From 9732617e402a5152ba822eb1459d6ff776aba15c Mon Sep 17 00:00:00 2001 From: Gustaf Rydholm Date: Tue, 13 Sep 2022 19:07:53 +0200 Subject: Add convnext module --- text_recognizer/networks/convnext/__init__.py | 2 + text_recognizer/networks/convnext/attention.py | 0 text_recognizer/networks/convnext/convnext.py | 75 +++++++++++++++++++++++++ text_recognizer/networks/convnext/downsample.py | 17 ++++++ text_recognizer/networks/convnext/norm.py | 15 +++++ text_recognizer/networks/convnext/residual.py | 12 ++++ 6 files changed, 121 insertions(+) create mode 100644 text_recognizer/networks/convnext/__init__.py create mode 100644 text_recognizer/networks/convnext/attention.py create mode 100644 text_recognizer/networks/convnext/convnext.py create mode 100644 text_recognizer/networks/convnext/downsample.py create mode 100644 text_recognizer/networks/convnext/norm.py create mode 100644 text_recognizer/networks/convnext/residual.py (limited to 'text_recognizer') diff --git a/text_recognizer/networks/convnext/__init__.py b/text_recognizer/networks/convnext/__init__.py new file mode 100644 index 0000000..8d8470f --- /dev/null +++ b/text_recognizer/networks/convnext/__init__.py @@ -0,0 +1,2 @@ +"""Convnext module.""" +from text_recognizer.networks.convnext.convnext import ConvNext diff --git a/text_recognizer/networks/convnext/attention.py b/text_recognizer/networks/convnext/attention.py new file mode 100644 index 0000000..e69de29 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) diff --git a/text_recognizer/networks/convnext/downsample.py b/text_recognizer/networks/convnext/downsample.py new file mode 100644 index 0000000..6e4306f --- /dev/null +++ b/text_recognizer/networks/convnext/downsample.py @@ -0,0 +1,17 @@ +from typing import Tuple + +from einops.layers.torch import Rearrange +from torch import nn, Tensor + + +class Downsample(nn.Module): + def __init__(self, dim: int, dim_out: int, factors: Tuple[int, int]) -> None: + super().__init__() + s1, s2 = factors + self.fn = nn.Sequential( + Rearrange("b c (h s1) (w s2) -> b (c s1 s2) h w", s1=s1, s2=s2), + nn.Conv2d(dim * s1 * s2, dim_out, 1), + ) + + def forward(self, x: Tensor) -> Tensor: + return self.fn(x) diff --git a/text_recognizer/networks/convnext/norm.py b/text_recognizer/networks/convnext/norm.py new file mode 100644 index 0000000..2d896e5 --- /dev/null +++ b/text_recognizer/networks/convnext/norm.py @@ -0,0 +1,15 @@ +"""Layer norm for conv layers.""" +import torch +from torch import nn, Tensor + + +class LayerNorm(nn.Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.gamma = nn.Parameter(torch.ones(1, dim, 1, 1)) + + def forward(self, x: Tensor) -> Tensor: + eps = 1e-5 if x.dtype == torch.float32 else 1e-3 + var = torch.var(x, dim=1, unbiased=False, keepdim=True) + mean = torch.mean(x, dim=1, keepdim=True) + return (x - mean) / (var + eps).sqrt() * self.gamma diff --git a/text_recognizer/networks/convnext/residual.py b/text_recognizer/networks/convnext/residual.py new file mode 100644 index 0000000..3f44390 --- /dev/null +++ b/text_recognizer/networks/convnext/residual.py @@ -0,0 +1,12 @@ +"""Generic residual layer.""" +from typing import Callable +from torch import nn, Tensor + + +class Residual(nn.Module): + def __init__(self, fn: Callable) -> None: + super().__init__() + self.fn = fn + + def forward(self, x: Tensor) -> Tensor: + return self.fn(x) + x -- cgit v1.2.3-70-g09d2