for model in models:
if model.find('caffemodel') == -1:
continue
if model.find('iter_120000') == -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(
"#########################################################################################################"
dt = {} # Detections from MATLAB
for model in models:
if model.find('caffemodel') == -1:
continue
caffemodel = train_test_outPath + model
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,
vis=visualizeDets,
redoInference=redoInference)
else:
if input_size == 320:
multi_scale_test_net_320(net,
imdb,
vis=visualizeDets,
redoInference=redoInference)
else:
multi_scale_test_net_512(net,
imdb,
vis=visualizeDets,
redoInference=redoInference)
mAP[iter] = cfg.mAP
mPrec[iter] = cfg.prec
