Fixed training script, able to train vqvae

8 files changed, 182 insertions, 310 deletions

diff --git a/text_recognizer/networks/__init__.py b/text_recognizer/networks/__init__.py
index 979149f..41fd43f 100644
--- a/text_recognizer/networks/__init__.py
+++ b/text_recognizer/networks/__init__.py
@@ -1,2 +1,2 @@
 """Network modules"""
-from .image_transformer import ImageTransformer
+from .vqvae import VQVAE
diff --git a/text_recognizer/networks/cnn_transformer.py b/text_recognizer/networks/cnn_transformer.py
index 9150b55..e23a15d 100644
--- a/text_recognizer/networks/cnn_transformer.py
+++ b/text_recognizer/networks/cnn_transformer.py
@@ -1,158 +1,165 @@
-"""A CNN-Transformer for image to text recognition."""
-from typing import Dict, Optional, Tuple
+"""A Transformer with a cnn backbone.
+
+The network encodes a image with a convolutional backbone to a latent representation,
+i.e. feature maps. A 2d positional encoding is applied to the feature maps for
+spatial information. The resulting feature are then set to a transformer decoder
+together with the target tokens.
+
+TODO: Local attention for lower layer in attention.
+
+"""
+import importlib
+import math
+from typing import Dict, Optional, Union, Sequence, Type
 
 from einops import rearrange
+from omegaconf import DictConfig, OmegaConf
 import torch
 from torch import nn
 from torch import Tensor
 
-from text_recognizer.networks.transformer import PositionalEncoding, Transformer
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.util import configure_backbone
+from text_recognizer.data.emnist import NUM_SPECIAL_TOKENS
+from text_recognizer.networks.transformer import (
+    Decoder,
+    DecoderLayer,
+    PositionalEncoding,
+    PositionalEncoding2D,
+    target_padding_mask,
+)
 
+NUM_WORD_PIECES = 1000
 
-class CNNTransformer(nn.Module):
-    """CNN+Transfomer for image to sequence prediction."""
 
+class CNNTransformer(nn.Module):
     def __init__(
         self,
-        num_encoder_layers: int,
-        num_decoder_layers: int,
-        hidden_dim: int,
-        vocab_size: int,
-        num_heads: int,
-        adaptive_pool_dim: Tuple,
-        expansion_dim: int,
-        dropout_rate: float,
-        trg_pad_index: int,
-        max_len: int,
-        backbone: str,
-        backbone_args: Optional[Dict] = None,
-        activation: str = "gelu",
-        pool_kernel: Optional[Tuple[int, int]] = None,
+        input_shape: Sequence[int],
+        output_shape: Sequence[int],
+        encoder: Union[DictConfig, Dict],
+        vocab_size: Optional[int] = None,
+        num_decoder_layers: int = 4,
+        hidden_dim: int = 256,
+        num_heads: int = 4,
+        expansion_dim: int = 1024,
+        dropout_rate: float = 0.1,
+        transformer_activation: str = "glu",
     ) -> None:
-        super().__init__()
-        self.trg_pad_index = trg_pad_index
-        self.vocab_size = vocab_size
-        self.backbone = configure_backbone(backbone, backbone_args)
-
-        if pool_kernel is not None:
-            self.max_pool = nn.MaxPool2d(pool_kernel, stride=2)
-        else:
-            self.max_pool = None
-
-        self.character_embedding = nn.Embedding(self.vocab_size, hidden_dim)
-
-        self.src_position_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim))
-        self.pos_dropout = nn.Dropout(p=dropout_rate)
-        self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
-
-        nn.init.normal_(self.character_embedding.weight, std=0.02)
-
-        self.adaptive_pool = (
-            nn.AdaptiveAvgPool2d((adaptive_pool_dim)) if adaptive_pool_dim else None
+        self.vocab_size = (
+            NUM_WORD_PIECES + NUM_SPECIAL_TOKENS if vocab_size is None else vocab_size
         )
+        self.hidden_dim = hidden_dim
+        self.max_output_length = output_shape[0]
 
-        self.transformer = Transformer(
-            num_encoder_layers,
-            num_decoder_layers,
-            hidden_dim,
-            num_heads,
-            expansion_dim,
-            dropout_rate,
-            activation,
+        # Image backbone
+        self.encoder = self._configure_encoder(encoder)
+        self.feature_map_encoding = PositionalEncoding2D(
+            hidden_dim=hidden_dim, max_h=input_shape[1], max_w=input_shape[2]
         )
 
-        self.head = nn.Sequential(
-            # nn.Linear(hidden_dim, hidden_dim * 2),
-            # activation_function(activation),
-            nn.Linear(hidden_dim, vocab_size),
-        )
+        # Target token embedding
+        self.trg_embedding = nn.Embedding(self.vocab_size, hidden_dim)
+        self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
 
-    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)
+        # Transformer decoder
+        self.decoder = Decoder(
+            decoder_layer=DecoderLayer(
+                hidden_dim=hidden_dim,
+                num_heads=num_heads,
+                expansion_dim=expansion_dim,
+                dropout_rate=dropout_rate,
+                activation=transformer_activation,
+            ),
+            num_layers=num_decoder_layers,
+            norm=nn.LayerNorm(hidden_dim),
         )
 
-    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
+        # Classification head
+        self.head = nn.Linear(hidden_dim, self.vocab_size)
 
-        Args:
-            src (Tensor): Input tensor.
+        # Initialize weights
+        self._init_weights()
 
-        Returns:
-            Tensor: A input src to the transformer.
+    def _init_weights(self) -> None:
+        """Initialize network weights."""
+        self.trg_embedding.weight.data.uniform_(-0.1, 0.1)
+        self.head.bias.data.zero_()
+        self.head.weight.data.uniform_(-0.1, 0.1)
 
-        """
-        # If batch dimension is missing, it needs to be added.
-        if len(src.shape) < 4:
-            src = src[(None,) * (4 - len(src.shape))]
-
-        src = self.backbone(src)
-
-        if self.max_pool is not None:
-            src = self.max_pool(src)
-
-        if self.adaptive_pool is not None and len(src.shape) == 4:
-            src = rearrange(src, "b c h w -> b w c h")
-            src = self.adaptive_pool(src)
-            src = src.squeeze(3)
-        elif len(src.shape) == 4:
-            src = rearrange(src, "b c h w -> b (h w) c")
+        nn.init.kaiming_normal_(
+            self.feature_map_encoding.weight.data,
+            a=0,
+            mode="fan_out",
+            nonlinearity="relu",
+        )
+        if self.feature_map_encoding.bias is not None:
+            _, fan_out = nn.init._calculate_fan_in_and_fan_out(
+                self.feature_map_encoding.weight.data
+            )
+            bound = 1 / math.sqrt(fan_out)
+            nn.init.normal_(self.feature_map_encoding.bias, -bound, bound)
+
+    @staticmethod
+    def _configure_encoder(encoder: Union[DictConfig, Dict]) -> Type[nn.Module]:
+        encoder = OmegaConf.create(encoder)
+        network_module = importlib.import_module("text_recognizer.networks")
+        encoder_class = getattr(network_module, encoder.type)
+        return encoder_class(**encoder.args)
+
+    def encode(self, image: Tensor) -> Tensor:
+        """Extracts image features with backbone.
 
-        b, t, _ = src.shape
+        Args:
+            image (Tensor): Image(s) of handwritten text.
 
-        src += self.src_position_embedding[:, :t]
-        src = self.pos_dropout(src)
+        Retuns:
+            Tensor: Image features.
 
-        return src
+        Shapes:
+            - image: :math: `(B, C, H, W)`
+            - latent: :math: `(B, T, C)`
 
-    def target_embedding(self, trg: Tensor) -> Tuple[Tensor, Tensor]:
-        """Encodes target tensor with embedding and postion.
+        """
+        # Extract image features.
+        image_features = self.encoder(image)
 
-        Args:
-            trg (Tensor): Target tensor.
+        # Add 2d encoding to the feature maps.
+        image_features = self.feature_map_encoding(image_features)
 
-        Returns:
-            Tuple[Tensor, Tensor]: Encoded target tensor and target mask.
+        # Collapse features maps height and width.
+        image_features = rearrange(image_features, "b c h w -> b (h w) c")
+        return image_features
 
-        """
-        trg = self.character_embedding(trg.long())
+    def decode(self, memory: Tensor, trg: Tensor) -> Tensor:
+        """Decodes image features with transformer decoder."""
+        trg_mask = target_padding_mask(trg=trg, pad_index=self.pad_index)
+        trg = self.trg_embedding(trg) * math.sqrt(self.hidden_dim)
         trg = self.trg_position_encoding(trg)
-        return trg
-
-    def decode_image_features(
-        self, image_features: 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(image_features, trg, trg_mask=trg_mask)
-
+        out = self.decoder(trg=trg, memory=memory, trg_mask=trg_mask, memory_mask=None)
         logits = self.head(out)
         return logits
 
-    def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
-        """Forward pass with CNN transfomer."""
-        image_features = self.extract_image_features(x)
-        logits = self.decode_image_features(image_features, trg)
-        return logits
+    def predict(self, image: Tensor) -> Tensor:
+        """Transcribes text in image(s)."""
+        bsz = image.shape[0]
+        image_features = self.encode(image)
+
+        output_tokens = (
+            (torch.ones((bsz, self.max_output_length)) * self.pad_index)
+            .type_as(image)
+            .long()
+        )
+        output_tokens[:, 0] = self.start_index
+        for i in range(1, self.max_output_length):
+            trg = output_tokens[:, :i]
+            output = self.decode(image_features, trg)
+            output = torch.argmax(output, dim=-1)
+            output_tokens[:, i] = output[-1:]
+
+        # Set all tokens after end token to be padding.
+        for i in range(1, self.max_output_length):
+            indices = output_tokens[:, i - 1] == self.end_index | (
+                output_tokens[:, i - 1] == self.pad_index
+            )
+            output_tokens[indices, i] = self.pad_index
+
+        return output_tokens
diff --git a/text_recognizer/networks/image_transformer.py b/text_recognizer/networks/image_transformer.py
deleted file mode 100644
index a6aaca4..0000000
--- a/text_recognizer/networks/image_transformer.py
+++ /dev/null
@@ -1,165 +0,0 @@
-"""A Transformer with a cnn backbone.
-
-The network encodes a image with a convolutional backbone to a latent representation,
-i.e. feature maps. A 2d positional encoding is applied to the feature maps for
-spatial information. The resulting feature are then set to a transformer decoder
-together with the target tokens.
-
-TODO: Local attention for lower layer in attention.
-
-"""
-import importlib
-import math
-from typing import Dict, Optional, Union, Sequence, Type
-
-from einops import rearrange
-from omegaconf import DictConfig, OmegaConf
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.data.emnist import NUM_SPECIAL_TOKENS
-from text_recognizer.networks.transformer import (
-    Decoder,
-    DecoderLayer,
-    PositionalEncoding,
-    PositionalEncoding2D,
-    target_padding_mask,
-)
-
-NUM_WORD_PIECES = 1000
-
-
-class ImageTransformer(nn.Module):
-    def __init__(
-        self,
-        input_shape: Sequence[int],
-        output_shape: Sequence[int],
-        encoder: Union[DictConfig, Dict],
-        vocab_size: Optional[int] = None,
-        num_decoder_layers: int = 4,
-        hidden_dim: int = 256,
-        num_heads: int = 4,
-        expansion_dim: int = 1024,
-        dropout_rate: float = 0.1,
-        transformer_activation: str = "glu",
-    ) -> None:
-        self.vocab_size = (
-            NUM_WORD_PIECES + NUM_SPECIAL_TOKENS if vocab_size is None else vocab_size
-        )
-        self.hidden_dim = hidden_dim
-        self.max_output_length = output_shape[0]
-
-        # Image backbone
-        self.encoder = self._configure_encoder(encoder)
-        self.feature_map_encoding = PositionalEncoding2D(
-            hidden_dim=hidden_dim, max_h=input_shape[1], max_w=input_shape[2]
-        )
-
-        # Target token embedding
-        self.trg_embedding = nn.Embedding(self.vocab_size, hidden_dim)
-        self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
-
-        # Transformer decoder
-        self.decoder = Decoder(
-            decoder_layer=DecoderLayer(
-                hidden_dim=hidden_dim,
-                num_heads=num_heads,
-                expansion_dim=expansion_dim,
-                dropout_rate=dropout_rate,
-                activation=transformer_activation,
-            ),
-            num_layers=num_decoder_layers,
-            norm=nn.LayerNorm(hidden_dim),
-        )
-
-        # Classification head
-        self.head = nn.Linear(hidden_dim, self.vocab_size)
-
-        # Initialize weights
-        self._init_weights()
-
-    def _init_weights(self) -> None:
-        """Initialize network weights."""
-        self.trg_embedding.weight.data.uniform_(-0.1, 0.1)
-        self.head.bias.data.zero_()
-        self.head.weight.data.uniform_(-0.1, 0.1)
-
-        nn.init.kaiming_normal_(
-            self.feature_map_encoding.weight.data,
-            a=0,
-            mode="fan_out",
-            nonlinearity="relu",
-        )
-        if self.feature_map_encoding.bias is not None:
-            _, fan_out = nn.init._calculate_fan_in_and_fan_out(
-                self.feature_map_encoding.weight.data
-            )
-            bound = 1 / math.sqrt(fan_out)
-            nn.init.normal_(self.feature_map_encoding.bias, -bound, bound)
-
-    @staticmethod
-    def _configure_encoder(encoder: Union[DictConfig, Dict]) -> Type[nn.Module]:
-        encoder = OmegaConf.create(encoder)
-        network_module = importlib.import_module("text_recognizer.networks")
-        encoder_class = getattr(network_module, encoder.type)
-        return encoder_class(**encoder.args)
-
-    def encode(self, image: Tensor) -> Tensor:
-        """Extracts image features with backbone.
-
-        Args:
-            image (Tensor): Image(s) of handwritten text.
-
-        Retuns:
-            Tensor: Image features.
-
-        Shapes:
-            - image: :math: `(B, C, H, W)`
-            - latent: :math: `(B, T, C)`
-
-        """
-        # Extract image features.
-        image_features = self.encoder(image)
-
-        # Add 2d encoding to the feature maps.
-        image_features = self.feature_map_encoding(image_features)
-
-        # Collapse features maps height and width.
-        image_features = rearrange(image_features, "b c h w -> b (h w) c")
-        return image_features
-
-    def decode(self, memory: Tensor, trg: Tensor) -> Tensor:
-        """Decodes image features with transformer decoder."""
-        trg_mask = target_padding_mask(trg=trg, pad_index=self.pad_index)
-        trg = self.trg_embedding(trg) * math.sqrt(self.hidden_dim)
-        trg = self.trg_position_encoding(trg)
-        out = self.decoder(trg=trg, memory=memory, trg_mask=trg_mask, memory_mask=None)
-        logits = self.head(out)
-        return logits
-
-    def predict(self, image: Tensor) -> Tensor:
-        """Transcribes text in image(s)."""
-        bsz = image.shape[0]
-        image_features = self.encode(image)
-
-        output_tokens = (
-            (torch.ones((bsz, self.max_output_length)) * self.pad_index)
-            .type_as(image)
-            .long()
-        )
-        output_tokens[:, 0] = self.start_index
-        for i in range(1, self.max_output_length):
-            trg = output_tokens[:, :i]
-            output = self.decode(image_features, trg)
-            output = torch.argmax(output, dim=-1)
-            output_tokens[:, i] = output[-1:]
-
-        # Set all tokens after end token to be padding.
-        for i in range(1, self.max_output_length):
-            indices = output_tokens[:, i - 1] == self.end_index | (
-                output_tokens[:, i - 1] == self.pad_index
-            )
-            output_tokens[indices, i] = self.pad_index
-
-        return output_tokens
diff --git a/text_recognizer/networks/residual_network.py b/text_recognizer/networks/residual_network.py
index c33f419..da7553d 100644
--- a/text_recognizer/networks/residual_network.py
+++ b/text_recognizer/networks/residual_network.py
@@ -20,7 +20,11 @@ class Conv2dAuto(nn.Conv2d):
 
 def conv_bn(in_channels: int, out_channels: int, *args, **kwargs) -> nn.Sequential:
     """3x3 convolution with batch norm."""
-    conv3x3 = partial(Conv2dAuto, kernel_size=3, bias=False,)
+    conv3x3 = partial(
+        Conv2dAuto,
+        kernel_size=3,
+        bias=False,
+    )
     return nn.Sequential(
         conv3x3(in_channels, out_channels, *args, **kwargs),
         nn.BatchNorm2d(out_channels),
diff --git a/text_recognizer/networks/transducer/transducer.py b/text_recognizer/networks/transducer/transducer.py
index d7e3d08..b10f93a 100644
--- a/text_recognizer/networks/transducer/transducer.py
+++ b/text_recognizer/networks/transducer/transducer.py
@@ -392,7 +392,12 @@ def load_transducer_loss(
         transitions = gtn.load(str(processed_path / transitions))
 
     preprocessor = Preprocessor(
-        data_dir, num_features, tokens_path, lexicon_path, use_words, prepend_wordsep,
+        data_dir,
+        num_features,
+        tokens_path,
+        lexicon_path,
+        use_words,
+        prepend_wordsep,
     )
 
     num_tokens = preprocessor.num_tokens
diff --git a/text_recognizer/networks/vqvae/decoder.py b/text_recognizer/networks/vqvae/decoder.py
index 8847aba..93a1e43 100644
--- a/text_recognizer/networks/vqvae/decoder.py
+++ b/text_recognizer/networks/vqvae/decoder.py
@@ -44,7 +44,12 @@ class Decoder(nn.Module):
 
         # Configure encoder.
         self.decoder = self._build_decoder(
-            channels, kernel_sizes, strides, num_residual_layers, activation, dropout,
+            channels,
+            kernel_sizes,
+            strides,
+            num_residual_layers,
+            activation,
+            dropout,
         )
 
     def _build_decompression_block(
@@ -72,8 +77,10 @@ class Decoder(nn.Module):
                 )
             )
 
-            if i < len(self.upsampling):
-                modules.append(nn.Upsample(size=self.upsampling[i]),)
+            if self.upsampling and i < len(self.upsampling):
+                modules.append(
+                    nn.Upsample(size=self.upsampling[i]),
+                )
 
             if dropout is not None:
                 modules.append(dropout)
@@ -102,7 +109,12 @@ class Decoder(nn.Module):
     ) -> nn.Sequential:
 
         self.res_block.append(
-            nn.Conv2d(self.embedding_dim, channels[0], kernel_size=1, stride=1,)
+            nn.Conv2d(
+                self.embedding_dim,
+                channels[0],
+                kernel_size=1,
+                stride=1,
+            )
         )
 
         # Bottleneck module.
diff --git a/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py
index d3adac5..b0cceed 100644
--- a/text_recognizer/networks/vqvae/encoder.py
+++ b/text_recognizer/networks/vqvae/encoder.py
@@ -1,5 +1,5 @@
 """CNN encoder for the VQ-VAE."""
-from typing import List, Optional, Tuple, Type
+from typing import Sequence, Optional, Tuple, Type
 
 import torch
 from torch import nn
@@ -11,7 +11,10 @@ from text_recognizer.networks.vqvae.vector_quantizer import VectorQuantizer
 
 class _ResidualBlock(nn.Module):
     def __init__(
-        self, in_channels: int, out_channels: int, dropout: Optional[Type[nn.Module]],
+        self,
+        in_channels: int,
+        out_channels: int,
+        dropout: Optional[Type[nn.Module]],
     ) -> None:
         super().__init__()
         self.block = [
@@ -36,9 +39,9 @@ class Encoder(nn.Module):
     def __init__(
         self,
         in_channels: int,
-        channels: List[int],
-        kernel_sizes: List[int],
-        strides: List[int],
+        channels: Sequence[int],
+        kernel_sizes: Sequence[int],
+        strides: Sequence[int],
         num_residual_layers: int,
         embedding_dim: int,
         num_embeddings: int,
@@ -77,12 +80,12 @@ class Encoder(nn.Module):
             self.num_embeddings, self.embedding_dim, self.beta
         )
 
+    @staticmethod
     def _build_compression_block(
-        self,
         in_channels: int,
         channels: int,
-        kernel_sizes: List[int],
-        strides: List[int],
+        kernel_sizes: Sequence[int],
+        strides: Sequence[int],
         activation: Type[nn.Module],
         dropout: Optional[Type[nn.Module]],
     ) -> nn.ModuleList:
@@ -109,8 +112,8 @@ class Encoder(nn.Module):
         self,
         in_channels: int,
         channels: int,
-        kernel_sizes: List[int],
-        strides: List[int],
+        kernel_sizes: Sequence[int],
+        strides: Sequence[int],
         num_residual_layers: int,
         activation: Type[nn.Module],
         dropout: Optional[Type[nn.Module]],
@@ -135,7 +138,12 @@ class Encoder(nn.Module):
         )
 
         encoder.append(
-            nn.Conv2d(channels[-1], self.embedding_dim, kernel_size=1, stride=1,)
+            nn.Conv2d(
+                channels[-1],
+                self.embedding_dim,
+                kernel_size=1,
+                stride=1,
+            )
         )
 
         return nn.Sequential(*encoder)
diff --git a/text_recognizer/networks/vqvae/vqvae.py b/text_recognizer/networks/vqvae/vqvae.py
index 50448b4..1f08e5e 100644
--- a/text_recognizer/networks/vqvae/vqvae.py
+++ b/text_recognizer/networks/vqvae/vqvae.py
@@ -1,8 +1,7 @@
 """The VQ-VAE."""
 
-from typing import List, Optional, Tuple, Type
+from typing import Any, Dict, List, Optional, Tuple
 
-import torch
 from torch import nn
 from torch import Tensor
 
@@ -25,6 +24,8 @@ class VQVAE(nn.Module):
         beta: float = 0.25,
         activation: str = "leaky_relu",
         dropout_rate: float = 0.0,
+        *args: Any,
+        **kwargs: Dict,
     ) -> None:
         super().__init__()