diff options
Diffstat (limited to 'text_recognizer/networks/quantizer')
| -rw-r--r-- | text_recognizer/networks/quantizer/__init__.py | 0 | ||||
| -rw-r--r-- | text_recognizer/networks/quantizer/codebook.py | 96 | ||||
| -rw-r--r-- | text_recognizer/networks/quantizer/kmeans.py | 32 | ||||
| -rw-r--r-- | text_recognizer/networks/quantizer/quantizer.py | 59 | ||||
| -rw-r--r-- | text_recognizer/networks/quantizer/utils.py | 26 |
5 files changed, 0 insertions, 213 deletions
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)) |