Delete convnext

1 files changed, 0 insertions, 248 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
-}