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-rw-r--r--text_recognizer/network/transformer/attend.py11
-rw-r--r--text_recognizer/network/transformer/attention.py19
-rw-r--r--text_recognizer/network/transformer/decoder.py8
-rw-r--r--text_recognizer/network/transformer/encoder.py4
-rw-r--r--text_recognizer/network/transformer/norm.py2
-rw-r--r--text_recognizer/network/transformer/swiglu.py2
-rw-r--r--text_recognizer/network/transformer/transformer.py48
-rw-r--r--text_recognizer/network/transformer/vit.py (renamed from text_recognizer/network/vit.py)29
8 files changed, 106 insertions, 17 deletions
diff --git a/text_recognizer/network/transformer/attend.py b/text_recognizer/network/transformer/attend.py
index a5c23c6..23a6487 100644
--- a/text_recognizer/network/transformer/attend.py
+++ b/text_recognizer/network/transformer/attend.py
@@ -1,10 +1,10 @@
-from typing import Optional
from collections import namedtuple
+from typing import Optional
import torch
-from torch import Tensor, einsum, nn
-from einops import rearrange
import torch.nn.functional as F
+from einops import rearrange
+from torch import Tensor, einsum, nn
Config = namedtuple(
"FlashAttentionConfig", ["enable_flash", "enable_math", "enable_mem_efficient"]
@@ -79,6 +79,11 @@ class Attend(nn.Module):
causal: bool,
mask: Optional[Tensor] = None,
) -> Tensor:
+ if k.ndim == 3:
+ k = rearrange(k, 'b ... -> b 1 ...').expand_as(q)
+ if v.ndim == 3:
+ v = rearrange(v, 'b ... -> b 1 ...').expand_as(q)
+
if mask is not None:
mask = rearrange(mask, "b j -> b 1 1 j")
if self.use_flash:
diff --git a/text_recognizer/network/transformer/attention.py b/text_recognizer/network/transformer/attention.py
index bae077f..9a4aa0d 100644
--- a/text_recognizer/network/transformer/attention.py
+++ b/text_recognizer/network/transformer/attention.py
@@ -1,11 +1,12 @@
"""Implements the attention module for the transformer."""
from typing import Optional
-from einops import rearrange
-from text_recognizer.network.transformer.swiglu import SwiGLU
import torch
+from einops import rearrange
from torch import Tensor, nn
+from text_recognizer.network.transformer.swiglu import SwiGLU
+
from .attend import Attend
from .embedding.rotary import RotaryEmbedding, apply_rotary_pos_emb
@@ -23,7 +24,8 @@ class Attention(nn.Module):
dropout_rate: float = 0.0,
use_flash: bool = True,
norm_context: bool = False,
- rotary_emb: Optional[RotaryEmbedding] = None,
+ use_rotary_emb: bool = False,
+ one_kv_head: bool = False,
) -> None:
super().__init__()
self.heads = heads
@@ -36,12 +38,13 @@ class Attention(nn.Module):
self.norm = nn.LayerNorm(dim)
self.context_norm = nn.LayerNorm(dim) if norm_context else nn.Identity()
self.to_q = nn.Linear(dim, inner_dim, bias=False)
- self.to_kv = nn.Linear(dim, 2 * inner_dim, bias=False)
+ self.kv_heads = 1 if one_kv_head else heads
+ self.to_kv = nn.Linear(dim, 2 * self.kv_heads * dim_head, bias=False)
self.attend = Attend(use_flash)
self.to_out = nn.Linear(inner_dim, dim, bias=False)
- self.rotary_emb = rotary_emb
+ self.rotary_emb = RotaryEmbedding(dim_head) if use_rotary_emb else None
self.pos_emb = None
ff_inner_dim = ff_mult * dim
@@ -68,9 +71,9 @@ class Attention(nn.Module):
k, v = self.to_kv(x if context is None else self.context_norm(context)).chunk(
2, dim=-1
)
-
- q, k, v = map(
- lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), (q, k, v)
+ q = rearrange(q, "b n (h d) -> b h n d", h=self.heads)
+ k, v = map(
+ lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.kv_heads), (k, v)
)
if self.rotary_emb is not None:
diff --git a/text_recognizer/network/transformer/decoder.py b/text_recognizer/network/transformer/decoder.py
index 4ebdd2c..60e426a 100644
--- a/text_recognizer/network/transformer/decoder.py
+++ b/text_recognizer/network/transformer/decoder.py
@@ -1,9 +1,9 @@
"""Transformer decoder module."""
from typing import Optional
+
from torch import Tensor, nn
from .attention import Attention
-from .embedding.rotary import RotaryEmbedding
class Decoder(nn.Module):
@@ -15,6 +15,7 @@ class Decoder(nn.Module):
dim_head: int,
depth: int,
dropout_rate: float = 0.0,
+ one_kv_head: bool = False,
) -> None:
super().__init__()
self.norm = nn.LayerNorm(dim)
@@ -31,7 +32,8 @@ class Decoder(nn.Module):
dropout_rate=dropout_rate,
use_flash=True,
norm_context=False,
- rotary_emb=RotaryEmbedding(dim_head),
+ use_rotary_emb=True,
+ one_kv_head=one_kv_head,
),
Attention(
dim=dim,
@@ -42,6 +44,8 @@ class Decoder(nn.Module):
dropout_rate=dropout_rate,
use_flash=True,
norm_context=False,
+ use_rotary_emb=False,
+ one_kv_head=one_kv_head,
),
]
)
diff --git a/text_recognizer/network/transformer/encoder.py b/text_recognizer/network/transformer/encoder.py
index 1728c61..ce30372 100644
--- a/text_recognizer/network/transformer/encoder.py
+++ b/text_recognizer/network/transformer/encoder.py
@@ -13,6 +13,8 @@ class Encoder(nn.Module):
ff_mult: int,
depth: int,
dropout_rate: float = 0.0,
+ use_rotary_emb: bool = False,
+ one_kv_head: bool = False,
) -> None:
super().__init__()
self.norm = nn.LayerNorm(dim)
@@ -27,6 +29,8 @@ class Encoder(nn.Module):
dropout_rate=dropout_rate,
use_flash=True,
norm_context=False,
+ use_rotary_emb=use_rotary_emb,
+ one_kv_head=one_kv_head,
)
for _ in range(depth)
]
diff --git a/text_recognizer/network/transformer/norm.py b/text_recognizer/network/transformer/norm.py
index 2737754..9ba35a8 100644
--- a/text_recognizer/network/transformer/norm.py
+++ b/text_recognizer/network/transformer/norm.py
@@ -5,8 +5,8 @@ Copied from lucidrains:
"""
import torch
-from torch import Tensor, nn
import torch.nn.functional as F
+from torch import Tensor, nn
class RMSNorm(nn.Module):
diff --git a/text_recognizer/network/transformer/swiglu.py b/text_recognizer/network/transformer/swiglu.py
index e61662a..7bafd06 100644
--- a/text_recognizer/network/transformer/swiglu.py
+++ b/text_recognizer/network/transformer/swiglu.py
@@ -1,5 +1,5 @@
-from torch import nn
import torch.nn.functional as F
+from torch import nn
class SwiGLU(nn.Module):
diff --git a/text_recognizer/network/transformer/transformer.py b/text_recognizer/network/transformer/transformer.py
new file mode 100644
index 0000000..298308e
--- /dev/null
+++ b/text_recognizer/network/transformer/transformer.py
@@ -0,0 +1,48 @@
+from torch import Tensor, nn
+
+from .decoder import Decoder
+from .embedding.token import TokenEmbedding
+from .vit import Vit
+
+
+class Transformer(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ num_classes: int,
+ encoder: Vit,
+ decoder: Decoder,
+ token_embedding: TokenEmbedding,
+ tie_embeddings: bool,
+ pad_index: int,
+ ) -> None:
+ super().__init__()
+ self.token_embedding = token_embedding
+ self.to_logits = (
+ nn.Linear(dim, num_classes)
+ if not tie_embeddings
+ else lambda t: t @ self.token_embedding.to_embedding.weight.t()
+ )
+ self.encoder = encoder
+ self.decoder = decoder
+ self.pad_index = pad_index
+
+ def encode(self, images: Tensor) -> Tensor:
+ return self.encoder(images)
+
+ def decode(self, text: Tensor, img_features: Tensor) -> Tensor:
+ text = text.long()
+ mask = text != self.pad_index
+ tokens = self.token_embedding(text)
+ output = self.decoder(tokens, context=img_features, mask=mask)
+ return self.to_logits(output)
+
+ def forward(
+ self,
+ img: Tensor,
+ text: Tensor,
+ ) -> Tensor:
+ """Applies decoder block on input signals."""
+ img_features = self.encode(img)
+ logits = self.decode(text, img_features)
+ return logits # [B, N, C]
diff --git a/text_recognizer/network/vit.py b/text_recognizer/network/transformer/vit.py
index a596792..3b600c3 100644
--- a/text_recognizer/network/vit.py
+++ b/text_recognizer/network/transformer/vit.py
@@ -1,8 +1,30 @@
+import torch
+from einops import rearrange
from einops.layers.torch import Rearrange
from torch import Tensor, nn
-from .transformer.embedding.sincos import sincos_2d
-from .transformer.encoder import Encoder
+from .embedding.sincos import sincos_2d
+from .encoder import Encoder
+
+
+class PatchDropout(nn.Module):
+ def __init__(self, prob):
+ super().__init__()
+ assert 0 <= prob < 1.
+ self.prob = prob
+
+ def forward(self, x):
+ if not self.training or self.prob == 0.:
+ return x
+
+ b, n, _, device = *x.shape, x.device
+
+ batch_indices = torch.arange(b, device = device)
+ batch_indices = rearrange(batch_indices, '... -> ... 1')
+ num_patches_keep = max(1, int(n * (1 - self.prob)))
+ patch_indices_keep = torch.randn(b, n, device = device).topk(num_patches_keep, dim = -1).indices
+
+ return x[batch_indices, patch_indices_keep]
class Vit(nn.Module):
@@ -15,6 +37,7 @@ class Vit(nn.Module):
dim: int,
encoder: Encoder,
channels: int = 1,
+ patch_dropout: float = 0.0,
) -> None:
super().__init__()
patch_dim = patch_height * patch_width * channels
@@ -32,8 +55,10 @@ class Vit(nn.Module):
h=image_height // patch_height, w=image_width // patch_width, dim=dim
)
self.encoder = encoder
+ self.patch_dropout = PatchDropout(patch_dropout)
def forward(self, images: Tensor) -> Tensor:
x = self.to_patch_embedding(images)
x = x + self.patch_embedding.to(images.device, dtype=images.dtype)
+ x = self.patch_dropout(x)
return self.encoder(x)
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2025-11-04 09:41:40 +0000