[GitHub] nswamy commented on a change in pull request #13411: [WIP] Gluon end to end tutorial

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nswamy commented on a change in pull request #13411: [WIP] Gluon end to end tutorial
URL: https://github.com/apache/incubator-mxnet/pull/13411#discussion_r237707450
 
 

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+# Gluon: from experiment to deployment, an end to end example
+
+## Overview
+
+MXNet Gluon API comes with a lot of great features and it can provide you everything you need from experiment to deploy the model.
+In this tutorial, we will walk you through a common used case on how to build a model using gluon, train it on your data, and deploy it for inference.
+
+Let's say you want to build a service that provides flower species recognition. A common use case is, you don't have enough data to train a good model like ResNet50.
+What you can do is utilize pre-trained model from Gluon, tweak the model according to your neeed, fine-tune the model on your small dataset, and deploy the model to integrate with your service.
+
+We will use the [Oxford 102 Category Flower Dateset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/) as an example to show you the steps.
+
+## Prepare training data
+
+You can use this [script](https://github.com/Arsey/keras-transfer-learning-for-oxford102/blob/master/bootstrap.py) to download and organize your data into train, test, and validation sets. Simply run:
+```python
+python bootstrap.py
+```
+
+Now your data will be organized into the following format, all the images belong to the same category will be put together
+```
+data
+├── train
+│   ├── 0
+│   │   ├── image_06736.jpg
+│   │   ├── image_06741.jpg
+...
+│   ├── 1
+│   │   ├── image_06755.jpg
+│   │   ├── image_06899.jpg
+...
+├── test
+│   ├── 0
+│   │   ├── image_00731.jpg
+│   │   ├── image_0002.jpg
+...
+│   ├── 1
+│   │   ├── image_00036.jpg
+│   │   ├── image_05011.jpg
+
+```
+
+
+# Training using Gluon
+### Define Hyper-paramerters
+Now let's first import neccesarry packages:
+```python
+import mxnet as mx
+import numpy as np
+import os, time
+
+from mxnet import gluon, init
+from mxnet import autograd as ag
+from mxnet.gluon import nn
+from mxnet.gluon.data.vision import transforms
+from gluoncv.model_zoo import get_model
+```
+
+and define the hyper parameter we will use for fine-tuning:
+```python
+classes = 102
+
+epochs = 1
+lr = 0.001
+per_device_batch_size = 32
+momentum = 0.9
+wd = 0.0001
+
+lr_factor = 0.75
+lr_steps = [10, 20, 30, np.inf]
+
+num_gpus = 0
+num_workers = 1
+ctx = [mx.gpu(i) for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
+batch_size = per_device_batch_size * max(num_gpus, 1)
+```
+
+### Data pre-processing
+
+We can use Gluon DataSet API, DataLoader API, and Transform API to load the images and do data augmentation:
+```python
+jitter_param = 0.4
+lighting_param = 0.1
+
+transform_train = transforms.Compose([
+    transforms.RandomResizedCrop(224),
+    transforms.RandomFlipLeftRight(),
+    transforms.RandomColorJitter(brightness=jitter_param, contrast=jitter_param,
+                                 saturation=jitter_param),
+    transforms.RandomLighting(lighting_param),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+transform_test = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+
+path = './data'
+train_path = os.path.join(path, 'train')
+val_path = os.path.join(path, 'valid')
+test_path = os.path.join(path, 'test')
+
+train_data = gluon.data.DataLoader(
+    gluon.data.vision.ImageFolderDataset(train_path).transform_first(transform_train),
+    batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+val_data = gluon.data.DataLoader(
+    gluon.data.vision.ImageFolderDataset(val_path).transform_first(transform_test),
+    batch_size=batch_size, shuffle=False, num_workers = num_workers)
+
+test_data = gluon.data.DataLoader(
+    gluon.data.vision.ImageFolderDataset(test_path).transform_first(transform_test),
+    batch_size=batch_size, shuffle=False, num_workers = num_workers)
+ ```
+
+
+### Loading pre-trained model
+
+We will use pre-trained ResNet50_v2 model, all you need to do is re-define the last softmax layer for your case. Specify the number of classes in your data and initialize the weights.
+You can also add layers to the network according to your needs.
+
+Before we go to training, one important part is to hybridize your model, it will convert your imperative code to mxnet symbolic graph. It's much more efficient to train a symbolic model,
 
 Review comment:
   What does Hybridize mean to the user, can you write in a single sentence and link to hybridize doc on website.

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