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))
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)