| 10 | |
| 11 | |
| 12 | class Linear_(nn.Module): |
| 13 | def __init__(self, in_features, out_features, bias=True, act="ReLU", is_folded=True): |
| 14 | super(Linear_, self).__init__() |
| 15 | self.in_features = in_features |
| 16 | self.out_features = out_features |
| 17 | self.bias = bias |
| 18 | self.act_type = act |
| 19 | self.is_folded = is_folded |
| 20 | self.linear = nn.Linear(in_features=self.in_features, |
| 21 | out_features=self.out_features, |
| 22 | bias=self.bias) |
| 23 | self.act = _act(self.act_type) |
| 24 | |
| 25 | def forward(self, inputs): |
| 26 | result_linear = self.linear(inputs) |
| 27 | result = self.act(result_linear) |
| 28 | return result |
| 29 | |
| 30 | @property |
| 31 | def multiply_adds(self): |
| 32 | result = self.in_features * self.out_features |
| 33 | return result |
| 34 | |
| 35 | @property |
| 36 | def params(self): |
| 37 | params = self.in_features * self.out_features |
| 38 | # TODO 不考虑fold方式，需要计算bias的参数量 |
| 39 | if self.bias is True and self.is_folded is False: |
| 40 | params += self.out_features |
| 41 | # print("%d" % self.out_features) |
| 42 | return params |
| 43 | |
| 44 | |
| 45 | class Identity_(nn.Module): |