Inital commit for refactoring to lightning

4 files changed, 0 insertions, 392 deletions

diff --git a/src/text_recognizer/networks/transformer/__init__.py b/src/text_recognizer/networks/transformer/__init__.py
deleted file mode 100644
index 9febc88..0000000
--- a/src/text_recognizer/networks/transformer/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-"""Transformer modules."""
-from .positional_encoding import PositionalEncoding
-from .transformer import Decoder, Encoder, EncoderLayer, Transformer
diff --git a/src/text_recognizer/networks/transformer/attention.py b/src/text_recognizer/networks/transformer/attention.py
deleted file mode 100644
index cce1ecc..0000000
--- a/src/text_recognizer/networks/transformer/attention.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""Implementes the attention module for the transformer."""
-from typing import Optional, Tuple
-
-from einops import rearrange
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-
-
-class MultiHeadAttention(nn.Module):
-    """Implementation of multihead attention."""
-
-    def __init__(
-        self, hidden_dim: int, num_heads: int = 8, dropout_rate: float = 0.0
-    ) -> None:
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.num_heads = num_heads
-        self.fc_q = nn.Linear(
-            in_features=hidden_dim, out_features=hidden_dim, bias=False
-        )
-        self.fc_k = nn.Linear(
-            in_features=hidden_dim, out_features=hidden_dim, bias=False
-        )
-        self.fc_v = nn.Linear(
-            in_features=hidden_dim, out_features=hidden_dim, bias=False
-        )
-        self.fc_out = nn.Linear(in_features=hidden_dim, out_features=hidden_dim)
-
-        self._init_weights()
-
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-    def _init_weights(self) -> None:
-        nn.init.normal_(
-            self.fc_q.weight,
-            mean=0,
-            std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)),
-        )
-        nn.init.normal_(
-            self.fc_k.weight,
-            mean=0,
-            std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)),
-        )
-        nn.init.normal_(
-            self.fc_v.weight,
-            mean=0,
-            std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)),
-        )
-        nn.init.xavier_normal_(self.fc_out.weight)
-
-    def scaled_dot_product_attention(
-        self, query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None
-    ) -> Tensor:
-        """Calculates the scaled dot product attention."""
-
-        # Compute the energy.
-        energy = torch.einsum("bhlk,bhtk->bhlt", [query, key]) / np.sqrt(
-            query.shape[-1]
-        )
-
-        # If we have a mask for padding some inputs.
-        if mask is not None:
-            energy = energy.masked_fill(mask == 0, -np.inf)
-
-        # Compute the attention from the energy.
-        attention = torch.softmax(energy, dim=3)
-
-        out = torch.einsum("bhlt,bhtv->bhlv", [attention, value])
-        out = rearrange(out, "b head l v -> b l (head v)")
-        return out, attention
-
-    def forward(
-        self, query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None
-    ) -> Tuple[Tensor, Tensor]:
-        """Forward pass for computing the multihead attention."""
-        # Get the query, key, and value tensor.
-        query = rearrange(
-            self.fc_q(query), "b l (head k) -> b head l k", head=self.num_heads
-        )
-        key = rearrange(
-            self.fc_k(key), "b t (head k) -> b head t k", head=self.num_heads
-        )
-        value = rearrange(
-            self.fc_v(value), "b t (head v) -> b head t v", head=self.num_heads
-        )
-
-        out, attention = self.scaled_dot_product_attention(query, key, value, mask)
-
-        out = self.fc_out(out)
-        out = self.dropout(out)
-        return out, attention
diff --git a/src/text_recognizer/networks/transformer/positional_encoding.py b/src/text_recognizer/networks/transformer/positional_encoding.py
deleted file mode 100644
index 1ba5537..0000000
--- a/src/text_recognizer/networks/transformer/positional_encoding.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""A positional encoding for the image features, as the transformer has no notation of the order of the sequence."""
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-
-
-class PositionalEncoding(nn.Module):
-    """Encodes a sense of distance or time for transformer networks."""
-
-    def __init__(
-        self, hidden_dim: int, dropout_rate: float, max_len: int = 1000
-    ) -> None:
-        super().__init__()
-        self.dropout = nn.Dropout(p=dropout_rate)
-        self.max_len = max_len
-
-        pe = torch.zeros(max_len, hidden_dim)
-        position = torch.arange(0, max_len).unsqueeze(1)
-        div_term = torch.exp(
-            torch.arange(0, hidden_dim, 2) * -(np.log(10000.0) / hidden_dim)
-        )
-
-        pe[:, 0::2] = torch.sin(position * div_term)
-        pe[:, 1::2] = torch.cos(position * div_term)
-        pe = pe.unsqueeze(0)
-        self.register_buffer("pe", pe)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Encodes the tensor with a postional embedding."""
-        x = x + self.pe[:, : x.shape[1]]
-        return self.dropout(x)
diff --git a/src/text_recognizer/networks/transformer/transformer.py b/src/text_recognizer/networks/transformer/transformer.py
deleted file mode 100644
index dd180c4..0000000
--- a/src/text_recognizer/networks/transformer/transformer.py
+++ /dev/null
@@ -1,264 +0,0 @@
-"""Transfomer module."""
-import copy
-from typing import Dict, Optional, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-import torch.nn.functional as F
-
-from text_recognizer.networks.transformer.attention import MultiHeadAttention
-from text_recognizer.networks.util import activation_function
-
-
-class GEGLU(nn.Module):
-    """GLU activation for improving feedforward activations."""
-
-    def __init__(self, dim_in: int, dim_out: int) -> None:
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward propagation."""
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-def _get_clones(module: Type[nn.Module], num_layers: int) -> nn.ModuleList:
-    return nn.ModuleList([copy.deepcopy(module) for _ in range(num_layers)])
-
-
-class _IntraLayerConnection(nn.Module):
-    """Preforms the residual connection inside the transfomer blocks and applies layernorm."""
-
-    def __init__(self, dropout_rate: float, hidden_dim: int) -> None:
-        super().__init__()
-        self.norm = nn.LayerNorm(normalized_shape=hidden_dim)
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-    def forward(self, src: Tensor, residual: Tensor) -> Tensor:
-        return self.norm(self.dropout(src) + residual)
-
-
-class _ConvolutionalLayer(nn.Module):
-    def __init__(
-        self,
-        hidden_dim: int,
-        expansion_dim: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-
-        in_projection = (
-            nn.Sequential(
-                nn.Linear(hidden_dim, expansion_dim), activation_function(activation)
-            )
-            if activation != "glu"
-            else GEGLU(hidden_dim, expansion_dim)
-        )
-
-        self.layer = nn.Sequential(
-            in_projection,
-            nn.Dropout(p=dropout_rate),
-            nn.Linear(in_features=expansion_dim, out_features=hidden_dim),
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.layer(x)
-
-
-class EncoderLayer(nn.Module):
-    """Transfomer encoding layer."""
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        num_heads: int,
-        expansion_dim: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
-        self.cnn = _ConvolutionalLayer(
-            hidden_dim, expansion_dim, dropout_rate, activation
-        )
-        self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
-        self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
-
-    def forward(self, src: Tensor, mask: Optional[Tensor] = None) -> Tensor:
-        """Forward pass through the encoder."""
-        # First block.
-        # Multi head attention.
-        out, _ = self.self_attention(src, src, src, mask)
-
-        # Add & norm.
-        out = self.block1(out, src)
-
-        # Second block.
-        # Apply 1D-convolution.
-        cnn_out = self.cnn(out)
-
-        # Add & norm.
-        out = self.block2(cnn_out, out)
-
-        return out
-
-
-class Encoder(nn.Module):
-    """Transfomer encoder module."""
-
-    def __init__(
-        self,
-        num_layers: int,
-        encoder_layer: Type[nn.Module],
-        norm: Optional[Type[nn.Module]] = None,
-    ) -> None:
-        super().__init__()
-        self.layers = _get_clones(encoder_layer, num_layers)
-        self.norm = norm
-
-    def forward(self, src: Tensor, src_mask: Optional[Tensor] = None) -> Tensor:
-        """Forward pass through all encoder layers."""
-        for layer in self.layers:
-            src = layer(src, src_mask)
-
-        if self.norm is not None:
-            src = self.norm(src)
-
-        return src
-
-
-class DecoderLayer(nn.Module):
-    """Transfomer decoder layer."""
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        num_heads: int,
-        expansion_dim: int,
-        dropout_rate: float = 0.0,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
-        self.multihead_attention = MultiHeadAttention(
-            hidden_dim, num_heads, dropout_rate
-        )
-        self.cnn = _ConvolutionalLayer(
-            hidden_dim, expansion_dim, dropout_rate, activation
-        )
-        self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
-        self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
-        self.block3 = _IntraLayerConnection(dropout_rate, hidden_dim)
-
-    def forward(
-        self,
-        trg: Tensor,
-        memory: Tensor,
-        trg_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass of the layer."""
-        out, _ = self.self_attention(trg, trg, trg, trg_mask)
-        trg = self.block1(out, trg)
-
-        out, _ = self.multihead_attention(trg, memory, memory, memory_mask)
-        trg = self.block2(out, trg)
-
-        out = self.cnn(trg)
-        out = self.block3(out, trg)
-
-        return out
-
-
-class Decoder(nn.Module):
-    """Transfomer decoder module."""
-
-    def __init__(
-        self,
-        decoder_layer: Type[nn.Module],
-        num_layers: int,
-        norm: Optional[Type[nn.Module]] = None,
-    ) -> None:
-        super().__init__()
-        self.layers = _get_clones(decoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-
-    def forward(
-        self,
-        trg: Tensor,
-        memory: Tensor,
-        trg_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass through the decoder."""
-        for layer in self.layers:
-            trg = layer(trg, memory, trg_mask, memory_mask)
-
-        if self.norm is not None:
-            trg = self.norm(trg)
-
-        return trg
-
-
-class Transformer(nn.Module):
-    """Transformer network."""
-
-    def __init__(
-        self,
-        num_encoder_layers: int,
-        num_decoder_layers: int,
-        hidden_dim: int,
-        num_heads: int,
-        expansion_dim: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-
-        # Configure encoder.
-        encoder_norm = nn.LayerNorm(hidden_dim)
-        encoder_layer = EncoderLayer(
-            hidden_dim, num_heads, expansion_dim, dropout_rate, activation
-        )
-        self.encoder = Encoder(num_encoder_layers, encoder_layer, encoder_norm)
-
-        # Configure decoder.
-        decoder_norm = nn.LayerNorm(hidden_dim)
-        decoder_layer = DecoderLayer(
-            hidden_dim, num_heads, expansion_dim, dropout_rate, activation
-        )
-        self.decoder = Decoder(decoder_layer, num_decoder_layers, decoder_norm)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self) -> None:
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-
-    def forward(
-        self,
-        src: Tensor,
-        trg: Tensor,
-        src_mask: Optional[Tensor] = None,
-        trg_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass through the transformer."""
-        if src.shape[0] != trg.shape[0]:
-            print(trg.shape)
-            raise RuntimeError("The batch size of the src and trg must be the same.")
-        if src.shape[2] != trg.shape[2]:
-            raise RuntimeError(
-                "The number of features for the src and trg must be the same."
-            )
-
-        memory = self.encoder(src, src_mask)
-        output = self.decoder(trg, memory, trg_mask, memory_mask)
-        return output