{ "cells": [ { "cell_type": "code", "execution_count": 7, "id": "136a80f5-10e1-40c4-973a-a7eb7939bb1f", "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", "from hydra import compose, initialize\n", "from omegaconf import OmegaConf\n", "from hydra.utils import instantiate\n", "from torchinfo import summary\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": 2, "id": "1a0fb9ca-1886-4fd4-839f-dc111a450cfd", "metadata": {}, "outputs": [], "source": [ "path = \"../training/conf/network/vqvae.yaml\"" ] }, { "cell_type": "code", "execution_count": 5, "id": "0182a614-5781-44a6-b659-008e7c584fa7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "encoder:\n", " _target_: text_recognizer.networks.vqvae.encoder.Encoder\n", " in_channels: 1\n", " hidden_dim: 32\n", " channels_multipliers:\n", " - 1\n", " - 2\n", " - 4\n", " dropout_rate: 0.0\n", " activation: mish\n", " use_norm: true\n", " num_residuals: 4\n", " residual_channels: 32\n", "decoder:\n", " _target_: text_recognizer.networks.vqvae.decoder.Decoder\n", " out_channels: 1\n", " hidden_dim: 32\n", " channels_multipliers:\n", " - 4\n", " - 2\n", " - 1\n", " dropout_rate: 0.0\n", " activation: mish\n", " use_norm: true\n", " num_residuals: 4\n", " residual_channels: 32\n", "_target_: text_recognizer.networks.vqvae.vqvae.VQVAE\n", "hidden_dim: 128\n", "embedding_dim: 32\n", "num_embeddings: 8192\n", "decay: 0.99\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'vqvae': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information\n", " warnings.warn(msg, UserWarning)\n" ] } ], "source": [ "with initialize(config_path=\"../training/conf/network/\", job_name=\"test_app\"):\n", " cfg = compose(config_name=\"vqvae\")\n", " print(OmegaConf.to_yaml(cfg))" ] }, { "cell_type": "code", "execution_count": 6, "id": "a500f94c-7dae-477e-a3fb-2a2d62ee7b72", "metadata": {}, "outputs": [], "source": [ "net = instantiate(cfg)" ] }, { "cell_type": "code", "execution_count": 9, "id": "7f3b3559-5e23-485e-bf57-9405568a1fbf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "====================================================================================================\n", "Layer (type:depth-idx) Output Shape Param #\n", "====================================================================================================\n", "VQVAE -- --\n", "├─Encoder: 1-1 [1, 128, 72, 80] --\n", "│ └─Sequential: 2-1 [1, 128, 72, 80] --\n", "│ │ └─Conv2d: 3-1 [1, 32, 576, 640] 320\n", "│ │ └─Normalize: 3-2 [1, 32, 576, 640] 64\n", "│ │ └─Mish: 3-3 [1, 32, 576, 640] --\n", "│ │ └─Mish: 3-4 [1, 32, 576, 640] --\n", "│ │ └─Mish: 3-5 [1, 32, 576, 640] --\n", "│ │ └─Conv2d: 3-6 [1, 32, 288, 320] 16,416\n", "│ │ └─Normalize: 3-7 [1, 32, 288, 320] 64\n", "│ │ └─Mish: 3-8 [1, 32, 288, 320] --\n", "│ │ └─Mish: 3-9 [1, 32, 288, 320] --\n", "│ │ └─Mish: 3-10 [1, 32, 288, 320] --\n", "│ │ └─Conv2d: 3-11 [1, 64, 144, 160] 32,832\n", "│ │ └─Normalize: 3-12 [1, 64, 144, 160] 128\n", "│ │ └─Mish: 3-13 [1, 64, 144, 160] --\n", "│ │ └─Mish: 3-14 [1, 64, 144, 160] --\n", "│ │ └─Mish: 3-15 [1, 64, 144, 160] --\n", "│ │ └─Conv2d: 3-16 [1, 128, 72, 80] 131,200\n", "│ │ └─Residual: 3-17 [1, 128, 72, 80] 41,280\n", "│ │ └─Residual: 3-18 [1, 128, 72, 80] 41,280\n", "│ │ └─Residual: 3-19 [1, 128, 72, 80] 41,280\n", "│ │ └─Residual: 3-20 [1, 128, 72, 80] 41,280\n", "├─Conv2d: 1-2 [1, 32, 72, 80] 4,128\n", "├─VectorQuantizer: 1-3 [1, 32, 72, 80] --\n", "├─Conv2d: 1-4 [1, 128, 72, 80] 4,224\n", "├─Decoder: 1-5 [1, 1, 576, 640] --\n", "│ └─Sequential: 2-2 [1, 1, 576, 640] --\n", "│ │ └─Residual: 3-21 [1, 128, 72, 80] 41,280\n", "│ │ └─Residual: 3-22 [1, 128, 72, 80] 41,280\n", "│ │ └─Residual: 3-23 [1, 128, 72, 80] 41,280\n", "│ │ └─Residual: 3-24 [1, 128, 72, 80] 41,280\n", "│ │ └─Normalize: 3-25 [1, 128, 72, 80] 256\n", "│ │ └─Mish: 3-26 [1, 128, 72, 80] --\n", "│ │ └─Mish: 3-27 [1, 128, 72, 80] --\n", "│ │ └─Mish: 3-28 [1, 128, 72, 80] --\n", "│ │ └─ConvTranspose2d: 3-29 [1, 64, 144, 160] 131,136\n", "│ │ └─Normalize: 3-30 [1, 64, 144, 160] 128\n", "│ │ └─Mish: 3-31 [1, 64, 144, 160] --\n", "│ │ └─Mish: 3-32 [1, 64, 144, 160] --\n", "│ │ └─Mish: 3-33 [1, 64, 144, 160] --\n", "│ │ └─ConvTranspose2d: 3-34 [1, 32, 288, 320] 32,800\n", "│ │ └─Normalize: 3-35 [1, 32, 288, 320] 64\n", "│ │ └─Mish: 3-36 [1, 32, 288, 320] --\n", "│ │ └─Mish: 3-37 [1, 32, 288, 320] --\n", "│ │ └─Mish: 3-38 [1, 32, 288, 320] --\n", "│ │ └─ConvTranspose2d: 3-39 [1, 32, 576, 640] 16,416\n", "│ │ └─Normalize: 3-40 [1, 32, 576, 640] 64\n", "│ │ └─Conv2d: 3-41 [1, 1, 576, 640] 289\n", "====================================================================================================\n", "Total params: 700,769\n", "Trainable params: 700,769\n", "Non-trainable params: 0\n", "Total mult-adds (G): 17.28\n", "====================================================================================================\n", "Input size (MB): 1.47\n", "Forward/backward pass size (MB): 659.13\n", "Params size (MB): 2.80\n", "Estimated Total Size (MB): 663.41\n", "====================================================================================================" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary(net, (1, 1, 576, 640), device=\"cpu\")" ] }, { "cell_type": "code", "execution_count": null, "id": "9f880b03-d641-4640-acd3-aa5666ca5184", "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }