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-rw-r--r--src/text_recognizer/models/crnn_model.py119
1 files changed, 0 insertions, 119 deletions
diff --git a/src/text_recognizer/models/crnn_model.py b/src/text_recognizer/models/crnn_model.py
deleted file mode 100644
index 1e01a83..0000000
--- a/src/text_recognizer/models/crnn_model.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""Defines the CRNNModel class."""
-from typing import Callable, Dict, Optional, Tuple, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-from torch.utils.data import Dataset
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets import EmnistMapper
-from text_recognizer.models.base import Model
-from text_recognizer.networks import greedy_decoder
-
-
-class CRNNModel(Model):
- """Model for predicting a sequence of characters from an image of a text line."""
-
- def __init__(
- self,
- network_fn: Type[nn.Module],
- dataset: Type[Dataset],
- network_args: Optional[Dict] = None,
- dataset_args: Optional[Dict] = None,
- metrics: Optional[Dict] = None,
- criterion: Optional[Callable] = None,
- criterion_args: Optional[Dict] = None,
- optimizer: Optional[Callable] = None,
- optimizer_args: Optional[Dict] = None,
- lr_scheduler: Optional[Callable] = None,
- lr_scheduler_args: Optional[Dict] = None,
- swa_args: Optional[Dict] = None,
- device: Optional[str] = None,
- ) -> None:
- super().__init__(
- network_fn,
- dataset,
- network_args,
- dataset_args,
- metrics,
- criterion,
- criterion_args,
- optimizer,
- optimizer_args,
- lr_scheduler,
- lr_scheduler_args,
- swa_args,
- device,
- )
-
- self.pad_token = dataset_args["args"]["pad_token"]
- if self._mapper is None:
- self._mapper = EmnistMapper(pad_token=self.pad_token,)
- self.tensor_transform = ToTensor()
-
- def criterion(self, output: Tensor, targets: Tensor) -> Tensor:
- """Computes the CTC loss.
-
- Args:
- output (Tensor): Model predictions.
- targets (Tensor): Correct output sequence.
-
- Returns:
- Tensor: The CTC loss.
-
- """
-
- # Input lengths on the form [T, B]
- input_lengths = torch.full(
- size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long,
- )
-
- # Configure target tensors for ctc loss.
- targets_ = Tensor([]).to(self.device)
- target_lengths = []
- for t in targets:
- # Remove padding symbol as it acts as the blank symbol.
- t = t[t < 79]
- targets_ = torch.cat([targets_, t])
- target_lengths.append(len(t))
-
- targets = targets_.type(dtype=torch.long)
- target_lengths = (
- torch.Tensor(target_lengths).type(dtype=torch.long).to(self.device)
- )
-
- return self._criterion(output, targets, input_lengths, target_lengths)
-
- @torch.no_grad()
- def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
- """Predict on a single input."""
- self.eval()
-
- if image.dtype == np.uint8:
- # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
- image = self.tensor_transform(image)
-
- # Rescale image between 0 and 1.
- if image.dtype == torch.uint8:
- # If the image is an unscaled tensor.
- image = image.type("torch.FloatTensor") / 255
-
- # Put the image tensor on the device the model weights are on.
- image = image.to(self.device)
- log_probs = self.forward(image)
-
- raw_pred, _ = greedy_decoder(
- predictions=log_probs,
- character_mapper=self.mapper,
- blank_label=79,
- collapse_repeated=True,
- )
-
- log_probs, _ = log_probs.max(dim=2)
-
- predicted_characters = "".join(raw_pred[0])
- confidence_of_prediction = log_probs.cumprod(dim=0)[-1].item()
-
- return predicted_characters, confidence_of_prediction