Delete convnext

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