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I'm using a custom dataset with the same structure of MVTec datasets. When inferencing with OpenVINO, if the image is not resized to the train image size, the prediction will give wrong results. I think the same problem is also #2136
Wrong prediction
Inferenced with inferencer.predict(image=image_path) where the images are in 1440x1440
in this dataset images are 1024x1024
I used patchcore to convert OPENVINO, and the following error occurred when using cpu: image = cv2.resize(image, tuple(list(self.input_blob.shape)[2:][::-1]))
RuntimeError: Exception from src/core/src/partial_shape.cpp:266:
to_shape was called on a dynamic shape. Do you know why? I also get an error when using GPU. Can I not use NVIDIA GPU?
Describe the bug
I'm using a custom dataset with the same structure of MVTec datasets. When inferencing with OpenVINO, if the image is not resized to the train image size, the prediction will give wrong results. I think the same problem is also #2136
Wrong prediction
Inferenced with
inferencer.predict(image=image_path)
where the images are in 1440x1440in this dataset images are 1024x1024
Correct prediction
Inferenced with
Torch inferencer
Using Torch inferencer the result is correct without resizing the images.
Code used for training:
Dataset
Other (please specify in the text field below)
Model
PADiM
Steps to reproduce the behavior
see above
OS information
OS information:
I'm using a custom dataset
Expected behavior
OpenVINOInferencer predicts correctly without resizing the images
Screenshots
Pip/GitHub
GitHub
What version/branch did you use?
No response
Configuration YAML
Logs
Code of Conduct
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