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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2021-03-31 21:55:10 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2021-03-31 21:55:10 +0200
commit3196144ec99e803cef218295ddea592748931c57 (patch)
tree867d38ed08c78b8186fdd9a8abab4257f14d05c7 /text_recognizer/networks/crnn.py
parentd21594211e29c40c135b753e33b248b0737cd76f (diff)
Removing legacy code
Diffstat (limited to 'text_recognizer/networks/crnn.py')
-rw-r--r--text_recognizer/networks/crnn.py110
1 files changed, 0 insertions, 110 deletions
diff --git a/text_recognizer/networks/crnn.py b/text_recognizer/networks/crnn.py
deleted file mode 100644
index 778e232..0000000
--- a/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