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Commit c1c4dea9 authored by Patrick von Platen's avatar Patrick von Platen
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correct tests ncsnpp

parent f4cd5a20
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tests/test_modeling_utils.py
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...@@ -742,18 +742,18 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase): ...@@ -742,18 +742,18 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
num_channels = 3 num_channels = 3
sizes = (32, 32) sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [10]).to(torch_device) time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad(): with torch.no_grad():
output = model(noise, time_step) output = model(noise, time_step)
output_slice = output[0, -3:, -3:, -1].flatten().cpu() output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off # fmt: off
expected_output_slice = torch.tensor([3.1909e-07, -8.5393e-08, 4.8460e-07, -4.5550e-07, -1.3205e-06, -6.3475e-07, 9.7837e-07, 2.9974e-07, 1.2345e-06]) expected_output_slice = torch.tensor([0.1315, 0.0741, 0.0393, 0.0455, 0.0556, 0.0180, -0.0832, -0.0644, -0.0856])
# fmt: on # fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3)) self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_ve_large(self): def test_output_pretrained_ve_large(self):
model = NCSNpp.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy") model = NCSNpp.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy")
...@@ -768,21 +768,21 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase): ...@@ -768,21 +768,21 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
num_channels = 3 num_channels = 3
sizes = (32, 32) sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [10]).to(torch_device) time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad(): with torch.no_grad():
output = model(noise, time_step) output = model(noise, time_step)
output_slice = output[0, -3:, -3:, -1].flatten().cpu() output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off # fmt: off
expected_output_slice = torch.tensor([-8.3299e-07, -9.0431e-07, 4.0585e-08, 9.7563e-07, 1.0280e-06, 1.0133e-06, 1.4979e-06, -2.9716e-07, -6.1817e-07]) expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
# fmt: on # fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3)) self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_vp(self): def test_output_pretrained_vp(self):
model = NCSNpp.from_pretrained("fusing/ddpm-cifar10-vp-dummy") model = NCSNpp.from_pretrained("fusing/cifar10-ddpmpp-vp")
model.eval() model.eval()
model.to(torch_device) model.to(torch_device)
...@@ -794,18 +794,18 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase): ...@@ -794,18 +794,18 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
num_channels = 3 num_channels = 3
sizes = (32, 32) sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [10]).to(torch_device) time_step = torch.tensor(batch_size * [9.]).to(torch_device)
with torch.no_grad(): with torch.no_grad():
output = model(noise, time_step) output = model(noise, time_step)
output_slice = output[0, -3:, -3:, -1].flatten().cpu() output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off # fmt: off
expected_output_slice = torch.tensor([-3.9086e-07, -1.1001e-05, 1.8881e-06, 1.1106e-05, 1.6629e-06, 2.9820e-06, 8.4978e-06, 8.0253e-07, 1.5435e-06]) expected_output_slice = torch.tensor([0.3303, -0.2275, -2.8872, -0.1309, -1.2861, 3.4567, -1.0083, 2.5325, -1.3866])
# fmt: on # fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3)) self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class VQModelTests(ModelTesterMixin, unittest.TestCase): class VQModelTests(ModelTesterMixin, unittest.TestCase):
......
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