Add new quantizer

6 files changed, 213 insertions, 141 deletions

diff --git a/text_recognizer/networks/quantizer/__init__.py b/text_recognizer/networks/quantizer/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/text_recognizer/networks/quantizer/__init__.py
diff --git a/text_recognizer/networks/quantizer/codebook.py b/text_recognizer/networks/quantizer/codebook.py
new file mode 100644
index 0000000..cb9bc59
--- /dev/null
+++ b/text_recognizer/networks/quantizer/codebook.py
@@ -0,0 +1,96 @@
+"""Codebook module."""
+from typing import Tuple
+
+import attr
+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,
+)
+
+
+@attr.s(eq=False)
+class CosineSimilarityCodebook(nn.Module):
+    """Cosine similarity codebook."""
+
+    dim: int = attr.ib()
+    codebook_size: int = attr.ib()
+    kmeans_init: bool = attr.ib(default=False)
+    kmeans_iters: int = attr.ib(default=10)
+    decay: float = attr.ib(default=0.8)
+    eps: float = attr.ib(default=1.0e-5)
+    threshold_dead: int = attr.ib(default=2)
+
+    def __attrs_pre_init__(self) -> None:
+        super().__init__()
+
+    def __attrs_post_init__(self) -> None:
+        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 = dist.max(dim=-1).indices
+        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
diff --git a/text_recognizer/networks/quantizer/kmeans.py b/text_recognizer/networks/quantizer/kmeans.py
new file mode 100644
index 0000000..a34c381
--- /dev/null
+++ b/text_recognizer/networks/quantizer/kmeans.py
@@ -0,0 +1,32 @@
+"""K-means clustering for embeddings."""
+from typing import Tuple
+
+from einops import repeat
+import torch
+from torch import Tensor
+
+from text_recognizer.networks.quantizer.utils import norm, sample_vectors
+
+
+def kmeans(
+    samples: Tensor, num_clusters: int, num_iters: int = 10
+) -> Tuple[Tensor, Tensor]:
+    """Compute k-means clusters."""
+    D = samples.shape[-1]
+
+    means = sample_vectors(samples, num_clusters)
+
+    for _ in range(num_iters):
+        dists = samples @ means.t()
+        buckets = dists.max(dim=-1).indices
+        bins = torch.bincount(buckets, minlength=num_clusters)
+        zero_mask = bins == 0
+        bins_min_clamped = bins.masked_fill(zero_mask, 1)
+
+        new_means = buckets.new_zeros(num_clusters, D).type_as(samples)
+        new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=D), samples)
+        new_means /= bins_min_clamped[..., None]
+        new_means = norm(new_means)
+        means = torch.where(zero_mask[..., None], means, new_means)
+
+    return means, bins
diff --git a/text_recognizer/networks/quantizer/quantizer.py b/text_recognizer/networks/quantizer/quantizer.py
new file mode 100644
index 0000000..3e8f0b2
--- /dev/null
+++ b/text_recognizer/networks/quantizer/quantizer.py
@@ -0,0 +1,59 @@
+"""Implementation of a Vector Quantized Variational AutoEncoder.
+
+Reference:
+https://github.com/AntixK/PyTorch-VAE/blob/master/models/vq_vae.py
+"""
+from typing import Tuple, Type
+
+import attr
+from einops import rearrange
+import torch
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+
+
+@attr.s(eq=False)
+class VectorQuantizer(nn.Module):
+    """Vector quantizer."""
+
+    input_dim: int = attr.ib()
+    codebook: Type[nn.Module] = attr.ib()
+    commitment: float = attr.ib(default=1.0)
+    project_in: nn.Linear = attr.ib(default=None, init=False)
+    project_out: nn.Linear = attr.ib(default=None, init=False)
+
+    def __attrs_pre_init__(self) -> None:
+        super().__init__()
+
+    def __attrs_post_init__(self) -> None:
+        require_projection = self.codebook.dim != self.input_dim
+        self.project_in = (
+            nn.Linear(self.input_dim, self.codebook.dim)
+            if require_projection
+            else nn.Identity()
+        )
+        self.project_out = (
+            nn.Linear(self.codebook.dim, self.input_dim)
+            if require_projection
+            else nn.Identity()
+        )
+
+    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
+        """Quantizes latent vectors."""
+        H, W = x.shape[-2:]
+        x = rearrange(x, "b d h w -> b (h w) d")
+        x = self.project_in(x)
+
+        quantized, indices = self.codebook(x)
+
+        if self.training:
+            commitment_loss = F.mse_loss(quantized.detach(), x) * self.commitment
+            quantized = x + (quantized - x).detach()
+        else:
+            commitment_loss = torch.tensor([0.0]).type_as(x)
+
+        quantized = self.project_out(quantized)
+        quantized = rearrange(quantized, "b (h w) d -> b d h w", h=H, w=W)
+
+        return quantized, indices, commitment_loss
diff --git a/text_recognizer/networks/quantizer/utils.py b/text_recognizer/networks/quantizer/utils.py
new file mode 100644
index 0000000..0502d49
--- /dev/null
+++ b/text_recognizer/networks/quantizer/utils.py
@@ -0,0 +1,26 @@
+"""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))
diff --git a/text_recognizer/networks/vqvae/quantizer.py b/text_recognizer/networks/vqvae/quantizer.py
deleted file mode 100644
index bba9b60..0000000
--- a/text_recognizer/networks/vqvae/quantizer.py
+++ /dev/null
@@ -1,141 +0,0 @@
-"""Implementation of a Vector Quantized Variational AutoEncoder.
-
-Reference:
-https://github.com/AntixK/PyTorch-VAE/blob/master/models/vq_vae.py
-"""
-from einops import rearrange
-import torch
-from torch import nn
-from torch import Tensor
-import torch.nn.functional as F
-
-
-class EmbeddingEMA(nn.Module):
-    """Embedding for Exponential Moving Average (EMA)."""
-
-    def __init__(self, num_embeddings: int, embedding_dim: int) -> None:
-        super().__init__()
-        weight = torch.zeros(num_embeddings, embedding_dim)
-        nn.init.kaiming_uniform_(weight, nonlinearity="linear")
-        self.register_buffer("weight", weight)
-        self.register_buffer("cluster_size", torch.zeros(num_embeddings))
-        self.register_buffer("weight_avg", weight.clone())
-
-
-class VectorQuantizer(nn.Module):
-    """The codebook that contains quantized vectors."""
-
-    def __init__(
-        self, num_embeddings: int, embedding_dim: int, decay: float = 0.99
-    ) -> None:
-        super().__init__()
-        self.num_embeddings = num_embeddings
-        self.embedding_dim = embedding_dim
-        self.decay = decay
-        self.embedding = EmbeddingEMA(self.num_embeddings, self.embedding_dim)
-
-    def _discretization_bottleneck(self, latent: Tensor) -> Tensor:
-        """Computes the code nearest to the latent representation.
-
-        First we compute the posterior categorical distribution, and then map
-        the latent representation to the nearest element of the embedding.
-
-        Args:
-            latent (Tensor): The latent representation.
-
-        Shape:
-            - latent :math:`(B x H x W, D)`
-
-        Returns:
-            Tensor: The quantized embedding vector.
-
-        """
-        # Store latent shape.
-        b, h, w, d = latent.shape
-
-        # Flatten the latent representation to 2D.
-        latent = rearrange(latent, "b h w d -> (b h w) d")
-
-        # Compute the L2 distance between the latents and the embeddings.
-        l2_distance = (
-            torch.sum(latent ** 2, dim=1, keepdim=True)
-            + torch.sum(self.embedding.weight ** 2, dim=1)
-            - 2 * latent @ self.embedding.weight.t()
-        )  # [BHW x K]
-
-        # Find the embedding k nearest to each latent.
-        encoding_indices = torch.argmin(l2_distance, dim=1).unsqueeze(1)  # [BHW, 1]
-
-        # Convert to one-hot encodings, aka discrete bottleneck.
-        one_hot_encoding = torch.zeros(
-            encoding_indices.shape[0], self.num_embeddings, device=latent.device
-        )
-        one_hot_encoding.scatter_(1, encoding_indices, 1)  # [BHW x K]
-
-        # Embedding quantization.
-        quantized_latent = one_hot_encoding @ self.embedding.weight  # [BHW, D]
-        quantized_latent = rearrange(
-            quantized_latent, "(b h w) d -> b h w d", b=b, h=h, w=w
-        )
-        if self.training:
-            self._compute_ema(one_hot_encoding=one_hot_encoding, latent=latent)
-
-        return quantized_latent
-
-    def _compute_ema(self, one_hot_encoding: Tensor, latent: Tensor) -> None:
-        """Computes the EMA update to the codebook."""
-        batch_cluster_size = one_hot_encoding.sum(axis=0)
-        batch_embedding_avg = (latent.t() @ one_hot_encoding).t()
-        self.embedding.cluster_size.data.mul_(self.decay).add_(
-            batch_cluster_size, alpha=1 - self.decay
-        )
-        self.embedding.weight_avg.data.mul_(self.decay).add_(
-            batch_embedding_avg, alpha=1 - self.decay
-        )
-        new_embedding = self.embedding.weight_avg / (
-            self.embedding.cluster_size + 1.0e-5
-        ).unsqueeze(1)
-        self.embedding.weight.data.copy_(new_embedding)
-
-    def _commitment_loss(self, latent: Tensor, quantized_latent: Tensor) -> Tensor:
-        """Vector Quantization loss.
-
-        The vector quantization algorithm allows us to create a codebook. The VQ
-        algorithm works by moving the embedding vectors towards the encoder outputs.
-
-        The embedding loss moves the embedding vector towards the encoder outputs. The
-        .detach() works as the stop gradient (sg) described in the paper.
-
-        Because the volume of the embedding space is dimensionless, it can arbitarily
-        grow if the embeddings are not trained as fast as the encoder parameters. To
-        mitigate this, a commitment loss is added in the second term which makes sure
-        that the encoder commits to an embedding and that its output does not grow.
-
-        Args:
-            latent (Tensor): The encoder output.
-            quantized_latent (Tensor): The quantized latent.
-
-        Returns:
-            Tensor: The combinded VQ loss.
-
-        """
-        loss = F.mse_loss(quantized_latent.detach(), latent)
-        return loss
-
-    def forward(self, latent: Tensor) -> Tensor:
-        """Forward pass that returns the quantized vector and the vq loss."""
-        # Rearrange latent representation s.t. the hidden dim is at the end.
-        latent = rearrange(latent, "b d h w -> b h w d")
-
-        # Maps latent to the nearest code in the codebook.
-        quantized_latent = self._discretization_bottleneck(latent)
-
-        loss = self._commitment_loss(latent, quantized_latent)
-
-        # Add residue to the quantized latent.
-        quantized_latent = latent + (quantized_latent - latent).detach()
-
-        # Rearrange the quantized shape back to the original shape.
-        quantized_latent = rearrange(quantized_latent, "b h w d -> b d h w")
-
-        return quantized_latent, loss