Configs, refactor with attrs, fix attr bug in iam

1 files changed, 201 insertions, 0 deletions

diff --git a/text_recognizer/networks/conv_transformer.py b/text_recognizer/networks/conv_transformer.py
new file mode 100644
index 0000000..4acdc36
--- /dev/null
+++ b/text_recognizer/networks/conv_transformer.py
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+"""Vision transformer for character recognition."""
+import math
+from typing import Tuple, Type
+
+import attr
+import torch
+from torch import nn, Tensor
+
+from text_recognizer.data.mappings import AbstractMapping
+from text_recognizer.networks.base import BaseNetwork
+from text_recognizer.networks.encoders.efficientnet import EfficientNet
+from text_recognizer.networks.transformer.layers import Decoder
+from text_recognizer.networks.transformer.positional_encodings import (
+    PositionalEncoding,
+    PositionalEncoding2D,
+)
+
+
+@attr.s(auto_attribs=True)
+class ConvTransformer(BaseNetwork):
+    # Parameters and placeholders,
+    input_dims: Tuple[int, int, int] = attr.ib()
+    hidden_dim: int = attr.ib()
+    dropout_rate: float = attr.ib()
+    max_output_len: int = attr.ib()
+    num_classes: int = attr.ib()
+    start_token: str = attr.ib()
+    start_index: Tensor = attr.ib(init=False)
+    end_token: str = attr.ib()
+    end_index: Tensor = attr.ib(init=False)
+    pad_token: str = attr.ib()
+    pad_index: Tensor = attr.ib(init=False)
+
+    # Modules.
+    encoder: EfficientNet = attr.ib()
+    decoder: Decoder = attr.ib()
+    mapping: Type[AbstractMapping] = attr.ib()
+
+    latent_encoder: nn.Sequential = attr.ib(init=False)
+    token_embedding: nn.Embedding = attr.ib(init=False)
+    token_pos_encoder: PositionalEncoding = attr.ib(init=False)
+    head: nn.Linear = attr.ib(init=False)
+
+    def __attrs_post_init__(self) -> None:
+        """Post init configuration."""
+        self.start_index = self.mapping.get_index(self.start_token)
+        self.end_index = self.mapping.get_index(self.end_token)
+        self.pad_index = self.mapping.get_index(self.pad_token)
+
+        # Latent projector for down sampling number of filters and 2d
+        # positional encoding.
+        self.latent_encoder = nn.Sequential(
+            nn.Conv2d(
+                in_channels=self.encoder.out_channels,
+                out_channels=self.hidden_dim,
+                kernel_size=1,
+            ),
+            PositionalEncoding2D(
+                hidden_dim=self.hidden_dim,
+                max_h=self.input_dims[1],
+                max_w=self.input_dims[2],
+            ),
+            nn.Flatten(start_dim=2),
+        )
+
+        # Token embedding.
+        self.token_embedding = nn.Embedding(
+            num_embeddings=self.num_classes, embedding_dim=self.hidden_dim
+        )
+
+        # Positional encoding for decoder tokens.
+        self.token_pos_encoder = PositionalEncoding(
+            hidden_dim=self.hidden_dim, dropout_rate=self.dropout_rate
+        )
+        # Head
+        self.head = nn.Linear(
+            in_features=self.hidden_dim, out_features=self.num_classes
+        )
+
+        # Initalize weights for encoder.
+        self.init_weights()
+
+    def init_weights(self) -> None:
+        """Initalize weights for decoder network and head."""
+        bound = 0.1
+        self.token_embedding.weight.data.uniform_(-bound, bound)
+        self.head.bias.data.zero_()
+        self.head.weight.data.uniform_(-bound, bound)
+        # TODO: Initalize encoder?
+
+    def encode(self, x: Tensor) -> Tensor:
+        """Encodes an image into a latent feature vector.
+
+        Args:
+            x (Tensor): Image tensor.
+
+        Shape:
+            - x: :math: `(B, C, H, W)`
+            - z: :math: `(B, Sx, E)`
+
+            where Sx is the length of the flattened feature maps projected from
+            the encoder. E latent dimension for each pixel in the projected
+            feature maps.
+
+        Returns:
+            Tensor: A Latent embedding of the image.
+        """
+        z = self.encoder(x)
+        z = self.latent_encoder(z)
+
+        # Permute tensor from [B, E, Ho * Wo] to [B, Sx, E]
+        z = z.permute(0, 2, 1)
+        return z
+
+    def decode(self, z: Tensor, context: Tensor) -> Tensor:
+        """Decodes latent images embedding into word pieces.
+
+        Args:
+            z (Tensor): Latent images embedding.
+            context (Tensor): Word embeddings.
+
+        Shapes:
+            - z: :math: `(B, Sx, E)`
+            - context: :math: `(B, Sy)`
+            - out: :math: `(B, Sy, T)`
+
+            where Sy is the length of the output and T is the number of tokens.
+
+        Returns:
+            Tensor: Sequence of word piece embeddings.
+        """
+        context_mask = context != self.pad_index
+        context = self.token_embedding(context) * math.sqrt(self.hidden_dim)
+        context = self.token_pos_encoder(context)
+        out = self.decoder(x=context, context=z, mask=context_mask)
+        logits = self.head(out)
+        return logits
+
+    def forward(self, x: Tensor, context: Tensor) -> Tensor:
+        """Encodes images into word piece logtis.
+
+        Args:
+            x (Tensor): Input image(s).
+            context (Tensor): Target word embeddings.
+
+        Shapes:
+            - x: :math: `(B, C, H, W)`
+            - context: :math: `(B, Sy, T)`
+
+            where B is the batch size, C is the number of input channels, H is
+            the image height and W is the image width.
+
+        Returns:
+            Tensor: Sequence of logits.
+        """
+        z = self.encode(x)
+        logits = self.decode(z, context)
+        return logits
+
+    def predict(self, x: Tensor) -> Tensor:
+        """Predicts text in image.
+        
+        Args:
+            x (Tensor): Image(s) to extract text from.
+
+        Shapes:
+            - x: :math: `(B, H, W)`
+            - output: :math: `(B, S)`
+
+        Returns:
+            Tensor: A tensor of token indices of the predictions from the model.
+        """
+        bsz = x.shape[0]
+
+        # Encode image(s) to latent vectors.
+        z = self.encode(x)
+
+        # Create a placeholder matrix for storing outputs from the network
+        output = torch.ones((bsz, self.max_output_len), dtype=torch.long).to(x.device)
+        output[:, 0] = self.start_index
+
+        for i in range(1, self.max_output_len):
+            context = output[:, :i]  # (bsz, i)
+            logits = self.decode(z, context)  # (i, bsz, c)
+            tokens = torch.argmax(logits, dim=-1)  # (i, bsz)
+            output[:, i : i + 1] = tokens[-1:]
+
+            # Early stopping of prediction loop if token is end or padding token.
+            if (
+                output[:, i - 1] == self.end_index | output[: i - 1] == self.pad_index
+            ).all():
+                break
+
+        # Set all tokens after end token to pad token.
+        for i in range(1, self.max_output_len):
+            idx = (
+                output[:, i - 1] == self.end_index | output[:, i - 1] == self.pad_index
+            )
+            output[idx, i] = self.pad_index
+
+        return output