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-rw-r--r--notebooks/04-convnext.ipynb248
-rw-r--r--text_recognizer/network/convnext/__init__.py7
-rw-r--r--text_recognizer/network/convnext/attention.py79
-rw-r--r--text_recognizer/network/convnext/convnext.py77
-rw-r--r--text_recognizer/network/convnext/downsample.py21
-rw-r--r--text_recognizer/network/convnext/norm.py18
-rw-r--r--text_recognizer/network/convnext/residual.py16
7 files changed, 0 insertions, 466 deletions
diff --git a/notebooks/04-convnext.ipynb b/notebooks/04-convnext.ipynb
deleted file mode 100644
index 5ab71c8..0000000
--- a/notebooks/04-convnext.ipynb
+++ /dev/null
@@ -1,248 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "7c02ae76-b540-4b16-9492-e9210b3b9249",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
- "import random\n",
- "\n",
- "%matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import numpy as np\n",
- "from omegaconf import OmegaConf\n",
- "\n",
- "%load_ext autoreload\n",
- "%autoreload 2\n",
- "\n",
- "from importlib.util import find_spec\n",
- "if find_spec(\"text_recognizer\") is None:\n",
- " import sys\n",
- " sys.path.append('..')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "ccdb6dde-47e5-429a-88f2-0764fb7e259a",
- "metadata": {},
- "outputs": [],
- "source": [
- "from hydra import compose, initialize\n",
- "from omegaconf import OmegaConf\n",
- "from hydra.utils import instantiate"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"../training/conf/network/convnext.yaml\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "e52ecb01-c975-4e55-925d-1182c7aea473",
- "metadata": {},
- "outputs": [],
- "source": [
- "with open(path, \"rb\") as f:\n",
- " cfg = OmegaConf.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'_target_': 'text_recognizer.network.convnext.ConvNext', 'dim': 16, 'dim_mults': [2, 4, 8], 'depths': [3, 3, 6], 'downsampling_factors': [[2, 2], [2, 2], [2, 2]], 'attn': {'_target_': 'text_recognizer.network.convnext.TransformerBlock', 'attn': {'_target_': 'text_recognizer.network.convnext.Attention', 'dim': 128, 'heads': 4, 'dim_head': 64, 'scale': 8}, 'ff': {'_target_': 'text_recognizer.network.convnext.FeedForward', 'dim': 128, 'mult': 4}}}"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "cfg"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "a2b420c1",
- "metadata": {},
- "outputs": [],
- "source": [
- "cfg.dim_mults = [2, 4, 8, 8]\n",
- "cfg.depths = [3, 3, 6, 6]\n",
- "cfg.downsampling_factors = [[2, 2], [2, 2], [2, 2], [2, 1]]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "c9589350",
- "metadata": {},
- "outputs": [],
- "source": [
- "net = instantiate(cfg)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
- "metadata": {},
- "outputs": [],
- "source": [
- "from torchinfo import summary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "25759b7b-8deb-4163-b75d-a1357c9fe88f",
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "====================================================================================================\n",
- "Layer (type:depth-idx) Output Shape Param #\n",
- "====================================================================================================\n",
- "ConvNext [2, 128, 72, 80] 165,408\n",
- "├─TransformerBlock: 1-5 [2, 128, 72, 80] (recursive)\n",
- "│ └─Attention: 2-6 [2, 128, 72, 80] (recursive)\n",
- "│ │ └─LayerNorm: 3-13 [2, 128, 72, 80] (recursive)\n",
- "├─Conv2d: 1-3 [2, 16, 576, 640] (recursive)\n",
- "├─TransformerBlock: 1 -- --\n",
- "│ └─Attention: 2 -- --\n",
- "│ │ └─Conv2d: 3-15 [2, 128, 72, 80] (recursive)\n",
- "│ └─FeedForward: 2-7 [2, 128, 72, 80] (recursive)\n",
- "│ │ └─Residual: 3-16 [2, 128, 72, 80] (recursive)\n",
- "│ │ │ └─Sequential: 4-26 [2, 128, 72, 80] (recursive)\n",
- "├─Conv2d: 1-3 [2, 16, 576, 640] (recursive)\n",
- "├─ModuleList: 1-4 -- --\n",
- "│ └─ModuleList: 2-3 -- --\n",
- "│ │ └─ConvNextBlock: 3-4 [2, 16, 576, 640] --\n",
- "│ │ │ └─Conv2d: 4-2 [2, 16, 576, 640] 800\n",
- "│ │ │ └─Sequential: 4-3 [2, 16, 576, 640] 9,280\n",
- "│ │ │ └─Identity: 4-4 [2, 16, 576, 640] --\n",
- "│ │ └─ModuleList: 3-5 -- --\n",
- "│ │ │ └─ConvNextBlock: 4-5 [2, 16, 576, 640] 10,080\n",
- "│ │ │ └─ConvNextBlock: 4-6 [2, 16, 576, 640] 10,080\n",
- "│ │ │ └─ConvNextBlock: 4-7 [2, 16, 576, 640] 10,080\n",
- "│ │ └─Downsample: 3-6 [2, 32, 288, 320] --\n",
- "│ │ │ └─Sequential: 4-8 [2, 32, 288, 320] 2,080\n",
- "│ └─ModuleList: 2-4 -- --\n",
- "│ │ └─ConvNextBlock: 3-7 [2, 32, 288, 320] --\n",
- "│ │ │ └─Conv2d: 4-9 [2, 32, 288, 320] 1,600\n",
- "│ │ │ └─Sequential: 4-10 [2, 32, 288, 320] 36,992\n",
- "│ │ │ └─Identity: 4-11 [2, 32, 288, 320] --\n",
- "│ │ └─ModuleList: 3-8 -- --\n",
- "│ │ │ └─ConvNextBlock: 4-12 [2, 32, 288, 320] 38,592\n",
- "│ │ │ └─ConvNextBlock: 4-13 [2, 32, 288, 320] 38,592\n",
- "│ │ │ └─ConvNextBlock: 4-14 [2, 32, 288, 320] 38,592\n",
- "│ │ └─Downsample: 3-9 [2, 64, 144, 160] --\n",
- "│ │ │ └─Sequential: 4-15 [2, 64, 144, 160] 8,256\n",
- "│ └─ModuleList: 2-5 -- --\n",
- "│ │ └─ConvNextBlock: 3-10 [2, 64, 144, 160] --\n",
- "│ │ │ └─Conv2d: 4-16 [2, 64, 144, 160] 3,200\n",
- "│ │ │ └─Sequential: 4-17 [2, 64, 144, 160] 147,712\n",
- "│ │ │ └─Identity: 4-18 [2, 64, 144, 160] --\n",
- "│ │ └─ModuleList: 3-11 -- --\n",
- "│ │ │ └─ConvNextBlock: 4-19 [2, 64, 144, 160] 150,912\n",
- "│ │ │ └─ConvNextBlock: 4-20 [2, 64, 144, 160] 150,912\n",
- "│ │ │ └─ConvNextBlock: 4-21 [2, 64, 144, 160] 150,912\n",
- "│ │ │ └─ConvNextBlock: 4-22 [2, 64, 144, 160] 150,912\n",
- "│ │ │ └─ConvNextBlock: 4-23 [2, 64, 144, 160] 150,912\n",
- "│ │ │ └─ConvNextBlock: 4-24 [2, 64, 144, 160] 150,912\n",
- "│ │ └─Downsample: 3-12 [2, 128, 72, 80] --\n",
- "│ │ │ └─Sequential: 4-25 [2, 128, 72, 80] 32,896\n",
- "├─TransformerBlock: 1-5 [2, 128, 72, 80] (recursive)\n",
- "│ └─Attention: 2-6 [2, 128, 72, 80] (recursive)\n",
- "│ │ └─LayerNorm: 3-13 [2, 128, 72, 80] (recursive)\n",
- "│ │ └─Conv2d: 3-14 [2, 768, 72, 80] 98,304\n",
- "│ │ └─Conv2d: 3-15 [2, 128, 72, 80] (recursive)\n",
- "│ └─FeedForward: 2-7 [2, 128, 72, 80] (recursive)\n",
- "│ │ └─Residual: 3-16 [2, 128, 72, 80] (recursive)\n",
- "│ │ │ └─Sequential: 4-26 [2, 128, 72, 80] (recursive)\n",
- "├─LayerNorm: 1-6 [2, 128, 72, 80] 128\n",
- "====================================================================================================\n",
- "Total params: 1,558,144\n",
- "Trainable params: 1,558,144\n",
- "Non-trainable params: 0\n",
- "Total mult-adds (G): 114.00\n",
- "====================================================================================================\n",
- "Input size (MB): 2.95\n",
- "Forward/backward pass size (MB): 3822.06\n",
- "Params size (MB): 5.57\n",
- "Estimated Total Size (MB): 3830.58\n",
- "===================================================================================================="
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "summary(net, (2, 1, 576, 640), device=\"cpu\", depth=4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "05c1d499",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
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
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-12-25 05:59:56 +0000