Inital commit for refactoring to lightning

1 files changed, 0 insertions, 75 deletions

diff --git a/src/text_recognizer/models/segmentation_model.py b/src/text_recognizer/models/segmentation_model.py
deleted file mode 100644
index 613108a..0000000
--- a/src/text_recognizer/models/segmentation_model.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""Segmentation model for detecting and segmenting lines."""
-from typing import Callable, Dict, Optional, 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.models.base import Model
-
-
-class SegmentationModel(Model):
-    """Model for segmenting lines in an image."""
-
-    def __init__(
-        self,
-        network_fn: str,
-        dataset: str,
-        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.tensor_transform = ToTensor()
-        self.softmax = nn.Softmax(dim=2)
-
-    @torch.no_grad()
-    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tensor:
-        """Predict on a single input."""
-        self.eval()
-
-        if image.dtype is 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 is torch.uint8 or image.dtype is torch.int64:
-            # If the image is an unscaled tensor.
-            image = image.type("torch.FloatTensor") / 255
-
-        if not torch.is_tensor(image):
-            image = Tensor(image)
-
-        # Put the image tensor on the device the model weights are on.
-        image = image.to(self.device)
-
-        logits = self.forward(image)
-
-        segmentation_mask = torch.argmax(logits, dim=1)
-
-        return segmentation_mask