Create test_consistency_model_utils.py

tests/test_models/test_editors/test_consistency_models/test_consistency_model_utils.py

@@ -0,0 +1,97 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import gc
+import platform
+from copy import deepcopy
+from unittest import TestCase
+
+import pytest
+import torch
+
+from mmagic.models.editors.consistency_models.consistencymodel_utils import (
+    DeterministicGenerator, DeterministicIndividualGenerator, DummyGenerator,
+    get_generator, get_sample_fn, karras_sample, sample_dpm, sample_euler,
+    sample_euler_ancestral, sample_heun, sample_onestep, sample_progdist,
+    stochastic_iterative_sampler)
+from mmagic.registry import MODELS
+from mmagic.utils import register_all_modules
+
+gc.collect()
+torch.cuda.empty_cache()
+register_all_modules()
+denoiser_config = dict(
+    type='KarrasDenoiser',
+    sigma_data=0.5,
+    sigma_max=80.0,
+    sigma_min=0.002,
+    weight_schedule='uniform',
+)
+
+unet_config = dict(
+    type='ConsistencyUNetModel',
+    image_size=64,
+    out_channels=3,
+    attention_resolutions=(2, 4, 8),
+    in_channels=3,
+    model_channels=192,
+    num_res_blocks=3,
+    dropout=0.0,
+    channel_mult=(1, 2, 3, 4),
+    use_checkpoint=False,
+    use_fp16=False,
+    num_head_channels=64,
+    num_heads=4,
+    num_heads_upsample=-1,
+    resblock_updown=True,
+    use_new_attention_order=False,
+    use_scale_shift_norm=True)
+
+
+@pytest.mark.skipif(
+    'win' in platform.system().lower(),
+    reason='skip on windows due to limited RAM.')
+class TestConsistencyModelUtils(TestCase):
+
+    def test_karras_sample(self):
+        unet_cfg = deepcopy(unet_config)
+        diffuse_cfg = deepcopy(denoiser_config)
+        unet = MODELS.build(unet_cfg)
+        diffuse = MODELS.build(diffuse_cfg)
+        image_size = 64
+        channel_num = 3
+        steps = 2
+        batch_size = 4
+        model_kwargs = {}
+        sample = karras_sample(
+            diffuse,
+            unet, (batch_size, channel_num, image_size, image_size),
+            steps=steps,
+            model_kwargs=model_kwargs)
+        assert sample.shape == (batch_size, channel_num, image_size,
+                                image_size)
+
+    def test_get_generator(self):
+        self.assertIsInstance(get_generator('dummy'), DummyGenerator)
+        self.assertIsInstance(get_generator('determ'), DeterministicGenerator)
+        self.assertIsInstance(
+            get_generator('determ-indiv'), DeterministicIndividualGenerator)
+        with pytest.raises(NotImplementedError):
+            get_generator('')
+
+    def test_sample_fn(self):
+        self.assertEqual(get_sample_fn('heun'), sample_heun)
+        self.assertEqual(get_sample_fn('dpm'), sample_dpm)
+        self.assertEqual(get_sample_fn('ancestral'), sample_euler_ancestral)
+        self.assertEqual(get_sample_fn('onestep'), sample_onestep)
+        self.assertEqual(get_sample_fn('progdist'), sample_progdist)
+        self.assertEqual(get_sample_fn('euler'), sample_euler)
+        self.assertEqual(
+            get_sample_fn('multistep'), stochastic_iterative_sampler)
+        with pytest.raises(KeyError):
+            get_sample_fn('')
+
+
+def teardown_module():
+    import gc
+    gc.collect()
+    globals().clear()
+    locals().clear()