def _build_multi_predictors_recognition_model(config, is_training):
if not isinstance(config, model_pb2.MultiPredictorsRecognitionModel):
raise ValueError(
'config not of type model_pb2.MultiPredictorsRecognitionModel')
spatial_transformer_object = None
if config.HasField('spatial_transformer'):
spatial_transformer_object = spatial_transformer_builder.build(
config.spatial_transformer, is_training)
feature_extractor_object = feature_extractor_builder.build(
config.feature_extractor, is_training=is_training)
predictors_dict = {
predictor_config.name: predictor_builder.build(predictor_config,
is_training=is_training)
for predictor_config in config.predictor
}
regression_loss_object = (None if not config.keypoint_supervision else
loss_builder.build(config.regression_loss))
model_object = multi_predictors_recognition_model.MultiPredictorsRecognitionModel(
spatial_transformer=spatial_transformer_object,
feature_extractor=feature_extractor_object,
predictors_dict=predictors_dict,
keypoint_supervision=config.keypoint_supervision,
regression_loss=regression_loss_object,
is_training=is_training,
)
return model_object
def test_sync_predictor_builder(self):
predictor_text_proto = """
attention_predictor {
rnn_cell {
lstm_cell {
num_units: 256
forget_bias: 1.0
initializer { orthogonal_initializer { } }
}
}
rnn_regularizer { l2_regularizer { weight: 1e-4 } }
num_attention_units: 128
max_num_steps: 10
multi_attention: false
beam_width: 1
reverse: false
label_map {
character_set {
text_string: "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
delimiter: ""
}
label_offset: 2
}
loss {
sequence_cross_entropy_loss {
sequence_normalize: false
sample_normalize: true
}
}
sync: true
}
"""
predictor_proto = predictor_pb2.Predictor()
text_format.Merge(predictor_text_proto, predictor_proto)
predictor_object = predictor_builder.build(predictor_proto, True)
feature_maps = [tf.random_uniform([2, 1, 10, 32], dtype=tf.float32)]
predictor_object.provide_groundtruth(
tf.constant([b'hello', b'world'], dtype=tf.string))
predictions_dict = predictor_object.predict(feature_maps)
loss = predictor_object.loss(predictions_dict)
with self.test_session() as sess:
sess.run(
[tf.global_variables_initializer(),
tf.tables_initializer()])
sess_outputs = sess.run({'loss': loss})
print(sess_outputs)