Python errorcounter.CountErrors方法代码示例

# 需要导入模块: import errorcounter [as 别名]
# 或者: from errorcounter import CountErrors [as 别名]
def testCountErrors(self):
    """Tests that the error counter works as expected.
    """
    truth_str = 'farm barn'
    counts = ec.CountErrors(ocr_text=truth_str, truth_text=truth_str)
    self.assertEqual(
        counts, ec.ErrorCounts(
            fn=0, fp=0, truth_count=9, test_count=9))
    # With a period on the end, we get a char error.
    dot_str = 'farm barn.'
    counts = ec.CountErrors(ocr_text=dot_str, truth_text=truth_str)
    self.assertEqual(
        counts, ec.ErrorCounts(
            fn=0, fp=1, truth_count=9, test_count=10))
    counts = ec.CountErrors(ocr_text=truth_str, truth_text=dot_str)
    self.assertEqual(
        counts, ec.ErrorCounts(
            fn=1, fp=0, truth_count=10, test_count=9))
    # Space is just another char.
    no_space = 'farmbarn'
    counts = ec.CountErrors(ocr_text=no_space, truth_text=truth_str)
    self.assertEqual(
        counts, ec.ErrorCounts(
            fn=1, fp=0, truth_count=9, test_count=8))
    counts = ec.CountErrors(ocr_text=truth_str, truth_text=no_space)
    self.assertEqual(
        counts, ec.ErrorCounts(
            fn=0, fp=1, truth_count=8, test_count=9))
    # Lose them all.
    counts = ec.CountErrors(ocr_text='', truth_text=truth_str)
    self.assertEqual(
        counts, ec.ErrorCounts(
            fn=9, fp=0, truth_count=9, test_count=0))
    counts = ec.CountErrors(ocr_text=truth_str, truth_text='')
    self.assertEqual(
        counts, ec.ErrorCounts(
            fn=0, fp=9, truth_count=0, test_count=9)) 

# 需要导入模块: import errorcounter [as 别名]
# 或者: from errorcounter import CountErrors [as 别名]
def SoftmaxEval(self, sess, model, num_steps):
    """Evaluate a model in softmax mode.

    Adds char, word recall and sequence error rate events to the sw summary
    writer, and returns them as well
    TODO(rays) Add LogisticEval.
    Args:
      sess:  A tensor flow Session.
      model: The model to run in the session. Requires a VGSLImageModel or any
        other class that has a using_ctc attribute and a RunAStep(sess) method
        that reurns a softmax result with corresponding labels.
      num_steps: Number of steps to evaluate for.
    Returns:
      ErrorRates named tuple.
    Raises:
      ValueError: If an unsupported number of dimensions is used.
    """
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    # Run the requested number of evaluation steps, gathering the outputs of the
    # softmax and the true labels of the evaluation examples.
    total_label_counts = ec.ErrorCounts(0, 0, 0, 0)
    total_word_counts = ec.ErrorCounts(0, 0, 0, 0)
    sequence_errors = 0
    for _ in xrange(num_steps):
      softmax_result, labels = model.RunAStep(sess)
      # Collapse softmax to same shape as labels.
      predictions = softmax_result.argmax(axis=-1)
      # Exclude batch from num_dims.
      num_dims = len(predictions.shape) - 1
      batch_size = predictions.shape[0]
      null_label = softmax_result.shape[-1] - 1
      for b in xrange(batch_size):
        if num_dims == 2:
          # TODO(rays) Support 2-d data.
          raise ValueError('2-d label data not supported yet!')
        else:
          if num_dims == 1:
            pred_batch = predictions[b, :]
            labels_batch = labels[b, :]
          else:
            pred_batch = [predictions[b]]
            labels_batch = [labels[b]]
          text = self.StringFromCTC(pred_batch, model.using_ctc, null_label)
          truth = self.StringFromCTC(labels_batch, False, null_label)
          # Note that recall_errs is false negatives (fn) aka drops/deletions.
          # Actual recall would be 1-fn/truth_words.
          # Likewise precision_errs is false positives (fp) aka adds/insertions.
          # Actual precision would be 1-fp/ocr_words.
          total_word_counts = ec.AddErrors(total_word_counts,
                                           ec.CountWordErrors(text, truth))
          total_label_counts = ec.AddErrors(total_label_counts,
                                            ec.CountErrors(text, truth))
          if text != truth:
            sequence_errors += 1

    coord.request_stop()
    coord.join(threads)
    return ec.ComputeErrorRates(total_label_counts, total_word_counts,
                                sequence_errors, num_steps * batch_size)