import unittest import numpy as np import torch from fastNLP import AccuracyMetric from fastNLP.core.metrics import _pred_topk, _accuracy_topk from fastNLP.core.vocabulary import Vocabulary from collections import Counter from fastNLP.core.metrics import SpanFPreRecMetric, CMRC2018Metric, ClassifyFPreRecMetric def _generate_tags(encoding_type, number_labels=4): """ :param encoding_type: 例如BIOES, BMES, BIO等 :param number_labels: 多少个label，大于1 :return: """ vocab = {} for i in range(number_labels): label = str(i) for tag in encoding_type: if tag == 'O': if tag not in vocab: vocab['O'] = len(vocab) + 1 continue vocab['{}-{}'.format(tag, label)] = len(vocab) + 1 # 其实表达的是这个的count return vocab def _convert_res_to_fastnlp_res(metric_result): allen_result = {} key_map = {'f1-measure-overall': "f", "recall-overall": "rec", "precision-overall": "pre"} for key, value in metric_result.items(): if key in key_map: key = key_map[key] else: label = key.split('-')[-1] if key.startswith('f1'): key = 'f-{}'.format(label) else: key = '{}-{}'.format(key[:3], label) allen_result[key] = round(value, 6) return allen_result class TestAccuracyMetric(unittest.TestCase): def test_AccuracyMetric1(self): # (1) only input, targets passed pred_dict = {"pred": torch.zeros(4, 3)} target_dict = {'target': torch.zeros(4)} metric = AccuracyMetric() metric(pred_dict=pred_dict, target_dict=target_dict) print(metric.get_metric()) def test_AccuracyMetric2(self): # (2) with corrupted size try: pred_dict = {"pred": torch.zeros(4, 3, 2)} target_dict = {'target': torch.zeros(4)} metric = AccuracyMetric() metric(pred_dict=pred_dict, target_dict=target_dict, ) print(metric.get_metric()) except Exception as e: print(e) return print("No exception catches.") def test_AccuracyMetric3(self): # (3) the second batch is corrupted size try: metric = AccuracyMetric() pred_dict = {"pred": torch.zeros(4, 3, 2)} target_dict = {'target': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) pred_dict = {"pred": torch.zeros(4, 3, 2)} target_dict = {'target': torch.zeros(4)} metric(pred_dict=pred_dict, target_dict=target_dict) print(metric.get_metric()) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_AccuaryMetric4(self): # (5) check reset metric = AccuracyMetric() pred_dict = {"pred": torch.randn(4, 3, 2)} target_dict = {'target': torch.ones(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) ans = torch.argmax(pred_dict["pred"], dim=2).to(target_dict["target"]) == target_dict["target"] res = metric.get_metric() self.assertTrue(isinstance(res, dict)) self.assertTrue("acc" in res) self.assertAlmostEqual(res["acc"], float(ans.float().mean()), places=3) def test_AccuaryMetric5(self): # (5) check reset metric = AccuracyMetric() pred_dict = {"pred": torch.randn(4, 3, 2)} target_dict = {'target': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) res = metric.get_metric(reset=False) ans = (torch.argmax(pred_dict["pred"], dim=2).float() == target_dict["target"]).float().mean() self.assertAlmostEqual(res["acc"], float(ans), places=4) def test_AccuaryMetric6(self): # (6) check numpy array is not acceptable try: metric = AccuracyMetric() pred_dict = {"pred": np.zeros((4, 3, 2))} target_dict = {'target': np.zeros((4, 3))} metric(pred_dict=pred_dict, target_dict=target_dict) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_AccuaryMetric7(self): # (7) check map, match metric = AccuracyMetric(pred='predictions', target='targets') pred_dict = {"predictions": torch.randn(4, 3, 2)} target_dict = {'targets': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) res = metric.get_metric() ans = (torch.argmax(pred_dict["predictions"], dim=2).float() == target_dict["targets"]).float().mean() self.assertAlmostEqual(res["acc"], float(ans), places=4) def test_AccuaryMetric8(self): try: metric = AccuracyMetric(pred='predictions', target='targets') pred_dict = {"prediction": torch.zeros(4, 3, 2)} target_dict = {'targets': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict, ) self.assertDictEqual(metric.get_metric(), {'acc': 1}) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_AccuaryMetric9(self): # (9) check map, include unused try: metric = AccuracyMetric(pred='prediction', target='targets') pred_dict = {"prediction": torch.zeros(4, 3, 2), 'unused': 1} target_dict = {'targets': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) self.assertDictEqual(metric.get_metric(), {'acc': 1}) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_AccuaryMetric10(self): # (10) check _fast_metric try: metric = AccuracyMetric() pred_dict = {"predictions": torch.zeros(4, 3, 2), "seq_len": torch.ones(3) * 3} target_dict = {'targets': torch.zeros(4, 3)} metric(pred_dict=pred_dict, target_dict=target_dict) self.assertDictEqual(metric.get_metric(), {'acc': 1}) except Exception as e: print(e) return self.assertTrue(True, False), "No exception catches." def test_duplicate(self): # 0.4.1的潜在bug，不能出现形参重复的情况 metric = AccuracyMetric(pred='predictions', target='targets') pred_dict = {"predictions": torch.zeros(4, 3, 2), "seq_len": torch.ones(4) * 3, 'pred':0} target_dict = {'targets':torch.zeros(4, 3), 'target': 0} metric(pred_dict=pred_dict, target_dict=target_dict) def test_seq_len(self): N = 256 seq_len = torch.zeros(N).long() seq_len[0] = 2 pred = {'pred': torch.ones(N, 2)} target = {'target': torch.ones(N, 2), 'seq_len': seq_len} metric = AccuracyMetric() metric(pred_dict=pred, target_dict=target) self.assertDictEqual(metric.get_metric(), {'acc': 1.}) seq_len[1:] = 1 metric(pred_dict=pred, target_dict=target) self.assertDictEqual(metric.get_metric(), {'acc': 1.}) class SpanFPreRecMetricTest(unittest.TestCase): def test_case1(self): from fastNLP.core.metrics import _bmes_tag_to_spans from fastNLP.core.metrics import _bio_tag_to_spans bmes_lst = ['M-8', 'S-2', 'S-0', 'B-9', 'B-6', 'E-5', 'B-7', 'S-2', 'E-7', 'S-8'] bio_lst = ['O-8', 'O-2', 'B-0', 'O-9', 'I-6', 'I-5', 'I-7', 'I-2', 'I-7', 'O-8'] expect_bmes_res = set() expect_bmes_res.update([('8', (0, 1)), ('2', (1, 2)), ('0', (2, 3)), ('9', (3, 4)), ('6', (4, 5)), ('5', (5, 6)), ('7', (6, 7)), ('2', (7, 8)), ('7', (8, 9)), ('8', (9, 10))]) expect_bio_res = set() expect_bio_res.update([('7', (8, 9)), ('0', (2, 3)), ('2', (7, 8)), ('5', (5, 6)), ('6', (4, 5)), ('7', (6, 7))]) self.assertSetEqual(expect_bmes_res, set(_bmes_tag_to_spans(bmes_lst))) self.assertSetEqual(expect_bio_res, set(_bio_tag_to_spans(bio_lst))) def test_case2(self): # 测试不带label的 from fastNLP.core.metrics import _bmes_tag_to_spans from fastNLP.core.metrics import _bio_tag_to_spans bmes_lst = ['B', 'E', 'B', 'S', 'B', 'M', 'E', 'M', 'B', 'E'] bio_lst = ['I', 'B', 'O', 'O', 'I', 'O', 'I', 'B', 'O', 'O'] expect_bmes_res = set() expect_bmes_res.update([('', (0, 2)), ('', (2, 3)), ('', (3, 4)), ('', (4, 7)), ('', (7, 8)), ('', (8, 10))]) expect_bio_res = set() expect_bio_res.update([('', (7, 8)), ('', (6, 7)), ('', (4, 5)), ('', (0, 1)), ('', (1, 2))]) self.assertSetEqual(expect_bmes_res, set(_bmes_tag_to_spans(bmes_lst))) self.assertSetEqual(expect_bio_res, set(_bio_tag_to_spans(bio_lst))) def test_case3(self): number_labels = 4 # bio tag fastnlp_bio_vocab = Vocabulary(unknown=None, padding=None) fastnlp_bio_vocab.word_count = Counter(_generate_tags('BIO', number_labels)) fastnlp_bio_metric = SpanFPreRecMetric(tag_vocab=fastnlp_bio_vocab, only_gross=False) bio_sequence = torch.FloatTensor([[[-0.4424, -0.4579, -0.7376, 1.8129, 0.1316, 1.6566, -1.2169, -0.3782, 0.8240], [-1.2348, -0.1876, -0.1462, -0.4834, -0.6692, -0.9735, 1.1563, -0.3562, -1.4116], [ 1.6550, -0.9555, 0.3782, -1.3160, -1.5835, -0.3443, -1.7858, 2.0023, 0.7075], [-0.3772, -0.5447, -1.5631, 1.1614, 1.4598, -1.2764, 0.5186, 0.3832, -0.1540], [-0.1011, 0.0600, 1.1090, -0.3545, 0.1284, 1.1484, -1.0120, -1.3508, -0.9513], [ 1.8948, 0.8627, -2.1359, 1.3740, -0.7499, 1.5019, 0.6919, -0.0842, -0.4294]], [[-0.2802, 0.6941, -0.4788, -0.3845, 1.7752, 1.2950, -1.9490, -1.4138, -0.8853], [-1.3752, -0.5457, -0.5305, 0.4018, 0.2934, 0.7931, 2.3845, -1.0726, 0.0364], [ 0.3621, 0.2609, 0.1269, -0.5950, 0.7212, 0.5959, 1.6264, -0.8836, -0.9320], [ 0.2003, -1.0758, -1.1560, -0.6472, -1.7549, 0.1264, 0.6044, -1.6857, 1.1571], [ 1.4277, -0.4915, 0.4496, 2.2027, 0.0730, -3.1792, -0.5125, -0.5837, 1.0184], [ 1.9495, 1.7145, -0.2143, -0.1230, -0.2205, 0.8250, 0.4943, -0.9025, 0.0864]]]) bio_target = torch.LongTensor([[3, 6, 0, 8, 2, 4], [4, 1, 7, 0, 4, 7]]) fastnlp_bio_metric({'pred': bio_sequence, 'seq_len': torch.LongTensor([6, 6])}, {'target': bio_target}) expect_bio_res = {'pre-1': 0.333333, 'rec-1': 0.333333, 'f-1': 0.333333, 'pre-2': 0.5, 'rec-2': 0.5, 'f-2': 0.5, 'pre-0': 0.0, 'rec-0': 0.0, 'f-0': 0.0, 'pre-3': 0.0, 'rec-3': 0.0, 'f-3': 0.0, 'pre': 0.222222, 'rec': 0.181818, 'f': 0.2} self.assertDictEqual(expect_bio_res, fastnlp_bio_metric.get_metric()) def test_case4(self): # bmes tag def _generate_samples(): target = [] seq_len = [] vocab = Vocabulary(unknown=None, padding=None) for i in range(3): target_i = [] seq_len_i = 0 for j in range(1, 10): word_len = np.random.randint(1, 5) seq_len_i += word_len if word_len==1: target_i.append('S') else: target_i.append('B') target_i.extend(['M']*(word_len-2)) target_i.append('E') vocab.add_word_lst(target_i) target.append(target_i) seq_len.append(seq_len_i) target_ = np.zeros((3, max(seq_len))) for i in range(3): target_i = [vocab.to_index(t) for t in target[i]] target_[i, :seq_len[i]] = target_i return target_, target, seq_len, vocab def get_eval(raw_target, pred, vocab, seq_len): pred = pred.argmax(dim=-1).tolist() tp = 0 gold = 0 seg = 0 pred_target = [] for i in range(len(seq_len)): tags = [vocab.to_word(p) for p in pred[i][:seq_len[i]]] spans = [] prev_bmes_tag = None for idx, tag in enumerate(tags): if tag in ('B', 'S'): spans.append([idx, idx]) elif tag in ('M', 'E') and prev_bmes_tag in ('B', 'M'): spans[-1][1] = idx else: spans.append([idx, idx]) prev_bmes_tag = tag tmp = [] for span in spans: if span[1]-span[0]>0: tmp.extend(['B'] + ['M']*(span[1]-span[0]-1) + ['E']) else: tmp.append('S') pred_target.append(tmp) for i in range(len(seq_len)): raw_pred = pred_target[i] start = 0 for j in range(seq_len[i]): if raw_target[i][j] in ('E', 'S'): flag = True for k in range(start, j+1): if raw_target[i][k]!=raw_pred[k]: flag = False break if flag: tp += 1 start = j + 1 gold += 1 if raw_pred[j] in ('E', 'S'): seg += 1 pre = round(tp/seg, 6) rec = round(tp/gold, 6) return {'f': round(2*pre*rec/(pre+rec), 6), 'pre': pre, 'rec':rec} target, raw_target, seq_len, vocab = _generate_samples() pred = torch.randn(3, max(seq_len), 4) expected_metric = get_eval(raw_target, pred, vocab, seq_len) metric = SpanFPreRecMetric(vocab, encoding_type='bmes') metric({'pred': pred, 'seq_len':torch.LongTensor(seq_len)}, {'target': torch.from_numpy(target)}) # print(metric.get_metric(reset=False)) # print(expected_metric) metric_value = metric.get_metric() for key, value in expected_metric.items(): self.assertAlmostEqual(value, metric_value[key], places=5) def test_auto_encoding_type_infer(self): # 检查是否可以自动check encode的类型 vocabs = {} import random for encoding_type in ['bio', 'bioes', 'bmeso']: vocab = Vocabulary(unknown=None, padding=None) for i in range(random.randint(10, 100)): label = str(random.randint(1, 10)) for tag in encoding_type: if tag!='o': vocab.add_word(f'{tag}-{label}') else: vocab.add_word('o') vocabs[encoding_type] = vocab for e in ['bio', 'bioes', 'bmeso']: with self.subTest(e=e): metric = SpanFPreRecMetric(tag_vocab=vocabs[e]) assert metric.encoding_type == e bmes_vocab = _generate_tags('bmes') vocab = Vocabulary() for tag, index in bmes_vocab.items(): vocab.add_word(tag) metric = SpanFPreRecMetric(vocab) assert metric.encoding_type == 'bmes' # 一些无法check的情况 vocab = Vocabulary() for i in range(10): vocab.add_word(str(i)) with self.assertRaises(Exception): metric = SpanFPreRecMetric(vocab) def test_encoding_type(self): # 检查传入的tag_vocab与encoding_type不符合时，是否会报错 vocabs = {} import random from itertools import product for encoding_type in ['bio', 'bioes', 'bmeso']: vocab = Vocabulary(unknown=None, padding=None) for i in range(random.randint(10, 100)): label = str(random.randint(1, 10)) for tag in encoding_type: if tag!='o': vocab.add_word(f'{tag}-{label}') else: vocab.add_word('o') vocabs[encoding_type] = vocab for e1, e2 in product(['bio', 'bioes', 'bmeso'], ['bio', 'bioes', 'bmeso']): with self.subTest(e1=e1, e2=e2): if e1==e2: metric = SpanFPreRecMetric(vocabs[e1], encoding_type=e2) else: s2 = set(e2) s2.update(set(e1)) if s2==set(e2): continue with self.assertRaises(AssertionError): metric = SpanFPreRecMetric(vocabs[e1], encoding_type=e2) for encoding_type in ['bio', 'bioes', 'bmeso']: with self.assertRaises(AssertionError): metric = SpanFPreRecMetric(vocabs[encoding_type], encoding_type='bmes') with self.assertWarns(Warning): vocab = Vocabulary(unknown=None, padding=None).add_word_lst(list('bmes')) metric = SpanFPreRecMetric(vocab, encoding_type='bmeso') vocab = Vocabulary().add_word_lst(list('bmes')) metric = SpanFPreRecMetric(vocab, encoding_type='bmeso') class TestCMRC2018Metric(unittest.TestCase): def test_case1(self): # 测试能否正确计算 import torch metric = CMRC2018Metric() raw_chars = [list("abcsdef"), list("123456s789")] context_len = torch.LongTensor([3, 6]) answers = [["abc", "abc", "abc"], ["12", "12", "12"]] pred_start = torch.randn(2, max(map(len, raw_chars))) pred_end = torch.randn(2, max(map(len, raw_chars))) pred_start[0, 0] = 1000 # 正好是abc pred_end[0, 2] = 1000 pred_start[1, 1] = 1000 # 取出234 pred_end[1, 3] = 1000 metric.evaluate(answers=answers, raw_chars=raw_chars, pred_start=pred_start, pred_end=pred_end, context_len=context_len) eval_res = metric.get_metric() self.assertDictEqual(eval_res, {'f1': 70.0, 'em': 50.0}) class TestUsefulFunctions(unittest.TestCase): # 测试metrics.py中一些看上去挺有用的函数 def test_case_1(self): # multi-class _ = _accuracy_topk(np.random.randint(0, 3, size=(10, 1)), np.random.randint(0, 3, size=(10, 1)), k=3) _ = _pred_topk(np.random.randint(0, 3, size=(10, 1))) # 跑通即可 class TestClassfiyFPreRecMetric(unittest.TestCase): def test_case_1(self): pred = torch.FloatTensor([[-0.1603, -1.3247, 0.2010, 0.9240, -0.6396], [-0.7316, -1.6028, 0.2281, 0.3558, 1.2500], [-1.2943, -1.7350, -0.7085, 1.1269, 1.0782], [ 0.1314, -0.2578, 0.7200, 1.0920, -1.0819], [-0.6787, -0.9081, -0.2752, -1.5818, 0.5538], [-0.2925, 1.1320, 2.8709, -0.6225, -0.6279], [-0.3320, -0.9009, -1.5762, 0.3810, -0.1220], [ 0.4601, -1.0509, 1.4242, 0.3427, 2.7014], [-0.5558, 1.0899, -1.9045, 0.3377, 1.3192], [-0.8251, -0.1558, -0.0871, -0.6755, -0.5905], [ 0.1019, 1.2504, -1.1627, -0.7062, 1.8654], [ 0.9016, -0.1984, -0.0831, -0.7646, 1.5309], [ 0.2073, 0.2250, -0.0879, 0.1608, -0.8915], [ 0.3624, 0.3806, 0.3159, -0.3603, -0.6672], [ 0.2714, 2.5086, -0.1053, -0.5188, 0.9229], [ 0.3258, -0.0303, 1.1439, -0.9123, 1.5180], [ 1.2496, -1.0298, -0.4463, 0.1186, -1.7089], [ 0.0788, 0.6300, -1.3336, -0.7122, 1.0164], [-1.1900, -0.9620, -0.3839, 0.1159, -1.2045], [-0.9037, -0.1447, 1.1834, -0.2617, 2.6112], [ 0.1507, 0.1686, -0.1535, -0.3669, -0.8425], [ 1.0537, 1.1958, -1.2309, 1.0405, 1.3018], [-0.9823, -0.9712, 1.1560, -0.6473, 1.0361], [ 0.8659, -0.2166, -0.8335, -0.3557, -0.5660], [-1.4742, -0.8773, -2.5237, 0.7410, 0.1506], [-1.3032, -1.7157, 0.7479, 1.0755, 1.0817], [-0.2988, 2.3745, 1.2072, 0.0054, 1.1877], [-0.0123, 1.6513, 0.2741, -0.7791, 0.6161], [ 1.6339, -1.0365, 0.3961, -0.9683, 0.2684], [-0.0278, -2.0856, -0.5376, 0.5129, -0.3169], [ 0.9386, 0.8317, 0.9518, -0.5050, -0.2808], [-0.6907, 0.5020, -0.9039, -1.1061, 0.1656]]) arg_max_pred = torch.Tensor([3, 2, 3, 3, 4, 2, 3, 4, 4, 2, 4, 4, 1, 1, 1, 4, 0, 4, 3, 4, 1, 4, 2, 0, 3, 4, 1, 1, 0, 3, 2, 1]) target = torch.Tensor([3, 3, 3, 3, 4, 1, 0, 2, 1, 2, 4, 4, 1, 1, 1, 4, 0, 4, 3, 4, 1, 4, 2, 0, 3, 4, 1, 1, 0, 3, 2, 1]) metric = ClassifyFPreRecMetric(f_type='macro') metric.evaluate(pred, target) result_dict = metric.get_metric(reset=True) ground_truth = {'f': 0.8362782, 'pre': 0.8269841, 'rec': 0.8668831} for keys in ['f', 'pre', 'rec']: self.assertAlmostEqual(result_dict[keys], ground_truth[keys], delta=0.0001) metric = ClassifyFPreRecMetric(f_type='micro') metric.evaluate(pred, target) result_dict = metric.get_metric(reset=True) ground_truth = {'f': 0.84375, 'pre': 0.84375, 'rec': 0.84375} for keys in ['f', 'pre', 'rec']: self.assertAlmostEqual(result_dict[keys], ground_truth[keys], delta=0.0001) metric = ClassifyFPreRecMetric(only_gross=False, f_type='micro') metric.evaluate(pred, target) result_dict = metric.get_metric(reset=True) ground_truth = {'f-0': 0.857143, 'pre-0': 0.75, 'rec-0': 1.0, 'f-1': 0.875, 'pre-1': 0.777778, 'rec-1': 1.0, 'f-2': 0.75, 'pre-2': 0.75, 'rec-2': 0.75, 'f-3': 0.857143, 'pre-3': 0.857143, 'rec-3': 0.857143, 'f-4': 0.842105, 'pre-4': 1.0, 'rec-4': 0.727273, 'f': 0.84375, 'pre': 0.84375, 'rec': 0.84375} for keys in ground_truth.keys(): self.assertAlmostEqual(result_dict[keys], ground_truth[keys], delta=0.0001)