From b11608c232da750ad5eecb19e7f2f20e14864886 Mon Sep 17 00:00:00 2001
From: huangyuxin <hyxin2014@126.com>
Date: Wed, 21 Jul 2021 08:44:12 +0000
Subject: [PATCH] added autoLog, but gpu_util is always 0.0%

---
 deepspeech/exps/deepspeech2/model.py | 34 +++++++++++++++++++++++++++-
 1 file changed, 33 insertions(+), 1 deletion(-)

diff --git a/deepspeech/exps/deepspeech2/model.py b/deepspeech/exps/deepspeech2/model.py
index 209e8b023..3810694a1 100644
--- a/deepspeech/exps/deepspeech2/model.py
+++ b/deepspeech/exps/deepspeech2/model.py
@@ -37,6 +37,11 @@ from deepspeech.utils import mp_tools
 from deepspeech.utils.log import Log
 logger = Log(__name__).getlog()
 
+import auto_log
+import os
+from paddle import inference
+
+
 
 class DeepSpeech2Trainer(Trainer):
     @classmethod
@@ -223,6 +228,27 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
 
     def __init__(self, config, args):
         super().__init__(config, args)
+        # added by hyx
+        pid = os.getpid()
+        infer_config = inference.Config()
+        infer_config.enable_use_gpu(10000, 2)
+        logger = None
+        autolog = auto_log.AutoLogger(
+             model_name="tiny_s0",
+             model_precision="fp32",
+             batch_size=config.decoding.batch_size,
+             data_shape="dynamic",
+             save_path="./output/auto_log.lpg",
+             inference_config=infer_config,
+             pids=pid,
+             process_name=None,
+             gpu_ids=2,
+             time_keys=[
+                     'preprocess_time', 'inference_time', 'postprocess_time'
+             ],
+             warmup=0)
+        self.autolog = autolog
+        logger = autolog.logger
 
     def ordid2token(self, texts, texts_len):
         """ ord() id to chr() chr """
@@ -248,6 +274,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         vocab_list = self.test_loader.collate_fn.vocab_list
 
         target_transcripts = self.ordid2token(texts, texts_len)
+        self.autolog.times.start()
+        self.autolog.times.stamp()
         result_transcripts = self.model.decode(
             audio,
             audio_len,
@@ -260,7 +288,9 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             cutoff_prob=cfg.cutoff_prob,
             cutoff_top_n=cfg.cutoff_top_n,
             num_processes=cfg.num_proc_bsearch)
-
+        self.autolog.times.stamp()
+        self.autolog.times.stamp()
+        self.autolog.times.end()
         for utt, target, result in zip(utts, target_transcripts,
                                        result_transcripts):
             errors, len_ref = errors_func(target, result)
@@ -291,6 +321,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         with open(self.args.result_file, 'w') as fout:
             for i, batch in enumerate(self.test_loader):
+                logger.info("batch: {}".format(i))
                 utts, audio, audio_len, texts, texts_len = batch
                 metrics = self.compute_metrics(utts, audio, audio_len, texts,
                                                texts_len, fout)
@@ -308,6 +339,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         msg += "Final error rate [%s] (%d/%d) = %f" % (
             error_rate_type, num_ins, num_ins, errors_sum / len_refs)
         logger.info(msg)
+        self.autolog.report()
 
     def run_test(self):
         self.resume_or_scratch()