1 files changed, 75 insertions, 0 deletions
diff --git a/text_recognizer/networks/convnext/convnext.py b/text_recognizer/networks/convnext/convnext.py
new file mode 100644
index 0000000..a4556a0
--- /dev/null
+++ b/text_recognizer/networks/convnext/convnext.py
@@ -0,0 +1,75 @@
+from typing import Sequence
+
+from einops import reduce, rearrange
+from einops.layers.torch import Rearrange
+import torch
+from torch import einsum, nn, Tensor
+import torch.nn.functional as F
+
+from text_recognizer.networks.convnext.downsample import Downsample
+from text_recognizer.networks.convnext.residual import Residual
+from text_recognizer.networks.convnext.norm import LayerNorm
+
+
+class ConvNextBlock(nn.Module):
+    def __init__(self, dim, dim_out, mult):
+        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):
+        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)),
+    ) -> None:
+        super().__init__()
+        dims = (dim, *map(lambda m: m * dim, dim_mults))
+        self.out_channels = dims[-1]
+        self.stem = nn.Conv2d(1, dims[0], kernel_size=(7, 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):
+        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)
+        return self.norm(x)