yolov3 darknet 转 TVM 推理输出、一文读懂-阿里云开发者社区

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🍊 计算机视觉： Yolo专栏、一文读懂
🍊 Yolo 系列推荐：yolov3 darknet 转 TVM Python 推理
📆 最近更新：2022年1月10日
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📕 tvm 源码安装

tvm 源码安装，可参考博文
【初识TVM】| LLVM编译 | tvm 源码安装 | deploy ONNX models with Relay 测试【一文读懂】

前置基础安装，有兴趣可参考上文、此处不再赘述
gcc、g++ 编译安装、一文读懂
LLVM 编译安装【此处不在赘述】
tvm 源码编译安装如下

git clone --recursive https://github.com/apache/tvm.git

cd tvm

mkdir build

cp cmake/config.cmake build

cmake ..

make -j8

直接 cmake .. 输出如下

-- Found Threads: TRUE  
-- Configuring done
-- Generating done
-- Build files have been written to: /home/moli/project/project21/modelTrans/tvm/build

make -j8 输出如下

[100%] Built target tvm_objs
[100%] Linking CXX shared library libtvm.so
[100%] Built target tvm

📕 此次运行Python代码如下

该代码支持 YOLO-V2 and YOLO-V3 DarkNet Models 转 TVM 推理输出

模型下载【代码自动下载、网速不佳、也可手动下载】
模型转换【DarkNet Models 转 TVM 】
模型推理【TVM 推理示例】


"""
Compile YOLO-V2 and YOLO-V3 in DarkNet Models
=============================================
**Author**: `Siju Samuel <https://siju-samuel.github.io/>`_

This article is an introductory tutorial to deploy darknet models with TVM.
All the required models and libraries will be downloaded from the internet by the script.
This script runs the YOLO-V2 and YOLO-V3 Model with the bounding boxes
Darknet parsing have dependancy with CFFI and CV2 library
Please install CFFI and CV2 before executing this script

.. code-block:: bash

  pip install cffi
  pip install opencv-python
"""

"""
**Second release Author**: 墨理学AI 
=============================================
CSDN 博客主页
<https://positive.blog.csdn.net/>

计算机视觉各领域交流群
<https://gitee.com/bravePatch/datasets/blob/master/jindachang.md>
"""

# numpy and matplotlib
import numpy as np
import matplotlib.pyplot as plt
import sys

# tvm, relay
import tvm
from tvm import te
from tvm import relay
from ctypes import *
from tvm.contrib.download import download_testdata
from tvm.relay.testing.darknet import __darknetffi__
import tvm.relay.testing.yolo_detection
import tvm.relay.testing.darknet

######################################################################
# Choose the model
# -----------------------
# Models are: 'yolov2', 'yolov3' or 'yolov3-tiny'

# Model name
MODEL_NAME = "yolov3"

######################################################################
# Download required files
# -----------------------
# Download cfg and weights file if first time.
CFG_NAME = MODEL_NAME + ".cfg"
WEIGHTS_NAME = MODEL_NAME + ".weights"
REPO_URL = "https://github.com/dmlc/web-data/blob/main/darknet/"
CFG_URL = REPO_URL + "cfg/" + CFG_NAME + "?raw=true"
WEIGHTS_URL = "https://pjreddie.com/media/files/" + WEIGHTS_NAME

cfg_path = download_testdata(CFG_URL, CFG_NAME, module="darknet")
weights_path = download_testdata(WEIGHTS_URL, WEIGHTS_NAME, module="darknet")

# Download and Load darknet library
if sys.platform in ["linux", "linux2"]:
    DARKNET_LIB = "libdarknet2.0.so"
    DARKNET_URL = REPO_URL + "lib/" + DARKNET_LIB + "?raw=true"
elif sys.platform == "darwin":
    DARKNET_LIB = "libdarknet_mac2.0.so"
    DARKNET_URL = REPO_URL + "lib_osx/" + DARKNET_LIB + "?raw=true"
else:
    err = "Darknet lib is not supported on {} platform".format(sys.platform)
    raise NotImplementedError(err)

lib_path = download_testdata(DARKNET_URL, DARKNET_LIB, module="darknet")

DARKNET_LIB = __darknetffi__.dlopen(lib_path)
net = DARKNET_LIB.load_network(cfg_path.encode("utf-8"), weights_path.encode("utf-8"), 0)
dtype = "float32"
batch_size = 1

data = np.empty([batch_size, net.c, net.h, net.w], dtype)
shape_dict = {"data": data.shape}
print("Converting darknet to relay functions...")
mod, params = relay.frontend.from_darknet(net, dtype=dtype, shape=data.shape)

######################################################################
# Import the graph to Relay
# -------------------------
# compile the model
target = tvm.target.Target("llvm", host="llvm")
dev = tvm.cpu(0)
data = np.empty([batch_size, net.c, net.h, net.w], dtype)
shape = {"data": data.shape}
print("Compiling the model...")
with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target=target, params=params)

[neth, netw] = shape["data"][2:]  # Current image shape is 608x608
######################################################################
# Load a test image
# -----------------
test_image = "dog.jpg"
print("Loading the test image...")
img_url = REPO_URL + "data/" + test_image + "?raw=true"
img_path = download_testdata(img_url, test_image, "data")

data = tvm.relay.testing.darknet.load_image(img_path, netw, neth)
######################################################################
# Execute on TVM Runtime
# ----------------------
# The process is no different from other examples.
from tvm.contrib import graph_executor

m = graph_executor.GraphModule(lib["default"](dev))

# set inputs
m.set_input("data", tvm.nd.array(data.astype(dtype)))
# execute
print("Running the test image...")

# detection
# thresholds
thresh = 0.5
nms_thresh = 0.45

m.run()
# get outputs
tvm_out = []
if MODEL_NAME == "yolov2":
    layer_out = {}
    layer_out["type"] = "Region"
    # Get the region layer attributes (n, out_c, out_h, out_w, classes, coords, background)
    layer_attr = m.get_output(2).numpy()
    layer_out["biases"] = m.get_output(1).numpy()
    out_shape = (layer_attr[0], layer_attr[1] // layer_attr[0], layer_attr[2], layer_attr[3])
    layer_out["output"] = m.get_output(0).numpy().reshape(out_shape)
    layer_out["classes"] = layer_attr[4]
    layer_out["coords"] = layer_attr[5]
    layer_out["background"] = layer_attr[6]
    tvm_out.append(layer_out)

elif MODEL_NAME == "yolov3":
    for i in range(3):
        layer_out = {}
        layer_out["type"] = "Yolo"
        # Get the yolo layer attributes (n, out_c, out_h, out_w, classes, total)
        layer_attr = m.get_output(i * 4 + 3).numpy()
        layer_out["biases"] = m.get_output(i * 4 + 2).numpy()
        layer_out["mask"] = m.get_output(i * 4 + 1).numpy()
        out_shape = (layer_attr[0], layer_attr[1] // layer_attr[0], layer_attr[2], layer_attr[3])
        layer_out["output"] = m.get_output(i * 4).numpy().reshape(out_shape)
        layer_out["classes"] = layer_attr[4]
        tvm_out.append(layer_out)

elif MODEL_NAME == "yolov3-tiny":
    for i in range(2):
        layer_out = {}
        layer_out["type"] = "Yolo"
        # Get the yolo layer attributes (n, out_c, out_h, out_w, classes, total)
        layer_attr = m.get_output(i * 4 + 3).numpy()
        layer_out["biases"] = m.get_output(i * 4 + 2).numpy()
        layer_out["mask"] = m.get_output(i * 4 + 1).numpy()
        out_shape = (layer_attr[0], layer_attr[1] // layer_attr[0], layer_attr[2], layer_attr[3])
        layer_out["output"] = m.get_output(i * 4).numpy().reshape(out_shape)
        layer_out["classes"] = layer_attr[4]
        tvm_out.append(layer_out)
        thresh = 0.560

# do the detection and bring up the bounding boxes
img = tvm.relay.testing.darknet.load_image_color(img_path)
_, im_h, im_w = img.shape
dets = tvm.relay.testing.yolo_detection.fill_network_boxes(
    (netw, neth), (im_w, im_h), thresh, 1, tvm_out
)
last_layer = net.layers[net.n - 1]
tvm.relay.testing.yolo_detection.do_nms_sort(dets, last_layer.classes, nms_thresh)

coco_name = "coco.names"
coco_url = REPO_URL + "data/" + coco_name + "?raw=true"
font_name = "arial.ttf"
font_url = REPO_URL + "data/" + font_name + "?raw=true"
coco_path = download_testdata(coco_url, coco_name, module="data")
font_path = download_testdata(font_url, font_name, module="data")

with open(coco_path) as f:
    content = f.readlines()

names = [x.strip() for x in content]

tvm.relay.testing.yolo_detection.show_detections(img, dets, thresh, names, last_layer.classes)
tvm.relay.testing.yolo_detection.draw_detections(
    font_path, img, dets, thresh, names, last_layer.classes
)

plt.imshow(img.transpose(1, 2, 0))
plt.show()
plt.savefig("yolov3_infer.png")

"""
# 代码运行输出如下：

python yolov3_darknet_infer.py

File /home/moli/.tvm_test_data/darknet/yolov3.cfg exists, skip.
File /home/moli/.tvm_test_data/darknet/yolov3.weights exists, skip.
File /home/moli/.tvm_test_data/darknet/libdarknet2.0.so exists, skip.
Converting darknet to relay functions...
Compiling the model...
One or more operators have not been tuned. Please tune your model for better performance. Use DEBUG logging level to see more details.
Loading the test image...
File /home/moli/.tvm_test_data/data/dog.jpg exists, skip.
Running the test image...
File /home/moli/.tvm_test_data/data/coco.names exists, skip.
File /home/moli/.tvm_test_data/data/arial.ttf exists, skip.
class:['dog 0.994'] left:127 right:227 top:316 bottom:533
class:['truck 0.9266'] left:471 right:83 top:689 bottom:169
class:['bicycle 0.9984'] left:111 right:113 top:577 bottom:447

"""