1 files changed, 0 insertions, 50 deletions
diff --git a/text_recognizer/networks/quantizer/utils.py b/text_recognizer/networks/quantizer/utils.py
deleted file mode 100644
index ec97949..0000000
--- a/text_recognizer/networks/quantizer/utils.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""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)