Randomization of transforms per image per batch #231
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Changes look fine to me for rand_color_jitter. Performance can be improved by doing some sort of kernel aggregation and selective image level filtering within the kernel. Once we have proved that rand_color_jitter is good for model accuracy/froc scores, can look into optimizing performance for this. |
gigony
approved these changes
Mar 24, 2022
| np_output = spt.rand_image_flip(arr_batch, prob=1.0, spatial_axis=(2, 3)) | ||
| np_output = spt.rand_image_flip(arr_batch, | ||
| prob=1.0, | ||
| spatial_axis=(2, 3), |
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Parameter input is not consistent. Here it uses (2, 3), but below code, it uses [2, 3].
| @@ -130,7 +131,8 @@ def image_rotate_90( | |||
| def rand_image_flip( | |||
| img: Any, | |||
| spatial_axis: tuple(), | |||
| prob: float = 0.1 | |||
| prob: float = 0.1, | |||
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What would be the good default value for this?
Having 0.5 for the default value looks more useful here?
Co-authored-by: Gigon Bae <[email protected]>
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Generally seems reasonable. Do we want to test Edit: Filed issue ( #259 ) to track this. |
jakirkham
approved these changes
Mar 31, 2022
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@gpucibot merge |
gigony
changed the title
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Can one of the admins verify this patch? |
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As per #225
Randomness updated to be applied to per image in the batch. Existing functionality of applying randomness on entire batch at once is unchanged.