From 3a4fa0f1cf3c381a3d81f39e6e9439ec192b2509 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 11 Sep 2023 22:14:43 +0200
Subject: Refactor vit to encoder only
---
text_recognizer/network/vit.py | 49 ++++++------------------------------------
1 file changed, 6 insertions(+), 43 deletions(-)
(limited to 'text_recognizer/network')
diff --git a/text_recognizer/network/vit.py b/text_recognizer/network/vit.py
index 1fbf3fc..a596792 100644
--- a/text_recognizer/network/vit.py
+++ b/text_recognizer/network/vit.py
@@ -1,16 +1,11 @@
-"""Transformer module."""
-from typing import Type
-
from einops.layers.torch import Rearrange
from torch import Tensor, nn
-from .transformer.embedding.token import TokenEmbedding
from .transformer.embedding.sincos import sincos_2d
-from .transformer.decoder import Decoder
from .transformer.encoder import Encoder
-class VisionTransformer(nn.Module):
+class Vit(nn.Module):
def __init__(
self,
image_height: int,
@@ -18,16 +13,11 @@ class VisionTransformer(nn.Module):
patch_height: int,
patch_width: int,
dim: int,
- num_classes: int,
encoder: Encoder,
- decoder: Decoder,
- token_embedding: TokenEmbedding,
- pos_embedding: Type[nn.Module],
- tie_embeddings: bool,
- pad_index: int,
+ channels: int = 1,
) -> None:
super().__init__()
- patch_dim = patch_height * patch_width
+ patch_dim = patch_height * patch_width * channels
self.to_patch_embedding = nn.Sequential(
Rearrange(
"b c (h ph) (w pw) -> b (h w) (ph pw c)",
@@ -41,36 +31,9 @@ class VisionTransformer(nn.Module):
self.patch_embedding = sincos_2d(
h=image_height // patch_height, w=image_width // patch_width, dim=dim
)
- self.pos_embedding = pos_embedding
- 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, img: Tensor) -> Tensor:
- x = self.to_patch_embedding(img)
- x += self.patch_embedding.to(img.device, dtype=img.dtype)
+ def forward(self, images: Tensor) -> Tensor:
+ x = self.to_patch_embedding(images)
+ x = x + self.patch_embedding.to(images.device, dtype=images.dtype)
return self.encoder(x)
-
- def decode(self, text: Tensor, img_features: Tensor) -> Tensor:
- text = text.long()
- mask = text != self.pad_index
- tokens = self.token_embedding(text)
- tokens = tokens + self.pos_embedding(tokens)
- 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]
--
cgit v1.2.3-70-g09d2