Deploy Single Shot Multibox Detector(SSD) model

Author: Yao Wang Leyuan Wang

This article is an introductory tutorial to deploy SSD models with TVM. We will use GluonCV pre-trained SSD model and convert it to Relay IR

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

Preliminary and Set parameters

Note

We support compiling SSD on bot CPUs and GPUs now.

To get best inference performance on CPU, change target argument according to your device and follow the tune_relay_x86 to tune x86 CPU and tune_relay_arm for arm CPU.

To get best performance fo SSD on Intel graphics, change target argument to ‘opencl -device=intel_graphics’

SSD with VGG as body network is not supported yet since x86 conv2d schedule doesn’t support dilation.

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()

Download and pre-process demo image

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)

Out:

File /workspace/.tvm_test_data/data/street_small.jpg exists, skip.

Convert and compile model 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

Create TVM runtime and do inference

def run(graph, lib, params, ctx):
    # Build TVM runtime
    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)
    # execute
    m.run()
    # get outputs
    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)

Display result

ax = utils.viz.plot_bbox(img, bounding_boxs.asnumpy()[0], scores.asnumpy()[0],
                         class_IDs.asnumpy()[0], class_names=block.classes)
plt.show()
../../_images/sphx_glr_deploy_ssd_gluoncv_001.png

Total running time of the script: ( 4 minutes 17.936 seconds)

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