From 7e8e54e84c63171e748bbf09516fd517e6821ace Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sat, 20 Mar 2021 18:09:06 +0100
Subject: Inital commit for refactoring to lightning

---
 src/text_recognizer/networks/crnn.py | 110 -----------------------------------
 1 file changed, 110 deletions(-)
 delete mode 100644 src/text_recognizer/networks/crnn.py

(limited to 'src/text_recognizer/networks/crnn.py')

diff --git a/src/text_recognizer/networks/crnn.py b/src/text_recognizer/networks/crnn.py
deleted file mode 100644
index 778e232..0000000
--- a/src/text_recognizer/networks/crnn.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""CRNN for handwritten text recognition."""
-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)
-
-            # Average 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.
-            x = self.backbone(x)
-            x = rearrange(x, "b c h w -> b w c h")
-            if self.adaptive_pool is not None:
-                x = self.adaptive_pool(x)
-            x = x.squeeze(3)
-
-        # Sequence predictions.
-        x, _ = self.rnn(x)
-
-        # Sequence to classification layer.
-        x = self.decoder(x)
-        return x
-- 
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