Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Enhancement] Mock LPIPS module in PPL metric #1490

Merged
merged 2 commits into from
Dec 5, 2022
Merged

[Enhancement] Mock LPIPS module in PPL metric #1490

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0f35bd1
mock LPIPS module in PPL metric
LeoXing1996 Nov 30, 2022
9665dae
mock cpu test
LeoXing1996 Nov 30, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
39 changes: 14 additions & 25 deletions tests/test_evaluation/test_metrics/test_ppl.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,17 +1,19 @@
# Copyright (c) OpenMMLab. All rights reserved.
import platform
from unittest.mock import MagicMock
from unittest.mock import patch

import pytest
import torch
import torch.nn as nn
from mmengine.runner import Runner

from mmedit.datasets import BasicImageDataset
from mmedit.datasets.transforms import PackEditInputs
from mmedit.evaluation import PerceptualPathLength
from mmedit.models import LSGAN, GenDataPreprocessor
from mmedit.models.editors.stylegan2 import StyleGAN2Generator
from mmedit.utils import register_all_modules

register_all_modules()


def process_fn(data_batch, predictions):
Expand All @@ -22,29 +24,17 @@ def process_fn(data_batch, predictions):
return data_batch, _predictions


class vgg_pytorch_classifier(nn.Module):

def __init__(self):
super().__init__()

def forward(self, x):
return torch.randn(x.shape[0], 4096)
class LPIPS_mock:

def __init__(self, *args, **kwargs):
pass

class vgg_mock(nn.Module):
def to(self, *args, **kwargs):
return self

def __init__(self, style):
super().__init__()
self.classifier = nn.Sequential(nn.Identity(), nn.Identity(),
nn.Identity(),
vgg_pytorch_classifier())
self.style = style

def forward(self, x, *args, **kwargs):
if self.style.upper() == 'STYLEGAN':
return torch.randn(x.shape[0], 4096)
else: # torch
return torch.randn(x.shape[0], 7 * 7 * 512)
def __call__(self, x1, x2, *args, **kwargs):
num_batche = x1.shape[0]
return torch.rand(num_batche, 1, 1, 1)


class TestPPL:
Expand All @@ -69,9 +59,7 @@ def setup_class(cls):
generator = StyleGAN2Generator(64, 8)
cls.module = LSGAN(generator, data_preprocessor=gan_data_preprocessor)

cls.mock_vgg_pytorch = MagicMock(
return_value=(vgg_mock('PyTorch'), 'False'))

@patch('lpips.LPIPS', LPIPS_mock)
@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
def test_ppl_cuda(self):
ppl = PerceptualPathLength(
Expand Down Expand Up @@ -104,6 +92,7 @@ def test_ppl_cuda(self):
ppl_res = ppl.compute_metrics(ppl.fake_results)
assert ppl_res['ppl_score'] >= 0

@patch('lpips.LPIPS', LPIPS_mock)
@pytest.mark.skipif(
'win' in platform.system().lower() and 'cu' in torch.__version__,
reason='skip on windows-cuda due to limited RAM.')
Expand Down