[GitHub] rahul003 commented on a change in pull request #10283: [MXNET-242][Tutorial] Fine-tuning ONNX model in Gluon

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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_r177911289
 
 

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 File path: docs/tutorials/onnx/fine_tuning_gluon.md
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+
+# 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:
   Should we add this to mxnet.gluon.data.vision.datasets as another option? So that other tutorials can also benefit from this.

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