Inital commit for refactoring to lightning

1 files changed, 0 insertions, 150 deletions

diff --git a/src/text_recognizer/networks/vit.py b/src/text_recognizer/networks/vit.py
deleted file mode 100644
index efb3701..0000000
--- a/src/text_recognizer/networks/vit.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""A Vision Transformer.
-
-Inspired by:
-https://openreview.net/pdf?id=YicbFdNTTy
-
-"""
-from typing import Optional, Tuple
-
-from einops import rearrange, repeat
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.transformer import Transformer
-
-
-class ViT(nn.Module):
-    """Transfomer for image to sequence prediction."""
-
-    def __init__(
-        self,
-        num_encoder_layers: int,
-        num_decoder_layers: int,
-        hidden_dim: int,
-        vocab_size: int,
-        num_heads: int,
-        expansion_dim: int,
-        patch_dim: Tuple[int, int],
-        image_size: Tuple[int, int],
-        dropout_rate: float,
-        trg_pad_index: int,
-        max_len: int,
-        activation: str = "gelu",
-    ) -> None:
-        super().__init__()
-
-        self.trg_pad_index = trg_pad_index
-        self.patch_dim = patch_dim
-        self.num_patches = image_size[-1] // self.patch_dim[1]
-
-        # Encoder
-        self.patch_to_embedding = nn.Linear(
-            self.patch_dim[0] * self.patch_dim[1], hidden_dim
-        )
-        self.cls_token = nn.Parameter(torch.randn(1, 1, hidden_dim))
-        self.character_embedding = nn.Embedding(vocab_size, hidden_dim)
-        self.pos_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim))
-        self.dropout = nn.Dropout(dropout_rate)
-        self._init()
-
-        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 _init(self) -> None:
-        nn.init.normal_(self.character_embedding.weight, std=0.02)
-        # nn.init.normal_(self.pos_embedding.weight, std=0.02)
-
-    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 extract_image_features(self, src: Tensor) -> Tensor:
-        """Extracts image features with a backbone neural network.
-
-        It seem like the winning idea was to swap channels and width dimension and collapse
-        the height dimension. The transformer is learning like a baby with this implementation!!! :D
-        Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D
-
-        Args:
-            src (Tensor): Input tensor.
-
-        Returns:
-            Tensor: A input src to the transformer.
-
-        """
-        # If batch dimension is missing, it needs to be added.
-        if len(src.shape) < 4:
-            src = src[(None,) * (4 - len(src.shape))]
-
-        patches = rearrange(
-            src,
-            "b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
-            p1=self.patch_dim[0],
-            p2=self.patch_dim[1],
-        )
-
-        # From patches to encoded sequence.
-        x = self.patch_to_embedding(patches)
-        b, n, _ = x.shape
-        cls_tokens = repeat(self.cls_token, "() n d -> b n d", b=b)
-        x = torch.cat((cls_tokens, x), dim=1)
-        x += self.pos_embedding[:, : (n + 1)]
-        x = self.dropout(x)
-
-        return x
-
-    def target_embedding(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.
-
-        """
-        _, n = trg.shape
-        trg = self.character_embedding(trg.long())
-        trg += self.pos_embedding[:, :n]
-        return trg
-
-    def decode_image_features(self, h: Tensor, trg: Optional[Tensor] = None) -> Tensor:
-        """Takes images features from the backbone and decodes them with the transformer."""
-        trg_mask = self._create_trg_mask(trg)
-        trg = self.target_embedding(trg)
-        out = self.transformer(h, trg, trg_mask=trg_mask)
-
-        logits = self.head(out)
-        return logits
-
-    def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
-        """Forward pass with CNN transfomer."""
-        h = self.extract_image_features(x)
-        logits = self.decode_image_features(h, trg)
-        return logits