TVM compile code for resnet50_v1 for ARMv7 Mali GPU

compile-resnet50-mali.py

#!/usr/bin/env python3

import nnvm
import nnvm.testing
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
output_file_name="model-cl"
model_name = 'resnet50_v1'

block = get_model(model_name, 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 = 'llvm'
#target = 'llvm -target=armv7l-linux-gnueabihf'
#target = tvm.target.intel_graphics()
#target = tvm.target.cuda()
#target = tvm.target.arm_cpu('rasp3b')
target = tvm.target.mali()

#target_host = None
target_host = 'llvm -target=armv7l-linux-gnueabihf'

print('model_name: ', model_name, ', target:', target, ', target_host: ', target_host, ', opt_level:', opt_level, ', data_shape:', data_shape)
print("Compiling...")

#from tvm.autotvm.measure.measure_methods import set_cuda_target_arch
#set_cuda_target_arch('sm_62')

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

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