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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 SiamMask.utils.bbox_helper import center2corner
from torch.autograd import Variable
from SiamMask.utils.anchors import Anchors
class SiamRPN(nn.Module):
def __init__(self, anchors=None):
super(SiamRPN, self).__init__()
self.anchors = anchors # anchor_cfg
self.anchor = Anchors(anchors)
self.anchor_num = self.anchor.anchor_num
self.features = None
self.rpn_model = None
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 _add_rpn_loss(self, label_cls, label_loc, lable_loc_weight, rpn_pred_cls,
rpn_pred_loc):
'''
:param compute_anchor_targets_fn: functions to produce anchors' learning targets.
:param rpn_pred_cls: [B, num_anchors * 2, h, w], output of rpn for classification.
:param rpn_pred_loc: [B, num_anchors * 4, h, w], output of rpn for localization.
:return: loss of classification and localization, respectively.
'''
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)
# classification accuracy, top1
acc = torch.zeros(1) # TODO
return rpn_loss_cls, rpn_loss_loc, acc
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)
if softmax:
rpn_pred_cls = self.softmax(rpn_pred_cls)
return rpn_pred_cls, rpn_pred_loc, 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']
rpn_pred_cls, rpn_pred_loc, template_feature, search_feature = self.run(template, search, softmax=self.training)
outputs = dict(predict=[], losses=[], accuracy=[])
outputs['predict'] = [rpn_pred_loc, rpn_pred_cls, template_feature, search_feature]
if self.training:
rpn_loss_cls, rpn_loss_loc, rpn_acc = self._add_rpn_loss(label_cls, label_loc, lable_loc_weight,
rpn_pred_cls, rpn_pred_loc)
outputs['losses'] = [rpn_loss_cls, rpn_loss_loc]
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 len(select.size()) == 0: return 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)
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