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"""VisionTransformer module.
Splits each image into patches and feeds them to a transformer.
"""
from typing import Dict, Optional, Tuple, Type
from einops import rearrange, reduce
from einops.layers.torch import Rearrange
from loguru import logger
import torch
from torch import nn
from torch import Tensor
from text_recognizer.networks.transformer import PositionalEncoding, Transformer
from text_recognizer.networks.util import configure_backbone
class VisionTransformer(nn.Module):
"""Linear projection+Transfomer for image to sequence prediction, sort of based on the ideas from ViT."""
def __init__(
self,
num_encoder_layers: int,
num_decoder_layers: int,
hidden_dim: int,
vocab_size: int,
num_heads: int,
max_len: int,
expansion_dim: int,
dropout_rate: float,
trg_pad_index: int,
mlp_dim: Optional[int] = None,
patch_size: Tuple[int, int] = (28, 28),
stride: Tuple[int, int] = (1, 14),
activation: str = "gelu",
backbone: Optional[str] = None,
backbone_args: Optional[Dict] = None,
) -> None:
super().__init__()
self.patch_size = patch_size
self.stride = stride
self.trg_pad_index = trg_pad_index
self.slidning_window = self._configure_sliding_window()
self.character_embedding = nn.Embedding(vocab_size, hidden_dim)
self.position_encoding = PositionalEncoding(hidden_dim, dropout_rate, max_len)
self.mlp_dim = mlp_dim
self.use_backbone = False
if backbone is None:
self.linear_projection = nn.Linear(
self.patch_size[0] * self.patch_size[1], hidden_dim
)
else:
self.backbone = configure_backbone(backbone, backbone_args)
if mlp_dim:
self.mlp = nn.Linear(mlp_dim, hidden_dim)
self.use_backbone = True
self.transformer = Transformer(
num_encoder_layers,
num_decoder_layers,
hidden_dim,
num_heads,
expansion_dim,
dropout_rate,
activation,
)
self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),)
def _configure_sliding_window(self) -> nn.Sequential:
return nn.Sequential(
nn.Unfold(kernel_size=self.patch_size, stride=self.stride),
Rearrange(
"b (c h w) t -> b t c h w",
h=self.patch_size[0],
w=self.patch_size[1],
c=1,
),
)
def _create_trg_mask(self, trg: Tensor) -> Tensor:
# Move this outside the transformer.
trg_pad_mask = (trg != self.trg_pad_index)[:, None, None]
trg_len = trg.shape[1]
trg_sub_mask = torch.tril(
torch.ones((trg_len, trg_len), device=trg.device)
).bool()
trg_mask = trg_pad_mask & trg_sub_mask
return trg_mask
def encoder(self, src: Tensor) -> Tensor:
"""Forward pass with the encoder of the transformer."""
return self.transformer.encoder(src)
def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor:
"""Forward pass with the decoder of the transformer + classification head."""
return self.head(
self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
)
def _backbone(self, x: Tensor) -> Tensor:
b, t = x.shape[:2]
if self.use_backbone:
x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t)
x = self.backbone(x)
if self.mlp_dim:
x = rearrange(x, "(b t) c h w -> b t (c h w)", b=b, t=t)
x = self.mlp(x)
else:
x = rearrange(x, "(b t) h -> b t h", b=b, t=t)
else:
x = rearrange(x, "b t c h w -> b t (c h w)", b=b, t=t)
x = self.linear_projection(x)
return x
def preprocess_input(self, src: Tensor) -> Tensor:
"""Encodes src with a backbone network and a positional encoding.
Args:
src (Tensor): Input tensor.
Returns:
Tensor: A input src to the transformer.
"""
# If batch dimenstion is missing, it needs to be added.
if len(src.shape) < 4:
src = src[(None,) * (4 - len(src.shape))]
src = self.slidning_window(src) # .squeeze(-2)
src = self._backbone(src)
src = self.position_encoding(src)
return src
def preprocess_target(self, trg: Tensor) -> Tuple[Tensor, Tensor]:
"""Encodes target tensor with embedding and postion.
Args:
trg (Tensor): Target tensor.
Returns:
Tuple[Tensor, Tensor]: Encoded target tensor and target mask.
"""
trg_mask = self._create_trg_mask(trg)
trg = self.character_embedding(trg.long())
trg = self.position_encoding(trg)
return trg, trg_mask
def forward(self, x: Tensor, trg: Tensor) -> Tensor:
"""Forward pass with vision transfomer."""
src = self.preprocess_input(x)
trg, trg_mask = self.preprocess_target(trg)
out = self.transformer(src, trg, trg_mask=trg_mask)
logits = self.head(out)
return logits
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