Inital commit for refactoring to lightning

1 files changed, 150 insertions, 0 deletions

diff --git a/text_recognizer/networks/vit.py b/text_recognizer/networks/vit.py
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+"""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