108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
import os
|
|
import sys
|
|
import unittest
|
|
import torch
|
|
from torch import Tensor
|
|
from typing import List, Tuple
|
|
|
|
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
|
|
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
|
|
sys.path.append(MODULE_ROOT)
|
|
|
|
from pytorch_wrapper.ptn_huber_loss import PtnHuberLoss
|
|
|
|
|
|
class TestPtnHuberLoss(unittest.TestCase):
|
|
def setUp(self) -> None:
|
|
"""
|
|
Set up the test instance by initializing the loss functions and test cases.
|
|
"""
|
|
self.node = PtnHuberLoss()
|
|
|
|
self.loss_fun_none_1 = self.node.f("none", 0.01)[0]
|
|
self.loss_fun_none_2 = self.node.f("none", 1)[0]
|
|
self.loss_fun_none_3 = self.node.f("none", 100)[0]
|
|
|
|
self.loss_fun_mean_1 = self.node.f("mean", 0.01)[0]
|
|
self.loss_fun_mean_2 = self.node.f("mean", 1)[0]
|
|
self.loss_fun_mean_3 = self.node.f("mean", 100)[0]
|
|
|
|
self.loss_fun_sum_1 = self.node.f("sum", 0.01)[0]
|
|
self.loss_fun_sum_2 = self.node.f("sum", 1)[0]
|
|
self.loss_fun_sum_3 = self.node.f("sum", 100)[0]
|
|
|
|
# Test cases: (predicted values y_hat, ground truth y)
|
|
self.test_cases: List[Tuple[Tensor, Tensor]] = [
|
|
(
|
|
torch.tensor([0.1, -1.0, 10, -100, 1000], dtype=torch.float32),
|
|
torch.tensor([0.09, -1.1, 9, -101, 999], dtype=torch.float32),
|
|
)
|
|
]
|
|
|
|
def unreduced_loss(self, y_hat: Tensor, y: Tensor, delta: float) -> Tensor:
|
|
return torch.nn.functional.huber_loss(y_hat, y, delta=delta, reduction="none")
|
|
|
|
def test_none_delta_001(self) -> None:
|
|
for y_hat, y in self.test_cases:
|
|
expected = self.unreduced_loss(y_hat, y, delta=0.01)
|
|
actual = self.loss_fun_none_1(y_hat, y)
|
|
self.assertTrue(torch.allclose(actual, expected, atol=1e-6))
|
|
|
|
def test_none_delta_1(self) -> None:
|
|
for y_hat, y in self.test_cases:
|
|
expected = self.unreduced_loss(y_hat, y, delta=1)
|
|
actual = self.loss_fun_none_2(y_hat, y)
|
|
self.assertTrue(torch.allclose(actual, expected, atol=1e-6))
|
|
|
|
def test_none_delta_100(self) -> None:
|
|
for y_hat, y in self.test_cases:
|
|
expected = self.unreduced_loss(y_hat, y, delta=100)
|
|
actual = self.loss_fun_none_3(y_hat, y)
|
|
self.assertTrue(torch.allclose(actual, expected, atol=1e-6))
|
|
|
|
def test_mean_delta_001(self) -> None:
|
|
for y_hat, y in self.test_cases:
|
|
loss = self.unreduced_loss(y_hat, y, delta=0.01)
|
|
expected = loss.mean(dim=0)
|
|
actual = self.loss_fun_mean_1(y_hat, y)
|
|
self.assertTrue(torch.equal(actual, expected))
|
|
|
|
def test_mean_delta_1(self) -> None:
|
|
for y_hat, y in self.test_cases:
|
|
loss = self.unreduced_loss(y_hat, y, delta=1)
|
|
expected = loss.mean(dim=0)
|
|
actual = self.loss_fun_mean_2(y_hat, y)
|
|
self.assertTrue(torch.equal(actual, expected))
|
|
|
|
def test_mean_delta_100(self) -> None:
|
|
for y_hat, y in self.test_cases:
|
|
loss = self.unreduced_loss(y_hat, y, delta=100)
|
|
expected = loss.mean(dim=0)
|
|
actual = self.loss_fun_mean_3(y_hat, y)
|
|
self.assertTrue(torch.equal(actual, expected))
|
|
|
|
def test_sum_delta_001(self) -> None:
|
|
for y_hat, y in self.test_cases:
|
|
loss = self.unreduced_loss(y_hat, y, delta=0.01)
|
|
expected = loss.sum(dim=0)
|
|
actual = self.loss_fun_sum_1(y_hat, y)
|
|
self.assertTrue(torch.equal(actual, expected))
|
|
|
|
def test_sum_delta_1(self) -> None:
|
|
for y_hat, y in self.test_cases:
|
|
loss = self.unreduced_loss(y_hat, y, delta=1)
|
|
expected = loss.sum(dim=0)
|
|
actual = self.loss_fun_sum_2(y_hat, y)
|
|
self.assertTrue(torch.equal(actual, expected))
|
|
|
|
def test_sum_delta_100(self) -> None:
|
|
for y_hat, y in self.test_cases:
|
|
loss = self.unreduced_loss(y_hat, y, delta=100)
|
|
expected = loss.sum(dim=0)
|
|
actual = self.loss_fun_sum_3(y_hat, y)
|
|
self.assertTrue(torch.equal(actual, expected))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|