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-rw-r--r--models/convnext/discriminator.py11
1 files changed, 8 insertions, 3 deletions
diff --git a/models/convnext/discriminator.py b/models/convnext/discriminator.py
index 571b915..5798bcf 100644
--- a/models/convnext/discriminator.py
+++ b/models/convnext/discriminator.py
@@ -5,7 +5,7 @@ from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
5from torch.nn import functional as F 5from torch.nn import functional as F
6 6
7 7
8class ConvNeXtDiscriminator(): 8class ConvNeXtDiscriminator:
9 def __init__(self, model: ConvNeXt, input_size: int) -> None: 9 def __init__(self, model: ConvNeXt, input_size: int) -> None:
10 self.net = model 10 self.net = model
11 11
@@ -22,8 +22,13 @@ class ConvNeXtDiscriminator():
22 img_mean = self.img_mean.to(device=img.device, dtype=img.dtype) 22 img_mean = self.img_mean.to(device=img.device, dtype=img.dtype)
23 img_std = self.img_std.to(device=img.device, dtype=img.dtype) 23 img_std = self.img_std.to(device=img.device, dtype=img.dtype)
24 24
25 img = ((img + 1.) / 2.).sub(img_mean).div(img_std) 25 img = ((img + 1.0) / 2.0).sub(img_mean).div(img_std)
26 26
27 img = F.interpolate(img, size=(self.input_size, self.input_size), mode='bicubic', align_corners=True) 27 img = F.interpolate(
28 img,
29 size=(self.input_size, self.input_size),
30 mode="bicubic",
31 align_corners=True,
32 )
28 pred = self.net(img) 33 pred = self.net(img)
29 return pred 34 return pred