1 files changed, 0 insertions, 77 deletions
diff --git a/text_recognizer/network/convnext/convnext.py b/text_recognizer/network/convnext/convnext.py
deleted file mode 100644
index 6acf059..0000000
--- a/text_recognizer/network/convnext/convnext.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""ConvNext module."""
-from typing import Optional, Sequence
-
-from torch import Tensor, nn
-
-from text_recognizer.network.convnext.attention import TransformerBlock
-from text_recognizer.network.convnext.downsample import Downsample
-from text_recognizer.network.convnext.norm import LayerNorm
-
-
-class ConvNextBlock(nn.Module):
-    """ConvNext block."""
-
-    def __init__(self, dim: int, dim_out: int, mult: int) -> None:
-        super().__init__()
-        self.ds_conv = nn.Conv2d(
-            dim, dim, kernel_size=(7, 7), padding="same", groups=dim
-        )
-        self.net = nn.Sequential(
-            LayerNorm(dim),
-            nn.Conv2d(dim, dim_out * mult, kernel_size=(3, 3), padding="same"),
-            nn.GELU(),
-            nn.Conv2d(dim_out * mult, dim_out, kernel_size=(3, 3), padding="same"),
-        )
-        self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
-
-    def forward(self, x: Tensor) -> Tensor:
-        h = self.ds_conv(x)
-        h = self.net(h)
-        return h + self.res_conv(x)
-
-
-class ConvNext(nn.Module):
-    def __init__(
-        self,
-        dim: int = 16,
-        dim_mults: Sequence[int] = (2, 4, 8),
-        depths: Sequence[int] = (3, 3, 6),
-        downsampling_factors: Sequence[Sequence[int]] = ((2, 2), (2, 2), (2, 2)),
-        attn: Optional[TransformerBlock] = None,
-    ) -> None:
-        super().__init__()
-        dims = (dim, *map(lambda m: m * dim, dim_mults))
-        self.attn = attn if attn is not None else nn.Identity()
-        self.out_channels = dims[-1]
-        self.stem = nn.Conv2d(1, dims[0], kernel_size=7, padding="same")
-        self.layers = nn.ModuleList([])
-
-        for i in range(len(dims) - 1):
-            dim_in, dim_out = dims[i], dims[i + 1]
-            self.layers.append(
-                nn.ModuleList(
-                    [
-                        ConvNextBlock(dim_in, dim_in, 2),
-                        nn.ModuleList(
-                            [ConvNextBlock(dim_in, dim_in, 2) for _ in range(depths[i])]
-                        ),
-                        Downsample(dim_in, dim_out, downsampling_factors[i]),
-                    ]
-                )
-            )
-        self.norm = LayerNorm(dims[-1])
-
-    def _init_weights(self, m):
-        if isinstance(m, (nn.Conv2d, nn.Linear)):
-            nn.init.trunc_normal_(m.weight, std=0.02)
-            nn.init.constant_(m.bias, 0)
-
-    def forward(self, x: Tensor) -> Tensor:
-        x = self.stem(x)
-        for init_block, blocks, down in self.layers:
-            x = init_block(x)
-            for fn in blocks:
-                x = fn(x)
-            x = down(x)
-        x = self.attn(x)
-        return self.norm(x)