| import torch | |
| from torch import nn | |
| class MixedOperationRandom(nn.Module): | |
| def __init__(self, search_ops): | |
| super(MixedOperationRandom, self).__init__() | |
| self.ops = nn.ModuleList(search_ops) | |
| self.num_ops = len(search_ops) | |
| def forward(self, x, x_path=None): | |
| if x_path is None: | |
| output = sum(op(x) for op in self.ops) / self.num_ops | |
| else: | |
| assert isinstance(x_path, (int, float)) and 0 <= x_path < self.num_ops or isinstance(x_path, torch.Tensor) | |
| if isinstance(x_path, (int, float)): | |
| x_path = int(x_path) | |
| assert 0 <= x_path < self.num_ops | |
| output = self.ops[x_path](x) | |
| elif isinstance(x_path, torch.Tensor): | |
| assert x_path.size(0) == x.size(0), 'batch_size should match length of y_idx' | |
| output = torch.cat([self.ops[int(x_path[i].item())](x.narrow(0, i, 1)) | |
| for i in range(x.size(0))], dim=0) | |
| return output |