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Diffstat (limited to 'notebooks')
| -rw-r--r-- | notebooks/04-convnext.ipynb | 248 |
1 files changed, 248 insertions, 0 deletions
diff --git a/notebooks/04-convnext.ipynb b/notebooks/04-convnext.ipynb new file mode 100644 index 0000000..5ab71c8 --- /dev/null +++ b/notebooks/04-convnext.ipynb @@ -0,0 +1,248 @@ +{ + "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 +} |