class SiamRPNPP():
def __init__(self,dataset=''):
if 'OTB' in dataset:
cfg_file = os.path.join(project_path_,'pysot/experiments/siamrpn_r50_l234_dwxcorr_otb/config.yaml')
snapshot = os.path.join(project_path_,'pysot/experiments/siamrpn_r50_l234_dwxcorr_otb/model.pth')
elif 'LT' in dataset:
cfg_file = os.path.join(project_path_, 'pysot/experiments/siamrpn_r50_l234_dwxcorr_lt/config.yaml')
snapshot = os.path.join(project_path_, 'pysot/experiments/siamrpn_r50_l234_dwxcorr_lt/model.pth')
else:
cfg_file = os.path.join(project_path_, 'pysot/experiments/siamrpn_r50_l234_dwxcorr/config.yaml')
snapshot = os.path.join(project_path_, 'pysot/experiments/siamrpn_r50_l234_dwxcorr/model.pth')
# load config
cfg.merge_from_file(cfg_file)
# create model
self.model = ModelBuilder()# A Neural Network.(a torch.nn.Module)
# load model
self.model = load_pretrain(self.model, snapshot).cuda().eval()
def get_heat_map(self, X_crop, softmax=False):
score_map = self.model.track(X_crop)['cls']#(N,2x5,25,25)
score_map = score_map.permute(1, 2, 3, 0).contiguous().view(2, -1).permute(1, 0)#(5HWN,2)
if softmax:
score_map = F.softmax(score_map, dim=1).data[:, 1]#(5HWN,)
return score_map
def get_cls_reg(self, X_crop, softmax=False):
outputs = self.model.track(X_crop)#(N,2x5,25,25)
score_map = outputs['cls'].permute(1, 2, 3, 0).contiguous().view(2, -1).permute(1, 0)#(5HWN,2)
reg_res = outputs['loc'].permute(1, 2, 3, 0).contiguous().view(4, -1)
if softmax:
score_map = F.softmax(score_map, dim=1).data[:, 1]#(5HWN,)
return score_map, reg_res
if args.mask:
extractor.pick = ('conv1', 'res2', 'res3', 'res4')
else:
extractor.pick = ('res3', 'res4', 'res5')
c_out = extractor(z)
for i in range(len(c_out)):
np.testing.assert_almost_equal(c_out[i].data, outs[i], decimal=5)
model.template(torch.FloatTensor(z))
chainer_model.template(z)
np.testing.assert_almost_equal(model.zf.detach().numpy(),
chainer_model.zs.data,
decimal=5)
t_out = model.track(torch.FloatTensor(x))
c_out = chainer_model.track(x)
t_conf = t_out['cls'].detach().numpy()
t_loc = t_out['loc'].detach().numpy()
np.testing.assert_almost_equal(t_conf, c_out[0].data, decimal=5)
np.testing.assert_almost_equal(t_loc, c_out[1].data, decimal=5)
if args.mask:
t_mask = t_out['mask'].detach().numpy()
np.testing.assert_almost_equal(t_mask, c_out[2].data, decimal=5)
pos = (12, 11)
t_out = model.mask_refine(pos).detach().numpy()
c_out = chainer_model.refine_mask(pos).data
np.testing.assert_almost_equal(t_out, c_out, decimal=5)
print('test done')