# -------------------------------------------------------- # SiamMask # Licensed under The MIT License # Written by Qiang Wang (wangqiang2015 at ia.ac.cn) # -------------------------------------------------------- import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from SiamMask.utils.anchors import Anchors class SiamMask(nn.Module): def __init__(self, anchors=None, o_sz=127, g_sz=127): super(SiamMask, self).__init__() self.anchors = anchors # anchor_cfg self.anchor_num = len(self.anchors["ratios"]) * len(self.anchors["scales"]) self.anchor = Anchors(anchors) self.features = None self.rpn_model = None self.mask_model = None self.o_sz = o_sz self.g_sz = g_sz self.upSample = nn.UpsamplingBilinear2d(size=[g_sz, g_sz]) self.all_anchors = None def set_all_anchors(self, image_center, size): # cx,cy,w,h if not self.anchor.generate_all_anchors(image_center, size): return all_anchors = self.anchor.all_anchors[1] # cx, cy, w, h self.all_anchors = torch.from_numpy(all_anchors).float().cuda() self.all_anchors = [self.all_anchors[i] for i in range(4)] def feature_extractor(self, x): return self.features(x) def rpn(self, template, search): pred_cls, pred_loc = self.rpn_model(template, search) return pred_cls, pred_loc def mask(self, template, search): pred_mask = self.mask_model(template, search) return pred_mask def _add_rpn_loss(self, label_cls, label_loc, lable_loc_weight, label_mask, label_mask_weight, rpn_pred_cls, rpn_pred_loc, rpn_pred_mask): rpn_loss_cls = select_cross_entropy_loss(rpn_pred_cls, label_cls) rpn_loss_loc = weight_l1_loss(rpn_pred_loc, label_loc, lable_loc_weight) rpn_loss_mask, iou_m, iou_5, iou_7 = select_mask_logistic_loss(rpn_pred_mask, label_mask, label_mask_weight) return rpn_loss_cls, rpn_loss_loc, rpn_loss_mask, iou_m, iou_5, iou_7 def run(self, template, search, softmax=False): """ run network """ template_feature = self.feature_extractor(template) feature, search_feature = self.features.forward_all(search) rpn_pred_cls, rpn_pred_loc = self.rpn(template_feature, search_feature) corr_feature = self.mask_model.mask.forward_corr(template_feature, search_feature) # (b, 256, w, h) rpn_pred_mask = self.refine_model(feature, corr_feature) if softmax: rpn_pred_cls = self.softmax(rpn_pred_cls) return rpn_pred_cls, rpn_pred_loc, rpn_pred_mask, template_feature, search_feature def softmax(self, cls): b, a2, h, w = cls.size() cls = cls.view(b, 2, a2//2, h, w) cls = cls.permute(0, 2, 3, 4, 1).contiguous() cls = F.log_softmax(cls, dim=4) return cls def forward(self, input): """ :param input: dict of input with keys of: 'template': [b, 3, h1, w1], input template image. 'search': [b, 3, h2, w2], input search image. 'label_cls':[b, max_num_gts, 5] or None(self.training==False), each gt contains x1,y1,x2,y2,class. :return: dict of loss, predict, accuracy """ template = input['template'] search = input['search'] if self.training: label_cls = input['label_cls'] label_loc = input['label_loc'] lable_loc_weight = input['label_loc_weight'] label_mask = input['label_mask'] label_mask_weight = input['label_mask_weight'] rpn_pred_cls, rpn_pred_loc, rpn_pred_mask, template_feature, search_feature = \ self.run(template, search, softmax=self.training) outputs = dict() outputs['predict'] = [rpn_pred_loc, rpn_pred_cls, rpn_pred_mask, template_feature, search_feature] if self.training: rpn_loss_cls, rpn_loss_loc, rpn_loss_mask, iou_acc_mean, iou_acc_5, iou_acc_7 = \ self._add_rpn_loss(label_cls, label_loc, lable_loc_weight, label_mask, label_mask_weight, rpn_pred_cls, rpn_pred_loc, rpn_pred_mask) outputs['losses'] = [rpn_loss_cls, rpn_loss_loc, rpn_loss_mask] outputs['accuracy'] = [iou_acc_mean, iou_acc_5, iou_acc_7] return outputs def template(self, z): self.zf = self.feature_extractor(z) cls_kernel, loc_kernel = self.rpn_model.template(self.zf) return cls_kernel, loc_kernel def track(self, x, cls_kernel=None, loc_kernel=None, softmax=False): xf = self.feature_extractor(x) rpn_pred_cls, rpn_pred_loc = self.rpn_model.track(xf, cls_kernel, loc_kernel) if softmax: rpn_pred_cls = self.softmax(rpn_pred_cls) return rpn_pred_cls, rpn_pred_loc def get_cls_loss(pred, label, select): if select.nelement() == 0: return pred.sum()*0. pred = torch.index_select(pred, 0, select) label = torch.index_select(label, 0, select) return F.nll_loss(pred, label) def select_cross_entropy_loss(pred, label): pred = pred.view(-1, 2) label = label.view(-1) pos = Variable(label.data.eq(1).nonzero().squeeze()).cuda() neg = Variable(label.data.eq(0).nonzero().squeeze()).cuda() loss_pos = get_cls_loss(pred, label, pos) loss_neg = get_cls_loss(pred, label, neg) return loss_pos * 0.5 + loss_neg * 0.5 def weight_l1_loss(pred_loc, label_loc, loss_weight): """ :param pred_loc: [b, 4k, h, w] :param label_loc: [b, 4k, h, w] :param loss_weight: [b, k, h, w] :return: loc loss value """ b, _, sh, sw = pred_loc.size() pred_loc = pred_loc.view(b, 4, -1, sh, sw) diff = (pred_loc - label_loc).abs() diff = diff.sum(dim=1).view(b, -1, sh, sw) loss = diff * loss_weight return loss.sum().div(b) def select_mask_logistic_loss(p_m, mask, weight, o_sz=63, g_sz=127): weight = weight.view(-1) pos = Variable(weight.data.eq(1).nonzero().squeeze()) if pos.nelement() == 0: return p_m.sum() * 0, p_m.sum() * 0, p_m.sum() * 0, p_m.sum() * 0 if len(p_m.shape) == 4: p_m = p_m.permute(0, 2, 3, 1).contiguous().view(-1, 1, o_sz, o_sz) p_m = torch.index_select(p_m, 0, pos) p_m = nn.UpsamplingBilinear2d(size=[g_sz, g_sz])(p_m) p_m = p_m.view(-1, g_sz * g_sz) else: p_m = torch.index_select(p_m, 0, pos) mask_uf = F.unfold(mask, (g_sz, g_sz), padding=0, stride=8) mask_uf = torch.transpose(mask_uf, 1, 2).contiguous().view(-1, g_sz * g_sz) mask_uf = torch.index_select(mask_uf, 0, pos) loss = F.soft_margin_loss(p_m, mask_uf) iou_m, iou_5, iou_7 = iou_measure(p_m, mask_uf) return loss, iou_m, iou_5, iou_7 def iou_measure(pred, label): pred = pred.ge(0) mask_sum = pred.eq(1).add(label.eq(1)) intxn = torch.sum(mask_sum == 2, dim=1).float() union = torch.sum(mask_sum > 0, dim=1).float() iou = intxn/union return torch.mean(iou), (torch.sum(iou > 0.5).float()/iou.shape[0]), (torch.sum(iou > 0.7).float()/iou.shape[0]) if __name__ == "__main__": p_m = torch.randn(4, 63*63, 25, 25) cls = torch.randn(4, 1, 25, 25) > 0.9 mask = torch.randn(4, 1, 255, 255) * 2 - 1 loss = select_mask_logistic_loss(p_m, mask, cls) print(loss)