backend .set_learning_phase (0 )
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graph_def , input_names , output_names = _get_graph_from_saved_model_v1 (model )
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+ except :
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+ keras_format = "saved_model_general"
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+ if keras_format == "saved_model_general" : # pargma: no cover
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+ try :
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+ from neural_compressor .adaptor .tf_utils .util import parse_saved_model
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+
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+ graph_def , _saved_model , _ , _ , input_names , output_names = parse_saved_model (
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+ temp_dir , True , input_tensor_names , output_tensor_names
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+ )
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except :
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raise ValueError ("Not supported keras model type..." )
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