Upload tests/test_utils.py

tests/test_utils.py

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+import cv2
+from facexlib.utils.face_restoration_helper import FaceRestoreHelper
+
+from gfpgan.archs.gfpganv1_arch import GFPGANv1
+from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
+from gfpgan.utils import GFPGANer
+
+
+def test_gfpganer():
+    # initialize with the clean model
+    restorer = GFPGANer(
+        model_path='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth',
+        upscale=2,
+        arch='clean',
+        channel_multiplier=2,
+        bg_upsampler=None)
+    # test attribute
+    assert isinstance(restorer.gfpgan, GFPGANv1Clean)
+    assert isinstance(restorer.face_helper, FaceRestoreHelper)
+
+    # initialize with the original model
+    restorer = GFPGANer(
+        model_path='experiments/pretrained_models/GFPGANv1.pth',
+        upscale=2,
+        arch='original',
+        channel_multiplier=1,
+        bg_upsampler=None)
+    # test attribute
+    assert isinstance(restorer.gfpgan, GFPGANv1)
+    assert isinstance(restorer.face_helper, FaceRestoreHelper)
+
+    # ------------------ test enhance ---------------- #
+    img = cv2.imread('tests/data/gt/00000000.png', cv2.IMREAD_COLOR)
+    result = restorer.enhance(img, has_aligned=False, paste_back=True)
+    assert result[0][0].shape == (512, 512, 3)
+    assert result[1][0].shape == (512, 512, 3)
+    assert result[2].shape == (1024, 1024, 3)
+
+    # with has_aligned=True
+    result = restorer.enhance(img, has_aligned=True, paste_back=False)
+    assert result[0][0].shape == (512, 512, 3)
+    assert result[1][0].shape == (512, 512, 3)
+    assert result[2] is None