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refinedet_test.py
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import _init_paths
from fast_rcnn.test import single_scale_test_net, multi_scale_test_net_320, multi_scale_test_net_512
from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list
from datasets.factory import get_imdb
import caffe
import os
if __name__ == '__main__':
GPU_ID = 0
single_scale = True # True: sinle scale test; False: multi scale test
test_set = 'voc_2007_test' # 'voc_2007_test' or 'voc_2012_test' or 'coco_2014_minival' or 'coco_2015_test-dev'
voc_path = 'models/VGGNet/VOC0712/refinedet_vgg16_320x320/'
coco_path = 'models/VGGNet/coco/refinedet_vgg16_320x320/'
cfg.single_scale_test = single_scale
if 'voc' in test_set:
path = voc_path
else:
path = coco_path
if '320' in path:
input_size = 320
else:
input_size = 512
caffe.set_mode_gpu()
caffe.set_device(GPU_ID)
imdb = get_imdb(test_set)
imdb.competition_mode(False)
if 'coco' in test_set:
if single_scale is True:
prototxt = path + 'single_test_deploy.prototxt'
else:
prototxt = path + 'multi_test_deploy.prototxt'
f = open(prototxt, 'r')
for line in f:
if 'confidence_threshold' in line:
line = line[:-1]
cfg.confidence_threshold = float(line.split(' ')[-1])
else:
prototxt = path + 'deploy.prototxt'
models = os.listdir(path)
mAP = {}
for model in models:
if model.find('caffemodel') == -1:
continue
caffemodel = path + model
print('Start evaluating: ' + caffemodel)
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
net.name = os.path.splitext(os.path.basename(model))[0]
cfg.net_name = net.name
try:
iter = int(net.name.split('_')[-1])
except:
iter = 000000
if single_scale is True:
single_scale_test_net(net, imdb, targe_size=input_size)
else:
if input_size == 320:
multi_scale_test_net_320(net, imdb)
else:
multi_scale_test_net_512(net, imdb)
mAP[iter] = cfg.mAP
keys = mAP.keys()
keys.sort()
templine = []
print("#########################################################################################################")
print("#########################################################################################################")
if 'voc' in test_set:
for key in keys:
value = mAP.get(key)
print("%d\t%.4f"%(key, value))
templine.append("%d\t%.4f\n"%(key, value))
with open(path+'mAP.txt', 'w+') as f:
f.writelines(templine)
elif 'coco' in test_set:
print("Iter\tAP@0.5:0.95\tAP@0.5\tAP@0.75\tAP@S\tAP@M\tAP@L\tAR@1\tAR@10\tAR@100\tAR@S\tAR@M\tAR@L")
templine.append("Iter\tAP@0.5:0.95\tAP@0.5\tAP@0.75\tAP@S\tAP@M\tAP@L\tAR@1\tAR@10\tAR@100\tAR@S\tAR@M\tAR@L\n")
for key in keys:
value = mAP.get(key) * 100
print("%d\t %.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f"
%(key,value[0],value[1],value[2],value[3],value[4],value[5],value[6],value[7],value[8],value[9],value[10],value[11]))
templine.append("%d\t %.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\n"
%(key,value[0],value[1],value[2],value[3],value[4],value[5],value[6],value[7],value[8],value[9],value[10],value[11]))
with open(path+'mAP.txt', 'w+') as f:
f.writelines(templine)
print("#########################################################################################################")
print("#########################################################################################################")