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)