diff options
Diffstat (limited to 'text_recognizer')
| -rw-r--r-- | text_recognizer/network/convnext/__init__.py | 7 | ||||
| -rw-r--r-- | text_recognizer/network/convnext/attention.py | 79 | ||||
| -rw-r--r-- | text_recognizer/network/convnext/convnext.py | 77 | ||||
| -rw-r--r-- | text_recognizer/network/convnext/downsample.py | 21 | ||||
| -rw-r--r-- | text_recognizer/network/convnext/norm.py | 18 | ||||
| -rw-r--r-- | text_recognizer/network/convnext/residual.py | 16 |
6 files changed, 0 insertions, 218 deletions
diff --git a/text_recognizer/network/convnext/__init__.py b/text_recognizer/network/convnext/__init__.py deleted file mode 100644 index dcff3fc..0000000 --- a/text_recognizer/network/convnext/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -"""Convnext module.""" -from text_recognizer.network.convnext.attention import ( - Attention, - FeedForward, - TransformerBlock, -) -from text_recognizer.network.convnext.convnext import ConvNext diff --git a/text_recognizer/network/convnext/attention.py b/text_recognizer/network/convnext/attention.py deleted file mode 100644 index 6bc9692..0000000 --- a/text_recognizer/network/convnext/attention.py +++ /dev/null @@ -1,79 +0,0 @@ -"""Convolution self attention block.""" - -import torch.nn.functional as F -from einops import rearrange -from torch import Tensor, einsum, nn - -from text_recognizer.network.convnext.norm import LayerNorm -from text_recognizer.network.convnext.residual import Residual - - -def l2norm(t: Tensor) -> Tensor: - return F.normalize(t, dim=-1) - - -class FeedForward(nn.Module): - def __init__(self, dim: int, mult: int = 4) -> None: - super().__init__() - inner_dim = int(dim * mult) - self.fn = Residual( - nn.Sequential( - LayerNorm(dim), - nn.Conv2d(dim, inner_dim, 1, bias=False), - nn.GELU(), - LayerNorm(inner_dim), - nn.Conv2d(inner_dim, dim, 1, bias=False), - ) - ) - - def forward(self, x: Tensor) -> Tensor: - return self.fn(x) - - -class Attention(nn.Module): - def __init__( - self, dim: int, heads: int = 4, dim_head: int = 64, scale: int = 8 - ) -> None: - super().__init__() - self.scale = scale - self.heads = heads - inner_dim = heads * dim_head - self.norm = LayerNorm(dim) - - self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias=False) - self.to_out = nn.Conv2d(inner_dim, dim, 1, bias=False) - - def forward(self, x: Tensor) -> Tensor: - h, w = x.shape[-2:] - - residual = x.clone() - - x = self.norm(x) - - q, k, v = self.to_qkv(x).chunk(3, dim=1) - q, k, v = map( - lambda t: rearrange(t, "b (h c) ... -> b h (...) c", h=self.heads), - (q, k, v), - ) - - q, k = map(l2norm, (q, k)) - - sim = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale - attn = sim.softmax(dim=-1) - - out = einsum("b h i j, b h j d -> b h i d", attn, v) - - out = rearrange(out, "b h (x y) d -> b (h d) x y", x=h, y=w) - return self.to_out(out) + residual - - -class TransformerBlock(nn.Module): - def __init__(self, attn: Attention, ff: FeedForward) -> None: - super().__init__() - self.attn = attn - self.ff = ff - - def forward(self, x: Tensor) -> Tensor: - x = self.attn(x) - x = self.ff(x) - return x 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) diff --git a/text_recognizer/network/convnext/downsample.py b/text_recognizer/network/convnext/downsample.py deleted file mode 100644 index a8a0466..0000000 --- a/text_recognizer/network/convnext/downsample.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Convnext downsample module.""" -from typing import Tuple - -from einops.layers.torch import Rearrange -from torch import Tensor, nn - - -class Downsample(nn.Module): - """Downsamples feature maps by patches.""" - - 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: - """Applies patch function.""" - return self.fn(x) diff --git a/text_recognizer/network/convnext/norm.py b/text_recognizer/network/convnext/norm.py deleted file mode 100644 index 3355de9..0000000 --- a/text_recognizer/network/convnext/norm.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Layer norm for conv layers.""" -import torch -from torch import Tensor, nn - - -class LayerNorm(nn.Module): - """Layer norm for convolutions.""" - - def __init__(self, dim: int) -> None: - super().__init__() - self.gamma = nn.Parameter(torch.ones(1, dim, 1, 1)) - - def forward(self, x: Tensor) -> Tensor: - """Applies layer norm.""" - 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/network/convnext/residual.py b/text_recognizer/network/convnext/residual.py deleted file mode 100644 index dfc2847..0000000 --- a/text_recognizer/network/convnext/residual.py +++ /dev/null @@ -1,16 +0,0 @@ -"""Generic residual layer.""" -from typing import Callable - -from torch import Tensor, nn - - -class Residual(nn.Module): - """Residual layer.""" - - def __init__(self, fn: Callable) -> None: - super().__init__() - self.fn = fn - - def forward(self, x: Tensor) -> Tensor: - """Applies residual fn.""" - return self.fn(x) + x |