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https://github.com/firestar5683/StarPilot.git
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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.
40 lines
1.9 KiB
Python
40 lines
1.9 KiB
Python
from tinygrad import Tensor
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from test.external.mlperf_retinanet.focal_loss import sigmoid_focal_loss as ref_sigmoid_focal_loss
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from test.external.mlperf_unet3d.dice import DiceCELoss
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from examples.mlperf.losses import dice_ce_loss, sigmoid_focal_loss, l1_loss
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import numpy as np
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import torch
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import unittest
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class ExternalTestLosses(unittest.TestCase):
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def setUp(self):
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np.random.seed(42)
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def _assert_loss(self, pred, tgt, tinygrad_metrics, ref_metrics, rtol=1e-07, atol=0, **kwargs):
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tinygrad_metrics_res = tinygrad_metrics(Tensor(pred), Tensor(tgt), **kwargs)
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ref_metrics_res = ref_metrics(torch.from_numpy(pred), torch.from_numpy(tgt), **kwargs)
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np.testing.assert_allclose(tinygrad_metrics_res.numpy(), ref_metrics_res.numpy(), rtol=rtol, atol=atol)
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def test_dice_ce_loss(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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tinygrad_metrics_res, ref_metrics_res = dice_ce_loss, DiceCELoss(True, True, "NCDHW", False)
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self._assert_loss(pred, label, tinygrad_metrics_res, ref_metrics_res, atol=1e-4)
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def test_sigmoid_focal_loss(self):
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def _apply_logit(p): return np.log(p / (1 - p))
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pred, tgt = _apply_logit(np.random.rand(5,2).astype(np.float32)), np.random.randint(0, 2, (5, 2)).astype(np.float32)
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for reduction in ["mean", "sum", "none"]:
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for alpha, gamma in zip([-1, 0.58], [0, 2]):
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self._assert_loss(pred, tgt, sigmoid_focal_loss, ref_sigmoid_focal_loss, rtol=1e-4, alpha=alpha, gamma=gamma, reduction=reduction)
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def test_l1_loss(self):
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N, C, H, W = 3, 4, 5, 6
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shapes = ((N, C), (N, C, H), (N, C, H, W))
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for reduction in ["mean", "sum", "none"]:
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for shape in shapes:
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pred, tgt = np.random.randint(shape).astype(np.float32), np.random.randint(shape)
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self._assert_loss(pred, tgt, l1_loss, torch.nn.functional.l1_loss, reduction=reduction)
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if __name__ == '__main__':
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unittest.main() |