def __init__(self, classes=65):
super(SuperEvent, self).__init__()
self.classes = classes
self.dropout = nn.Dropout(0.7)
self.add_module('d', self.dropout)
self.super_event = tsf.TSF(3)
self.add_module('sup', self.super_event)
self.super_event2 = tsf.TSF(3)
self.add_module('sup2', self.super_event2)
# we have 2xD*3
# we want to learn a per-class weighting
# to take 2xD*3 to D*3
self.cls_wts = nn.Parameter(torch.Tensor(classes))
self.sup_mat = nn.Parameter(torch.Tensor(1, classes, 1024))
stdv = 1./np.sqrt(1024+1024)
self.sup_mat.data.uniform_(-stdv, stdv)
self.per_frame = nn.Conv3d(1024, classes, (1,1,1))
self.per_frame.weight.data.uniform_(-stdv, stdv)
self.per_frame.bias.data.uniform_(-stdv, stdv)
self.add_module('pf', self.per_frame)
def __init__(self, inp, classes=8):
super(Hierarchy, self).__init__()
self.classes = classes
self.dropout = nn.Dropout()
self.add_module('d', self.dropout)
self.super_event = tsf.TSF(3)
self.add_module('sup', self.super_event)
self.super_event2 = tsf.TSF(3)
self.add_module('sup2', self.super_event2)
# we have 2xD
# we want to learn a per-class weighting
# to take 2xD to D
self.cls_wts = nn.Parameter(torch.Tensor(classes))
self.sup_mat = nn.Parameter(torch.Tensor(1, classes, inp))
stdv = 1. / np.sqrt(inp + inp)
self.sup_mat.data.uniform_(-stdv, stdv)
self.sub_event1 = TGM(inp, 16, 5, c_in=1, c_out=8, soft=False)
self.sub_event2 = TGM(inp, 16, 5, c_in=8, c_out=8, soft=False)
self.sub_event3 = TGM(inp, 16, 5, c_in=8, c_out=8, soft=False)
self.h = nn.Conv1d(inp + 1 * inp + classes, 512, 1)
self.classify = nn.Conv1d(512, classes, 1)
self.inp = inp