Python run_cifar Examples

Example #1

    def _run_and_report_benchmark(self):
        """Executes benchmark and reports result."""
        start_time_sec = time.time()
        stats = cifar_main.run_cifar(flags.FLAGS)
        wall_time_sec = time.time() - start_time_sec

        examples_per_sec_hook = None
        for hook in stats['train_hooks']:
            if isinstance(hook, hooks.ExamplesPerSecondHook):
                examples_per_sec_hook = hook
                break

        eval_results = stats['eval_results']
        extras = {}
        extras['accuracy_top_1'] = self._json_description(
            eval_results['accuracy'].item(), priority=0)
        extras['accuracy_top_5'] = self._json_description(
            eval_results['accuracy_top_5'].item())
        if examples_per_sec_hook:
            exp_per_second_list = examples_per_sec_hook.current_examples_per_sec_list
            # ExamplesPerSecondHook skips the first 10 steps.
            exp_per_sec = sum(exp_per_second_list) / (len(exp_per_second_list))
            extras['exp_per_second'] = self._json_description(exp_per_sec)

        self.report_benchmark(iters=eval_results['global_step'],
                              wall_time=wall_time_sec,
                              extras=extras)

Example #2

  def _run_and_report_benchmark(self):
    """Executes benchmark and reports result."""
    start_time_sec = time.time()
    stats = cifar_main.run_cifar(flags.FLAGS)
    wall_time_sec = time.time() - start_time_sec

    examples_per_sec_hook = None
    for hook in stats['train_hooks']:
      if isinstance(hook, hooks.ExamplesPerSecondHook):
        examples_per_sec_hook = hook
        break

    eval_results = stats['eval_results']
    metrics = []
    metrics.append({'name': 'accuracy_top_1',
                    'value': eval_results['accuracy'].item()})
    metrics.append({'name': 'accuracy_top_5',
                    'value': eval_results['accuracy_top_5'].item()})
    if examples_per_sec_hook:
      exp_per_second_list = examples_per_sec_hook.current_examples_per_sec_list
      # ExamplesPerSecondHook skips the first 10 steps.
      exp_per_sec = sum(exp_per_second_list) / (len(exp_per_second_list))
      metrics.append({'name': 'exp_per_second',
                      'value': exp_per_sec})

    self.report_benchmark(
        iters=eval_results['global_step'],
        wall_time=wall_time_sec,
        metrics=metrics)

Example #3

File: estimator_cifar_benchmark.py Project: Exscotticus/models

  def _run_and_report_benchmark(self):
    """Executes benchmark and reports result."""
    start_time_sec = time.time()
    stats = cifar_main.run_cifar(flags.FLAGS)
    wall_time_sec = time.time() - start_time_sec

    examples_per_sec_hook = None
    for hook in stats['train_hooks']:
      if isinstance(hook, hooks.ExamplesPerSecondHook):
        examples_per_sec_hook = hook
        break

    eval_results = stats['eval_results']
    extras = {}
    extras['accuracy_top_1'] = self._json_description(
        eval_results['accuracy'].item(),
        priority=0)
    extras['accuracy_top_5'] = self._json_description(
        eval_results['accuracy_top_5'].item())
    if examples_per_sec_hook:
      exp_per_second_list = examples_per_sec_hook.current_examples_per_sec_list
      # ExamplesPerSecondHook skips the first 10 steps.
      exp_per_sec = sum(exp_per_second_list) / (len(exp_per_second_list))
      extras['exp_per_second'] = self._json_description(exp_per_sec)

    self.report_benchmark(
        iters=eval_results['global_step'],
        wall_time=wall_time_sec,
        extras=extras)

Example #4

File: estimator_benchmark.py Project: ytianxing/models

  def _run_and_report_benchmark(self):
    """Executes benchmark and reports result."""
    start_time_sec = time.time()
    stats = cifar_main.run_cifar(flags.FLAGS)
    wall_time_sec = time.time() - start_time_sec

    self._report_benchmark(stats,
                           wall_time_sec,
                           top_1_min=0.926,
                           top_1_max=0.938)

Example #5

File: estimator_benchmark.py Project: rder96/models

  def _run_and_report_benchmark(self):
    """Executes benchmark and reports result."""
    start_time_sec = time.time()
    stats = cifar_main.run_cifar(flags.FLAGS)
    wall_time_sec = time.time() - start_time_sec

    self._report_benchmark(stats,
                           wall_time_sec,
                           top_1_min=0.926,
                           top_1_max=0.938)

Example #6

File: estimator_cifar_benchmark.py Project: Fellfalla/tensorflow_models

 def resnet56_fp16_2_gpu(self):
     """Test layers FP16 model with Estimator and dist_strat. 2 GPUs."""
     self._setup()
     flags.FLAGS.num_gpus = 2
     flags.FLAGS.data_dir = DATA_DIR
     flags.FLAGS.batch_size = 128
     flags.FLAGS.train_epochs = 182
     flags.FLAGS.model_dir = self._get_model_dir('resnet56_fp16_2_gpu')
     flags.FLAGS.resnet_size = 56
     flags.FLAGS.dtype = 'fp16'
     stats = cifar_main.run_cifar(flags.FLAGS)
     self._fill_report_object(stats)

Example #7

File: estimator_cifar_benchmark.py Project: pooyadavoodi/models

 def resnet56_fp16_2_gpu(self):
   """Test layers FP16 model with Estimator and dist_strat. 2 GPUs."""
   self._setup()
   flags.FLAGS.num_gpus = 2
   flags.FLAGS.data_dir = DATA_DIR
   flags.FLAGS.batch_size = 128
   flags.FLAGS.train_epochs = 182
   flags.FLAGS.model_dir = self._get_model_dir('resnet56_fp16_2_gpu')
   flags.FLAGS.resnet_size = 56
   flags.FLAGS.dtype = 'fp16'
   stats = cifar_main.run_cifar(flags.FLAGS)
   self._fill_report_object(stats)

Example #8

File: estimator_cifar_benchmark.py Project: zhenggr/models

    def _run_and_report_benchmark(self):
        start_time_sec = time.time()
        stats = cifar_main.run_cifar(flags.FLAGS)
        wall_time_sec = time.time() - start_time_sec

        self.report_benchmark(
            iters=stats['global_step'],
            wall_time=wall_time_sec,
            extras={
                'accuracy_top_1':
                self._json_description(stats['accuracy'].item(), priority=0),
                'accuracy_top_5':
                self._json_description(stats['accuracy_top_5'].item()),
            })