TVM Compiler中文教程：TVM部署SSD模型

部署SSD模型

我们使用GluonCV预训练SSD模型和转换成Relay IR中间表示

import tvm
from matplotlib import pyplot as plt
from tvm.relay.testing.config import ctx_list
from tvm import relay
from tvm.contrib import graph_runtime
from tvm.contrib.download import download_testdata
from gluoncv import model_zoo, data, utils
supported_model = [
    'ssd_512_resnet50_v1_voc',
    'ssd_512_resnet50_v1_coco',
    'ssd_512_resnet101_v2_voc',
    'ssd_512_mobilenet1.0_voc',
    'ssd_512_mobilenet1.0_coco',
]
model_name = supported_model[0]
dshape = (1, 3, 512, 512)
target_list = ctx_list()
# 下载和预处理demo图像
im_fname = download_testdata('https://github.com/dmlc/web-data/blob/master/' +
                             'gluoncv/detection/street_small.jpg?raw=true',
                             'street_small.jpg', module='data')
x, img = data.transforms.presets.ssd.load_test(im_fname, short=512)
#转换和编译模型for CPU
block = model_zoo.get_model(model_name, pretrained=True)
def build(target):
    net, params = relay.frontend.from_mxnet(block,{ "data": dshape})
    with relay.build_config(opt_level=3):
        graph, lib, params = relay.build(net,target,params=params)
        return graph, lib, params
#创建TVM运行时和推理
def run(graph, lib, params, ctx):
    #创建TVM运行时
    m = graph_runtime.create(graph, lib, ctx)
    tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)
    m.set_input('data', tvm_input)
    m.set_input(**params)
    #执行
    m.run()
    #获取输出
    class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)
    return class_IDs, scores, bounding_boxs
for target, ctx in target_list:
    graph, lib, params = build(target)
    class_IDs, scores, bounding_boxs = run(graph, lib, params, ctx)
#显示结果
ax = utils.viz.plot_bbox(img, bounding_boxs,asnumpy()[0], scores.asnumpy()[0],class_IDs.asnumpy()[0],class_names=block.classes)
plt.show