def test_AccuaryMetric5(self):
# (5) check reset
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)
self.assertDictEqual(metric.get_metric(reset=False), {'acc': 1})
pred_dict = {"pred": torch.zeros(4, 3, 2)}
target_dict = {'target': torch.zeros(4, 3) + 1}
metric(pred_dict=pred_dict, target_dict=target_dict)
self.assertDictEqual(metric.get_metric(), {'acc': 0.5})
def test_AccuaryMetric7(self):
# (7) check map, match
metric = AccuracyMetric(pred='predictions', target='targets')
pred_dict = {"predictions": 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})
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_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_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_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_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_AccuaryMetric8(self):
# (8) check map, does not match. use stop_fast_param to stop fast param map
try:
metric = AccuracyMetric(pred='predictions', target='targets')
pred_dict = {"prediction": torch.zeros(4, 3, 2), "stop_fast_param": 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), "masks": torch.zeros(4, 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_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
self.assertTrue(True, False), "No exception catches."