Remove VQVAE stuff, did not work...

18 files changed, 0 insertions, 1052 deletions

diff --git a/text_recognizer/criterion/n_layer_discriminator.py b/text_recognizer/criterion/n_layer_discriminator.py
deleted file mode 100644
index a9f47f9..0000000
--- a/text_recognizer/criterion/n_layer_discriminator.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""Pix2pix discriminator loss."""
-from torch import nn, Tensor
-
-from text_recognizer.networks.vqvae.norm import Normalize
-
-
-class NLayerDiscriminator(nn.Module):
-    """Defines a PatchGAN discriminator loss in Pix2Pix."""
-
-    def __init__(
-        self, in_channels: int = 1, num_channels: int = 32, num_layers: int = 3
-    ) -> None:
-        super().__init__()
-        self.in_channels = in_channels
-        self.num_channels = num_channels
-        self.num_layers = num_layers
-        self.discriminator = self._build_discriminator()
-
-    def _build_discriminator(self) -> nn.Sequential:
-        """Builds discriminator."""
-        discriminator = [
-            nn.Sigmoid(),
-            nn.Conv2d(
-                in_channels=self.in_channels,
-                out_channels=self.num_channels,
-                kernel_size=4,
-                stride=2,
-                padding=1,
-            ),
-            nn.Mish(inplace=True),
-        ]
-        in_channels = self.num_channels
-        for n in range(1, self.num_layers):
-            discriminator += [
-                nn.Conv2d(
-                    in_channels=in_channels,
-                    out_channels=in_channels * n,
-                    kernel_size=4,
-                    stride=2,
-                    padding=1,
-                ),
-                # Normalize(num_channels=in_channels * n),
-                nn.Mish(inplace=True),
-            ]
-            in_channels *= n
-
-        discriminator += [
-            nn.Conv2d(
-                in_channels=self.num_channels * (self.num_layers - 1),
-                out_channels=1,
-                kernel_size=4,
-                padding=1,
-            )
-        ]
-        return nn.Sequential(*discriminator)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass through discriminator."""
-        return self.discriminator(x)
diff --git a/text_recognizer/criterion/vqgan_loss.py b/text_recognizer/criterion/vqgan_loss.py
deleted file mode 100644
index 8e8b65b..0000000
--- a/text_recognizer/criterion/vqgan_loss.py
+++ /dev/null
@@ -1,123 +0,0 @@
-"""VQGAN loss for PyTorch Lightning."""
-from typing import Optional, Tuple
-
-import torch
-from torch import nn, Tensor
-import torch.nn.functional as F
-
-from text_recognizer.criterion.n_layer_discriminator import NLayerDiscriminator
-
-
-def _adopt_weight(
-    weight: Tensor, global_step: int, threshold: int = 0, value: float = 0.0
-) -> float:
-    """Sets weight to value after the threshold is passed."""
-    if global_step < threshold:
-        weight = value
-    return weight
-
-
-class VQGANLoss(nn.Module):
-    """VQGAN loss."""
-
-    def __init__(
-        self,
-        reconstruction_loss: nn.L1Loss,
-        discriminator: NLayerDiscriminator,
-        commitment_weight: float = 1.0,
-        discriminator_weight: float = 1.0,
-        discriminator_factor: float = 1.0,
-        discriminator_iter_start: int = 1000,
-    ) -> None:
-        super().__init__()
-        self.reconstruction_loss = reconstruction_loss
-        self.discriminator = discriminator
-        self.commitment_weight = commitment_weight
-        self.discriminator_weight = discriminator_weight
-        self.discriminator_factor = discriminator_factor
-        self.discriminator_iter_start = discriminator_iter_start
-
-    @staticmethod
-    def adversarial_loss(logits_real: Tensor, logits_fake: Tensor) -> Tensor:
-        """Calculates the adversarial loss."""
-        loss_real = torch.mean(F.relu(1.0 - logits_real))
-        loss_fake = torch.mean(F.relu(1.0 + logits_fake))
-        d_loss = (loss_real + loss_fake) / 2.0
-        return d_loss
-
-    def _adaptive_weight(
-        self, rec_loss: Tensor, g_loss: Tensor, decoder_last_layer: Tensor
-    ) -> Tensor:
-        rec_grads = torch.autograd.grad(
-            rec_loss, decoder_last_layer, retain_graph=True
-        )[0]
-        g_grads = torch.autograd.grad(g_loss, decoder_last_layer, retain_graph=True)[0]
-        d_weight = torch.norm(rec_grads) / (torch.norm(g_grads) + 1.0e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1.0e4).detach()
-        d_weight *= self.discriminator_weight
-        return d_weight
-
-    def forward(
-        self,
-        data: Tensor,
-        reconstructions: Tensor,
-        commitment_loss: Tensor,
-        decoder_last_layer: Tensor,
-        optimizer_idx: int,
-        global_step: int,
-        stage: str,
-    ) -> Optional[Tuple]:
-        """Calculates the VQGAN loss."""
-        rec_loss: Tensor = self.reconstruction_loss(reconstructions, data)
-
-        # GAN part.
-        if optimizer_idx == 0:
-            logits_fake = self.discriminator(reconstructions)
-            g_loss = -torch.mean(logits_fake)
-
-            if self.training:
-                d_weight = self._adaptive_weight(
-                    rec_loss=rec_loss,
-                    g_loss=g_loss,
-                    decoder_last_layer=decoder_last_layer,
-                )
-            else:
-                d_weight = torch.tensor(0.0)
-
-            d_factor = _adopt_weight(
-                self.discriminator_factor,
-                global_step=global_step,
-                threshold=self.discriminator_iter_start,
-            )
-
-            loss: Tensor = (
-                rec_loss
-                + d_factor * d_weight * g_loss
-                + self.commitment_weight * commitment_loss
-            )
-            log = {
-                f"{stage}/total_loss": loss,
-                f"{stage}/commitment_loss": commitment_loss,
-                f"{stage}/rec_loss": rec_loss,
-                f"{stage}/g_loss": g_loss,
-            }
-            return loss, log
-
-        if optimizer_idx == 1:
-            logits_fake = self.discriminator(reconstructions.detach())
-            logits_real = self.discriminator(data.detach())
-
-            d_factor = _adopt_weight(
-                self.discriminator_factor,
-                global_step=global_step,
-                threshold=self.discriminator_iter_start,
-            )
-
-            d_loss = d_factor * self.adversarial_loss(
-                logits_real=logits_real, logits_fake=logits_fake
-            )
-
-            log = {
-                f"{stage}/d_loss": d_loss,
-            }
-            return d_loss, log
diff --git a/text_recognizer/models/vq_transformer.py b/text_recognizer/models/vq_transformer.py
deleted file mode 100644
index 8ec28fd..0000000
--- a/text_recognizer/models/vq_transformer.py
+++ /dev/null
@@ -1,94 +0,0 @@
-"""PyTorch Lightning model for base Transformers."""
-from typing import Tuple, Type
-
-import attr
-import torch
-from torch import Tensor
-
-from text_recognizer.models.transformer import TransformerLitModel
-
-
-@attr.s(auto_attribs=True, eq=False)
-class VqTransformerLitModel(TransformerLitModel):
-    """A PyTorch Lightning model for transformer networks."""
-
-    def forward(self, data: Tensor) -> Tensor:
-        """Forward pass with the transformer network."""
-        return self.predict(data)
-
-    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
-        """Training step."""
-        data, targets = batch
-        logits, commitment_loss = self.network(data, targets[:, :-1])
-        loss = self.loss_fn(logits, targets[:, 1:]) + commitment_loss
-        self.log("train/loss", loss)
-        self.log("train/commitment_loss", commitment_loss)
-        return loss
-
-    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Validation step."""
-        data, targets = batch
-        logits, commitment_loss = self.network(data, targets[:, :-1])
-        loss = self.loss_fn(logits, targets[:, 1:]) + commitment_loss
-        self.log("val/loss", loss, prog_bar=True)
-        self.log("val/commitment_loss", commitment_loss)
-
-        # Get the token prediction.
-        # pred = self(data)
-        # self.val_cer(pred, targets)
-        # self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
-        # self.test_acc(pred, targets)
-        # self.log("val/acc", self.test_acc, on_step=False, on_epoch=True)
-
-    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Test step."""
-        data, targets = batch
-        pred = self(data)
-        self.test_cer(pred, targets)
-        self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
-        self.test_acc(pred, targets)
-        self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
-
-    def predict(self, x: Tensor) -> Tensor:
-        """Predicts text in image.
-        
-        Args:
-            x (Tensor): Image(s) to extract text from.
-
-        Shapes:
-            - x: :math: `(B, H, W)`
-            - output: :math: `(B, S)`
-
-        Returns:
-            Tensor: A tensor of token indices of the predictions from the model.
-        """
-        bsz = x.shape[0]
-
-        # Encode image(s) to latent vectors.
-        z, _ = self.network.encode(x)
-
-        # Create a placeholder matrix for storing outputs from the network
-        output = torch.ones((bsz, self.max_output_len), dtype=torch.long).to(x.device)
-        output[:, 0] = self.start_index
-
-        for Sy in range(1, self.max_output_len):
-            context = output[:, :Sy]  # (B, Sy)
-            logits = self.network.decode(z, context)  # (B, C, Sy)
-            tokens = torch.argmax(logits, dim=1)  # (B, Sy)
-            output[:, Sy : Sy + 1] = tokens[:, -1:]
-
-            # Early stopping of prediction loop if token is end or padding token.
-            if (
-                (output[:, Sy - 1] == self.end_index)
-                | (output[:, Sy - 1] == self.pad_index)
-            ).all():
-                break
-
-        # Set all tokens after end token to pad token.
-        for Sy in range(1, self.max_output_len):
-            idx = (output[:, Sy - 1] == self.end_index) | (
-                output[:, Sy - 1] == self.pad_index
-            )
-            output[idx, Sy] = self.pad_index
-
-        return output
diff --git a/text_recognizer/models/vqgan.py b/text_recognizer/models/vqgan.py
deleted file mode 100644
index 6a90e06..0000000
--- a/text_recognizer/models/vqgan.py
+++ /dev/null
@@ -1,116 +0,0 @@
-"""PyTorch Lightning model for base Transformers."""
-from typing import Tuple
-
-import attr
-from torch import Tensor
-
-from text_recognizer.criterion.vqgan_loss import VQGANLoss
-from text_recognizer.models.base import BaseLitModel
-
-
-@attr.s(auto_attribs=True, eq=False)
-class VQGANLitModel(BaseLitModel):
-    """A PyTorch Lightning model for transformer networks."""
-
-    loss_fn: VQGANLoss = attr.ib()
-    latent_loss_weight: float = attr.ib(default=0.25)
-
-    def forward(self, data: Tensor) -> Tensor:
-        """Forward pass with the transformer network."""
-        return self.network(data)
-
-    def training_step(
-        self, batch: Tuple[Tensor, Tensor], batch_idx: int, optimizer_idx: int
-    ) -> Tensor:
-        """Training step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-
-        if optimizer_idx == 0:
-            loss, log = self.loss_fn(
-                data=data,
-                reconstructions=reconstructions,
-                commitment_loss=commitment_loss,
-                decoder_last_layer=self.network.decoder.decoder[-1].weight,
-                optimizer_idx=optimizer_idx,
-                global_step=self.global_step,
-                stage="train",
-            )
-            self.log(
-                "train/loss", loss, prog_bar=True,
-            )
-            self.log_dict(log, logger=True, on_step=True, on_epoch=True)
-            return loss
-
-        if optimizer_idx == 1:
-            loss, log = self.loss_fn(
-                data=data,
-                reconstructions=reconstructions,
-                commitment_loss=commitment_loss,
-                decoder_last_layer=self.network.decoder.decoder[-1].weight,
-                optimizer_idx=optimizer_idx,
-                global_step=self.global_step,
-                stage="train",
-            )
-            self.log(
-                "train/discriminator_loss", loss, prog_bar=True,
-            )
-            self.log_dict(log, logger=True, on_step=True, on_epoch=True)
-            return loss
-
-    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Validation step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-
-        loss, log = self.loss_fn(
-            data=data,
-            reconstructions=reconstructions,
-            commitment_loss=commitment_loss,
-            decoder_last_layer=self.network.decoder.decoder[-1].weight,
-            optimizer_idx=0,
-            global_step=self.global_step,
-            stage="val",
-        )
-        self.log(
-            "val/loss", loss, prog_bar=True,
-        )
-        self.log_dict(log)
-
-        _, log = self.loss_fn(
-            data=data,
-            reconstructions=reconstructions,
-            commitment_loss=commitment_loss,
-            decoder_last_layer=self.network.decoder.decoder[-1].weight,
-            optimizer_idx=1,
-            global_step=self.global_step,
-            stage="val",
-        )
-        self.log_dict(log)
-
-    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Test step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-
-        _, log = self.loss_fn(
-            data=data,
-            reconstructions=reconstructions,
-            commitment_loss=commitment_loss,
-            decoder_last_layer=self.network.decoder.decoder[-1].weight,
-            optimizer_idx=0,
-            global_step=self.global_step,
-            stage="test",
-        )
-        self.log_dict(log)
-
-        _, log = self.loss_fn(
-            data=data,
-            reconstructions=reconstructions,
-            commitment_loss=commitment_loss,
-            decoder_last_layer=self.network.decoder.decoder[-1].weight,
-            optimizer_idx=1,
-            global_step=self.global_step,
-            stage="test",
-        )
-        self.log_dict(log)
diff --git a/text_recognizer/models/vqvae.py b/text_recognizer/models/vqvae.py
deleted file mode 100644
index 4898852..0000000
--- a/text_recognizer/models/vqvae.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""PyTorch Lightning model for base Transformers."""
-from typing import Tuple
-
-import attr
-from torch import Tensor
-
-from text_recognizer.models.base import BaseLitModel
-
-
-@attr.s(auto_attribs=True, eq=False)
-class VQVAELitModel(BaseLitModel):
-    """A PyTorch Lightning model for transformer networks."""
-
-    commitment: float = attr.ib(default=0.25)
-
-    def forward(self, data: Tensor) -> Tensor:
-        """Forward pass with the transformer network."""
-        return self.network(data)
-
-    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
-        """Training step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-        loss = self.loss_fn(reconstructions, data)
-        loss = loss + self.commitment * commitment_loss
-        self.log("train/commitment_loss", commitment_loss)
-        self.log("train/loss", loss)
-        return loss
-
-    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Validation step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-        loss = self.loss_fn(reconstructions, data)
-        self.log("val/commitment_loss", commitment_loss)
-        self.log("val/loss", loss, prog_bar=True)
-
-    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
-        """Test step."""
-        data, _ = batch
-        reconstructions, commitment_loss = self(data)
-        loss = self.loss_fn(reconstructions, data)
-        loss = loss + self.commitment * commitment_loss
-        self.log("test/commitment_loss", commitment_loss)
-        self.log("test/loss", loss)
diff --git a/text_recognizer/networks/quantizer/__init__.py b/text_recognizer/networks/quantizer/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/text_recognizer/networks/quantizer/__init__.py
+++ /dev/null
diff --git a/text_recognizer/networks/quantizer/codebook.py b/text_recognizer/networks/quantizer/codebook.py
deleted file mode 100644
index cb9bc59..0000000
--- a/text_recognizer/networks/quantizer/codebook.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""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
deleted file mode 100644
index a34c381..0000000
--- a/text_recognizer/networks/quantizer/kmeans.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""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
deleted file mode 100644
index 3e8f0b2..0000000
--- a/text_recognizer/networks/quantizer/quantizer.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""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
deleted file mode 100644
index 0502d49..0000000
--- a/text_recognizer/networks/quantizer/utils.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""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/vq_transformer.py b/text_recognizer/networks/vq_transformer.py
deleted file mode 100644
index a2bd81b..0000000
--- a/text_recognizer/networks/vq_transformer.py
+++ /dev/null
@@ -1,84 +0,0 @@
-"""Vector quantized encoder, transformer decoder."""
-from typing import Optional, Tuple, Type
-
-from torch import nn, Tensor
-
-from text_recognizer.networks.conv_transformer import ConvTransformer
-from text_recognizer.networks.quantizer.quantizer import VectorQuantizer
-from text_recognizer.networks.transformer.layers import Decoder
-
-
-class VqTransformer(ConvTransformer):
-    """Convolutional encoder and transformer decoder network."""
-
-    def __init__(
-        self,
-        input_dims: Tuple[int, int, int],
-        hidden_dim: int,
-        num_classes: int,
-        pad_index: Tensor,
-        encoder: nn.Module,
-        decoder: Decoder,
-        pixel_pos_embedding: Type[nn.Module],
-        quantizer: VectorQuantizer,
-        token_pos_embedding: Optional[Type[nn.Module]] = None,
-    ) -> None:
-        super().__init__(
-            input_dims=input_dims,
-            hidden_dim=hidden_dim,
-            num_classes=num_classes,
-            pad_index=pad_index,
-            encoder=encoder,
-            decoder=decoder,
-            pixel_pos_embedding=pixel_pos_embedding,
-            token_pos_embedding=token_pos_embedding,
-        )
-        self.quantizer = quantizer
-
-    def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Encodes an image into a discrete (VQ) latent representation.
-
-        Args:
-            x (Tensor): Image tensor.
-
-        Shape:
-            - x: :math: `(B, C, H, W)`
-            - z: :math: `(B, Sx, E)`
-
-            where Sx is the length of the flattened feature maps projected from
-            the encoder. E latent dimension for each pixel in the projected
-            feature maps.
-
-        Returns:
-            Tensor: A Latent embedding of the image.
-        """
-        z = self.encoder(x)
-        z = self.conv(z)
-        z, _, commitment_loss = self.quantizer(z)
-        z = self.pixel_pos_embedding(z)
-        z = z.flatten(start_dim=2)
-
-        # Permute tensor from [B, E, Ho * Wo] to [B, Sx, E]
-        z = z.permute(0, 2, 1)
-        return z, commitment_loss
-
-    def forward(self, x: Tensor, context: Tensor) -> Tensor:
-        """Encodes images into word piece logtis.
-
-        Args:
-            x (Tensor): Input image(s).
-            context (Tensor): Target word embeddings.
-
-        Shapes:
-            - x: :math: `(B, C, H, W)`
-            - context: :math: `(B, Sy, T)`
-
-            where B is the batch size, C is the number of input channels, H is
-            the image height and W is the image width.
-
-        Returns:
-            Tensor: Sequence of logits.
-        """
-        z, commitment_loss = self.encode(x)
-        logits = self.decode(z, context)
-        return logits, commitment_loss
diff --git a/text_recognizer/networks/vqvae/__init__.py b/text_recognizer/networks/vqvae/__init__.py
deleted file mode 100644
index e1f05fa..0000000
--- a/text_recognizer/networks/vqvae/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""VQ-VAE module."""
diff --git a/text_recognizer/networks/vqvae/decoder.py b/text_recognizer/networks/vqvae/decoder.py
deleted file mode 100644
index 7734a5a..0000000
--- a/text_recognizer/networks/vqvae/decoder.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""CNN decoder for the VQ-VAE."""
-from typing import Sequence
-
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.vqvae.norm import Normalize
-from text_recognizer.networks.vqvae.residual import Residual
-
-
-class Decoder(nn.Module):
-    """A CNN encoder network."""
-
-    def __init__(
-        self,
-        out_channels: int,
-        hidden_dim: int,
-        channels_multipliers: Sequence[int],
-        dropout_rate: float,
-        activation: str = "mish",
-        use_norm: bool = False,
-        num_residuals: int = 4,
-        residual_channels: int = 32,
-    ) -> None:
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_dim = hidden_dim
-        self.channels_multipliers = tuple(channels_multipliers)
-        self.activation = activation
-        self.dropout_rate = dropout_rate
-        self.use_norm = use_norm
-        self.num_residuals = num_residuals
-        self.residual_channels = residual_channels
-        self.decoder = self._build_decompression_block()
-
-    def _build_decompression_block(self,) -> nn.Sequential:
-        decoder = []
-        in_channels = self.hidden_dim * self.channels_multipliers[0]
-        for _ in range(self.num_residuals):
-            decoder += [
-                Residual(
-                    in_channels=in_channels,
-                    residual_channels=self.residual_channels,
-                    use_norm=self.use_norm,
-                    activation=self.activation,
-                ),
-            ]
-
-        activation_fn = activation_function(self.activation)
-        out_channels_multipliers = self.channels_multipliers + (1,)
-        num_blocks = len(self.channels_multipliers)
-
-        for i in range(num_blocks):
-            in_channels = self.hidden_dim * self.channels_multipliers[i]
-            out_channels = self.hidden_dim * out_channels_multipliers[i + 1]
-            if self.use_norm:
-                decoder += [
-                    Normalize(num_channels=in_channels,),
-                ]
-            decoder += [
-                activation_fn,
-                nn.ConvTranspose2d(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    kernel_size=4,
-                    stride=2,
-                    padding=1,
-                ),
-            ]
-
-        if self.use_norm:
-            decoder += [
-                Normalize(
-                    num_channels=self.hidden_dim * out_channels_multipliers[-1],
-                    num_groups=self.hidden_dim * out_channels_multipliers[-1] // 4,
-                ),
-            ]
-
-        decoder += [
-            nn.Conv2d(
-                in_channels=self.hidden_dim * out_channels_multipliers[-1],
-                out_channels=self.out_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-            ),
-        ]
-        return nn.Sequential(*decoder)
-
-    def forward(self, z_q: Tensor) -> Tensor:
-        """Reconstruct input from given codes."""
-        return self.decoder(z_q)
diff --git a/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py
deleted file mode 100644
index 4761486..0000000
--- a/text_recognizer/networks/vqvae/encoder.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""CNN encoder for the VQ-VAE."""
-from typing import List, Tuple
-
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.vqvae.norm import Normalize
-from text_recognizer.networks.vqvae.residual import Residual
-
-
-class Encoder(nn.Module):
-    """A CNN encoder network."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        hidden_dim: int,
-        channels_multipliers: List[int],
-        dropout_rate: float,
-        activation: str = "mish",
-        use_norm: bool = False,
-        num_residuals: int = 4,
-        residual_channels: int = 32,
-    ) -> None:
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_dim = hidden_dim
-        self.channels_multipliers = tuple(channels_multipliers)
-        self.activation = activation
-        self.dropout_rate = dropout_rate
-        self.use_norm = use_norm
-        self.num_residuals = num_residuals
-        self.residual_channels = residual_channels
-        self.encoder = self._build_compression_block()
-
-    def _build_compression_block(self) -> nn.Sequential:
-        """Builds encoder network."""
-        num_blocks = len(self.channels_multipliers)
-        channels_multipliers = (1,) + self.channels_multipliers
-        activation_fn = activation_function(self.activation)
-
-        encoder = [
-            nn.Conv2d(
-                in_channels=self.in_channels,
-                out_channels=self.hidden_dim,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-            ),
-        ]
-
-        for i in range(num_blocks):
-            in_channels = self.hidden_dim * channels_multipliers[i]
-            out_channels = self.hidden_dim * channels_multipliers[i + 1]
-            if self.use_norm:
-                encoder += [
-                    Normalize(num_channels=in_channels,),
-                ]
-            encoder += [
-                activation_fn,
-                nn.Conv2d(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    kernel_size=4,
-                    stride=2,
-                    padding=1,
-                ),
-            ]
-
-        for _ in range(self.num_residuals):
-            encoder += [
-                Residual(
-                    in_channels=out_channels,
-                    residual_channels=self.residual_channels,
-                    use_norm=self.use_norm,
-                    activation=self.activation,
-                )
-            ]
-
-        return nn.Sequential(*encoder)
-
-    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Encodes input into a discrete representation."""
-        return self.encoder(x)
diff --git a/text_recognizer/networks/vqvae/norm.py b/text_recognizer/networks/vqvae/norm.py
deleted file mode 100644
index d73f9f8..0000000
--- a/text_recognizer/networks/vqvae/norm.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""Normalizer block."""
-import attr
-from torch import nn, Tensor
-
-
-@attr.s(eq=False)
-class Normalize(nn.Module):
-    num_channels: int = attr.ib()
-    num_groups: int = attr.ib(default=32)
-    norm: nn.GroupNorm = attr.ib(init=False)
-
-    def __attrs_post_init__(self) -> None:
-        """Post init configuration."""
-        super().__init__()
-        self.norm = nn.GroupNorm(
-            num_groups=self.num_groups,
-            num_channels=self.num_channels,
-            eps=1.0e-6,
-            affine=True,
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Applies group normalization."""
-        return self.norm(x)
diff --git a/text_recognizer/networks/vqvae/residual.py b/text_recognizer/networks/vqvae/residual.py
deleted file mode 100644
index bdff9eb..0000000
--- a/text_recognizer/networks/vqvae/residual.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""Residual block."""
-import attr
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.vqvae.norm import Normalize
-
-
-@attr.s(eq=False)
-class Residual(nn.Module):
-    in_channels: int = attr.ib()
-    residual_channels: int = attr.ib()
-    use_norm: bool = attr.ib(default=False)
-    activation: str = attr.ib(default="relu")
-
-    def __attrs_post_init__(self) -> None:
-        """Post init configuration."""
-        super().__init__()
-        self.block = self._build_res_block()
-
-    def _build_res_block(self) -> nn.Sequential:
-        """Build residual block."""
-        block = []
-        activation_fn = activation_function(activation=self.activation)
-
-        if self.use_norm:
-            block.append(Normalize(num_channels=self.in_channels))
-
-        block += [
-            activation_fn,
-            nn.Conv2d(
-                self.in_channels,
-                self.residual_channels,
-                kernel_size=3,
-                padding=1,
-                bias=False,
-            ),
-        ]
-
-        if self.use_norm:
-            block.append(Normalize(num_channels=self.residual_channels))
-
-        block += [
-            activation_fn,
-            nn.Conv2d(
-                self.residual_channels, self.in_channels, kernel_size=1, bias=False
-            ),
-        ]
-        return nn.Sequential(*block)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Apply the residual forward pass."""
-        return x + self.block(x)
diff --git a/text_recognizer/networks/vqvae/resize.py b/text_recognizer/networks/vqvae/resize.py
deleted file mode 100644
index 8d67d02..0000000
--- a/text_recognizer/networks/vqvae/resize.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""Up and down-sample with linear interpolation."""
-from torch import nn, Tensor
-import torch.nn.functional as F
-
-
-class Upsample(nn.Module):
-    """Upsamples by a factor 2."""
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Applies upsampling."""
-        return F.interpolate(x, scale_factor=2.0, mode="nearest")
-
-
-class Downsample(nn.Module):
-    """Downsampling by a factor 2."""
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Applies downsampling."""
-        return F.avg_pool2d(x, kernel_size=2, stride=2)
diff --git a/text_recognizer/networks/vqvae/vqvae.py b/text_recognizer/networks/vqvae/vqvae.py
deleted file mode 100644
index 5560e12..0000000
--- a/text_recognizer/networks/vqvae/vqvae.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""The VQ-VAE."""
-from typing import Tuple
-
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.quantizer.quantizer import VectorQuantizer
-
-
-class VQVAE(nn.Module):
-    """Vector Quantized Variational AutoEncoder."""
-
-    def __init__(
-        self,
-        encoder: nn.Module,
-        decoder: nn.Module,
-        quantizer: VectorQuantizer,
-    ) -> None:
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.quantizer = quantizer
-
-    def encode(self, x: Tensor) -> Tensor:
-        """Encodes input to a latent code."""
-        return self.encoder(x)
-
-    def quantize(self, z_e: Tensor) -> Tuple[Tensor, Tensor]:
-        """Quantizes the encoded latent vectors."""
-        z_q, _, commitment_loss = self.quantizer(z_e)
-        return z_q, commitment_loss
-
-    def decode(self, z_q: Tensor) -> Tensor:
-        """Reconstructs input from latent codes."""
-        return self.decoder(z_q)
-
-    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Compresses and decompresses input."""
-        z_e = self.encode(x)
-        z_q, commitment_loss = self.quantize(z_e)
-        x_hat = self.decode(z_q)
-        return x_hat, commitment_loss