TVM yolov3优化代码修改（编译运行OK）
yolov3_quantize_sample.py
附https://github.com/makihiro/tvm_yolov3_sample代码：
yolov3_quantize_sample.py
import nnvm
import nnvm.frontend.darknet
import nnvm.testing.yolo_detection
import nnvm.testing.darknet
import matplotlib.pyplot as plt
import numpy as np
import tvm
from tvm import rpc
import sys
import cv2
import time
from ctypes import *
from tvm.contrib import util
from tvm.contrib.download import download
from nnvm.testing.darknet import __darknetffi__
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/siju-samuel/darknet/blob/master/'
CFG_URL = REPO_URL + 'cfg/' + CFG_NAME + '?raw=true'
WEIGHTS_URL = 'https://pjreddie.com/media/files/' + WEIGHTS_NAME
download(CFG_URL, CFG_NAME)
download(WEIGHTS_URL, WEIGHTS_NAME)
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)
download(DARKNET_URL, DARKNET_LIB)
DARKNET_LIB = __darknetffi__.dlopen('./' + DARKNET_LIB)
cfg = "./" + str(CFG_NAME)
weights = "./" + str(WEIGHTS_NAME)
net = DARKNET_LIB.load_network(cfg.encode('utf-8'), weights.encode('utf-8'), 0)
dtype = 'float32'
batch_size = 1
print("Converting darknet to nnvm symbols...")
sym, params = nnvm.frontend.darknet.from_darknet(net, dtype)
######################################################################
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
######################################################################
Execute on TVM Runtime
----------------------
The process is no different from other examples.
from tvm.contrib import graph_runtime
if local:
remote = rpc.LocalSession()
ctx = remote.cpu(0)
else:
# The following is my environment, change this to the IP address of your target device
host = 'localhost'
port = 9090
remote = rpc.connect(host, port)
ctx = remote.gpu(0)
upload the library to remote device and load it
remote.upload(lib_fname)
rlib = remote.load_module('model.tar')
create the remote runtime module
m = graph_runtime.create(graph, rlib, ctx)
m.set_input(**params)
thresh = 0.5
nms_thresh = 0.45
coco_name = 'coco.names'
coco_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + coco_name + '?raw=true'
font_name = 'arial.ttf'
font_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + font_name + '?raw=true'
download(coco_url, coco_name)
download(font_url, font_name)
with open(coco_name) as f:
content = f.readlines()
names = [x.strip() for x in content]
test image demo
test_image = 'dog.jpg'
print("Loading the test image...")
img_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + \
test_image + '?raw=true'
download(img_url, test_image)
data = nnvm.testing.darknet.load_image(test_image, netw, neth)
set inputs
m.set_input('data', tvm.nd.array(data.astype(dtype)))
execute
print("Running the test image...")
m.run()
get outputs
tvm_out = []
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).asnumpy()
layer_out['biases'] = m.get_output(i*4+2).asnumpy()
layer_out['mask'] = m.get_output(i*4+1).asnumpy()
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).asnumpy().reshape(out_shape)
layer_out['classes'] = layer_attr[4]
tvm_out.append(layer_out)
img = nnvm.testing.darknet.load_image_color(test_image)
_, im_h, im_w = img.shape
dets = nnvm.testing.yolo_detection.fill_network_boxes((netw, neth), (im_w, im_h), thresh,
1, tvm_out)
last_layer = net.layers[net.n - 1]
nnvm.testing.yolo_detection.do_nms_sort(dets, last_layer.classes, nms_thresh)
nnvm.testing.yolo_detection.draw_detections(img, dets, thresh, names, last_layer.classes)
plt.imshow(img.transpose(1, 2, 0))
plt.show()
video demo
video_demo = False
if video_demo:
#vcap = cv2.VideoCapture("video.mp4")
vcap = cv2.VideoCapture(0)
n_frames = 0
seconds = 0.0
fps = 0.0
while True:
# Start time
start = time.time()
# Capture frame-by-frame
n_frames = n_frames + 1
ret, frame = vcap.read()
img = np.array(frame)
img = img.transpose((2, 0, 1))
img = np.divide(img, 255.0)
img = np.flip(img, 0)
data = nnvm.testing.darknet._letterbox_image(img, netw, neth)
# set inputs
m.set_input('data', tvm.nd.array(data.astype(dtype)))
# execute
print("Running the test image...")
m.run()
# get outputs
tvm_out = []
#tvm_output_list = []
# for i in range(0, 3):
# tvm_output = m.get_output(i)
# tvm_output_list.append(tvm_output.asnumpy())
#print(tvm_output_list)
#print(m.get_num_outputs())
#layer_attr = [m.get_output(i).asnumpy() for i in range(m.get_num_outputs())]
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).asnumpy()
layer_out['biases'] = m.get_output(i*4+2).asnumpy()
layer_out['mask'] = m.get_output(i*4+1).asnumpy()
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).asnumpy().reshape(out_shape)
layer_out['classes'] = layer_attr[4]
tvm_out.append(layer_out)
_, im_h, im_w = img.shape
dets = nnvm.testing.yolo_detection.fill_network_boxes((netw, neth), (im_w, im_h), thresh,
1, tvm_out)
last_layer = net.layers[net.n - 1]
nnvm.testing.yolo_detection.do_nms_sort(dets, last_layer.classes, nms_thresh)
nnvm.testing.yolo_detection.draw_detections(img, dets, thresh, names, last_layer.classes)
# End time
end = time.time()
# Time elapsed
seconds = (end - start)
# Calculate frames per second
fps = (fps + (1 / seconds)) / 2
print(fps)
cv2.putText(img, str(fps), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
cv2.imshow('Video', img.transpose(1, 2, 0))
#cv2.waitKey(3)
# Press Q to stop!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
参考链接：
https://github.com/makihiro/tvm_yolov3_sample
Original: https://blog.csdn.net/wujianing_110117/article/details/121449316
Author: wujianming_110117
Title: TVM yolov3优化代码修改（编译运行OK）
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