def detect(cfgfile, weightfile, imgfile):
if cfgfile.find('.prototxt') >= 0:
from caffenet import CaffeNet
m = CaffeNet(cfgfile)
else:
m = Darknet(cfgfile)
m.print_network()
m.load_weights(weightfile)
print('Loading weights from %s... Done!' % (weightfile))
if m.num_classes == 20:
namesfile = 'data/voc.names'
elif m.num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/names'
use_cuda = 1
if use_cuda:
m.cuda()
img = Image.open(imgfile).convert('RGB')
sized = img.resize((m.width, m.height))
for i in range(2):
start = time.time()
boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
finish = time.time()
if i == 1:
print('%s: Predicted in %f seconds.' % (imgfile, (finish-start)))
class_names = load_class_names(namesfile)
plot_boxes(img, boxes, 'predictions.jpg', class_names)
def detect(cfgfile, weightfile, imgfile):
if cfgfile.find('.prototxt') >= 0:
from caffenet import CaffeNet
m = CaffeNet(cfgfile)
else:
m = Darknet(cfgfile)
m.print_network()
m.load_weights(weightfile)
print('Loading weights from %s... Done!' % (weightfile))
if m.num_classes == 20:
namesfile = 'data/voc.names'
elif m.num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/names'
use_cuda = 1
if use_cuda:
m.cuda()
img = Image.open(imgfile).convert('RGB')
sized = img.resize((m.width, m.height))
for i in range(2):
start = time.time()
boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
finish = time.time()
if i == 1:
print('%s: Predicted in %f seconds.' % (imgfile, (finish - start)))
class_names = load_class_names(namesfile)
plot_boxes(img, boxes, 'predictions.jpg', class_names)
test_interval = 99999999
snapshot = int(solver['snapshot'])
snapshot_prefix = solver['snapshot_prefix']
stepvalues = solver['stepvalue']
stepvalues = [int(item) for item in stepvalues]
if args.lr != None:
base_lr = args.lr
#torch.manual_seed(int(time.time()))
#if args.gpu:
# torch.cuda.manual_seed(int(time.time()))
#print(protofile)
net = CaffeNet(protofile)
if args.weights:
net.load_weights(args.weights)
#net.set_verbose(False)
net.set_train_outputs('mbox_loss')
if args.gpu:
device_ids = args.gpu.split(',')
device_ids = [int(i) for i in device_ids]
print('device_ids', device_ids)
if len(device_ids) > 1:
print('---- Multi GPUs ----')
net = ParallelCaffeNet(net.cuda(), device_ids=device_ids)
else:
print('---- Single GPU ----')
net.cuda()
print(net)
weightfile1 = 'reid.weights'
cfgfile2 = 'reid_nbn.cfg'
weightfile2 = 'reid_nbn.weights'
cfgfile3 = 'reid_nbn.prototxt'
weightfile3 = 'reid_nbn.caffemodel'
m1 = Darknet(cfgfile1)
m1.load_weights(weightfile1)
m1.eval()
m2 = Darknet(cfgfile2)
m2.load_weights(weightfile2)
m2.eval()
m3 = CaffeNet(cfgfile3)
m3.load_weights(weightfile3)
m3.eval()
img = torch.rand(8, 3, 128, 64)
img = Variable(img)
output1 = m1(img).clone()
output2 = m2(img).clone()
output3 = m3(img).clone()
print('----- output1 ------------------')
print(output1.data.storage()[0:100])
print('----- output2 ------------------')
print(output2.data.storage()[0:100])
print('----- output3 ------------------')
print(output3.data.storage()[0:100])