(self, inchannels, midchannels=512)
| 99 | |
| 100 | class FTB(nn.Module): |
| 101 | def __init__(self, inchannels, midchannels=512): |
| 102 | super(FTB, self).__init__() |
| 103 | self.in1 = inchannels |
| 104 | self.mid = midchannels |
| 105 | self.conv1 = nn.Conv2d(in_channels=self.in1, out_channels=self.mid, kernel_size=3, padding=1, stride=1, |
| 106 | bias=True) |
| 107 | # NN.BatchNorm2d |
| 108 | self.conv_branch = nn.Sequential(nn.ReLU(inplace=True), \ |
| 109 | nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3, |
| 110 | padding=1, stride=1, bias=True), \ |
| 111 | nn.BatchNorm2d(num_features=self.mid), \ |
| 112 | nn.ReLU(inplace=True), \ |
| 113 | nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3, |
| 114 | padding=1, stride=1, bias=True)) |
| 115 | self.relu = nn.ReLU(inplace=True) |
| 116 | |
| 117 | self.init_params() |
| 118 | |
| 119 | def forward(self, x): |
| 120 | x = self.conv1(x) |
nothing calls this directly
no test coverage detected