diff options
Diffstat (limited to 'text_recognizer/networks/vqvae')
| -rw-r--r-- | text_recognizer/networks/vqvae/quantizer.py | 141 |
1 files changed, 0 insertions, 141 deletions
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 |