Fixed shape error, allowing arbitary image sizes for EfficientAD (#1537)

* Fixed shape error, allowing arbitrary image sizes. Replaced integer parsing by floor operation

* Replaced calculation by ceil operation. Solution of shape error is to round up and not down for the last upsample layer

* Add comment for ceil oepration

* Formatting with pre-commit hook

src/anomalib/models/efficient_ad/torch_model.py

@@ -6,6 +6,7 @@
 from __future__ import annotations
 
 import logging
+import math
 import random
 from enum import Enum
 
@@ -147,9 +148,10 @@ class Decoder(nn.Module):
     def __init__(self, out_channels, padding, img_size, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
         self.img_size = img_size
+        # use ceil to match output shape of PDN
         self.last_upsample = (
-            int(img_size[0] / 4) if padding else int(img_size[0] / 4) - 8,
-            int(img_size[1] / 4) if padding else int(img_size[1] / 4) - 8,
+            math.ceil(img_size[0] / 4) if padding else math.ceil(img_size[0] / 4) - 8,
+            math.ceil(img_size[1] / 4) if padding else math.ceil(img_size[1] / 4) - 8,
         )
         self.deconv1 = nn.Conv2d(64, 64, kernel_size=4, stride=1, padding=2)
         self.deconv2 = nn.Conv2d(64, 64, kernel_size=4, stride=1, padding=2)
@@ -167,22 +169,22 @@ def __init__(self, out_channels, padding, img_size, *args, **kwargs) -> None:
         self.dropout6 = nn.Dropout(p=0.2)
 
     def forward(self, x):
-        x = F.interpolate(x, size=(int(self.img_size[0] / 64) - 1, int(self.img_size[1] / 64) - 1), mode="bilinear")
+        x = F.interpolate(x, size=(self.img_size[0] // 64 - 1, self.img_size[1] // 64 - 1), mode="bilinear")
         x = F.relu(self.deconv1(x))
         x = self.dropout1(x)
-        x = F.interpolate(x, size=(int(self.img_size[0] / 32), int(self.img_size[1] / 32)), mode="bilinear")
+        x = F.interpolate(x, size=(self.img_size[0] // 32, self.img_size[1] // 32), mode="bilinear")
         x = F.relu(self.deconv2(x))
         x = self.dropout2(x)
-        x = F.interpolate(x, size=(int(self.img_size[0] / 16) - 1, int(self.img_size[1] / 16) - 1), mode="bilinear")
+        x = F.interpolate(x, size=(self.img_size[0] // 16 - 1, self.img_size[1] // 16 - 1), mode="bilinear")
         x = F.relu(self.deconv3(x))
         x = self.dropout3(x)
-        x = F.interpolate(x, size=(int(self.img_size[0] / 8), int(self.img_size[1] / 8)), mode="bilinear")
+        x = F.interpolate(x, size=(self.img_size[0] // 8, self.img_size[1] // 8), mode="bilinear")
         x = F.relu(self.deconv4(x))
         x = self.dropout4(x)
-        x = F.interpolate(x, size=(int(self.img_size[0] / 4) - 1, int(self.img_size[1] / 4) - 1), mode="bilinear")
+        x = F.interpolate(x, size=(self.img_size[0] // 4 - 1, self.img_size[1] // 4 - 1), mode="bilinear")
         x = F.relu(self.deconv5(x))
         x = self.dropout5(x)
-        x = F.interpolate(x, size=(int(self.img_size[0] / 2) - 1, int(self.img_size[1] / 2) - 1), mode="bilinear")
+        x = F.interpolate(x, size=(self.img_size[0] // 2 - 1, self.img_size[1] // 2 - 1), mode="bilinear")
         x = F.relu(self.deconv6(x))
         x = self.dropout6(x)
         x = F.interpolate(x, size=self.last_upsample, mode="bilinear")