pass transforms around instead of making duplicates #416
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…#416) Summary: Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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Summary: Pull Request resolved: facebookresearch#422 Sobol generators would return the incorrect shape when handling multi stimuli generation. This would not cause problem because of the ask converted inadvertantly avoided the problem. Fixed and clarified the docstring what should happen. Sort of a bandaid fix, tensor shapes may need to be unified more carefully when n-choice models are implemented. Differential Revision: D65239074
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…esearch#401) Summary: Pull Request resolved: facebookresearch#401 Parameter transforms will be handled by wrapping generator and model objects. The wrappers surfaces the base object API completely and even appears to be the wrapped object upon type inspection. Methods that requires the transformations are overridden by the wrapper to apply the required (un)transforms. The wrappers expects transforms from BoTorch and new transforms should follow BoTorch's InputTransforms. As a baseline a log10 transform is implemented. Differential Revision: D64129439
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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…#416) Summary: Pull Request resolved: facebookresearch#416 Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects. Differential Revision: D65155103
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Summary: Instead of creating duplicate transforms whenever we need one, we create a single transform from the config and initialize the wrapped model and wrapped generators with that one transform. This passes the same transform object around and allows the transformations to learn parameters and still be synced up across wrapped objects.
Differential Revision: D65155103