Working cnn transformer.

1 files changed, 0 insertions, 73 deletions

diff --git a/src/text_recognizer/networks/cnn_transformer_encoder.py b/src/text_recognizer/networks/cnn_transformer_encoder.py
deleted file mode 100644
index 93626bf..0000000
--- a/src/text_recognizer/networks/cnn_transformer_encoder.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Network with a CNN backend and a transformer encoder head."""
-from typing import Dict
-
-from einops import rearrange
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.transformer import PositionalEncoding
-from text_recognizer.networks.util import configure_backbone
-
-
-class CNNTransformerEncoder(nn.Module):
-    """A CNN backbone with Transformer Encoder frontend for sequence prediction."""
-
-    def __init__(
-        self,
-        backbone: str,
-        backbone_args: Dict,
-        mlp_dim: int,
-        d_model: int,
-        nhead: int = 8,
-        dropout_rate: float = 0.1,
-        activation: str = "relu",
-        num_layers: int = 6,
-        num_classes: int = 80,
-        num_channels: int = 256,
-        max_len: int = 97,
-    ) -> None:
-        super().__init__()
-        self.d_model = d_model
-        self.nhead = nhead
-        self.dropout_rate = dropout_rate
-        self.activation = activation
-        self.num_layers = num_layers
-
-        self.backbone = configure_backbone(backbone, backbone_args)
-        self.position_encoding = PositionalEncoding(d_model, dropout_rate)
-        self.encoder = self._configure_encoder()
-
-        self.conv = nn.Conv2d(num_channels, max_len, kernel_size=1)
-
-        self.mlp = nn.Linear(mlp_dim, d_model)
-
-        self.head = nn.Linear(d_model, num_classes)
-
-    def _configure_encoder(self) -> nn.TransformerEncoder:
-        encoder_layer = nn.TransformerEncoderLayer(
-            d_model=self.d_model,
-            nhead=self.nhead,
-            dropout=self.dropout_rate,
-            activation=self.activation,
-        )
-        norm = nn.LayerNorm(self.d_model)
-        return nn.TransformerEncoder(
-            encoder_layer=encoder_layer, num_layers=self.num_layers, norm=norm
-        )
-
-    def forward(self, x: Tensor, targets: Tensor = None) -> Tensor:
-        """Forward pass through the network."""
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-
-        x = self.conv(self.backbone(x))
-        x = rearrange(x, "b c h w -> b c (h w)")
-        x = self.mlp(x)
-        x = self.position_encoding(x)
-        x = rearrange(x, "b c h-> c b h")
-        x = self.encoder(x)
-        x = rearrange(x, "c b h-> b c h")
-        logits = self.head(x)
-
-        return logits