87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
import os
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import sys
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import unittest
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import torch
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from torch import Tensor
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from typing import List, Tuple
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PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
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MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
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sys.path.append(MODULE_ROOT)
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from pytorch_wrapper.pt_compute_loss import PtComputeLoss
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from pytorch_wrapper.ptn_l1_loss import PtnL1Loss
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class TestComputeLoss(unittest.TestCase):
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def setUp(self) -> None:
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"""
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Set up the test instance by initializing the loss functions and test cases.
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"""
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self.compute_node = PtComputeLoss()
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self.loss_node = PtnL1Loss()
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self.loss_fun_none = self.loss_node.f("none")[0]
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self.loss_fun_mean = self.loss_node.f("mean")[0]
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self.loss_fun_sum = self.loss_node.f("sum")[0]
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# Test cases: (predicted values y_hat, ground truth y)
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self.test_cases: List[Tuple[Tensor, Tensor]] = [
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(
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torch.tensor([0.1, -1.0, 10, -100, 1000], dtype=torch.float32),
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torch.tensor([0.09, -1.1, 9, -101, 999], dtype=torch.float32),
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)
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]
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def unreduced_loss(self, y_hat: Tensor, y: Tensor) -> Tensor:
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"""
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Compute the unreduced L1 loss.
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Args:
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y_hat (Tensor): Predicted values.
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y (Tensor): Ground truth values.
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Returns:
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Tensor: Unreduced element-wise L1 loss.
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"""
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return torch.abs(y_hat - y)
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def test_none(self) -> None:
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"""
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Test L1 loss with reduction='none'.
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"""
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for y_hat, y in self.test_cases:
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with self.subTest(y_hat=y_hat):
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actual = self.compute_node.f(y_hat, y, self.loss_fun_none)[0]
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expected = self.unreduced_loss(y_hat, y)
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actual = self.loss_fun_none(y_hat, y)
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self.assertTrue(torch.allclose(actual, expected, atol=1e-6),
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f"Expected {expected}, got {actual}")
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def test_mean(self) -> None:
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"""
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Test L1 loss with reduction='mean'.
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"""
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for y_hat, y in self.test_cases:
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with self.subTest(y_hat=y_hat):
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loss = self.unreduced_loss(y_hat, y)
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expected = loss.mean(dim=0)
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actual = self.compute_node.f(y_hat, y, self.loss_fun_mean)[0]
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self.assertTrue(torch.equal(actual, expected),
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f"Expected {expected}, got {actual}")
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def test_sum(self) -> None:
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"""
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Test L1 loss with reduction='sum'.
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"""
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for y_hat, y in self.test_cases:
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with self.subTest(y_hat=y_hat):
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loss = self.unreduced_loss(y_hat, y)
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expected = loss.sum(dim=0)
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actual = self.compute_node.f(y_hat, y, self.loss_fun_sum)[0]
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self.assertTrue(torch.equal(actual, expected),
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f"Expected {expected}, got {actual}")
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if __name__ == "__main__":
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unittest.main()
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