(self, in_channels, out_channels, stride=1, num_kernels=6, init_weight=True)
| 4 | |
| 5 | class Inception_Block_V1(nn.Module): |
| 6 | def __init__(self, in_channels, out_channels, stride=1, num_kernels=6, init_weight=True): |
| 7 | super(Inception_Block_V1, self).__init__() |
| 8 | self.in_channels = in_channels |
| 9 | self.out_channels = out_channels |
| 10 | self.num_kernels = num_kernels |
| 11 | self.stride = stride |
| 12 | kernels = [] |
| 13 | for i in range(self.num_kernels): |
| 14 | kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i, stride=stride)) |
| 15 | self.kernels = nn.ModuleList(kernels) |
| 16 | if init_weight: |
| 17 | self._initialize_weights() |
| 18 | |
| 19 | def _initialize_weights(self): |
| 20 | for m in self.modules(): |
no test coverage detected