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| from caffe2.python import workspace, crf |
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| from caffe2.python.cnn import CNNModelHelper |
| from caffe2.python.crf_predict import crf_update_predictions |
| from caffe2.python.test_util import TestCase |
| import hypothesis.strategies as st |
| from hypothesis import given, settings |
| import numpy as np |
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| class TestCrfDecode(TestCase): |
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| @given(num_tags=st.integers(2, 4), num_words=st.integers(2, 15)) |
| @settings(deadline=2000) |
| def test_crf_viterbi(self, num_tags, num_words): |
| model = CNNModelHelper(name='external') |
| predictions = np.random.randn(num_words, num_tags).astype(np.float32) |
| transitions = np.random.uniform( |
| low=-1, high=1, size=(num_tags + 2, num_tags + 2) |
| ).astype(np.float32) |
| predictions_blob, transitions_blob = ( |
| model.net.AddExternalInputs('predictions', 'crf_transitions') |
| ) |
| workspace.FeedBlob(str(transitions_blob), transitions) |
| workspace.FeedBlob(str(predictions_blob), predictions) |
| crf_layer = crf.CRFWithLoss(model, num_tags, transitions_blob) |
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| updated_predictions = crf_update_predictions( |
| model, crf_layer, predictions_blob |
| ) |
| ref_predictions = crf_layer.update_predictions(predictions_blob) |
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| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
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| updated_predictions = workspace.FetchBlob(str(updated_predictions)) |
| ref_predictions = workspace.FetchBlob(str(ref_predictions)) |
| np.testing.assert_allclose( |
| updated_predictions, |
| ref_predictions, |
| atol=1e-4, rtol=1e-4, err_msg='Mismatch in CRF predictions' |
| ) |
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