1 files changed, 50 insertions, 0 deletions
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 einsum, 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))
+
+
+def log(t: Tensor, eps: float = 1e-20) -> Tensor:
+    return torch.log(t.clamp(min=eps))
+
+
+def gumbel_noise(t: Tensor) -> Tensor:
+    noise = torch.zeros_like(t).uniform_(0, 1)
+    return -log(-log(noise))
+
+
+def gumbel_sample(t: Tensor, temperature: float = 1.0, dim: int = -1) -> Tensor:
+    if temperature == 0:
+        return t.argmax(dim=dim)
+    return ((t / temperature) + gumbel_noise(t)).argmax(dim=dim)
+
+
+def orthgonal_loss_fn(t: Tensor) -> Tensor:
+    # eq (2) from https://arxiv.org/abs/2112.00384
+    n = t.shape[0]
+    normed_codes = norm(t)
+    identity = torch.eye(n, device=t.device)
+    cosine_sim = einsum("i d, j d -> i j", normed_codes, normed_codes)
+    return ((cosine_sim - identity) ** 2).sum() / (n ** 2)