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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
42 lines
2.3 KiB
Python
42 lines
2.3 KiB
Python
from tinygrad import Tensor
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from test.external.mlperf_unet3d.dice import DiceScore
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from examples.mlperf.metrics import dice_score, log_perplexity
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import numpy as np
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import torch
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import unittest, math
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class ExternalTestMetrics(unittest.TestCase):
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def _test_metrics(self, tinygrad_metrics, orig_metrics, pred, label, atol=1e-8, rtol=1e-7):
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tinygrad_metrics_res = tinygrad_metrics(Tensor(pred), Tensor(label)).squeeze().numpy()
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orig_metrics_res = orig_metrics(torch.from_numpy(pred), torch.from_numpy(label)).numpy()
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np.testing.assert_allclose(tinygrad_metrics_res, orig_metrics_res, atol=atol, rtol=rtol)
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def test_dice(self):
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pred, label = np.random.rand(1, 3, 128, 128, 128).astype(np.float32), np.ones((1, 1, 128, 128, 128)).astype(np.uint8)
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self._test_metrics(dice_score, DiceScore(), pred, label)
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def test_log_perplexity(self):
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# equally likely
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np.testing.assert_allclose(log_perplexity(Tensor([[[1.0, 1, 1, 1]]]), Tensor([[2]])).numpy(), math.log(4))
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np.testing.assert_allclose(log_perplexity(Tensor([[[1.0]*256]*32]), Tensor([[2]*32])).numpy(), math.log(256), rtol=1e-6)
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# pretty correct and incorrect
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np.testing.assert_allclose(log_perplexity(Tensor([[[10000., 0, 0, 0]]]), Tensor([[0]])).numpy(), 0)
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np.testing.assert_allclose(log_perplexity(Tensor([[[0.0, 10000, 10000, 10000]]]), Tensor([[0]])).numpy(), 10000, rtol=1e-3)
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# higher logit -> lower loss
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x = Tensor([[[4.0, 3, 2, 1]]])
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for i in range(x.numel()-1): self.assertLess(log_perplexity(x, Tensor([[i]])).item(), log_perplexity(x, Tensor([[i+1]])).item())
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# torch eval examples
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np.testing.assert_allclose(
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log_perplexity(Tensor([[[0.3659, 0.7025, 0.3104], [0.0097, 0.6577, 0.1947]]]), Tensor([[2, 1]])).exp().numpy(),
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2.7593, rtol=1e-5)
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np.testing.assert_allclose(
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log_perplexity(Tensor([[[0.3, 0.7, 0.3, 0.1], [0.5, 0.4, 0.1, 0.4],[0.1, 0.1, 0.2, 0.5]],
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[[0.1, 0.6, 0.1, 0.5], [0.3, 0.7, 0.3, 0.4], [0.3, 0.7, 0.3, 0.4]]]), Tensor([[2, 1, 3], [1, 0, 1]])).exp().numpy(),
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3.6216, rtol=1e-5)
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np.testing.assert_allclose(
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log_perplexity(Tensor([[[0.3659, 0.7025, 0.3104], [0.0097, 0.6577, 0.1947]]]), Tensor([[2, 1]]), ignore_index=1).exp().numpy(),
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3.5372, rtol=1e-4)
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if __name__ == '__main__':
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unittest.main() |