(self,fea_dim)
| 31 | |
| 32 | class bC3D(torch.nn.Module): |
| 33 | def __init__(self,fea_dim): |
| 34 | super(bC3D, self).__init__() |
| 35 | # self.video_dim = 4096 |
| 36 | self.video_dim = 2048 |
| 37 | self.attention = Attention(dim=128,heads=4) |
| 38 | |
| 39 | self.linear_video = nn.Sequential(torch.nn.Linear(self.video_dim, fea_dim),torch.nn.ReLU()) |
| 40 | |
| 41 | self.classifier = nn.Linear(fea_dim,2) |
| 42 | |
| 43 | def forward(self, **kwargs): |
| 44 | c3d = kwargs['c3d'] |