Ishitori commented on a change in pull request #12542: [MXNET-949] Module API to Gluon API tutorial URL: https://github.com/apache/incubator-mxnet/pull/12542#discussion_r217832716
########## File path: docs/tutorials/python/module_to_gluon.md ########## @@ -0,0 +1,297 @@ + +# Converting Module API code to the Gluon API + +Sometimes, you find yourself in the situation where the model you want to use has been written using the symbolic Module API rather than the simpler, easier-to-debug, more flexible, imperative Gluon API. In this tutorial, we will give you a comprehensive guide you can use in order to see how you can transform your Module code, to work with the Gluon API. + +The different steps to take into consideration are: + +I) Data loading + +II) Model definition + +III) Loss + +IV) Training Loop + +V) Exporting Models + +In the following section we will look at 1:1 mappings between the Module and the Gluon ways of training a neural networks. + +## I - Data Loading + + +```python +import logging +logging.basicConfig(level=logging.INFO) + +import numpy as np +import mxnet as mx +from mxnet.gluon.data import ArrayDataset, DataLoader +from mxnet.gluon import nn +from mxnet import gluon + +batch_size = 5 +dataset_length = 200 +``` + +#### Module + +When using the Module API we use a [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter), in addition to the data itself, the [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) contains information about the name of the input symbols. + +Let's create some random data, following the same format as grayscale 28x28 images. + + +```python +train_data = np.random.rand(dataset_length, 28,28).astype('float32') +train_label = np.random.randint(0, 10, (dataset_length,)).astype('float32') +``` + + +```python +data_iter = mx.io.NDArrayIter(data=train_data, label=train_label, batch_size=batch_size, shuffle=False, data_name='data', label_name='softmax_label') +for batch in data_iter: + print(batch.data[0].shape, batch.label[0]) + break; +``` + + (5, 28, 28) + [5. 0. 3. 4. 9.] + <NDArray 5 @cpu(0)> + + +#### Gluon + +With Gluon, the preferred method is to use a [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) that make use of a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) to prefetch asynchronously the data. + + +```python +dataset = ArrayDataset(train_data, train_label) +dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0) +for data, label in dataloader: + print(data.shape, label) + break +``` + + (5, 28, 28) + [5. 0. 3. 4. 9.] + <NDArray 5 @cpu(0)> + + +#### Notable differences + +- Gluon keeps a strict separation between data holding, and data loading / fetching. The `Dataset` role is to hold onto some data, in or out of memory, and the `DataLoader` role is to request certain indices of the dataset, in the main thread or through multi-processing workers. This flexible API allows to efficiently pre-fetch data and separate the concerns. +- In the module API, `DataIter`s are responsible for both holding the data and iterating through it. Some `DataIter` support multi-threading like the [`ImageRecordIter`](https://mxnet.incubator.apache.org/api/python/io/io.html#mxnet.io.ImageRecordIter), while other don't like the `NDArrayIter`. + +You can checkout the [`Dataset` and `DataLoader` tutorial](https://mxnet.incubator.apache.org/tutorials/gluon/datasets.html). You can either rewrite your code in order to use one of the provided [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) class, like the [`ArrayDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=arraydataset#mxnet.gluon.data.ArrayDataset) or the [`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset), or you can simply wrap your existing [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) to have a similar usage pattern as a `DataLoader`: + + +```python +class DataIterLoader(): + def __init__(self, data_iter): + self.data_iter = data_iter + + def __iter__(self): + self.data_iter.reset() + return self + + def __next__(self): + batch = self.data_iter.__next__() + assert len(batch.data) == len(batch.label) == 1 + data = batch.data[0] + label = batch.label[0] + return data, label + + def next(self): + return self.__next__() # for Python 2 +``` + + +```python +data_iter = mx.io.NDArrayIter(data=train_data, label=train_label, batch_size=batch_size) +data_iter_loader = DataIterLoader(data_iter) +for data, label in data_iter_loader: + print(data.shape, label) + break +``` + + (5, 28, 28) + [5. 0. 3. 4. 9.] + <NDArray 5 @cpu(0)> + + +## II - Model definition + +Let's look at the model definition from the [MNIST Module Tutorial](https://mxnet.incubator.apache.org/tutorials/python/mnist.html): + + +```python +ctx = mx.gpu() Review comment: Let's not expect people to have gpu ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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