Alex Stoken
5 years ago
committed by
GitHub
commit
f517ba81c7
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# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
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# Download command: bash ./data/get_voc.sh
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# Train command: python train.py --data voc.yaml
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# Dataset should be placed next to yolov5 folder:
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# /parent_folder
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# /VOC
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# /yolov5
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start=`date +%s`
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# handle optional download dir
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if [ -z "$1" ]
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then
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# navigate to ~/tmp
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echo "navigating to ../tmp/ ..."
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mkdir -p ../tmp
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cd ../tmp/
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else
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# check if is valid directory
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if [ ! -d $1 ]; then
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echo $1 "is not a valid directory"
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exit 0
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fi
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echo "navigating to" $1 "..."
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cd $1
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fi
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echo "Downloading VOC2007 trainval ..."
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# Download the data.
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curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
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echo "Downloading VOC2007 test data ..."
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curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
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echo "Done downloading."
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# Extract data
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echo "Extracting trainval ..."
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tar -xf VOCtrainval_06-Nov-2007.tar
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echo "Extracting test ..."
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tar -xf VOCtest_06-Nov-2007.tar
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echo "removing tars ..."
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rm VOCtrainval_06-Nov-2007.tar
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rm VOCtest_06-Nov-2007.tar
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end=`date +%s`
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runtime=$((end-start))
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echo "Completed in" $runtime "seconds"
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start=`date +%s`
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# handle optional download dir
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if [ -z "$1" ]
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then
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# navigate to ~/tmp
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echo "navigating to ../tmp/ ..."
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mkdir -p ../tmp
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cd ../tmp/
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else
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# check if is valid directory
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if [ ! -d $1 ]; then
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echo $1 "is not a valid directory"
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exit 0
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fi
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echo "navigating to" $1 "..."
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cd $1
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fi
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echo "Downloading VOC2012 trainval ..."
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# Download the data.
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curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
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echo "Done downloading."
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# Extract data
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echo "Extracting trainval ..."
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tar -xf VOCtrainval_11-May-2012.tar
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echo "removing tar ..."
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rm VOCtrainval_11-May-2012.tar
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end=`date +%s`
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runtime=$((end-start))
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echo "Completed in" $runtime "seconds"
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cd ../tmp
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echo "Spliting dataset..."
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python3 - "$@" <<END
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import xml.etree.ElementTree as ET
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import pickle
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import os
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from os import listdir, getcwd
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from os.path import join
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sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
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classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
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def convert(size, box):
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dw = 1./(size[0])
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dh = 1./(size[1])
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x = (box[0] + box[1])/2.0 - 1
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y = (box[2] + box[3])/2.0 - 1
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w = box[1] - box[0]
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h = box[3] - box[2]
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x = x*dw
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w = w*dw
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y = y*dh
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h = h*dh
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return (x,y,w,h)
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def convert_annotation(year, image_id):
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in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
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out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
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tree=ET.parse(in_file)
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root = tree.getroot()
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size = root.find('size')
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w = int(size.find('width').text)
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h = int(size.find('height').text)
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for obj in root.iter('object'):
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difficult = obj.find('difficult').text
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cls = obj.find('name').text
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if cls not in classes or int(difficult)==1:
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continue
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cls_id = classes.index(cls)
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xmlbox = obj.find('bndbox')
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b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
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bb = convert((w,h), b)
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out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
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wd = getcwd()
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for year, image_set in sets:
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if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
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os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
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image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
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list_file = open('%s_%s.txt'%(year, image_set), 'w')
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for image_id in image_ids:
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list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
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convert_annotation(year, image_id)
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list_file.close()
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END
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cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt
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cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt
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python3 - "$@" <<END
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import shutil
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import os
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os.system('mkdir ../VOC/')
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os.system('mkdir ../VOC/images')
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os.system('mkdir ../VOC/images/train')
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os.system('mkdir ../VOC/images/val')
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os.system('mkdir ../VOC/labels')
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os.system('mkdir ../VOC/labels/train')
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os.system('mkdir ../VOC/labels/val')
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import os
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print(os.path.exists('../tmp/train.txt'))
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f = open('../tmp/train.txt', 'r')
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lines = f.readlines()
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for line in lines:
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#print(line.split('/')[-1][:-1])
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line = "/".join(line.split('/')[2:])
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#print(line)
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if (os.path.exists("../" + line[:-1])):
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os.system("cp ../"+ line[:-1] + " ../VOC/images/train")
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print(os.path.exists('../tmp/train.txt'))
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f = open('../tmp/train.txt', 'r')
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lines = f.readlines()
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for line in lines:
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#print(line.split('/')[-1][:-1])
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line = "/".join(line.split('/')[2:])
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line = line.replace('JPEGImages', 'labels')
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line = line.replace('jpg', 'txt')
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#print(line)
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if (os.path.exists("../" + line[:-1])):
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os.system("cp ../"+ line[:-1] + " ../VOC/labels/train")
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print(os.path.exists('../tmp/2007_test.txt'))
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f = open('../tmp/2007_test.txt', 'r')
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lines = f.readlines()
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for line in lines:
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#print(line.split('/')[-1][:-1])
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line = "/".join(line.split('/')[2:])
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if (os.path.exists("../" + line[:-1])):
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os.system("cp ../"+ line[:-1] + " ../VOC/images/val")
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print(os.path.exists('../tmp/2007_test.txt'))
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f = open('../tmp/2007_test.txt', 'r')
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lines = f.readlines()
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for line in lines:
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#print(line.split('/')[-1][:-1])
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line = "/".join(line.split('/')[2:])
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line = line.replace('JPEGImages', 'labels')
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line = line.replace('jpg', 'txt')
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#print(line)
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if (os.path.exists("../" + line[:-1])):
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os.system("cp ../"+ line[:-1] + " ../VOC/labels/val")
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END
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rm -rf ../tmp # remove temporary directory
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echo "VOC download done."
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# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
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# Download command: bash ./data/get_voc.sh
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# Train command: python train.py --data voc.yaml
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# Dataset should be placed next to yolov5 folder:
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# /parent_folder
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# /VOC
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# /yolov5
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# train and val datasets (image directory or *.txt file with image paths)
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train: ../VOC/images/train/
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val: ../VOC/images/val/
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# number of classes
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nc: 20
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# class names
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names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
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'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
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"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
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Usage:
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$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
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"""
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import argparse
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from models.common import *
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from utils import google_utils
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
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parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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opt = parser.parse_args()
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opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
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print(opt)
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# Input
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img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection
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# Load PyTorch model
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google_utils.attempt_download(opt.weights)
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model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
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model.eval()
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model.model[-1].export = True # set Detect() layer export=True
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y = model(img) # dry run
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# TorchScript export
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try:
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print('\nStarting TorchScript export with torch %s...' % torch.__version__)
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f = opt.weights.replace('.pt', '.torchscript') # filename
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ts = torch.jit.trace(model, img)
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ts.save(f)
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print('TorchScript export success, saved as %s' % f)
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except Exception as e:
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print('TorchScript export failure: %s' % e)
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# ONNX export
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try:
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import onnx
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print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
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f = opt.weights.replace('.pt', '.onnx') # filename
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model.fuse() # only for ONNX
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torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
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output_names=['classes', 'boxes'] if y is None else ['output'])
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# Checks
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onnx_model = onnx.load(f) # load onnx model
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onnx.checker.check_model(onnx_model) # check onnx model
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print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
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print('ONNX export success, saved as %s' % f)
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except Exception as e:
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print('ONNX export failure: %s' % e)
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# CoreML export
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try:
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import coremltools as ct
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print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
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model = ct.convert(ts, inputs=[ct.ImageType(name='images', shape=img.shape)]) # convert
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f = opt.weights.replace('.pt', '.mlmodel') # filename
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model.save(f)
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print('CoreML export success, saved as %s' % f)
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except Exception as e:
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print('CoreML export failure: %s' % e)
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# Finish
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print('\nExport complete. Visualize with https://github.com/lutzroeder/netron.')
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@ -1,42 +0,0 @@
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"""Exports a pytorch *.pt model to *.onnx format
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Usage:
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$ export PYTHONPATH="$PWD" && python models/onnx_export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
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"""
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import argparse
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import onnx
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from models.common import *
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from utils import google_utils
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
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parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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opt = parser.parse_args()
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print(opt)
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# Parameters
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f = opt.weights.replace('.pt', '.onnx') # onnx filename
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img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size, (1, 3, 320, 192) iDetection
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# Load pytorch model
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google_utils.attempt_download(opt.weights)
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model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
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model.eval()
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model.fuse()
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# Export to onnx
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model.model[-1].export = True # set Detect() layer export=True
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_ = model(img) # dry run
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torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['images'],
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output_names=['output']) # output_names=['classes', 'boxes']
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# Check onnx model
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model = onnx.load(f) # load onnx model
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onnx.checker.check_model(model) # check onnx model
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print(onnx.helper.printable_graph(model.graph)) # print a human readable representation of the graph
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print('Export complete. ONNX model saved to %s\nView with https://github.com/lutzroeder/netron' % f)
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