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

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merged 6 commits into from
Jan 23, 2024
Merged

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

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911a892
Fixed shape error, allowing arbitrary image sizes. Replaced integer p…
holzweber Dec 10, 2023
7d5d1a2
Replaced calculation by ceil operation. Solution of shape error is to…
holzweber Dec 10, 2023
06b9f4b
Merge branch 'main' into main
holzweber Dec 14, 2023
f56680b
Add comment for ceil oepration
holzweber Jan 12, 2024
f2a7bc0
Merge branch 'main' of https://github.com/holzweber/anomalib
holzweber Jan 12, 2024
bc40854
Formatting with pre-commit hook
holzweber Jan 13, 2024
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18 changes: 10 additions & 8 deletions src/anomalib/models/efficient_ad/torch_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
from __future__ import annotations

import logging
import math
import random
from enum import Enum

Expand Down Expand Up @@ -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,
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)
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)
Expand All @@ -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")
Expand Down
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