rahul003 commented on a change in pull request #10283: [MXNET-242][Tutorial] Fine-tuning ONNX model in Gluon URL: https://github.com/apache/incubator-mxnet/pull/10283#discussion_r178376584
########## File path: docs/tutorials/onnx/fine_tuning_gluon.md ########## @@ -0,0 +1,441 @@ + +# Fine-tuning an ONNX model with MXNet/Gluon + +Fine-tuning is a common practice in Transfer Learning. One can take advantage of the pre-trained weights of a network, and use them as an initializer for their own task. Indeed, quite often it is difficult to gather a dataset large enough that it would allow training from scratch deep and complex networks such as ResNet152 or VGG16. For example in an image classification task, using a network trained on a large dataset like ImageNet gives a good base from which the weights can be slightly updated, or fine-tuned, to predict accurately the new classes. We will see in this tutorial that this can be achieved even with a relatively small number of new training examples. + + +[Open Neural Network Exchange (ONNX)](https://github.com/onnx/onnx) provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. + +In this tutorial we will: + +- learn how to pick a specific layer from a pre-trained .onnx model file +- learn how to load this model in Gluon and fine-tune it on a different dataset + +## Pre-requisite + +To run the tutorial you will need to have installed the following python modules: +- [MXNet](http://mxnet.incubator.apache.org/install/index.html) +- [onnx](https://github.com/onnx/onnx) +- matplotlib +- wget + +We recommend that you have done this tutorial: +- [Inference using an ONNX model on MXNet Gluon](https://mxnet.incubator.apache.org/tutorials/onnx/inference_on_onnx_model.html) + + +```python +import numpy as np +import mxnet as mx +from mxnet import gluon, nd, autograd +from mxnet.gluon.data.vision.datasets import ImageFolderDataset +from mxnet.gluon.data import DataLoader +import mxnet.contrib.onnx as onnx_mxnet +%matplotlib inline +import matplotlib.pyplot as plt +import tarfile, os +import wget +import json +import multiprocessing +``` + + +### Downloading supporting files +These are images and a vizualisation script + + +```python +image_folder = "images" +utils_file = "utils.py" # contain utils function to plot nice visualization +images = ['wrench', 'dolphin', 'lotus'] +base_url = "https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/{}?raw=true"; + +if not os.path.isdir(image_folder): + os.makedirs(image_folder) + for image in images: + wget.download(base_url.format("{}/{}.jpg".format(image_folder, image)), image_folder) +if not os.path.isfile(utils_file): + wget.download(base_url.format(utils_file)) +``` + + +```python +from utils import * +``` + +## Downloading a model from the ONNX model zoo + +We download a pre-trained model, in our case the [vgg16](https://arxiv.org/abs/1409.1556) model, trained on [ImageNet](http://www.image-net.org/) from the [ONNX model zoo](https://github.com/onnx/models). The model comes packaged in an archive `tar.gz` file containing an `model.onnx` model file and some sample input/output data. + + +```python +base_url = "https://s3.amazonaws.com/download.onnx/models/"; +current_model = "vgg16" +model_folder = "model" +archive_file = "{}.tar.gz".format(current_model) +archive_path = os.path.join(model_folder, archive_file) +url = "{}{}".format(base_url, archive_file) +onnx_path = os.path.join(model_folder, current_model, 'model.onnx') + +# Create the model folder and download the zipped model +if not os.path.isdir(model_folder): + os.makedirs(model_folder) +if not os.path.isfile(archive_path): + print('Downloading the {} model to {}...'.format(current_model, archive_path)) + wget.download(url, model_folder) + print('{} downloaded'.format(current_model)) + +# Extract the model +if not os.path.isdir(os.path.join(model_folder, current_model)): + print('Extracting {} in {}...'.format(archive_path, model_folder)) + tar = tarfile.open(archive_path, "r:gz") + tar.extractall(model_folder) + tar.close() + print('Model extracted.') +``` + +## Downloading the Caltech101 dataset + +The [Caltech101 dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) is made of pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. + +*L. Fei-Fei, R. Fergus and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. IEEE. CVPR 2004, Workshop on Generative-Model +Based Vision. 2004* + + +```python +data_folder = "data" +dataset_name = "101_ObjectCategories" +archive_file = "{}.tar.gz".format(dataset_name) +archive_path = os.path.join(data_folder, archive_file) +data_url = "https://s3.us-east-2.amazonaws.com/mxnet-public/"; +if not os.path.isdir(data_folder): + os.makedirs(data_folder) +if not os.path.isfile(archive_path): + print('Downloading {} in {}...'.format(archive_file, data_folder)) + wget.download("{}{}".format(data_url, archive_file), data_folder) + print('Extracting {} in {}...'.format(archive_file, data_folder)) + tar = tarfile.open(archive_path, "r:gz") + tar.extractall(data_folder) + tar.close() + print('Data extracted.') +``` Review comment: Also, It might be better to rename the folder to say Caltech101 and not just 101_ObjectCategories in the s3 bucket? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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