# --------------------------------------------------------
# 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 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)
