def __init__(self, do_cuda=True):
self.lock = RLock()
self.opts = rfnetOptions(do_cuda)
print(self.opts)
print('rfnet init')
#print(f"{gct()} model init")
print("model init")
det = RFDetSO(
cfg.TRAIN.score_com_strength,
cfg.TRAIN.scale_com_strength,
cfg.TRAIN.NMS_THRESH,
cfg.TRAIN.NMS_KSIZE,
cfg.TRAIN.TOPK,
cfg.MODEL.GAUSSIAN_KSIZE,
cfg.MODEL.GAUSSIAN_SIGMA,
cfg.MODEL.KSIZE,
cfg.MODEL.padding,
cfg.MODEL.dilation,
cfg.MODEL.scale_list,
)
des = HardNetNeiMask(cfg.HARDNET.MARGIN, cfg.MODEL.COO_THRSH)
model = RFNetSO(
det, des, cfg.LOSS.SCORE, cfg.LOSS.PAIR, cfg.PATCH.SIZE, cfg.TRAIN.TOPK
)
device = torch.device("cuda")
model = model.to(device)
#resume = args.resume
#resume = "/home/cviss3/PycharmProjects/gensynth_dev_env/pyslam/thirdparty/rfnet/runs/10_24_09_25/model/e121_NN_0.480_NNT_0.655_NNDR_0.813_MeanMS_0.649.pth.tar"
resume = "/content/RFnetpyslam/thirdparty/rfnet/runs/10_24_09_25/model/e121_NN_0.480_NNT_0.655_NNDR_0.813_MeanMS_0.649.pth.tar"
print('==> Loading pre-trained network.')
checkpoint = torch.load(resume)
model.load_state_dict(checkpoint["state_dict"])
self.fe = model
print('==> Successfully loaded pre-trained network.')
self.device = device
self.pts = []
self.kps = []
self.des = []
self.img = []
self.heatmap = []
self.frame = None
self.frameFloat = None
self.keypoint_size = 20 # just a representative size for visualization and in order to convert extracted points to cv2.KeyPoint
det_lr=cfg.TRAIN.DET_LR,
des_lr=cfg.TRAIN.DES_LR,
det_wd=cfg.TRAIN.DET_WD,
des_wd=cfg.TRAIN.DES_WD,
mgpu=mgpu,
)
###############################################################################
# resume model if exists
###############################################################################
if args.resume:
if os.path.isfile(args.resume):
print(f"{gct()} : Loading checkpoint {args.resume}")
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint["epoch"]
model.load_state_dict(checkpoint["state_dict"])
det_optim.load_state_dict(checkpoint["det_optim"])
des_optim.load_state_dict(checkpoint["des_optim"])
else:
print(f"{gct()} : Cannot found checkpoint {args.resume}")
else:
args.start_epoch = 0
###############################################################################
# Visualization
###############################################################################
train_writer = SummaryWriter(f"{args.save}/log/train")
test_writer = SummaryWriter(f"{args.save}/log/test")
###############################################################################
# Training function
cfg.TRAIN.NMS_THRESH,
cfg.TRAIN.NMS_KSIZE,
cfg.TRAIN.TOPK,
cfg.MODEL.GAUSSIAN_KSIZE,
cfg.MODEL.GAUSSIAN_SIGMA,
cfg.MODEL.KSIZE,
cfg.MODEL.padding,
cfg.MODEL.dilation,
cfg.MODEL.scale_list,
)
des = HardNetNeiMask(cfg.HARDNET.MARGIN, cfg.MODEL.COO_THRSH)
model = RFNetSO(det, des, cfg.LOSS.SCORE, cfg.LOSS.PAIR, cfg.PATCH.SIZE,
cfg.TRAIN.TOPK)
model = model.to(device)
checkpoint = torch.load(model_file)
model.load_state_dict(checkpoint["state_dict"])
model.eval()
def distance_matrix_vector(anchor, positive):
eps = 1e-8
FeatSimi_Mat = 2 - 2 * torch.mm(anchor, positive.t()) # [0, 4]
FeatSimi_Mat = FeatSimi_Mat.clamp(min=eps, max=4.0)
FeatSimi_Mat = torch.sqrt(FeatSimi_Mat) # euc [0, 2]
return FeatSimi_Mat
def pairwise_distances(x, y=None):
x_norm = (x**2).sum(1).view(-1, 1)
if y is not None: