squeezeDet
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BichenWuUCB
error in running demo.py
When I ran python ./src/demo.py, i got the following error:
Traceback (most recent call last):
File "./src/demo.py", line 217, in
Any idea why this happens?
Thanks,
Thanks for your question. That's a bug due to a recent update. Now it should be fixed. Could you please pull the update and try again?
It works now. On speed performance, I found SqueezeDet is slower than tiny-yolo model of Darkflow on afirefly-3399 platform: SqueezeDet: 0.9138s/image vs Tiny-Yolo: 0.6s/image. This is a surprise to me as I expect SqueezeDet should run faster.
Thanks,
Seems slow. What resolution are you using as input?
On Sat, 27 May 2017 at 2:15 PM, kaishijeng [email protected] wrote:
It works now. On speed performance, I found SqueezeDet is slower than tiny-yolo model of Darkflow on afirefly-3399 platform: SqueezeDet: 0.9138s/image vs Tiny-Yolo: 0.6s/image. This is a surprise to me as I expect SqueezeDet should run faster.
Thanks,
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This measurement is from demo.py. The time is measured below:
t_start = time.time()
det_boxes, det_probs, det_class = sess.run(
[model.det_boxes, model.det_probs, model.det_class],
feed_dict={model.image_input:[input_image]})
t_end = time.time()
times['detect'] = t_end - t_start
Firefly-3399 has 2 A72 core running at 2Ghz and 4 A53 cores (??Ghz). Tensorflow version: 1.0.1
Thanks,
I have converted VOC2012 datasets to KITTI format required by squeezedet. Training is running OK, but converge is very slow if I keep original image size 1248x384 in kitti_squeezeDet_config.py The issue of aspect ratio is quite distorted. If I change image size to 480x384, I got the following error during training:
Traceback (most recent call last):
File "./src/train.py", line 345, in
Any other files should I change in order to to use this new image size?
Thanks,
Here is what I did:
- Follow Training YOLO on VOC in https://pjreddie.com/darknet/yolo to download Pascal VOC dataset.
- Use my modified voc_pascal_new.py (below) to generate squeezeDet format of label.
import xml.etree.ElementTree as ET import pickle import os from os import listdir, getcwd from os.path import join
sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
def convert(size, box): dw = 1./size[0] dh = 1./size[1] x = (box[0] + box[1])/2.0 y = (box[2] + box[3])/2.0 w = box[1] - box[0] h = box[3] - box[2] x = xdw w = wdw y = ydh h = hdh return (box[0], box[2], box[1], box[3])
return (x,y,w,h)
def convert_annotation(year, image_id): in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id)) out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w') tree=ET.parse(in_file) root = tree.getroot() size = root.find('size') w = int(size.find('width').text) h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
bb = convert((w,h), b)
out_file.write(classes[cls_id] + " "+"0.0"+ " " + "0" +" " + "0.0" + " " + " ".join([str(a) for a in bb]) + " " +"0.0 0.0 0.0" +" " +"0.0 0.0 0.0"+" "+"0.0 0.0"+'\n')
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets: if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)): os.makedirs('VOCdevkit/VOC%s/labels/'%(year)) image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split() list_file = open('%s_%s.txt'%(year, image_set), 'w') for image_id in image_ids: list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id)) convert_annotation(year, image_id) list_file.close()
