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| # -------------------------------------------------------- | |
| # 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=63, 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) | |
| search_feature = self.feature_extractor(search) | |
| rpn_pred_cls, rpn_pred_loc = self.rpn(template_feature, search_feature) | |
| rpn_pred_mask = self.mask(template_feature, search_feature) # (b, 63*63, w, h) | |
| 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 | |
| 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) | |
| mask_uf = F.unfold(mask, (g_sz, g_sz), padding=32, 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) | |