Minor bug fixes etc.

1 files changed, 49 insertions, 14 deletions

diff --git a/src/text_recognizer/networks/line_lstm_ctc.py b/src/text_recognizer/networks/line_lstm_ctc.py
index 5c57479..9009f94 100644
--- a/src/text_recognizer/networks/line_lstm_ctc.py
+++ b/src/text_recognizer/networks/line_lstm_ctc.py
@@ -1,9 +1,11 @@
 """LSTM with CTC for handwritten text recognition within a line."""
 import importlib
+from pathlib import Path
 from typing import Callable, Dict, List, Optional, Tuple, Type, Union
 
 from einops import rearrange, reduce
 from einops.layers.torch import Rearrange, Reduce
+from loguru import logger
 import torch
 from torch import nn
 from torch import Tensor
@@ -14,40 +16,72 @@ class LineRecurrentNetwork(nn.Module):
 
     def __init__(
         self,
-        encoder: str,
-        encoder_args: Dict = None,
+        backbone: str,
+        backbone_args: Dict = None,
         flatten: bool = True,
         input_size: int = 128,
         hidden_size: int = 128,
+        bidirectional: bool = False,
         num_layers: int = 1,
         num_classes: int = 80,
         patch_size: Tuple[int, int] = (28, 28),
         stride: Tuple[int, int] = (1, 14),
     ) -> None:
         super().__init__()
-        self.encoder_args = encoder_args or {}
+        self.backbone_args = backbone_args or {}
         self.patch_size = patch_size
         self.stride = stride
         self.sliding_window = self._configure_sliding_window()
         self.input_size = input_size
         self.hidden_size = hidden_size
-        self.encoder = self._configure_encoder(encoder)
+        self.backbone = self._configure_backbone(backbone)
+        self.bidirectional = bidirectional
         self.flatten = flatten
-        self.fc = nn.Linear(in_features=self.input_size, out_features=self.hidden_size)
+
+        if self.flatten:
+            self.fc = nn.Linear(
+                in_features=self.input_size, out_features=self.hidden_size
+            )
+
         self.rnn = nn.LSTM(
             input_size=self.hidden_size,
             hidden_size=self.hidden_size,
+            bidirectional=bidirectional,
             num_layers=num_layers,
         )
+
+        decoder_size = self.hidden_size * 2 if self.bidirectional else self.hidden_size
+
         self.decoder = nn.Sequential(
-            nn.Linear(in_features=self.hidden_size, out_features=num_classes),
+            nn.Linear(in_features=decoder_size, out_features=num_classes),
             nn.LogSoftmax(dim=2),
         )
 
-    def _configure_encoder(self, encoder: str) -> Type[nn.Module]:
+    def _configure_backbone(self, backbone: str) -> Type[nn.Module]:
         network_module = importlib.import_module("text_recognizer.networks")
-        encoder_ = getattr(network_module, encoder)
-        return encoder_(**self.encoder_args)
+        backbone_ = getattr(network_module, backbone)
+
+        if "pretrained" in self.backbone_args:
+            logger.info("Loading pretrained backbone.")
+            checkpoint_file = Path(__file__).resolve().parents[
+                2
+            ] / self.backbone_args.pop("pretrained")
+
+            # Loading state directory.
+            state_dict = torch.load(checkpoint_file)
+            network_args = state_dict["network_args"]
+            weights = state_dict["model_state"]
+
+            # Initializes the network with trained weights.
+            backbone = backbone_(**network_args)
+            backbone.load_state_dict(weights)
+            if "freeze" in self.backbone_args and self.backbone_args["freeze"] is True:
+                for params in backbone.parameters():
+                    params.requires_grad = False
+
+            return backbone
+        else:
+            return backbone_(**self.backbone_args)
 
     def _configure_sliding_window(self) -> nn.Sequential:
         return nn.Sequential(
@@ -69,13 +103,14 @@ class LineRecurrentNetwork(nn.Module):
         # Rearrange from a sequence of patches for feedforward network.
         b, t = x.shape[:2]
         x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t)
-        x = self.encoder(x)
+        x = self.backbone(x)
 
         # Avgerage pooling.
-        x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t) if self.flatten else x
-
-        # Linear layer between CNN and RNN
-        x = self.fc(x)
+        x = (
+            self.fc(reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t))
+            if self.flatten
+            else rearrange(x, "(b t) h -> t b h", b=b, t=t)
+        )
 
         # Sequence predictions.
         x, _ = self.rnn(x)