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Running
on
T4
| # -------------------------------------------------------- | |
| # SiamMask | |
| # Licensed under The MIT License | |
| # Written by Qiang Wang (wangqiang2015 at ia.ac.cn) | |
| # -------------------------------------------------------- | |
| from __future__ import division | |
| from SiamMask.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) | |