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-rw-r--r--text_recognizer/networks/quantizer/utils.py26
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diff --git a/text_recognizer/networks/quantizer/utils.py b/text_recognizer/networks/quantizer/utils.py
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+++ b/text_recognizer/networks/quantizer/utils.py
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+"""Helper functions for quantization."""
+from typing import Tuple
+
+import torch
+from torch import Tensor
+import torch.nn.functional as F
+
+
+def sample_vectors(samples: Tensor, num: int) -> Tensor:
+ """Subsamples a set of vectors."""
+ B, device = samples.shape[0], samples.device
+ if B >= num:
+ indices = torch.randperm(B, device=device)[:num]
+ else:
+ indices = torch.randint(0, B, (num,), device=device)[:num]
+ return samples[indices]
+
+
+def norm(t: Tensor) -> Tensor:
+ """Applies L2-normalization."""
+ return F.normalize(t, p=2, dim=-1)
+
+
+def ema_inplace(moving_avg: Tensor, new: Tensor, decay: float) -> None:
+ """Applies exponential moving average."""
+ moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))