Address minor WinCLIP issues (#1889)

* fix minor winclip issues * update changelog
openvinotoolkit · Mar 21, 2024 · a07d47e · a07d47e
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -14,6 +14,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Use right interpolation method in WinCLIP resize (<https://github.com/openvinotoolkit/anomalib/pull/1889>)
 - 🐞 Fix the error if the device in masks_to_boxes is not both CPU and CUDA by @danylo-boiko in https://github.com/openvinotoolkit/anomalib/pull/1839
 
 ## [v1.0.0] - 2024-02-29

diff --git a/src/anomalib/models/image/winclip/README.md b/src/anomalib/models/image/winclip/README.md
@@ -22,13 +22,11 @@ WinCLIP is a zero-shot model, which means that we can directly evaluate the mode
 
 ### 0-Shot
 
-`anomalib test --model WinClip --data MVTec --data.image_size 240 --data.normalization clip`
+`anomalib test --model WinClip --data MVTec`
 
 ### 1-Shot
 
-`anomalib test --model WinClip --model.k_shot  1 --data MVTec --data.image_size 240 --data.normalization clip`
-
-> **Note:** The `data.image_size` and `data.normalization` parameters must be set to the above values to match the configuration in which the pre-trained CLIP model weights were obtained.
+`anomalib test --model WinClip --model.k_shot  1 --data MVTec`
 
 ## Parameters
 

diff --git a/src/anomalib/models/image/winclip/lightning_model.py b/src/anomalib/models/image/winclip/lightning_model.py
@@ -13,7 +13,7 @@
 
 import torch
 from torch.utils.data import DataLoader
-from torchvision.transforms.v2 import Compose, Normalize, Resize, Transform
+from torchvision.transforms.v2 import Compose, InterpolationMode, Normalize, Resize, Transform
 
 from anomalib import LearningType
 from anomalib.data.predict import PredictDataset
@@ -174,7 +174,7 @@ def configure_transforms(self, image_size: tuple[int, int] | None = None) -> Tra
             logger.warning("Image size is not used in WinCLIP. The input image size is determined by the model.")
         return Compose(
             [
-                Resize((240, 240), antialias=True),
+                Resize((240, 240), antialias=True, interpolation=InterpolationMode.BICUBIC),
                 Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711)),
             ],
         )