add unit test for deepspeech2online inference

tests/unit/asr/deepspeech2_online_model_test.py

@@ -15,9 +15,12 @@ import unittest
 
 import numpy as np
 import paddle
+import pickle
+import os
+from paddle import inference
 
 from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline
-
+from paddlespeech.s2t.models.ds2_online import DeepSpeech2InferModelOnline
 
 class TestDeepSpeech2ModelOnline(unittest.TestCase):
     def setUp(self):
@@ -182,5 +185,85 @@ class TestDeepSpeech2ModelOnline(unittest.TestCase):
                 paddle.allclose(final_state_c_box, final_state_c_box_chk), True)
 
 
+
+
+class TestDeepSpeech2StaticModelOnline(unittest.TestCase):
+    
+    def setUp(self):
+        export_prefix = "exp/deepspeech2_online/checkpoints/test_export"
+        os.makedirs( os.path.dirname(export_prefix), mode=0o755)
+        infer_model =  DeepSpeech2InferModelOnline(
+            feat_size=161,
+            dict_size=4233,
+            num_conv_layers=2,
+            num_rnn_layers=5,
+            rnn_size=1024,
+            num_fc_layers=0,
+            fc_layers_size_list=[-1],
+            use_gru=False)
+        static_model = infer_model.export()
+        paddle.jit.save(static_model, export_prefix)
+
+        with open("test_data/static_ds2online_inputs.pickle", "rb") as f:
+            self.data_dict = pickle.load(f)
+        
+        self.setup_model(export_prefix)
+    
+
+    def setup_model(self, export_prefix):
+        deepspeech_config = inference.Config(
+            export_prefix + ".pdmodel",
+            export_prefix + ".pdiparams")
+        if ('CUDA_VISIBLE_DEVICES' in os.environ.keys() and os.environ['CUDA_VISIBLE_DEVICES'].strip() != ''):
+            deepspeech_config.enable_use_gpu(100, 0)
+            deepspeech_config.enable_memory_optim()
+        deepspeech_predictor = inference.create_predictor(deepspeech_config)
+        self.predictor = deepspeech_predictor
+    
+    def test_unit(self):
+        input_names = self.predictor.get_input_names()
+        audio_handle = self.predictor.get_input_handle(input_names[0])
+        audio_len_handle = self.predictor.get_input_handle(input_names[1])
+        h_box_handle = self.predictor.get_input_handle(input_names[2])
+        c_box_handle = self.predictor.get_input_handle(input_names[3])
+        
+
+        x_chunk = self.data_dict["audio_chunk"]
+        x_chunk_lens = self.data_dict["audio_chunk_lens"]
+        chunk_state_h_box = self.data_dict["chunk_state_h_box"]
+        chunk_state_c_box = self.data_dict["chunk_state_c_bos"]
+
+        audio_handle.reshape(x_chunk.shape)
+        audio_handle.copy_from_cpu(x_chunk)
+
+        audio_len_handle.reshape(x_chunk_lens.shape)
+        audio_len_handle.copy_from_cpu(x_chunk_lens)
+
+        h_box_handle.reshape(chunk_state_h_box.shape)
+        h_box_handle.copy_from_cpu(chunk_state_h_box)
+
+        c_box_handle.reshape(chunk_state_c_box.shape)
+        c_box_handle.copy_from_cpu(chunk_state_c_box)
+
+
+
+        output_names = self.predictor.get_output_names()
+        output_handle = self.predictor.get_output_handle(
+            output_names[0])
+        output_lens_handle = self.predictor.get_output_handle(
+            output_names[1])
+        output_state_h_handle = self.predictor.get_output_handle(
+            output_names[2])
+        output_state_c_handle = self.predictor.get_output_handle(
+            output_names[3])
+        self.predictor.run()
+
+        output_chunk_probs = output_handle.copy_to_cpu()
+        output_chunk_lens = output_lens_handle.copy_to_cpu()
+        chunk_state_h_box = output_state_h_handle.copy_to_cpu()
+        chunk_state_c_box = output_state_c_handle.copy_to_cpu()
+        return True
+    
+
 if __name__ == '__main__':
     unittest.main()