DJL currently supports symbolic model loading from MXNet. A gluon HybridBlock can be converted into a symbol for loading by doing as follows:
from mxnet import nd
from mxnet.gluon import nn
# create a simple HybridSequential block
net = nn.HybridSequential()
net.add(nn.Dense(256, activation='relu'))
net.add(nn.Dense(10))
# initialize and hybridize the block
net.initialize()
net.hybridize()
# create sample input and run forward once
x = nd.random.uniform(shape=(2, 20))
net(x)
# export your model
net.export("sample_model")
After this is run, you will find sample_model-0000.params and sample_model-symbol.json in your local path.
These can be loaded in DJL.
In real applications, you may want to create and train a HybridBlock before exporting it. The code block below shows how you can convert a GluonCV pretrained model:
import mxnet as mx
from gluoncv import model_zoo
# get the pretrained model from the gluon model zoo
net = model_zoo.get_model('resnet18_v1', pretrained=True)
net.hybridize()
# create a sample input and run forward once (required for tracing)
x = nd.random.uniform(shape=(1, 3, 224, 224))
net(x)
# export your model
net.export("sample_model")