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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import platform
from unittest import TestCase
import numpy as np
import pytest
import torch
from mmengine import load
from numpy.testing import assert_array_almost_equal
from mmaction.evaluation import AccMetric, ConfusionMatrix, MultiSportsMetric
from mmaction.evaluation.functional import ava_eval
from mmaction.registry import METRICS
from mmaction.structures import ActionDataSample
def generate_data(num_classes=5, random_label=False, multi_label=False):
data_batch = []
data_samples = []
for i in range(num_classes * 10):
scores = torch.randn(num_classes)
if multi_label:
label = torch.ones_like(scores)
elif random_label:
label = torch.randint(num_classes, size=[1])
else:
label = torch.LongTensor([scores.argmax().item()])
data_sample = dict(pred_score=scores, gt_label=label)
data_samples.append(data_sample)
return data_batch, data_samples
def test_acc_metric():
num_classes = 32
metric = AccMetric(metric_list=('top_k_accuracy', 'mean_class_accuracy'))
data_batch, predictions = generate_data(
num_classes=num_classes, random_label=True)
metric.process(data_batch, predictions)
eval_results = metric.compute_metrics(metric.results)
assert 0.0 <= eval_results['top1'] <= eval_results['top5'] <= 1.0
assert 0.0 <= eval_results['mean1'] <= 1.0
metric.results.clear()
data_batch, predictions = generate_data(
num_classes=num_classes, random_label=False)
metric.process(data_batch, predictions)
eval_results = metric.compute_metrics(metric.results)
assert eval_results['top1'] == eval_results['top5'] == 1.0
assert eval_results['mean1'] == 1.0
metric = AccMetric(
metric_list=('mean_average_precision', 'mmit_mean_average_precision'))
data_batch, predictions = generate_data(
num_classes=num_classes, multi_label=True)
metric.process(data_batch, predictions)
eval_results = metric.compute_metrics(metric.results)
assert eval_results['mean_average_precision'] == 1.0
assert eval_results['mmit_mean_average_precision'] == 1.0
@pytest.mark.skipif(platform.system() == 'Windows', reason='Multiprocess Fail')
def test_ava_detection():
data_prefix = osp.normpath(
osp.join(osp.dirname(__file__), '../../data/eval_detection'))
gt_path = osp.join(data_prefix, 'gt.csv')
result_path = osp.join(data_prefix, 'pred.csv')
label_map = osp.join(data_prefix, 'action_list.txt')
# eval bbox
detection = ava_eval(result_path, 'mAP', label_map, gt_path, None)
assert_array_almost_equal(detection['overall'], 0.09385522)
def test_multisport_detection():
data_prefix = osp.normpath(
osp.join(osp.dirname(__file__), '../../data/eval_multisports'))
gt_path = osp.join(data_prefix, 'gt.pkl')
result_path = osp.join(data_prefix, 'data_samples.pkl')
result_datasamples = load(result_path)
metric = MultiSportsMetric(gt_path)
metric.process(None, result_datasamples)
eval_result = metric.compute_metrics(metric.results)
assert eval_result['frameAP'] == 83.6506
assert eval_result['v_map@0.2'] == 37.5
assert eval_result['v_map@0.5'] == 37.5
assert eval_result['v_map_0.10:0.90'] == 29.1667
class TestConfusionMatrix(TestCase):
def test_evaluate(self):
"""Test using the metric in the same way as Evalutor."""
pred = [
ActionDataSample().set_pred_score(i).set_pred_label(
j).set_gt_label(k).to_dict() for i, j, k in zip([
torch.tensor([0.7, 0.0, 0.3]),
torch.tensor([0.5, 0.2, 0.3]),
torch.tensor([0.4, 0.5, 0.1]),
torch.tensor([0.0, 0.0, 1.0]),
torch.tensor([0.0, 0.0, 1.0]),
torch.tensor([0.0, 0.0, 1.0]),
], [0, 0, 1, 2, 2, 2], [0, 0, 1, 2, 1, 0])
]
# Test with score (use score instead of label if score exists)
metric = METRICS.build(dict(type='ConfusionMatrix'))
metric.process(None, pred)
res = metric.evaluate(6)
self.assertIsInstance(res, dict)
self.assertTensorEqual(
res['confusion_matrix/result'],
torch.tensor([
[2, 0, 1],
[0, 1, 1],
[0, 0, 1],
]))
# Test with label
for sample in pred:
del sample['pred_score']
metric = METRICS.build(dict(type='ConfusionMatrix'))
metric.process(None, pred)
with self.assertRaisesRegex(AssertionError,
'Please specify the `num_classes`'):
metric.evaluate(6)
metric = METRICS.build(dict(type='ConfusionMatrix', num_classes=3))
metric.process(None, pred)
self.assertIsInstance(res, dict)
self.assertTensorEqual(
res['confusion_matrix/result'],
torch.tensor([
[2, 0, 1],
[0, 1, 1],
[0, 0, 1],
]))
def test_calculate(self):
y_true = np.array([0, 0, 1, 2, 1, 0])
y_label = torch.tensor([0, 0, 1, 2, 2, 2])
y_score = [
[0.7, 0.0, 0.3],
[0.5, 0.2, 0.3],
[0.4, 0.5, 0.1],
[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
]
# Test with score
cm = ConfusionMatrix.calculate(y_score, y_true)
self.assertIsInstance(cm, torch.Tensor)
self.assertTensorEqual(
cm, torch.tensor([
[2, 0, 1],
[0, 1, 1],
[0, 0, 1],
]))
# Test with label
with self.assertRaisesRegex(AssertionError,
'Please specify the `num_classes`'):
ConfusionMatrix.calculate(y_label, y_true)
cm = ConfusionMatrix.calculate(y_label, y_true, num_classes=3)
self.assertIsInstance(cm, torch.Tensor)
self.assertTensorEqual(
cm, torch.tensor([
[2, 0, 1],
[0, 1, 1],
[0, 0, 1],
]))
# Test with invalid inputs
with self.assertRaisesRegex(TypeError, "<class 'str'> is not"):
ConfusionMatrix.calculate(y_label, 'hi')
def test_plot(self):
import matplotlib.pyplot as plt
cm = torch.tensor([[2, 0, 1], [0, 1, 1], [0, 0, 1]])
fig = ConfusionMatrix.plot(cm, include_values=True, show=False)
self.assertIsInstance(fig, plt.Figure)
def assertTensorEqual(self,
tensor: torch.Tensor,
value: float,
msg=None,
**kwarg):
tensor = tensor.to(torch.float32)
value = torch.tensor(value).float()
try:
torch.testing.assert_allclose(tensor, value, **kwarg)
except AssertionError as e:
self.fail(self._formatMessage(msg, str(e)))
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