def _create_signature(model, class_names: List[str], top_k: Optional[int]):
serialized_tf_example = array_ops.placeholder(tf.string, name="tf_example")
feature_configs = {"x": tf.io.FixedLenFeature([], tf.string)}
tf_example = tf.io.parse_example(serialized_tf_example, feature_configs)
jpegs = tf_example["x"]
x = tf.map_fn(_preprocess_image, jpegs, dtype=tf.float32)
y = model(x)
top_k = min(top_k or len(class_names), len(class_names))
values, indices = tf.nn.top_k(y, top_k)
table_class_names = lookup_ops.index_to_string_table_from_tensor(
vocabulary_list=tf.constant(class_names),
default_value="UNK",
name=None)
classification_inputs = build_tensor_info(serialized_tf_example)
prediction_class_names = table_class_names.lookup(
tf.cast(indices, dtype=dtypes.int64))
classification_outputs_class_names = build_tensor_info(
prediction_class_names)
classification_outputs_scores = build_tensor_info(values)
classification_signature = build_signature_def(
inputs={sig_consts_v1.CLASSIFY_INPUTS: classification_inputs},
outputs={
sig_consts_v1.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_class_names,
sig_consts_v1.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores,
},
method_name=sig_consts_v1.CLASSIFY_METHOD_NAME,
)
# Ensure valid signature
if not is_valid_signature(classification_signature):
raise ValueError("Invalid classification signature!")
return classification_signature
def _assertInvalidSignature(self, inputs, outputs, method_name):
signature_def = signature_def_utils_impl.build_signature_def(
inputs, outputs, method_name)
self.assertFalse(
signature_def_utils_impl.is_valid_signature(signature_def))
def _assertInvalidSignature(self, inputs, outputs, method_name):
signature_def = signature_def_utils_impl.build_signature_def(
inputs, outputs, method_name)
self.assertFalse(
signature_def_utils_impl.is_valid_signature(signature_def))
