Tvm（1） SSD 模型

TVM 就不多介紹了，本文涉及TVM的安裝和SSD 模型的測試。

1. TVM 安裝

$ git clone --recursive https://github.com/dmlc/tvm
$ sudo apt-get update
$ sudo apt-get install -y python python-dev python-setuptools gcc libtinfo-dev zlib1g-dev
$ cd tvm
$ mkdir build
$ cp cmake/config.cmake build
$
$

LLVM 配置

安裝 TVM還需要安裝好LLVM， 一種是自編譯好的在build/config.cmake 設置參數*set(USE_LLVM /path/to/your/llvm/bin/llvm-config)* ，另一種是設置*set(USE_LLVM ON)* 自動查找。
同時set(USE_SORT ON)設置

$ cd build
$ cmake ..
$ make -j4

編譯python

$ cd python; python setup.py install --user; cd ..
$ cd topi/python; python setup.py install --user; cd ../..
$ cd nnvm/python; python setup.py install --user; cd ../..

(注意：如果沒有 –user 可能會出錯)
2. SSD

import os
import zipfile
import tvm
import mxnet as mx
import cv2
import numpy as np

from nnvm import compiler
from nnvm.frontend import from_mxnet
from tvm.contrib.download import download
from tvm.contrib import graph_runtime
from mxnet.model import load_checkpoint

model_name = "ssd_resnet50_512"
model_file = "%s.zip" % model_name
test_image = "dog.jpg"
dshape = (1, 3, 512, 512)
dtype = "float32"
target = "llvm"
ctx = tvm.cpu()

model_url = "https://github.com/zhreshold/mxnet-ssd/releases/download/v0.6/" \
            "resnet50_ssd_512_voc0712_trainval.zip"
image_url = "https://cloud.githubusercontent.com/assets/3307514/20012567/" \
            "cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg"
inference_symbol_folder = "c1904e900848df4548ce5dfb18c719c7-a28c4856c827fe766aa3da0e35bad41d44f0fb26"
inference_symbol_url = "https://gist.github.com/kevinthesun/c1904e900848df4548ce5dfb18c719c7/" \
                       "archive/a28c4856c827fe766aa3da0e35bad41d44f0fb26.zip"

dir = "ssd_model"
if not os.path.exists(dir):
    os.makedirs(dir)
model_file_path = "%s/%s" % (dir, model_file)
test_image_path = "%s/%s" % (dir, test_image)
inference_symbol_path = "%s/inference_model.zip" % dir
download(model_url, model_file_path)
download(image_url, test_image_path)
download(inference_symbol_url, inference_symbol_path)

zip_ref = zipfile.ZipFile(model_file_path, 'r')
zip_ref.extractall(dir)
zip_ref.close()
zip_ref = zipfile.ZipFile(inference_symbol_path)
zip_ref.extractall(dir)
zip_ref.close()


sym = mx.sym.load("%s/%s/ssd_resnet50_inference.json" % (dir, inference_symbol_folder))
_, arg_params, aux_params = load_checkpoint("%s/%s" % (dir, model_name), 0)
net, params = from_mxnet(sym, arg_params, aux_params)
with compiler.build_config(opt_level=3):
    graph, lib, params = compiler.build(net, target, {"data": dshape}, params=params)

# Preprocess image
image = cv2.imread(test_image_path)
img_data = cv2.resize(image, (dshape[2], dshape[3]))
img_data = img_data[:, :, (2, 1, 0)].astype(np.float32)
img_data -= np.array([123, 117, 104])
img_data = np.transpose(np.array(img_data), (2, 0, 1))
img_data = np.expand_dims(img_data, axis=0)
# Build TVM runtime
m = graph_runtime.create(graph, lib, ctx)
m.set_input('data', tvm.nd.array(img_data.astype(dtype)))
m.set_input(**params)
# execute
m.run()
# get outputs
_, oshape = compiler.graph_util.infer_shape(graph, shape={"data": dshape})
tvm_output = m.get_output(0, tvm.nd.empty(tuple(oshape[0]), dtype))

class_names = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair",
               "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant",
               "sheep", "sofa", "train", "tvmonitor"]
def display(img, out, thresh=0.5):
    import random
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    mpl.rcParams['figure.figsize'] = (10,10)
    pens = dict()
    plt.clf()
    plt.imshow(img)
    for det in out:
        cid = int(det[0])
        if cid < 0:
            continue
        score = det[1]
        if score < thresh:
            continue
        if cid not in pens:
            pens[cid] = (random.random(), random.random(), random.random())
        scales = [img.shape[1], img.shape[0]] * 2
        xmin, ymin, xmax, ymax = [int(p * s) for p, s in zip(det[2:6].tolist(), scales)]
        rect = plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False,
                             edgecolor=pens[cid], linewidth=3)
        plt.gca().add_patch(rect)
        text = class_names[cid]
        plt.gca().text(xmin, ymin-2, '{:s} {:.3f}'.format(text, score),
                       bbox=dict(facecolor=pens[cid], alpha=0.5),
                       fontsize=12, color='white')
    plt.show()

image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
display(image, tvm_output.asnumpy()[0], thresh=0.45)

錯誤 ：src/codegen/codegen.cc:27: Check failed: bf != nullptr Target llvm is not enabled
原因：LLVM 沒有裝好，又或者沒有設置好

參考文獻

1. https://docs.tvm.ai/tutorials/nnvm/deploy_ssd.html#sphx-glr-tutorials-nnvm-deploy-ssd-py
2. https://docs.tvm.ai/install/index.html