@@ -1,11 +1,11 @@
"""
Compile YOLO-V2 in DarkNet Models
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 NNVM.
All the required models and libraries will be downloaded from the internet by the script.
This script runs the YOLO-V2 Model with the bounding boxes
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
@@ -17,6 +17,7 @@
import nnvm
import nnvm .frontend .darknet
import nnvm .testing .yolo_detection
import nnvm .testing .darknet
import matplotlib .pyplot as plt
import numpy as np
@@ -28,7 +29,7 @@
from nnvm .testing .darknet import __darknetffi__
# Model name
MODEL_NAME = 'yolo '
MODEL_NAME = 'yolov3 '
######################################################################
# Download required files
@@ -105,24 +106,45 @@
m .run ()
# get outputs
out_shape = (net .outputs ,)
tvm_out = m .get_output (0 ).asnumpy ().flatten ()
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 (1 , tvm .nd .empty ((7 ,), dtype = 'int' )).asnumpy ()
layer_out ['biases' ] = m .get_output (2 ).asnumpy ()
out_shape = (layer_attr [0 ], layer_attr [1 ]// layer_attr [0 ],
layer_attr [2 ], layer_attr [3 ])
layer_out ['output' ] = m .get_output (3 ).asnumpy ().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 + 1 , tvm .nd .empty ((6 ,), dtype = 'int' )).asnumpy ()
layer_out ['biases' ] = m .get_output (i * 4 + 2 ).asnumpy ()
layer_out ['mask' ] = m .get_output (i * 4 + 3 ).asnumpy ().astype ('int' )
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 + 4 ).asnumpy ().reshape (out_shape )
layer_out ['classes' ] = layer_attr [4 ]
tvm_out .append (layer_out )
# do the detection and bring up the bounding boxes
thresh = 0.24
thresh = 0.5
hier_thresh = 0.5
img = nnvm .testing .darknet .load_image_color (test_image )
_ , im_h , im_w = img .shape
probs = []
boxes = []
region_layer = net .layers [net .n - 1 ]
boxes , probs = nnvm .testing .yolo2_detection .get_region_boxes (
region_layer , im_w , im_h , net .w , net .h ,
thresh , probs , boxes , 1 , tvm_out )
boxes , probs = nnvm .testing .yolo2_detection .do_nms_sort (
boxes , probs ,
region_layer .w * region_layer .h * region_layer .n , region_layer .classes , 0.3 )
nms = 0.45
dets = nnvm .testing .yolo_detection .fill_network_boxes (net .w , net .h , im_w , im_h , thresh ,
hier_thresh , 1 , tvm_out )
last_layer = net .layers [net .n - 1 ]
nnvm .testing .yolo_detection .do_nms_sort (dets , last_layer .classes , 0.45 )
coco_name = 'coco.names'
coco_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + coco_name + '?raw=true'
@@ -136,8 +158,6 @@
names = [x .strip () for x in content ]
nnvm .testing .yolo2_detection .draw_detections (
img , region_layer .w * region_layer .h * region_layer .n ,
thresh , boxes , probs , names , region_layer .classes )
nnvm .testing .yolo_detection .draw_detections (img , dets , thresh , names , last_layer .classes )
plt .imshow (img .transpose (1 , 2 , 0 ))
plt .show ()