Training working, multiple bug fixes

1 files changed, 19 insertions, 23 deletions

diff --git a/text_recognizer/networks/conv_transformer.py b/text_recognizer/networks/conv_transformer.py
index 09cc654..f3ba49d 100644
--- a/text_recognizer/networks/conv_transformer.py
+++ b/text_recognizer/networks/conv_transformer.py
@@ -2,7 +2,6 @@
 import math
 from typing import Tuple
 
-import attr
 from torch import nn, Tensor
 
 from text_recognizer.networks.encoders.efficientnet import EfficientNet
@@ -13,32 +12,28 @@ from text_recognizer.networks.transformer.positional_encodings import (
 )
 
 
-@attr.s(eq=False)
 class ConvTransformer(nn.Module):
     """Convolutional encoder and transformer decoder network."""
 
-    def __attrs_pre_init__(self) -> None:
+    def __init__(
+        self,
+        input_dims: Tuple[int, int, int],
+        hidden_dim: int,
+        dropout_rate: float,
+        num_classes: int,
+        pad_index: Tensor,
+        encoder: EfficientNet,
+        decoder: Decoder,
+    ) -> None:
         super().__init__()
+        self.input_dims = input_dims
+        self.hidden_dim = hidden_dim
+        self.dropout_rate = dropout_rate
+        self.num_classes = num_classes
+        self.pad_index = pad_index
+        self.encoder = encoder
+        self.decoder = decoder
 
-    # 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()
-    pad_index: Tensor = attr.ib()
-
-    # Modules.
-    encoder: EfficientNet = attr.ib()
-    decoder: Decoder = 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."""
         # Latent projector for down sampling number of filters and 2d
         # positional encoding.
         self.latent_encoder = nn.Sequential(
@@ -126,7 +121,8 @@ class ConvTransformer(nn.Module):
         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)
+        logits = self.head(out)  # [B, Sy, T]
+        logits = logits.permute(0, 2, 1)  # [B, T, Sy]
         return logits
 
     def forward(self, x: Tensor, context: Tensor) -> Tensor: