def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False):
self.model_path = 'data/pretrained_model/resnet101_caffe.pth'
self.dout_base_model = 1024
self.pretrained = pretrained
self.class_agnostic = class_agnostic
_fasterRCNN.__init__(self, classes, class_agnostic)
def __init__(self, classes, pretrained=False, class_agnostic=False):
self.model_path = 'Data/pretrained_model/vgg16_caffe.pth'
self.dout_base_model = 512
self.pretrained = pretrained
self.class_agnostic = class_agnostic
_fasterRCNN.__init__(self, classes, class_agnostic)
def __init__(self, classes, pretrained=False, class_agnostic=False):
#self.model_path = 'data/pretrained_model/vgg16_caffe.pth'
self.model_path = '/data/qingbeiguo/work/faster-rcnn.pytorch-pytorch-1.0/faster-rcnn.pytorch-pytorch-1.0/lib/model/faster_rcnn/pretrained_model/vgg16_caffe.pth'
self.dout_base_model = 512
self.pretrained = pretrained
self.class_agnostic = class_agnostic
_fasterRCNN.__init__(self, classes, class_agnostic)
def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False):
#self.model_path = 'data/pretrained_model/resnet101_caffe.pth'
#self.model_path = '/data/qingbeiguo/work/faster-rcnn.pytorch-pytorch-1.0/faster-rcnn.pytorch-pytorch-1.0/lib/model/faster_rcnn/pretrained_model/resnet101_caffe.pth'
self.model_path = '/data/qingbeiguo/work/gcnn-Faster-rcnn/faster-rcnn.pytorch-pytorch-1.0-1-res50-coco-600-G80/lib/model/faster_rcnn/pretrained_model/model_training_G80.pth'
self.dout_base_model = 1792
self.pretrained = pretrained
self.class_agnostic = class_agnostic
_fasterRCNN.__init__(self, classes, class_agnostic)
def __init__(self, classes, pretrained=False, class_agnostic=False):
# 预训练VGG权重
self.model_path = 'data/pretrained_model/vgg16_caffe.pth'
# 全图CNN特征通道数
self.dout_base_model = 512
# 是否使用预训练权重
self.pretrained = pretrained
# 是否使用类别无关box回归
self.class_agnostic = class_agnostic
# 初始化faster-RCNN
_fasterRCNN.__init__(self, classes, class_agnostic)
def __init__(self, classes,num_layers=101,base_model ='resnet50',
n_segments =8,n_div =8 , place = 'blockres',pretrain = 'imagenet',
shift = 'true',class_agnostic = 'false',loss_type = 'sigmoid',pathway='two_pathway'):
self.dout_base_model = 1024
self.pretrain = pretrain
self.shift = shift
self.n_segments = n_segments
self.shift_div = n_div
self.shift_place = place
self.class_agnostic = class_agnostic
self.loss_type = loss_type
self.pathway =pathway
_fasterRCNN.__init__(self, classes, class_agnostic,loss_type,pathway)
def __init__(self,
classes,
layer ,
pretrained_path=None,
class_agnostic=False,
):
self.pretrained_path = pretrained_path
self.class_agnostic = class_agnostic
self.dout_base_model = 256
self.layer = layer
self.dout_lh_base_model = 245
_fasterRCNN.__init__(self,
classes,
class_agnostic,
compact_mode=True)