Add quantizer

1 files changed, 104 insertions, 0 deletions

diff --git a/text_recognizer/networks/quantizer/cosine_codebook.py b/text_recognizer/networks/quantizer/cosine_codebook.py
new file mode 100644
index 0000000..3b6af0f
--- /dev/null
+++ b/text_recognizer/networks/quantizer/cosine_codebook.py
@@ -0,0 +1,104 @@
+"""Codebook module."""
+from typing import Tuple
+
+from einops import rearrange
+import torch
+from torch import nn, Tensor
+import torch.nn.functional as F
+
+from text_recognizer.networks.quantizer.kmeans import kmeans
+from text_recognizer.networks.quantizer.utils import (
+    ema_inplace,
+    norm,
+    sample_vectors,
+    gumbel_sample,
+)
+
+
+class CosineSimilarityCodebook(nn.Module):
+    """Cosine similarity codebook."""
+
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+        kmeans_init: bool = False,
+        kmeans_iters: int = 10,
+        decay: float = 0.8,
+        eps: float = 1.0e-5,
+        threshold_dead: int = 2,
+        temperature: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.dim = dim
+        self.codebook_size = codebook_size
+        self.kmeans_init = kmeans_init
+        self.kmeans_iters = kmeans_iters
+        self.decay = decay
+        self.eps = eps
+        self.threshold_dead = threshold_dead
+        self.temperature = temperature
+
+        if not self.kmeans_init:
+            embeddings = norm(torch.randn(self.codebook_size, self.dim))
+        else:
+            embeddings = torch.zeros(self.codebook_size, self.dim)
+        self.register_buffer("initalized", Tensor([not self.kmeans_init]))
+        self.register_buffer("cluster_size", torch.zeros(self.codebook_size))
+        self.register_buffer("embeddings", embeddings)
+
+    def _initalize_embedding(self, data: Tensor) -> None:
+        embeddings, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
+        self.embeddings.data.copy_(embeddings)
+        self.cluster_size.data.copy_(cluster_size)
+        self.initalized.data.copy_(Tensor([True]))
+
+    def _replace(self, samples: Tensor, mask: Tensor) -> None:
+        samples = norm(samples)
+        modified_codebook = torch.where(
+            mask[..., None],
+            sample_vectors(samples, self.codebook_size),
+            self.embeddings,
+        )
+        self.embeddings.data.copy_(modified_codebook)
+
+    def _replace_dead_codes(self, batch_samples: Tensor) -> None:
+        if self.threshold_dead == 0:
+            return
+        dead_codes = self.cluster_size < self.threshold_dead
+        if not torch.any(dead_codes):
+            return
+        batch_samples = rearrange(batch_samples, "... d -> (...) d")
+        self._replace(batch_samples, mask=dead_codes)
+
+    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
+        """Quantizes tensor."""
+        shape = x.shape
+        flatten = rearrange(x, "... d -> (...) d")
+        flatten = norm(flatten)
+
+        if not self.initalized:
+            self._initalize_embedding(flatten)
+
+        embeddings = norm(self.embeddings)
+        dist = flatten @ embeddings.t()
+        indices = gumbel_sample(dist, dim=-1, temperature=self.temperature)
+        one_hot = F.one_hot(indices, self.codebook_size).type_as(x)
+        indices = indices.view(*shape[:-1])
+
+        quantized = F.embedding(indices, self.embeddings)
+
+        if self.training:
+            bins = one_hot.sum(0)
+            ema_inplace(self.cluster_size, bins, self.decay)
+            zero_mask = bins == 0
+            bins = bins.masked_fill(zero_mask, 1.0)
+
+            embed_sum = flatten.t() @ one_hot
+            embed_norm = (embed_sum / bins.unsqueeze(0)).t()
+            embed_norm = norm(embed_norm)
+            embed_norm = torch.where(zero_mask[..., None], embeddings, embed_norm)
+            ema_inplace(self.embeddings, embed_norm, self.decay)
+            self._replace_dead_codes(x)
+
+        return quantized, indices