Implemented training script with hydra

1 files changed, 260 insertions, 260 deletions

diff --git a/text_recognizer/networks/transformer/transformer.py b/text_recognizer/networks/transformer/transformer.py
index 5ac2787..d49c85a 100644
--- a/text_recognizer/networks/transformer/transformer.py
+++ b/text_recognizer/networks/transformer/transformer.py
@@ -1,260 +1,260 @@
-"""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 FeedForward(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.mlp = FeedForward(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.
-        mlp_out = self.mlp(out)
-
-        # Add & norm.
-        out = self.block2(mlp_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.mlp = FeedForward(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.mlp(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
+# """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 FeedForward(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.mlp = FeedForward(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.
+#         mlp_out = self.mlp(out)
+#
+#         # Add & norm.
+#         out = self.block2(mlp_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.mlp = FeedForward(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.mlp(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