def main(argv):
if len(argv) > 1:
raise RuntimeError('Too many command-line arguments.')
# Read solution.
print('Reading solution...')
public_solution, private_solution, ignored_ids = dataset_file_io.ReadSolution(
cmd_args.solution_path, dataset_file_io.RECOGNITION_TASK_ID)
print('done!')
# Read predictions.
print('Reading predictions...')
public_predictions, private_predictions = dataset_file_io.ReadPredictions(
cmd_args.predictions_path, set(public_solution.keys()),
set(private_solution.keys()), set(ignored_ids),
dataset_file_io.RECOGNITION_TASK_ID)
print('done!')
# Global Average Precision.
print('**********************************************')
print('(Public) Global Average Precision: %f' %
metrics.GlobalAveragePrecision(public_predictions, public_solution))
print(
'(Private) Global Average Precision: %f' %
metrics.GlobalAveragePrecision(private_predictions, private_solution))
# Global Average Precision ignoring non-landmark queries.
print('**********************************************')
print(
'(Public) Global Average Precision ignoring non-landmark queries: %f'
% metrics.GlobalAveragePrecision(public_predictions,
public_solution,
ignore_non_gt_test_images=True))
print(
'(Private) Global Average Precision ignoring non-landmark queries: %f'
% metrics.GlobalAveragePrecision(private_predictions,
private_solution,
ignore_non_gt_test_images=True))
# Top-1 accuracy.
print('**********************************************')
print('(Public) Top-1 accuracy: %.2f' %
(100.0 * metrics.Top1Accuracy(public_predictions, public_solution)))
print(
'(Private) Top-1 accuracy: %.2f' %
(100.0 * metrics.Top1Accuracy(private_predictions, private_solution)))
def testGlobalAveragePrecisionWorks(self):
# Define input.
predictions = _CreateRecognitionPredictions()
solution = _CreateRecognitionSolution()
# Run tested function.
gap = metrics.GlobalAveragePrecision(predictions, solution)
# Define expected results.
expected_gap = 0.166667
# Compare actual and expected results.
self.assertAllClose(gap, expected_gap)
def testGlobalAveragePrecisionIgnoreNonGroundTruthWorks(self):
# Define input.
predictions = _CreateRecognitionPredictions()
solution = _CreateRecognitionSolution()
# Run tested function.
gap = metrics.GlobalAveragePrecision(predictions,
solution,
ignore_non_gt_test_images=True)
# Define expected results.
expected_gap = 0.333333
# Compare actual and expected results.
self.assertAllClose(gap, expected_gap)