Efficient net and non working transformer model.

1 files changed, 0 insertions, 38 deletions

diff --git a/text_recognizer/networks/coat/patch_embedding.py b/text_recognizer/networks/coat/patch_embedding.py
deleted file mode 100644
index 3b7b76a..0000000
--- a/text_recognizer/networks/coat/patch_embedding.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""Patch embedding for images and feature maps."""
-from typing import Sequence, Tuple
-
-from einops import rearrange
-from loguru import logger
-from torch import nn
-from torch import Tensor
-
-
-class PatchEmbedding(nn.Module):
-    """Patch embedding of images."""
-
-    def __init__(
-        self,
-        image_shape: Sequence[int],
-        patch_size: int = 16,
-        in_channels: int = 1,
-        embedding_dim: int = 512,
-    ) -> None:
-        if image_shape[0] % patch_size == 0 and image_shape[1] % patch_size == 0:
-            logger.error(
-                f"Image shape {image_shape} not divisable by patch size {patch_size}"
-            )
-
-        self.patch_size = patch_size
-        self.embedding = nn.Conv2d(
-            in_channels, embedding_dim, kernel_size=patch_size, stride=patch_size
-        )
-        self.norm = nn.LayerNorm(embedding_dim)
-
-    def forward(self, x: Tensor) -> Tuple[Tensor, Tuple[int, int]]:
-        """Embeds image or feature maps with patch embedding."""
-        _, _, h, w = x.shape
-        h_out, w_out = h // self.patch_size, w // self.patch_size
-        x = self.embedding(x)
-        x = rearrange(x, "b c h w -> b (h w) c")
-        x = self.norm(x)
-        return x, (h_out, w_out)