new updates

1 files changed, 108 insertions, 0 deletions

diff --git a/src/text_recognizer/networks/crnn.py b/src/text_recognizer/networks/crnn.py
new file mode 100644
index 0000000..9747429
--- /dev/null
+++ b/src/text_recognizer/networks/crnn.py
@@ -0,0 +1,108 @@
+"""LSTM with CTC for handwritten text recognition within a line."""
+from typing import Dict, Tuple
+
+from einops import rearrange, reduce
+from einops.layers.torch import Rearrange
+from loguru import logger
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.util import configure_backbone
+
+
+class ConvolutionalRecurrentNetwork(nn.Module):
+    """Network that takes a image of a text line and predicts tokens that are in the image."""
+
+    def __init__(
+        self,
+        backbone: str,
+        backbone_args: Dict = None,
+        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),
+        recurrent_cell: str = "lstm",
+        avg_pool: bool = False,
+        use_sliding_window: bool = True,
+    ) -> None:
+        super().__init__()
+        self.backbone_args = backbone_args or {}
+        self.patch_size = patch_size
+        self.stride = stride
+        self.sliding_window = (
+            self._configure_sliding_window() if use_sliding_window else None
+        )
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.backbone = configure_backbone(backbone, backbone_args)
+        self.bidirectional = bidirectional
+        self.avg_pool = avg_pool
+
+        if recurrent_cell.upper() in ["LSTM", "GRU"]:
+            recurrent_cell = getattr(nn, recurrent_cell)
+        else:
+            logger.warning(
+                f"Option {recurrent_cell} not valid, defaulting to LSTM cell."
+            )
+            recurrent_cell = nn.LSTM
+
+        self.rnn = recurrent_cell(
+            input_size=self.input_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=decoder_size, out_features=num_classes),
+            nn.LogSoftmax(dim=2),
+        )
+
+    def _configure_sliding_window(self) -> nn.Sequential:
+        return nn.Sequential(
+            nn.Unfold(kernel_size=self.patch_size, stride=self.stride),
+            Rearrange(
+                "b (c h w) t -> b t c h w",
+                h=self.patch_size[0],
+                w=self.patch_size[1],
+                c=1,
+            ),
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Converts images to sequence of patches, feeds them to a CNN, then predictions are made with an LSTM."""
+        if len(x.shape) < 4:
+            x = x[(None,) * (4 - len(x.shape))]
+
+        if self.sliding_window is not None:
+            # Create image patches with a sliding window kernel.
+            x = self.sliding_window(x)
+
+            # 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.backbone(x)
+
+            # Avgerage pooling.
+            if self.avg_pool:
+                x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t)
+            else:
+                x = rearrange(x, "(b t) h -> t b h", b=b, t=t)
+        else:
+            # Encode the entire image with a CNN, and use the channels as temporal dimension.
+            b = x.shape[0]
+            x = self.backbone(x)
+            x = rearrange(x, "b c h w -> c b (h w)", b=b)
+
+        # Sequence predictions.
+        x, _ = self.rnn(x)
+
+        # Sequence to classifcation layer.
+        x = self.decoder(x)
+        return x