Running GluonCV object detection model on Android - mxnet

Question

I need to run a custom GluonCV object detection module on Android.
I already fine-tuned the model (ssd_512_mobilenet1.0_custom) on a custom dataset, I tried running inference with it (loading the .params file produced during the training) and everything works perfectly on my computer. Now, I need to export this to Android.
I was referring to this answer to figure out the procedure, there are 3 suggested options:
You can use ONNX to convert models to other runtimes, for example [...] NNAPI for Android
You can use TVM
You can use SageMaker Neo + DLR runtime [...]
Regarding the first one, I converted my model to ONNX.
However, in order to use it with NNAPI, it is necessary to convert it to daq. In the repository, they provide a precomplied AppImage of onnx2daq to make the conversion, but the script returns an error. I checked the issues section, and they report that "It actually fails for all onnx object detection models".
Then, I gave a try to DLR, since it's suggested to be the easiest way.
As I understand, in order to use my custom model with DLR, I would first need to compile it with TVM (which also covers the second point mentioned in the linked post). In the repo, they provide a Docker image with some conversion scripts for different frameworks.
I modified the 'compile_gluoncv.py' script, and now I have:
#!/usr/bin/env python3
from tvm import relay
import mxnet as mx
from mxnet.gluon.model_zoo.vision import get_model
from tvm_compiler_utils import tvm_compile
shape_dict = {'data': (1, 3, 300, 300)}
dtype='float32'
ctx = [mx.cpu(0)]
classes_custom = ["CML_mug"]
block = get_model('ssd_512_mobilenet1.0_custom', classes=classes_custom, pretrained_base=False, ctx=ctx)
block.load_parameters("ep_035.params", ctx=ctx) ### this is the file produced by training on the custom dataset
for arch in ["arm64-v8a", "armeabi-v7a", "x86_64", "x86"]:
sym, params = relay.frontend.from_mxnet(block, shape=shape_dict, dtype=dtype)
func = sym["main"]
func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_params, func.attrs)
tvm_compile(func, params, arch, dlr_model_name)
However, when I run the script it returns the error:
ValueError: Model ssd_512_mobilenet1.0_custom is not supported. Available options are
alexnet
densenet121
densenet161
densenet169
densenet201
inceptionv3
mobilenet0.25
mobilenet0.5
mobilenet0.75
mobilenet1.0
mobilenetv2_0.25
mobilenetv2_0.5
mobilenetv2_0.75
mobilenetv2_1.0
resnet101_v1
resnet101_v2
resnet152_v1
resnet152_v2
resnet18_v1
resnet18_v2
resnet34_v1
resnet34_v2
resnet50_v1
resnet50_v2
squeezenet1.0
squeezenet1.1
vgg11
vgg11_bn
vgg13
vgg13_bn
vgg16
vgg16_bn
vgg19
vgg19_bn
Am I doing something wrong? Is this thing even possible?
As a side note, after this I'd need to deploy on Android a pose detection model (simple_pose_resnet18_v1b) and an activity recognition one (i3d_nl10_resnet101_v1_kinetics400) as well.

Answer 1

You actually can run GluonCV model directly on Android with Deep Java Library (DJL)
What you need to do is:
hyridize your GluonCV model and save as MXNet model
Build MXNet engine for android, MXNET already support Android build
Include MXNet shared library into your android project
Use DJL in your android project, you can follow this DJL Android demo for PyTorch

Answer 2

The error message is self-explanatory - there is no model "ssd_512_mobilenet1.0_custom" supported by mxnet.gluon.model_zoo.vision.get_model. You are confusing GluonCV's get_model with MXNet Gluon's get_model.
Replace
block = get_model('ssd_512_mobilenet1.0_custom',
classes=classes_custom, pretrained_base=False, ctx=ctx)
with
import gluoncv
block = gluoncv.model_zoo.get_model('ssd_512_mobilenet1.0_custom',
classes=classes_custom, pretrained_base=False, ctx=ctx)