mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Xu Yang
43cb4a564b
* Commit init framework * Co-authored-by: Yuante Li (FESCO Adecco Human Resources) <v-yuanteli@microsoft.com> Co-authored-by: XianBW <XianBW@users.noreply.github.com> * add an import * refine the whole framework * benchmark related framework * fix black and isort errors * move requirements to folder * fix black again --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: xuyang1 <xuyang1@microsoft.com>
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
import numpy as np
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import torch
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def shape_evaluator(target, prediction):
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if target is None or prediction is None:
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return None, 0
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tar_shape = target.shape
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pre_shape = prediction.shape
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diff = []
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for i in range(max(len(tar_shape), len(pre_shape))):
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dim_tar = tar_shape[i] if i < len(tar_shape) else 0
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dim_pre = pre_shape[i] if i < len(pre_shape) else 0
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diff.append(abs(dim_tar - dim_pre))
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metric = 1 / (np.exp(np.mean(diff)) + 1)
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return diff, metric
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def reshape_tensor(original_tensor, target_shape):
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new_tensor = torch.zeros(target_shape)
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for i, dim in enumerate(original_tensor.shape):
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new_tensor = new_tensor.narrow(i, 0, dim).copy_(original_tensor)
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return new_tensor
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def value_evaluator(target, prediction):
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if target is None or prediction is None:
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return None, 0
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tar_shape = target.shape
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pre_shape = prediction.shape
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# Determine the shape of the padded tensors
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dims = [
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max(s1, s2)
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for s1, s2 in zip(
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tar_shape + (1,) * (len(pre_shape) - len(tar_shape)),
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pre_shape + (1,) * (len(tar_shape) - len(pre_shape)),
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)
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]
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# Reshape both tensors to the determined shape
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target = target.reshape(*tar_shape, *(1,) * (max(len(tar_shape), len(pre_shape)) - len(tar_shape)))
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prediction = prediction.reshape(*pre_shape, *(1,) * (max(len(tar_shape), len(pre_shape)) - len(pre_shape)))
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target_padded = reshape_tensor(target, dims)
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prediction_padded = reshape_tensor(prediction, dims)
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# Calculate the mean absolute difference
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diff = torch.abs(target_padded - prediction_padded)
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metric = 1 / (1 + np.exp(torch.mean(diff).item()))
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return diff, metric
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if __name__ == "__main__":
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tar = torch.rand(4, 5, 5)
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pre = torch.rand(4, 1)
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print(shape_evaluator(tar, pre))
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print(value_evaluator(tar, pre)[1])
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