|
import pytest |
|
import torch |
|
from ding.rl_utils.value_rescale import value_inv_transform, value_transform, symlog, inv_symlog |
|
|
|
|
|
@pytest.mark.unittest |
|
class TestValueRescale: |
|
|
|
def test_value_transform(self): |
|
for _ in range(10): |
|
t = torch.rand((2, 3)) |
|
assert isinstance(value_transform(t), torch.Tensor) |
|
assert value_transform(t).shape == t.shape |
|
|
|
def test_value_inv_transform(self): |
|
for _ in range(10): |
|
t = torch.rand((2, 3)) |
|
assert isinstance(value_inv_transform(t), torch.Tensor) |
|
assert value_inv_transform(t).shape == t.shape |
|
|
|
def test_trans_inverse(self): |
|
for _ in range(10): |
|
t = torch.rand((4, 16)) |
|
diff = value_inv_transform(value_transform(t)) - t |
|
assert pytest.approx(diff.abs().max().item(), abs=2e-5) == 0 |
|
assert pytest.approx(diff.abs().max().item(), abs=2e-5) == 0 |
|
|
|
|
|
@pytest.mark.unittest |
|
class TestSymlog: |
|
|
|
def test_symlog(self): |
|
for _ in range(10): |
|
t = torch.rand((3, 4)) |
|
assert isinstance(symlog(t), torch.Tensor) |
|
assert symlog(t).shape == t.shape |
|
|
|
def test_inv_symlog(self): |
|
for _ in range(10): |
|
t = torch.rand((3, 4)) |
|
assert isinstance(inv_symlog(t), torch.Tensor) |
|
assert inv_symlog(t).shape == t.shape |
|
|
|
def test_trans_inverse(self): |
|
for _ in range(10): |
|
t = torch.rand((4, 16)) |
|
diff = inv_symlog(symlog(t)) - t |
|
assert pytest.approx(diff.abs().max().item(), abs=2e-5) == 0 |
|
assert pytest.approx(diff.abs().max().item(), abs=2e-5) == 0 |
|
|
