def __init__(self, depth=34, pretrained=False):
self.model_path = '/home/agwang/Deeplearning/pytorch_dir/pretrainedmodels/resnet-34-kinetics-cpu.pth'
self.pretrained = pretrained
self.depth = depth
self.shortcut_type = 'A' if depth in [18, 34] else 'B'
self.dout_base_model = 256 if depth in [18, 34] else 1024
self.model_path = '/home/agwang/Deeplearning/pytorch_dir/pretrainedmodels/resnet-{}-kinetics.pth'.format(depth)
self.dout_top_model = 512 if depth in [18, 34] else 2048
_TDCNN.__init__(self)
def __init__(self, pretrained=False):
self.model_path = 'data/pretrained_model/eco_lite_rgb_16F_kinetics_v2.pth'
self.dout_base_model = 256
self.pretrained = pretrained
_TDCNN.__init__(self)
def __init__(self, pretrained=False):
self.model_path = 'data/pretrained_model/activitynet_iter_30000_3fps-caffe.pth' #ucf101-caffe.pth' #c3d_sports1M.pth'
self.dout_base_model = 512
self.pretrained = pretrained
_TDCNN.__init__(self)
def __init__(self, class_agnostic=False, pretrained=False):
self.model_path = 'data/pretrained_model/rgb_imagenet.pkl'
self.dout_base_model = 832
self.pretrained = pretrained
_TDCNN.__init__(self, class_agnostic)
def __init__(self, pretrained=False):
self.model_path = '/content/SD/MyDrive/activitynet_iter_30000_3fps-caffe.pth' #ucf101-caffe.pth' #c3d_sports1M.pth' #activitynet_iter_30000_3fps-caffe.pth
self.dout_base_model = 512
self.pretrained = pretrained
_TDCNN.__init__(self)
def __init__(self, class_agnostic=False, pretrained=False):
self.model_path = 'data/pretrained_model/ucf101-caffe.pth' #c3d_sports1M.pth'
self.dout_base_model = 512
self.pretrained = pretrained
_TDCNN.__init__(self, class_agnostic)
def __init__(self, pretrained=False):
self.model_path = 'data/pretrained_model/activitynet_iter_30000_3fps-caffe.pth' #ucf101-caffe.pth' #c3d_sports1M.pth' #activitynet_iter_30000_3fps-caffe.pth
self.dout_base_model = 512 # 特征图维度
self.pretrained = pretrained
_TDCNN.__init__(self) # 这句话猜测意思就是将c3d和tdcnn联系起来合成一个完整的工程 当在trainval_net中的c3d_tdcnn(pretrained = true)时先初始化该类，之后再初始化TDCNN类