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-rw-r--r--text_recognizer/networks/transformer/__init__.py6
-rw-r--r--text_recognizer/networks/transformer/attention.py109
-rw-r--r--text_recognizer/networks/transformer/decoder.py41
-rw-r--r--text_recognizer/networks/transformer/decoder_block.py44
-rw-r--r--text_recognizer/networks/transformer/embeddings/__init__.py1
-rw-r--r--text_recognizer/networks/transformer/embeddings/axial.py104
-rw-r--r--text_recognizer/networks/transformer/embeddings/rotary.py67
-rw-r--r--text_recognizer/networks/transformer/ff.py45
-rw-r--r--text_recognizer/networks/transformer/norm.py51
9 files changed, 0 insertions, 468 deletions
diff --git a/text_recognizer/networks/transformer/__init__.py b/text_recognizer/networks/transformer/__init__.py
deleted file mode 100644
index 0d17deb..0000000
--- a/text_recognizer/networks/transformer/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Transformer modules."""
-from text_recognizer.networks.transformer.attention import Attention
-from text_recognizer.networks.transformer.decoder import Decoder, DecoderBlock
-from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
-from text_recognizer.networks.transformer.ff import FeedForward
-from text_recognizer.networks.transformer.norm import RMSNorm
diff --git a/text_recognizer/networks/transformer/attention.py b/text_recognizer/networks/transformer/attention.py
deleted file mode 100644
index 85f513e..0000000
--- a/text_recognizer/networks/transformer/attention.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""Implementes the attention module for the transformer."""
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from einops import rearrange
-from torch import Tensor, einsum, nn
-
-from text_recognizer.networks.transformer.embeddings.rotary import (
- RotaryEmbedding,
-)
-
-
-class Attention(nn.Module):
- """Standard attention."""
-
- def __init__(
- self,
- dim: int,
- num_heads: int,
- causal: bool = False,
- dim_head: int = 64,
- dropout_rate: float = 0.0,
- ) -> None:
- super().__init__()
- self.dim = dim
- self.scale = self.dim**-0.5
- self.num_heads = num_heads
- self.dim_head = dim_head
-
- self.causal = causal
- self.dropout_rate = dropout_rate
-
- # Single key/value head
- k_dim = dim_head
- v_dim = dim_head
-
- out_dim = self.num_heads * self.dim_head
-
- self.to_q = nn.Linear(self.dim, out_dim, bias=False)
- self.to_k = nn.Linear(self.dim, k_dim, bias=False)
- self.to_v = nn.Linear(self.dim, v_dim, bias=False)
-
- self.dropout = nn.Dropout(p=self.dropout_rate)
-
- # Feedforward
- self.fc = nn.Linear(out_dim, self.dim)
-
- def forward(
- self,
- x: Tensor,
- context: Optional[Tensor] = None,
- mask: Optional[Tensor] = None,
- rotary_embedding: Optional[RotaryEmbedding] = None,
- ) -> Tensor:
- """Computes the attention."""
- b, device = x.shape[0], x.device
-
- q = self.to_q(x)
- q = rearrange(q, "b n (h d) -> b h n d", h=self.num_heads)
- k = self.to_k(context) if context is not None else self.to_k(x)
- v = self.to_v(context) if context is not None else self.to_v(x)
-
- if rotary_embedding is not None:
- q, k, v = map(lambda t: rotary_embedding.rotate(t), (q, k, v))
-
- energy = einsum("b h i d, b j d -> b h i j", q, k) * self.scale
- mask_value = -torch.finfo(energy.dtype).max
- energy = apply_input_mask(b, k, energy, mask, mask_value, device)
- if self.causal:
- energy = apply_causal_mask(energy, mask, mask_value, device)
-
- attn = F.softmax(energy, dim=-1)
- attn = self.dropout(attn)
- out = einsum("b h i j, b j d -> b h i d", attn, v)
- out = rearrange(out, "b h n d -> b n (h d)")
- out = self.fc(out)
- return out
-
-
-def apply_input_mask(
- b: int,
- k: Tensor,
- energy: Tensor,
- mask: Optional[Tensor],
- mask_value: Tensor,
- device: str,
-) -> Tensor:
- """Applies an input mask."""
- if mask is not None:
- k_mask = torch.ones((b, k.shape[-2]), device=device).bool()
- q_mask = rearrange(mask, "b i -> b () i ()")
- k_mask = rearrange(k_mask, "b j -> b () () j")
- input_mask = q_mask * k_mask
-
- energy = energy.masked_fill_(~input_mask, mask_value)
- return energy
-
-
-def apply_causal_mask(
- energy: Tensor, mask: Tensor, mask_value: Tensor, device: str
-) -> Tensor:
- """Applies a causal mask to the energy tensor."""
- i, j = energy.shape[-2:]
- r = torch.arange(i, device=device)
- mask = rearrange(r, "i -> () () i ()") < rearrange(r, "j -> () () () j")
- mask = F.pad(mask, (j - i, 0), value=False)
- energy.masked_fill_(mask, mask_value)
- return energy
diff --git a/text_recognizer/networks/transformer/decoder.py b/text_recognizer/networks/transformer/decoder.py
deleted file mode 100644
index 826bc13..0000000
--- a/text_recognizer/networks/transformer/decoder.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Transformer decoder module."""
-from copy import deepcopy
-from typing import Optional
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.decoder_block import DecoderBlock
-from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
-
-
-class Decoder(nn.Module):
- """Decoder Network."""
-
- def __init__(
- self,
- depth: int,
- dim: int,
- block: DecoderBlock,
- rotary_embedding: RotaryEmbedding,
- ) -> None:
- super().__init__()
- self.depth = depth
- self.rotary_embedding = rotary_embedding
- self.blocks = nn.ModuleList([deepcopy(block) for _ in range(self.depth)])
- self.ln = nn.LayerNorm(dim)
-
- def forward(
- self,
- x: Tensor,
- context: Optional[Tensor] = None,
- mask: Optional[Tensor] = None,
- ) -> Tensor:
- """Applies attention blocks."""
- for block in self.blocks:
- x = block(
- x=x,
- context=context,
- mask=mask,
- rotary_embedding=self.rotary_embedding,
- )
- return self.ln(x)
diff --git a/text_recognizer/networks/transformer/decoder_block.py b/text_recognizer/networks/transformer/decoder_block.py
deleted file mode 100644
index b8eb5c4..0000000
--- a/text_recognizer/networks/transformer/decoder_block.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Transformer decoder module."""
-from copy import deepcopy
-from typing import Optional, Type
-
-from torch import Tensor, nn
-
-from text_recognizer.networks.transformer.attention import Attention
-from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
-from text_recognizer.networks.transformer.ff import FeedForward
-
-
-class DecoderBlock(nn.Module):
- """Residual decoder block."""
-
- def __init__(
- self,
- self_attn: Attention,
- norm: Type[nn.Module],
- ff: FeedForward,
- cross_attn: Optional[Attention] = None,
- ) -> None:
- super().__init__()
- self.ln_attn = norm
- self.attn = self_attn
- self.ln_cross_attn = deepcopy(norm)
- self.cross_attn = cross_attn
- self.ln_ff = deepcopy(norm)
- self.ff = ff
-
- def forward(
- self,
- x: Tensor,
- rotary_embedding: RotaryEmbedding,
- context: Optional[Tensor] = None,
- mask: Optional[Tensor] = None,
- ) -> Tensor:
- """Applies decoder block on input signals."""
- x = x + self.attn(self.ln_attn(x), mask=mask, rotary_embedding=rotary_embedding)
- x = x + self.cross_attn(
- x=self.ln_cross_attn(x),
- context=context,
- )
- x = x + self.ff(self.ln_ff(x))
- return x
diff --git a/text_recognizer/networks/transformer/embeddings/__init__.py b/text_recognizer/networks/transformer/embeddings/__init__.py
deleted file mode 100644
index bb3f904..0000000
--- a/text_recognizer/networks/transformer/embeddings/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Positional encodings for transformers."""
diff --git a/text_recognizer/networks/transformer/embeddings/axial.py b/text_recognizer/networks/transformer/embeddings/axial.py
deleted file mode 100644
index 9b872a9..0000000
--- a/text_recognizer/networks/transformer/embeddings/axial.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""Axial attention for multi-dimensional data.
-
-Stolen from:
- https://github.com/lucidrains/axial-attention/blob/
- eff2c10c2e76c735a70a6b995b571213adffbbb7/axial_attention/axial_attention.py#L100
-"""
-from functools import reduce
-from operator import mul
-from typing import Optional, Sequence
-
-import torch
-from torch import Tensor, nn
-
-
-class AxialPositionalEmbedding(nn.Module):
- def __init__(
- self,
- dim: int,
- axial_shape: Sequence[int],
- axial_dims: Optional[Sequence[int]] = None,
- ) -> None:
- super().__init__()
-
- self.dim = dim
- self.shape = axial_shape
- self.max_seq_len = reduce(mul, axial_shape, 1)
-
- self.summed = axial_dims is None
- axial_dims = ((dim,) * len(axial_shape)) if self.summed else axial_dims
-
- assert len(self.shape) == len(
- axial_dims
- ), "number of axial dimensions must equal the number of dimensions in the shape"
- assert (
- self.summed or not self.summed and sum(axial_dims) == dim
- ), f"axial dimensions must sum up to the target dimension {dim}"
-
- self.weights = ParameterList(self, "weights", len(axial_shape))
-
- for ind, (shape, axial_dim) in enumerate(zip(self.shape, axial_dims)):
- ax_shape = [1] * len(self.shape)
- ax_shape[ind] = shape
- ax_shape = (1, *ax_shape, axial_dim)
- ax_emb = nn.Parameter(torch.zeros(ax_shape).normal_(0, 1))
- self.weights.append(ax_emb)
-
- def forward(self, x: Tensor) -> Tensor:
- """Returns axial positional embedding."""
- b, t, _ = x.shape
- assert (
- t <= self.max_seq_len
- ), f"Sequence length ({t}) must be less than the maximum sequence length allowed ({self.max_seq_len})"
- embs = []
-
- for ax_emb in self.weights.to_list():
- axial_dim = ax_emb.shape[-1]
- expand_shape = (b, *self.shape, axial_dim)
- emb = ax_emb.expand(expand_shape).reshape(b, self.max_seq_len, axial_dim)
- embs.append(emb)
-
- pos_emb = sum(embs) if self.summed else torch.cat(embs, dim=-1)
- return pos_emb[:, :t].to(x)
-
-
-# a mock parameter list object until below issue is resolved
-# https://github.com/pytorch/pytorch/issues/36035
-class ParameterList(object):
- def __init__(self, kls, prefix, length):
- self.ind = 0
- self.kls = kls
- self.prefix = prefix
- self.length = length
-
- def _keyname(self, prefix, ind):
- return f"{prefix}_{ind}"
-
- def append(self, x):
- setattr(self.kls, self._keyname(self.prefix, self.ind), x)
- self.ind += 1
-
- def to_list(self):
- return [
- getattr(self.kls, self._keyname(self.prefix, i)) for i in range(self.length)
- ]
-
-
-class AxialPositionalEmbeddingImage(nn.Module):
- def __init__(
- self,
- dim: int,
- axial_shape: Sequence[int],
- axial_dims: Optional[Sequence[int]] = None,
- ) -> None:
- super().__init__()
- axial_dims = (dim // 2, dim // 2) if axial_dims is None else axial_dims
- assert len(axial_shape) == 2, "Axial shape must have 2 dimensions for images"
- self.dim = dim
- self.pos_emb = AxialPositionalEmbedding(dim, axial_shape, axial_dims)
-
- def forward(self, img):
- b, c, h, w = img.shape
- img = img.permute(0, 2, 3, 1).reshape(b, h * w, c)
- pos_emb = self.pos_emb(img)
- return pos_emb.reshape(b, h, w, self.dim).permute(0, 3, 1, 2)
diff --git a/text_recognizer/networks/transformer/embeddings/rotary.py b/text_recognizer/networks/transformer/embeddings/rotary.py
deleted file mode 100644
index ca0a260..0000000
--- a/text_recognizer/networks/transformer/embeddings/rotary.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""Roatary embedding.
-
-Stolen from lucidrains:
- https://github.com/lucidrains/rotary-embedding-torch
-
-Explanation of roatary:
- https://blog.eleuther.ai/rotary-embeddings/
-"""
-from inspect import isfunction
-
-from einops import rearrange, repeat
-import torch
-from torch import Tensor, nn
-
-
-class RotaryEmbedding(nn.Module):
- """Rotary positional embedding."""
-
- def __init__(self, dim: int) -> None:
- super().__init__()
- inv_freqs = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
- self.register_buffer("inv_freqs", inv_freqs)
- self.cache = {}
-
- def rotate(self, t: Tensor, dim: int = -2) -> Tensor:
- """Rotate vector."""
- device, n = t.device, t.shape[dim]
- freqs = self.forward(lambda: torch.arange(n, device=device), cache_key=n)
- return apply_rotary_emb(t, freqs)
-
- def forward(self, t: Tensor, cache_key: int) -> Tensor:
- """Encodes tensor x with rotary embeddings."""
- if cache_key in self.cache:
- return self.cache[cache_key]
-
- if isfunction(t):
- t = t()
-
- freqs = self.inv_freqs
- freqs = torch.einsum("..., f -> ... f", t.type(freqs.dtype), freqs)
- freqs = repeat(freqs, "... n -> ... (n r)", r=2)
- self.cache[cache_key] = freqs
- return freqs
-
-
-def rotate_half(x: Tensor) -> Tensor:
- x = rearrange(x, "... (d r) -> ... d r", r=2)
- x1, x2 = x.unbind(dim=-1)
- x = torch.stack((-x2, x1), dim=-1)
- return rearrange(x, "... d r -> ... (d r)")
-
-
-def apply_rotary_emb(t: Tensor, freqs: Tensor, start_index: int = 0) -> Tensor:
- freqs = freqs.to(t)
- rot_dim = freqs.shape[-1]
- end_index = start_index + rot_dim
- assert rot_dim <= t.shape[-1], (
- f"feature dimension {t.shape[-1]} is not of sufficient size to rotate"
- f"in all the positions {rot_dim}"
- )
- t_left, t, t_right = (
- t[..., :start_index],
- t[..., start_index:end_index],
- t[..., end_index:],
- )
- t = (t * freqs.cos()) + (rotate_half(t) * freqs.sin())
- return torch.cat((t_left, t, t_right), dim=-1)
diff --git a/text_recognizer/networks/transformer/ff.py b/text_recognizer/networks/transformer/ff.py
deleted file mode 100644
index 3ccf5b5..0000000
--- a/text_recognizer/networks/transformer/ff.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Feedforward layer in transformer.
-
-Stolen from lucidrains:
- https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
-"""
-from typing import Optional
-
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-
-class GEGLU(nn.Module):
- def __init__(self, dim_in: int, dim_out: int) -> None:
- super().__init__()
- self.fc = nn.Linear(dim_in, dim_out * 2)
-
- def forward(self, x: Tensor) -> Tensor:
- x, gate = self.fc(x).chunk(2, dim=-1)
- return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
- def __init__(
- self,
- dim: int,
- dim_out: Optional[int] = None,
- expansion_factor: int = 4,
- glu: bool = True,
- dropout_rate: float = 0.0,
- ) -> None:
- super().__init__()
- inner_dim = dim * expansion_factor
- dim_out = dim_out if dim_out is not None else dim
- in_projection = (
- nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
- if not glu
- else GEGLU(dim, inner_dim)
- )
-
- self.mlp = nn.Sequential(
- in_projection, nn.Dropout(dropout_rate), nn.Linear(inner_dim, dim_out)
- )
-
- def forward(self, x: Tensor) -> Tensor:
- return self.mlp(x)
diff --git a/text_recognizer/networks/transformer/norm.py b/text_recognizer/networks/transformer/norm.py
deleted file mode 100644
index 1431327..0000000
--- a/text_recognizer/networks/transformer/norm.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""Normalization layers for transfromers.
-
-Copied from lucidrains:
- https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
-
-"""
-from typing import Optional, Type
-
-import torch
-from torch import Tensor, nn
-
-
-class RMSNorm(nn.Module):
- """Root mean square layer normalization."""
-
- def __init__(self, dim: int, eps: float = 1e-8) -> None:
- super().__init__()
- self.scale = dim**-0.5
- self.eps = eps
- self.g = nn.Parameter(torch.ones(dim))
-
- def forward(self, x: Tensor) -> Tensor:
- """Applies normalization."""
- norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
- return x / norm.clamp(min=self.eps) * self.g
-
-
-class PreNorm(nn.Module):
- """Applies layer normalization then function."""
-
- def __init__(
- self,
- normalized_shape: int,
- fn: Type[nn.Module],
- context_dim: Optional[int] = None,
- ) -> None:
- super().__init__()
- self.norm = nn.LayerNorm(normalized_shape)
- self.fn = fn
- self.norm_context = (
- nn.LayerNorm(context_dim) if context_dim is not None else None
- )
-
- def forward(self, x: Tensor, **kwargs) -> Tensor:
- """Applies pre norm."""
- x = self.norm(x)
- if self.norm_context is not None:
- context = kwargs["context"]
- normed_context = self.norm_context(context)
- kwargs.update(context=normed_context)
- return self.fn(x, **kwargs)
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-12-25 19:31:01 +0000