add tests code

tests/test_modules/test_cyclegan_archs.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from copy import deepcopy
+
+import pytest
+import torch
+
+from mmgen.models.architectures.cyclegan import ResnetGenerator
+
+
+class TestResnetGenerator:
+
+    @classmethod
+    def setup_class(cls):
+        cls.default_cfg = dict(
+            in_channels=3,
+            out_channels=3,
+            base_channels=64,
+            norm_cfg=dict(type='IN'),
+            use_dropout=False,
+            num_blocks=9,
+            padding_mode='reflect',
+            init_cfg=dict(type='normal', gain=0.02))
+
+    def test_cyclegan_generator_cpu(self):
+        # test with default cfg
+        real_a = torch.randn((2, 3, 256, 256))
+        gen = ResnetGenerator(**self.default_cfg)
+        fake_b = gen(real_a)
+        assert fake_b.shape == (2, 3, 256, 256)
+
+        # test args system
+        cfg = deepcopy(self.default_cfg)
+        cfg['num_blocks'] = 8
+        gen = ResnetGenerator(**cfg)
+        fake_b = gen(real_a)
+        assert fake_b.shape == (2, 3, 256, 256)
+
+    @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
+    def test_cyclegan_generator_cuda(self):
+        # test with default cfg
+        real_a = torch.randn((2, 3, 256, 256)).cuda()
+        gen = ResnetGenerator(**self.default_cfg).cuda()
+        fake_b = gen(real_a)
+        assert fake_b.shape == (2, 3, 256, 256)
+
+        # test args system
+        cfg = deepcopy(self.default_cfg)
+        cfg['num_blocks'] = 8
+        gen = ResnetGenerator(**cfg).cuda()
+        fake_b = gen(real_a)
+        assert fake_b.shape == (2, 3, 256, 256)