def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False):
self.model_path = 'data/pretrained_model/detnet59.pth'
self.pretrained = pretrained
self.class_agnostic = class_agnostic
self.dout_base_model = 256
_FPN.__init__(self, classes, class_agnostic)
def __init__(self, classes, num_layers=101, pretrained=False, class_agnostic=False):
self.model_path = '/home/lab30202/lq/ai_future/galaxy_star_detect/fpn_galaxy_star/data/pretrained_model/best_galaxy_star_IN.pth'
self.dout_base_model = 64 ## 256 -> 24
self.pretrained = pretrained
self.class_agnostic = class_agnostic
_FPN.__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.dout_base_model = 256
self.pretrained = pretrained
self.class_agnostic = class_agnostic
_FPN.__init__(self, classes, class_agnostic)
def __init__(self,
classes,
num_layers=50,
pretrained=False,
class_agnostic=False):
self.model_path = 'data/pretrained_model/seresnet50-60a8950a85b2b.pkl'
self.dout_base_model = 256
self.pretrained = pretrained
self.class_agnostic = class_agnostic
self.num_classes = len(classes)
_FPN.__init__(self, classes, class_agnostic)
def __init__(self,
classes,
num_layers=101,
pretrained=False,
class_agnostic=False):
self.num_layers = num_layers
self.model_path = './pretrained/resnet{}_caffe.pth'.format(
self.num_layers)
self.dout_base_model = 256 # different
self.pretrained = pretrained
self.class_agnostic = class_agnostic
_FPN.__init__(self, classes, class_agnostic)
def __init__(self,
classes,
num_layers=101,
pretrained=False,
class_agnostic=False,
use_RCNN_sigmoid=False,
att_type=None):
self.model_path = 'data/pretrained_model/resnet%d_caffe.pth' % num_layers
self.dout_base_model = 256
self.pretrained = pretrained
self.class_agnostic = class_agnostic
self.num_layers = num_layers
self.att_type = att_type
if use_RCNN_sigmoid:
raise AssertionError('use_RCNN_sigmoid is not supported!')
_FPN.__init__(self, classes, class_agnostic)