compile-cl.py

#!/usr/bin/env python3

import nnvm
import tvm
import numpy as np

from mxnet.gluon.model_zoo.vision import get_model

batch_size = 1
image_shape = (3, 224, 224)
data_shape = (batch_size,) + image_shape

block = get_model('resnet18_v1', pretrained=True)
#block = get_model('squeezenet1.1', pretrained=True)

sym, params = nnvm.frontend.from_mxnet(block)
# we want a probability so add a softmax operator
sym = nnvm.sym.softmax(sym)

opt_level = 3
target = tvm.target.mali()
#target_host = "llvm -target=armv7l-linux-gnueabihf"
print('target:', target, ', opt_level:', opt_level, ', data_shape:', data_shape)
print("Compiling...")

with nnvm.compiler.build_config(opt_level=opt_level):
    graph, lib, params = nnvm.compiler.build(sym, target, shape={"data": data_shape}, params=params)
print("Compilation done")

print("Saving files")
# save the graph, lib and params into separate files
path_lib = "model-cl.tar"
lib.export_library(path_lib)
with open("model-cl.json", "w") as fo:
    fo.write(graph.json())
with open("model-cl.params", "wb") as fo:
    fo.write(nnvm.compiler.save_param_dict(params))
print("Files saved")