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"""Tests for object_detection.matchers.argmax_matcher.""" |
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import numpy as np |
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import tensorflow as tf |
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from object_detection.matchers import argmax_matcher |
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from object_detection.utils import test_case |
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class ArgMaxMatcherTest(test_case.TestCase): |
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def test_return_correct_matches_with_default_thresholds(self): |
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def graph_fn(similarity_matrix): |
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matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=None) |
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match = matcher.match(similarity_matrix) |
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matched_cols = match.matched_column_indicator() |
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unmatched_cols = match.unmatched_column_indicator() |
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match_results = match.match_results |
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return (matched_cols, unmatched_cols, match_results) |
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similarity = np.array([[1., 1, 1, 3, 1], |
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[2, -1, 2, 0, 4], |
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[3, 0, -1, 0, 0]], dtype=np.float32) |
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expected_matched_rows = np.array([2, 0, 1, 0, 1]) |
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(res_matched_cols, res_unmatched_cols, |
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res_match_results) = self.execute(graph_fn, [similarity]) |
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self.assertAllEqual(res_match_results[res_matched_cols], |
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expected_matched_rows) |
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self.assertAllEqual(np.nonzero(res_matched_cols)[0], [0, 1, 2, 3, 4]) |
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self.assertFalse(np.all(res_unmatched_cols)) |
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def test_return_correct_matches_with_empty_rows(self): |
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def graph_fn(similarity_matrix): |
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matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=None) |
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match = matcher.match(similarity_matrix) |
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return match.unmatched_column_indicator() |
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similarity = 0.2 * np.ones([0, 5], dtype=np.float32) |
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res_unmatched_cols = self.execute(graph_fn, [similarity]) |
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self.assertAllEqual(np.nonzero(res_unmatched_cols)[0], np.arange(5)) |
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def test_return_correct_matches_with_matched_threshold(self): |
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def graph_fn(similarity): |
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matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3.) |
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match = matcher.match(similarity) |
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matched_cols = match.matched_column_indicator() |
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unmatched_cols = match.unmatched_column_indicator() |
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match_results = match.match_results |
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return (matched_cols, unmatched_cols, match_results) |
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similarity = np.array([[1, 1, 1, 3, 1], |
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[2, -1, 2, 0, 4], |
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[3, 0, -1, 0, 0]], dtype=np.float32) |
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expected_matched_cols = np.array([0, 3, 4]) |
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expected_matched_rows = np.array([2, 0, 1]) |
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expected_unmatched_cols = np.array([1, 2]) |
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(res_matched_cols, res_unmatched_cols, |
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match_results) = self.execute(graph_fn, [similarity]) |
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self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows) |
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self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols) |
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self.assertAllEqual(np.nonzero(res_unmatched_cols)[0], |
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expected_unmatched_cols) |
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def test_return_correct_matches_with_matched_and_unmatched_threshold(self): |
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def graph_fn(similarity): |
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matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3., |
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unmatched_threshold=2.) |
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match = matcher.match(similarity) |
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matched_cols = match.matched_column_indicator() |
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unmatched_cols = match.unmatched_column_indicator() |
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match_results = match.match_results |
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return (matched_cols, unmatched_cols, match_results) |
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similarity = np.array([[1, 1, 1, 3, 1], |
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[2, -1, 2, 0, 4], |
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[3, 0, -1, 0, 0]], dtype=np.float32) |
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expected_matched_cols = np.array([0, 3, 4]) |
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expected_matched_rows = np.array([2, 0, 1]) |
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expected_unmatched_cols = np.array([1]) |
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(res_matched_cols, res_unmatched_cols, |
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match_results) = self.execute(graph_fn, [similarity]) |
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self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows) |
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self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols) |
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self.assertAllEqual(np.nonzero(res_unmatched_cols)[0], |
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expected_unmatched_cols) |
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def test_return_correct_matches_negatives_lower_than_unmatched_false(self): |
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def graph_fn(similarity): |
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matcher = argmax_matcher.ArgMaxMatcher( |
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matched_threshold=3., |
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unmatched_threshold=2., |
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negatives_lower_than_unmatched=False) |
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match = matcher.match(similarity) |
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matched_cols = match.matched_column_indicator() |
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unmatched_cols = match.unmatched_column_indicator() |
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match_results = match.match_results |
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return (matched_cols, unmatched_cols, match_results) |
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similarity = np.array([[1, 1, 1, 3, 1], |
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[2, -1, 2, 0, 4], |
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[3, 0, -1, 0, 0]], dtype=np.float32) |
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expected_matched_cols = np.array([0, 3, 4]) |
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expected_matched_rows = np.array([2, 0, 1]) |
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expected_unmatched_cols = np.array([2]) |
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(res_matched_cols, res_unmatched_cols, |
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match_results) = self.execute(graph_fn, [similarity]) |
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self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows) |
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self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols) |
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self.assertAllEqual(np.nonzero(res_unmatched_cols)[0], |
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expected_unmatched_cols) |
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def test_return_correct_matches_unmatched_row_not_using_force_match(self): |
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def graph_fn(similarity): |
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matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3., |
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unmatched_threshold=2.) |
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match = matcher.match(similarity) |
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matched_cols = match.matched_column_indicator() |
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unmatched_cols = match.unmatched_column_indicator() |
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match_results = match.match_results |
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return (matched_cols, unmatched_cols, match_results) |
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similarity = np.array([[1, 1, 1, 3, 1], |
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[-1, 0, -2, -2, -1], |
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[3, 0, -1, 2, 0]], dtype=np.float32) |
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expected_matched_cols = np.array([0, 3]) |
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expected_matched_rows = np.array([2, 0]) |
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expected_unmatched_cols = np.array([1, 2, 4]) |
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(res_matched_cols, res_unmatched_cols, |
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match_results) = self.execute(graph_fn, [similarity]) |
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self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows) |
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self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols) |
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self.assertAllEqual(np.nonzero(res_unmatched_cols)[0], |
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expected_unmatched_cols) |
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def test_return_correct_matches_unmatched_row_while_using_force_match(self): |
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def graph_fn(similarity): |
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matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3., |
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unmatched_threshold=2., |
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force_match_for_each_row=True) |
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match = matcher.match(similarity) |
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matched_cols = match.matched_column_indicator() |
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unmatched_cols = match.unmatched_column_indicator() |
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match_results = match.match_results |
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return (matched_cols, unmatched_cols, match_results) |
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similarity = np.array([[1, 1, 1, 3, 1], |
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[-1, 0, -2, -2, -1], |
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[3, 0, -1, 2, 0]], dtype=np.float32) |
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expected_matched_cols = np.array([0, 1, 3]) |
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expected_matched_rows = np.array([2, 1, 0]) |
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expected_unmatched_cols = np.array([2, 4]) |
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(res_matched_cols, res_unmatched_cols, |
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match_results) = self.execute(graph_fn, [similarity]) |
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self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows) |
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self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols) |
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self.assertAllEqual(np.nonzero(res_unmatched_cols)[0], |
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expected_unmatched_cols) |
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def test_return_correct_matches_using_force_match_padded_groundtruth(self): |
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def graph_fn(similarity, valid_rows): |
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matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3., |
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unmatched_threshold=2., |
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force_match_for_each_row=True) |
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match = matcher.match(similarity, valid_rows) |
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matched_cols = match.matched_column_indicator() |
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unmatched_cols = match.unmatched_column_indicator() |
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match_results = match.match_results |
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return (matched_cols, unmatched_cols, match_results) |
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similarity = np.array([[1, 1, 1, 3, 1], |
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[-1, 0, -2, -2, -1], |
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[0, 0, 0, 0, 0], |
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[3, 0, -1, 2, 0], |
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[0, 0, 0, 0, 0]], dtype=np.float32) |
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valid_rows = np.array([True, True, False, True, False]) |
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expected_matched_cols = np.array([0, 1, 3]) |
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expected_matched_rows = np.array([3, 1, 0]) |
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expected_unmatched_cols = np.array([2, 4]) |
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(res_matched_cols, res_unmatched_cols, |
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match_results) = self.execute(graph_fn, [similarity, valid_rows]) |
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self.assertAllEqual(match_results[res_matched_cols], expected_matched_rows) |
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self.assertAllEqual(np.nonzero(res_matched_cols)[0], expected_matched_cols) |
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self.assertAllEqual(np.nonzero(res_unmatched_cols)[0], |
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expected_unmatched_cols) |
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def test_valid_arguments_corner_case(self): |
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argmax_matcher.ArgMaxMatcher(matched_threshold=1, |
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unmatched_threshold=1) |
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def test_invalid_arguments_corner_case_negatives_lower_than_thres_false(self): |
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with self.assertRaises(ValueError): |
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argmax_matcher.ArgMaxMatcher(matched_threshold=1, |
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unmatched_threshold=1, |
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negatives_lower_than_unmatched=False) |
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def test_invalid_arguments_no_matched_threshold(self): |
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with self.assertRaises(ValueError): |
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argmax_matcher.ArgMaxMatcher(matched_threshold=None, |
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unmatched_threshold=4) |
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def test_invalid_arguments_unmatched_thres_larger_than_matched_thres(self): |
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with self.assertRaises(ValueError): |
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argmax_matcher.ArgMaxMatcher(matched_threshold=1, |
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unmatched_threshold=2) |
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if __name__ == '__main__': |
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tf.test.main() |
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