def __init__(self, classes, class_agnostic): super(_hyper_rcnn, self).__init__() #self.classes = classes self.n_classes = classes self.class_agnostic = class_agnostic # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 self.rcnn_din = 4096 self.rpn_din = 512 # define rpn self.RCNN_rpn = _RPN(self.dout_base_model, self.rpn_din) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) # self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) # self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0/16.0) self.downSample = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.RCNN_roi_pool = ROIPoolingLayer((cfg.POOLING_SIZE, cfg.POOLING_SIZE), 1.0/16.0) self.RCNN_roi_align = ROIAlignLayer((cfg.POOLING_SIZE, cfg.POOLING_SIZE), 1.0/16.0, 0) self.downBeat = nn.Conv2d(3584, self.rpn_din, kernel_size=1) self.RCNN_top = nn.Sequential(OrderedDict([ ('fc6_new',nn.Linear(self.dout_base_model * cfg.POOLING_SIZE * cfg.POOLING_SIZE, self.rcnn_din)), ('fc6_relu', nn.ReLU(inplace=True)), ('fc7_new', nn.Linear(self.rcnn_din, self.rcnn_din, bias=True)), ('fc7_relu', nn.ReLU(inplace=True))]))
def __init__(self, classes, class_agnostic): super(pva_faster_rcnn, self).__init__() self.n_classes = classes self.class_agnostic = class_agnostic # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 self.rcnn_din = cfg.MODEL.RCNN_CIN self.rcnn_last_din = cfg.MODEL.RCNN_LAST self.rpn_din = cfg.MODEL.RPN_CIN # define rpn self.RCNN_rpn = _RPN(self.dout_base_model, self.rpn_din) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) if cfg.POOLING_MODE == 'align': self.RCNN_roi_pool = ROIAlignLayer( (cfg.POOLING_SIZE, cfg.POOLING_SIZE), 1.0 / cfg.FEAT_STRIDE[0], 2) elif cfg.POOLING_MODE == 'pool': self.RCNN_roi_pool = ROIPoolingLayer( (cfg.POOLING_SIZE, cfg.POOLING_SIZE), 1.0 / cfg.FEAT_STRIDE[0]) self.RCNN_top = nn.Sequential( OrderedDict([('fc6_new', nn.Linear( self.dout_base_model * cfg.POOLING_SIZE * cfg.POOLING_SIZE, self.rcnn_din)), ('fc6_relu', nn.ReLU(inplace=True)), ('fc7_new', nn.Linear(self.rcnn_din, self.rcnn_last_din, bias=True)), ('fc7_relu', nn.ReLU(inplace=True))]))
def __init__(self, classes, class_agnostic): super(_fasterRCNN, self).__init__() #self.classes = classes self.n_classes = classes self.class_agnostic = class_agnostic # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 # define rpn self.RCNN_rpn = _RPN(self.dout_base_model, self.rpn_din) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) self.RCNN_roi_pool = ROIPoolingLayer( (cfg.POOLING_SIZE, cfg.POOLING_SIZE), 1.0 / 16.0) self.RCNN_roi_align = ROIAlignLayer( (cfg.POOLING_SIZE, cfg.POOLING_SIZE), 1.0 / 16.0, 0)