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@@ -102,3 +102,5 @@ venv.bak/ | |
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| # mypy | ||
| .mypy_cache/ | ||
| *.DS_Store | ||
| *.swp | ||
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%matplotlib inline" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "2. GluonCV C++ Inference Demo\n================================\n\nThis is a demo tutorial which illustrates how to use existing GluonCV\nmodels in c++ environments given exported JSON and PARAMS files.\n\nPlease checkout `Export Network <sphx_glr_build_examples_deployment_export_network.py>` for instructions of how to export pre-trained models.\n\nDemo usage\n----------\n\n.. code:: bash\n\n # with yolo3_darknet53_voc-0000.params and yolo3_darknet53-symbol.json on disk\n ./gluoncv-detect yolo3_darknet53_voc demo.jpg\n\n.. figure:: https://user-images.githubusercontent.com/3307514/45458507-d76ff600-b6a8-11e8-92e1-0b1966e4344f.jpg\n :alt: demo\n\n demo\n\nUsage:\n\n::\n\n SYNOPSIS\n ./gluoncv-detect <model file> <image file> [-o <outfile>] [--class-file <classfile>] [-e\n <epoch>] [--gpu <gpu>] [-q] [--no-disp] [-t <thresh>]\n\n OPTIONS\n -o, --output <outfile>\n output image, by default no output\n\n --class-file <classfile>\n plain text file for class names, one name per line\n\n -e, --epoch <epoch>\n Epoch number to load parameters, by default is 0\n\n --gpu <gpu> Which gpu to use, by default is -1, means cpu only.\n -q, --quite Quite mode, no screen output\n --no-disp Do not display image\n -t, --thresh <thresh>\n Visualize threshold, from 0 to 1, default 0.3.\n\nSource Code and Build Instructions\n----------------------------------\n\nThe C++ demo code and build instructions are available in our repository `scripts\n<https://github.com/dmlc/gluon-cv/tree/master/scripts/deployment/cpp-inference>`_.\n\n.. hint::\n\n Prebuilt binaries are `available <https://github.com/dmlc/gluon-cv/tree/master/scripts/deployment/cpp-inference#download-prebuilt-binaries>`_ for Linus, Mac OS and Windows.\n\n And you can also build MXNet from `source <https://github.com/dmlc/gluon-cv/tree/master/scripts/deployment/cpp-inference#build-from-source>`_ to support C++ inference demo.\n\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "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": "3.7.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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| @@ -0,0 +1,129 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%matplotlib inline" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "1. Export trained GluonCV network to JSON\n============================================\n\nIt is awesome if you are enjoy using GluonCV in Python for training and testing.\nAt some point, you might ask: \"Is it possible to deploy the existing models to somewhere out of Python environments?\"\n\nThe answer is \"Absolutely!\", and it's super easy actually.\n\nThis article will show you how to export networks/models to be used somewhere other than Python.\n\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import gluoncv as gcv\nfrom gluoncv.utils import export_block" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "First of all, we need a network to play with, a pre-trained one is perfect\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "net = gcv.model_zoo.get_model('resnet18_v1', pretrained=True)\nexport_block('resnet18_v1', net, preprocess=True, layout='HWC')\nprint('Done.')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| ".. hint::\n\n Use ``preprocess=True`` will add a default preprocess layer above the network,\n which will subtract mean [123.675, 116.28, 103.53], divide\n std [58.395, 57.12, 57.375], and convert original image (B, H, W, C and range [0, 255]) to\n tensor (B, C, H, W) as network input. This is the default preprocess behavior of all GluonCV\n pre-trained models. With this preprocess head, you can use raw RGB image in C++ without\n explicitly applying these operations.\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The above code generates two files: xxxx.json and xxxx.params\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import glob\nprint(glob.glob('*.json') + glob.glob('*.params'))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "JSON file includes computational graph and params file includes pre-trained weights.\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The exportable networks are not limited to image classification models.\nWe can export detection and segmentation networks as well:\n\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# YOLO\nnet = gcv.model_zoo.get_model('yolo3_darknet53_coco', pretrained=True)\nexport_block('yolo3_darknet53_coco', net)\n\n# FCN\nnet = gcv.model_zoo.get_model('fcn_resnet50_ade', pretrained=True)\nexport_block('fcn_resnet50_ade', net)\n\n# MaskRCNN\nnet = gcv.model_zoo.get_model('mask_rcnn_resnet50_v1b_coco', pretrained=True)\nexport_block('mask_rcnn_resnet50_v1b_coco', net)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We are all set here. Please checkout the other tutorials of how to use the JSON and params files.\n\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "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": "3.7.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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