{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!mkdir ../checkpoints\n", "!wget https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth -P ../checkpoints" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "is_executing": true } }, "outputs": [], "source": [ "import torch\n", "import mmcv\n", "import matplotlib.pyplot as plt\n", "from mmengine.model.utils import revert_sync_batchnorm\n", "from mmseg.apis import init_model, inference_model, show_result_pyplot\n", "from mmseg.utils import register_all_modules\n", "register_all_modules()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "is_executing": true } }, "outputs": [], "source": [ "config_file = '../configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'\n", "checkpoint_file = '../checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# build the model from a config file and a checkpoint file\n", "model = init_model(config_file, checkpoint_file, device='cuda:0')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# test a single image\n", "img = 'demo.png'\n", "if not torch.cuda.is_available():\n", " model = revert_sync_batchnorm(model)\n", "result = inference_model(model, img)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# show the results\n", "vis_result = show_result_pyplot(model, img, result)\n", "plt.imshow(mmcv.bgr2rgb(vis_result))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.4 ('pt1.11-v2')", "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": "3.10.4" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } }, "vscode": { "interpreter": { "hash": "fdab7187f8cbd4ce42bbf864ddb4c4693e7329271a15a7fa96e4bdb82b9302c9" } } }, "nbformat": 4, "nbformat_minor": 4 }