Saving and Loading Gluon Models¶
Training large models take a lot of time and it is a good idea to save the trained models to files to avoid training them again and again. There are a number of reasons to do this. For example, you might want to do inference on a machine that is different from the one where the model was trained. Sometimes model’s performance on validation set decreases towards the end of the training because of overfitting. If you saved your model parameters after every epoch, at the end you can decide to use the model that performs best on the validation set. Another reason would be to train your model using one language (like Python that has a lot of tools for training) and run inference using a different language (like Scala probably because your application is built on Scala).
In this tutorial, we will learn ways to save and load Gluon models. There are two ways to save/load Gluon models:
1. Save/load model parameters only
Parameters of any Gluon model can be saved using the save_parameters and load_parameters method. This does not save model architecture. This method is used to save parameters of dynamic (non-hybrid) models. Model architecture cannot be saved for dynamic models because model architecture changes during execution.
2. Save/load model parameters AND architecture
The Model architecture of Hybrid models stays static and don’t change during execution. Therefore both model parameters AND architecture can be saved and loaded using export, imports methods.
Let’s look at the above methods in more detail. Let’s start by importing the modules we’ll need.
[1]:
from __future__ import print_function
import mxnet as mx
from mxnet import np, npx, autograd, gluon
from mxnet.gluon.data.vision import transforms
import numpy as onp
Setup: build and train a simple model¶
We need a trained model before we can save it to a file. So let’s go ahead and build a very simple convolutional network and train it on MNIST data.
Let’s define a helper function to build a LeNet model and another helper to train LeNet with MNIST.
[2]:
# Use GPU if one exists, else use CPU
device = mx.gpu() if mx.device.num_gpus() else mx.cpu()
# MNIST images are 28x28. Total pixels in input layer is 28x28 = 784
num_inputs = 784
# Clasify the images into one of the 10 digits
num_outputs = 10
# 64 images in a batch
batch_size = 64
# Load the training data
train_data = gluon.data.DataLoader(gluon.data.vision.MNIST(train=True).transform_first(transforms.ToTensor()),
batch_size, shuffle=True)
# Build a simple convolutional network
def build_lenet(net):
# First convolution
net.add(gluon.nn.Conv2D(channels=20, kernel_size=5, activation='relu'))
net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
# Second convolution
net.add(gluon.nn.Conv2D(channels=50, kernel_size=5, activation='relu'))
net.add(gluon.nn.MaxPool2D(pool_size=2, strides=2))
# Flatten the output before the fully connected layers
net.add(gluon.nn.Flatten())
# First fully connected layers with 512 neurons
net.add(gluon.nn.Dense(512, activation="relu"))
# Second fully connected layer with as many neurons as the number of classes
net.add(gluon.nn.Dense(num_outputs))
return net
# Train a given model using MNIST data
def train_model(model):
# Initialize the parameters with Xavier initializer
model.initialize(mx.init.Xavier(), device=device)
# Use cross entropy loss
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
# Use Adam optimizer
trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': .001})
# Train for one epoch
for epoch in range(1):
# Iterate through the images and labels in the training data
for batch_num, (data, label) in enumerate(train_data):
# get the images and labels
data = data.to_device(device)
label = label.to_device(device)
# Ask autograd to record the forward pass
with autograd.record():
# Run the forward pass
output = model(data)
# Compute the loss
loss = softmax_cross_entropy(output, label)
# Compute gradients
loss.backward()
# Update parameters
trainer.step(data.shape[0])
# Print loss once in a while
if batch_num % 50 == 0:
curr_loss = np.mean(loss).item()
print("Epoch: %d; Batch %d; Loss %f" % (epoch, batch_num, curr_loss))
Downloading /home/jenkins_slave/.mxnet/datasets/mnist/train-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/train-images-idx3-ubyte.gz...
Downloading /home/jenkins_slave/.mxnet/datasets/mnist/train-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/train-labels-idx1-ubyte.gz...
[04:46:12] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU
Let’s build a model and train it. After training, we will save and restore this model from a file.
[3]:
net = build_lenet(gluon.nn.Sequential())
train_model(net)
[04:46:15] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for GPU
[04:46:16] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable
[04:46:17] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable
Epoch: 0; Batch 0; Loss 2.282379
Epoch: 0; Batch 50; Loss 0.485588
Epoch: 0; Batch 100; Loss 0.155776
Epoch: 0; Batch 150; Loss 0.054582
Epoch: 0; Batch 200; Loss 0.027289
Epoch: 0; Batch 250; Loss 0.019336
Epoch: 0; Batch 300; Loss 0.039793
Epoch: 0; Batch 350; Loss 0.194553
Epoch: 0; Batch 400; Loss 0.074898
Epoch: 0; Batch 450; Loss 0.018919
Epoch: 0; Batch 500; Loss 0.021001
Epoch: 0; Batch 550; Loss 0.046894
Epoch: 0; Batch 600; Loss 0.006695
Epoch: 0; Batch 650; Loss 0.033298
Epoch: 0; Batch 700; Loss 0.022084
Epoch: 0; Batch 750; Loss 0.171372
Epoch: 0; Batch 800; Loss 0.099912
Epoch: 0; Batch 850; Loss 0.041335
Epoch: 0; Batch 900; Loss 0.093764
Epoch: 0; Batch 0; Loss 2.288904 Epoch: 0; Batch 50; Loss 0.269372 Epoch: 0; Batch 100; Loss 0.238990 Epoch: 0; Batch 150; Loss 0.320592 Epoch: 0; Batch 200; Loss 0.048619 Epoch: 0; Batch 250; Loss 0.121555 Epoch: 0; Batch 300; Loss 0.083645 Epoch: 0; Batch 350; Loss 0.040627 Epoch: 0; Batch 400; Loss 0.195946 Epoch: 0; Batch 450; Loss 0.155514 Epoch: 0; Batch 500; Loss 0.031762 Epoch: 0; Batch 550; Loss 0.056516 Epoch: 0; Batch 600; Loss 0.095174 Epoch: 0; Batch 650; Loss 0.054901 Epoch: 0; Batch 700; Loss 0.030067 Epoch: 0; Batch 750; Loss 0.102611 Epoch: 0; Batch 800; Loss 0.010036 Epoch: 0; Batch 850; Loss 0.051853 Epoch: 0; Batch 900; Loss 0.008402
Saving model parameters to file¶
Okay, we now have a model (net) that we can save to a file. Let’s save the parameters of this model to a file using the save_parameters function.
[4]:
file_name = "net.params"
net.save_parameters(file_name)
We have successfully saved the parameters of the model into a file.
Loading model parameters from file¶
Let’s now create a network with the parameters we saved into the file. We build the network again using the helper first and then load the weights from the file we saved using the load_parameters function.
[5]:
new_net = build_lenet(gluon.nn.Sequential())
new_net.load_parameters(file_name, device=device)
Note that to do this, we need the definition of the network as Python code. If we want to recreate this network on a different machine using the saved weights, we need the same Python code (build_lenet) that created the network to create the new_net object shown above. This means Python code needs to be copied over to any machine where we want to run this network.
If our network is Hybrid, we can even save the network architecture into files and we won’t need the network definition in a Python file to load the network. We’ll see how to do it in the next section.
Let’s test the model we just loaded from file.
[6]:
import matplotlib.pyplot as plt
def verify_loaded_model(net):
"""Run inference using ten random images.
Print both input and output of the model"""
def transform(data, label):
return data.astype(np.float32)/255, label.astype(np.float32)
# Load ten random images from the test dataset
sample_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=False).transform(transform),
10, shuffle=True)
for data, label in sample_data:
# Display the images
img = np.transpose(data, (1,0,2,3))
img = npx.reshape(img, (28,10*28,1))
imtiles = np.tile(img, (1,1,3))
plt.imshow(imtiles.asnumpy())
plt.show()
# Display the predictions
data = np.transpose(data, (0, 3, 1, 2))
out = net(data.to_device(device))
predictions = np.argmax(out, axis=1)
print('Model predictions: ', predictions.asnumpy())
break
verify_loaded_model(new_net)
Downloading /home/jenkins_slave/.mxnet/datasets/mnist/t10k-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/t10k-images-idx3-ubyte.gz...
Downloading /home/jenkins_slave/.mxnet/datasets/mnist/t10k-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/t10k-labels-idx1-ubyte.gz...
Model predictions: [5 4 6 0 9 0 4 9 1 9]
Model predictions: [1. 1. 4. 5. 0. 5. 7. 0. 3. 6.]
Saving model parameters AND architecture to file¶
Hybrid models can be serialized as JSON files using the export function. Once serialized, these models can be loaded from other language bindings like C++ or Scala for faster inference or inference in different environments.
Note that the network we created above is not a Hybrid network and therefore cannot be serialized into a JSON file. So, let’s create a Hybrid version of the same network and train it.
[7]:
net = build_lenet(gluon.nn.HybridSequential())
net.hybridize()
train_model(net)
Epoch: 0; Batch 0; Loss 2.297905
Epoch: 0; Batch 50; Loss 0.279512
Epoch: 0; Batch 100; Loss 0.320809
Epoch: 0; Batch 150; Loss 0.122951
Epoch: 0; Batch 200; Loss 0.172625
Epoch: 0; Batch 250; Loss 0.063134
Epoch: 0; Batch 300; Loss 0.162333
Epoch: 0; Batch 350; Loss 0.061574
Epoch: 0; Batch 400; Loss 0.063895
Epoch: 0; Batch 450; Loss 0.204456
Epoch: 0; Batch 500; Loss 0.107851
Epoch: 0; Batch 550; Loss 0.025380
Epoch: 0; Batch 600; Loss 0.056878
Epoch: 0; Batch 650; Loss 0.044420
Epoch: 0; Batch 700; Loss 0.027201
Epoch: 0; Batch 750; Loss 0.013869
Epoch: 0; Batch 800; Loss 0.048232
Epoch: 0; Batch 850; Loss 0.038755
Epoch: 0; Batch 900; Loss 0.037504
Epoch: 0; Batch 0; Loss 2.323284 Epoch: 0; Batch 50; Loss 0.444733 Epoch: 0; Batch 100; Loss 0.103407 Epoch: 0; Batch 150; Loss 0.166772 Epoch: 0; Batch 200; Loss 0.227569 Epoch: 0; Batch 250; Loss 0.069515 Epoch: 0; Batch 300; Loss 0.074086 Epoch: 0; Batch 350; Loss 0.074382 Epoch: 0; Batch 400; Loss 0.026569 Epoch: 0; Batch 450; Loss 0.097248 Epoch: 0; Batch 500; Loss 0.059895 Epoch: 0; Batch 550; Loss 0.053194 Epoch: 0; Batch 600; Loss 0.076294 Epoch: 0; Batch 650; Loss 0.047274 Epoch: 0; Batch 700; Loss 0.007898 Epoch: 0; Batch 750; Loss 0.039478 Epoch: 0; Batch 800; Loss 0.031342 Epoch: 0; Batch 850; Loss 0.059289 Epoch: 0; Batch 900; Loss 0.037809
We now have a trained hybrid network. This can be exported into files using the export function. The export function will export the model architecture into a .json file and model parameters into a .params file.
[8]:
net.export("lenet", epoch=1)
[8]:
('lenet-symbol.json', 'lenet-0001.params')
export in this case creates lenet-symbol.json and lenet-0001.params in the current directory.
Loading model parameters AND architecture from file¶
From Python¶
Serialized Hybrid networks (saved as .JSON and .params file) can be loaded and used inside Python frontend using gluon.nn.SymbolBlock. To demonstrate that, let’s load the network we serialized above.
[9]:
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
deserialized_net = gluon.nn.SymbolBlock.imports("lenet-symbol.json", ['data'], "lenet-0001.params", device=device)
deserialized_net now contains the network we deserialized from files. Let’s test the deserialized network to make sure it works.
[10]:
verify_loaded_model(deserialized_net)
Model predictions: [2 0 2 2 4 5 9 8 4 7]
Model predictions: [4. 8. 0. 1. 5. 5. 8. 8. 1. 9.]
That’s all! We learned how to save and load Gluon networks from files. Parameters of any Gluon network can be persisted into files. For hybrid networks, both the architecture of the network and the parameters can be saved to and loaded from files.