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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)
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