TVM darknet yolov3算子優化與量化代碼的配置方法

使用以下接口函數
? tvm.relay.optimize
? quantize.quantize
實際代碼：

convert nnvm to relay

print(“convert nnvm symbols into relay function…”)
#from nnvm.to_relay import to_relay
func, params = to_relay(sym, shape, ‘float32’, params=params)

optimization

print(“optimize relay graph…”)
with tvm.relay.build_config(opt_level=2):
func = tvm.relay.optimize(func, target, params)

quantize

print(“apply quantization…”)
from tvm.relay import quantize
with quantize.qconfig():
func = quantize.quantize(func, params)

參考鏈接：
https://github.com/makihiro/tvm_yolov3_sample/blob/master/yolov3_quantize_sample.py

完全代碼如下
早期版本，可以使用新的TVM版本修改。

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“”"
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
“”"

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)
######################################################################

Compile the model on NNVM

-------------------------

compile the model

local = True

if local:
target = ‘llvm’
ctx = tvm.cpu(0)
else:
target = ‘cuda’
ctx = tvm.gpu(0)

data = np.empty([batch_size, net.c, net.h, net.w], dtype)
shape = {‘data’: data.shape}

dtype_dict = {}

convert nnvm to relay

print(“convert nnvm symbols into relay function…”)
#from nnvm.to_relay import to_relay
func, params = to_relay(sym, shape, ‘float32’, params=params)

optimization

print(“optimize relay graph…”)
with tvm.relay.build_config(opt_level=2):
func = tvm.relay.optimize(func, target, params)

quantize

print(“apply quantization…”)
from tvm.relay import quantize
with quantize.qconfig():
func = quantize.quantize(func, params)

Relay build

print(“Compiling the model…”)
print(func.astext(show_meta_data=False))
with tvm.relay.build_config(opt_level=3):
graph, lib, params = tvm.relay.build(func, target=target, params=params)

Save the model

tmp = util.tempdir()
lib_fname = tmp.relpath(‘model.tar’)
lib.export_library(lib_fname)

NNVM

with nnvm.compiler.build_config(opt_level=2):

graph, lib, params = nnvm.compiler.build(sym, target, shape, dtype_dict, params)

#[neth, netw] = shape[‘data’][2:] # Current image shape is 608x608
######################################################################

######################################################################

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")

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()

參考鏈接：
https://github.com/makihiro/tvm_yolov3_sample/blob/master/yolov3_quantize_sample.py
https://tvm.apache.org/docs/tutorials/frontend/from_darknet.html#sphx-glr-tutorials-frontend-from-darknet-py

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