From 8bb76745e43c6b4967c8e91ebaf4c4295d0b8d0b Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 12 Jun 2022 23:16:20 +0200
Subject: Remove conformer

---
 text_recognizer/networks/conformer/subsampler.py | 50 ------------------------
 1 file changed, 50 deletions(-)
 delete mode 100644 text_recognizer/networks/conformer/subsampler.py

(limited to 'text_recognizer/networks/conformer/subsampler.py')

diff --git a/text_recognizer/networks/conformer/subsampler.py b/text_recognizer/networks/conformer/subsampler.py
deleted file mode 100644
index 133b53a..0000000
--- a/text_recognizer/networks/conformer/subsampler.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Simple convolutional network."""
-from typing import Tuple
-
-from einops import rearrange
-from torch import nn, Tensor
-
-from text_recognizer.networks.transformer import AxialPositionalEmbedding
-
-
-class Subsampler(nn.Module):
-    def __init__(
-        self,
-        channels: int,
-        dim: int,
-        depth: int,
-        height: int,
-        pixel_pos_embedding: AxialPositionalEmbedding,
-        dropout: float = 0.1,
-    ) -> None:
-        super().__init__()
-        self.pixel_pos_embedding = pixel_pos_embedding
-        self.subsampler, self.projector = self._build(
-            channels, height, dim, depth, dropout
-        )
-
-    def _build(
-        self, channels: int, height: int, dim: int, depth: int, dropout: float
-    ) -> Tuple[nn.Sequential, nn.Sequential]:
-        subsampler = []
-        for i in range(depth):
-            subsampler.append(
-                nn.Conv2d(
-                    in_channels=1 if i == 0 else channels,
-                    out_channels=channels,
-                    kernel_size=3,
-                    stride=2,
-                )
-            )
-            subsampler.append(nn.Mish(inplace=True))
-        projector = nn.Sequential(
-            nn.Linear(channels * height, dim), nn.Dropout(dropout)
-        )
-        return nn.Sequential(*subsampler), projector
-
-    def forward(self, x: Tensor) -> Tensor:
-        x = self.subsampler(x)
-        x = self.pixel_pos_embedding(x)
-        x = rearrange(x, "b c h w -> b w (c h)")
-        x = self.projector(x)
-        return x
-- 
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