(self, in_channels, out_channels, stride=1, num_kernels=6, init_weight=True)
| 33 | |
| 34 | class Inception_Trans_Block_V1(nn.Module): |
| 35 | def __init__(self, in_channels, out_channels, stride=1, num_kernels=6, init_weight=True): |
| 36 | super(Inception_Trans_Block_V1, self).__init__() |
| 37 | self.in_channels = in_channels |
| 38 | self.out_channels = out_channels |
| 39 | self.num_kernels = num_kernels |
| 40 | self.stride = stride |
| 41 | |
| 42 | kernels = [] |
| 43 | for i in range(self.num_kernels): |
| 44 | kernels.append( |
| 45 | nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i, stride=stride)) |
| 46 | self.kernels = nn.ModuleList(kernels) |
| 47 | if init_weight: |
| 48 | self._initialize_weights() |
| 49 | |
| 50 | def _initialize_weights(self): |
| 51 | for m in self.modules(): |
nothing calls this directly
no test coverage detected