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# -------------------------------------------------------- | |
# SiamMask | |
# Licensed under The MIT License | |
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn) | |
# -------------------------------------------------------- | |
from __future__ import division | |
from utils.anchors import Anchors | |
class TrackerConfig(object): | |
# These are the default hyper-params for SiamMask | |
penalty_k = 0.09 | |
window_influence = 0.39 | |
lr = 0.38 | |
seg_thr = 0.3 # for mask | |
windowing = 'cosine' # to penalize large displacements [cosine/uniform] | |
# Params from the network architecture, have to be consistent with the training | |
exemplar_size = 127 # input z size | |
instance_size = 255 # input x size (search region) | |
total_stride = 8 | |
out_size = 63 # for mask | |
base_size = 8 | |
score_size = (instance_size-exemplar_size)//total_stride+1+base_size | |
context_amount = 0.5 # context amount for the exemplar | |
ratios = [0.33, 0.5, 1, 2, 3] | |
scales = [8, ] | |
anchor_num = len(ratios) * len(scales) | |
round_dight = 0 | |
anchor = [] | |
def update(self, newparam=None, anchors=None): | |
if newparam: | |
for key, value in newparam.items(): | |
setattr(self, key, value) | |
if anchors is not None: | |
if isinstance(anchors, dict): | |
anchors = Anchors(anchors) | |
if isinstance(anchors, Anchors): | |
self.total_stride = anchors.stride | |
self.ratios = anchors.ratios | |
self.scales = anchors.scales | |
self.round_dight = anchors.round_dight | |
self.renew() | |
def renew(self): | |
self.score_size = (self.instance_size - self.exemplar_size) // self.total_stride + 1 + self.base_size | |
self.anchor_num = len(self.ratios) * len(self.scales) | |