def test_ConfusionMatrixMetric1(self):
pred_dict = {"pred": torch.zeros(4, 3)}
target_dict = {'target': torch.zeros(4)}
metric = ConfusionMatrixMetric()
metric(pred_dict=pred_dict, target_dict=target_dict)
print(metric.get_metric())
def test_ConfusionMatrixMetric6(self):
# (6) check map, match
metric = ConfusionMatrixMetric(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()
print(res)
def test_ConfusionMatrixMetric4(self):
# (4) check reset
metric = ConfusionMatrixMetric()
pred_dict = {"pred": torch.randn(4, 3, 2)}
target_dict = {'target': torch.ones(4, 3)}
metric(pred_dict=pred_dict, target_dict=target_dict)
res = metric.get_metric()
self.assertTrue(isinstance(res, dict))
print(res)
def test_vocab(self):
vocab = Vocabulary()
word_list = "this is a word list".split()
vocab.update(word_list)
pred_dict = {"pred": torch.zeros(4, 3)}
target_dict = {'target': torch.zeros(4)}
metric = ConfusionMatrixMetric(vocab=vocab)
metric(pred_dict=pred_dict, target_dict=target_dict)
print(metric.get_metric())
def test_duplicate(self):
# 0.4.1的潜在bug，不能出现形参重复的情况
metric = ConfusionMatrixMetric(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)
print(metric.get_metric())
def test_ConfusionMatrixMetric8(self):
# (8) check _fast_metric
with self.assertRaises(Exception):
metric = ConfusionMatrixMetric()
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)
print(metric.get_metric())
def test_ConfusionMatrixMetric3(self):
# (3) the second batch is corrupted size
with self.assertRaises(Exception):
metric = ConfusionMatrixMetric()
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())
def test_ConfusionMatrixMetric2(self):
# (2) with corrupted size
with self.assertRaises(Exception):
pred_dict = {"pred": torch.zeros(4, 3, 2)}
target_dict = {'target': torch.zeros(4)}
metric = ConfusionMatrixMetric()
metric(
pred_dict=pred_dict,
target_dict=target_dict,
)
print(metric.get_metric())
def test_ConfusionMatrixMetric5(self):
# (5) check numpy array is not acceptable
with self.assertRaises(Exception):
metric = ConfusionMatrixMetric()
pred_dict = {"pred": np.zeros((4, 3, 2))}
target_dict = {'target': np.zeros((4, 3))}
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 = ConfusionMatrixMetric()
metric(pred_dict=pred, target_dict=target)
metric.get_metric(reset=False)
seq_len[1:] = 1
metric(pred_dict=pred, target_dict=target)
metric.get_metric()
def test_ConfusionMatrixMetric7(self):
# (7) check map, include unused
metric = ConfusionMatrixMetric(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)