| 14 | return net; |
| 15 | |
| 16 | class S(nn.Module): |
| 17 | def __init__(self, num_layers_in_fc_layers = 1024): |
| 18 | super(S, self).__init__(); |
| 19 | |
| 20 | self.__nFeatures__ = 24; |
| 21 | self.__nChs__ = 32; |
| 22 | self.__midChs__ = 32; |
| 23 | |
| 24 | self.netcnnaud = nn.Sequential( |
| 25 | nn.Conv2d(1, 64, kernel_size=(3,3), stride=(1,1), padding=(1,1)), |
| 26 | nn.BatchNorm2d(64), |
| 27 | nn.ReLU(inplace=True), |
| 28 | nn.MaxPool2d(kernel_size=(1,1), stride=(1,1)), |
| 29 | |
| 30 | nn.Conv2d(64, 192, kernel_size=(3,3), stride=(1,1), padding=(1,1)), |
| 31 | nn.BatchNorm2d(192), |
| 32 | nn.ReLU(inplace=True), |
| 33 | nn.MaxPool2d(kernel_size=(3,3), stride=(1,2)), |
| 34 | |
| 35 | nn.Conv2d(192, 384, kernel_size=(3,3), padding=(1,1)), |
| 36 | nn.BatchNorm2d(384), |
| 37 | nn.ReLU(inplace=True), |
| 38 | |
| 39 | nn.Conv2d(384, 256, kernel_size=(3,3), padding=(1,1)), |
| 40 | nn.BatchNorm2d(256), |
| 41 | nn.ReLU(inplace=True), |
| 42 | |
| 43 | nn.Conv2d(256, 256, kernel_size=(3,3), padding=(1,1)), |
| 44 | nn.BatchNorm2d(256), |
| 45 | nn.ReLU(inplace=True), |
| 46 | nn.MaxPool2d(kernel_size=(3,3), stride=(2,2)), |
| 47 | |
| 48 | nn.Conv2d(256, 512, kernel_size=(5,4), padding=(0,0)), |
| 49 | nn.BatchNorm2d(512), |
| 50 | nn.ReLU(), |
| 51 | ); |
| 52 | |
| 53 | self.netfcaud = nn.Sequential( |
| 54 | nn.Linear(512, 512), |
| 55 | nn.BatchNorm1d(512), |
| 56 | nn.ReLU(), |
| 57 | nn.Linear(512, num_layers_in_fc_layers), |
| 58 | ); |
| 59 | |
| 60 | self.netfclip = nn.Sequential( |
| 61 | nn.Linear(512, 512), |
| 62 | nn.BatchNorm1d(512), |
| 63 | nn.ReLU(), |
| 64 | nn.Linear(512, num_layers_in_fc_layers), |
| 65 | ); |
| 66 | |
| 67 | self.netcnnlip = nn.Sequential( |
| 68 | nn.Conv3d(3, 96, kernel_size=(5,7,7), stride=(1,2,2), padding=0), |
| 69 | nn.BatchNorm3d(96), |
| 70 | nn.ReLU(inplace=True), |
| 71 | nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2)), |
| 72 | |
| 73 | nn.Conv3d(96, 256, kernel_size=(1,5,5), stride=(1,2,2), padding=(0,1,1)), |