(self, x)
| 137 | nn.init.constant_(m.bias, 0) |
| 138 | |
| 139 | def forward(self, x): |
| 140 | features = self.features(x) |
| 141 | out = F.relu(features, inplace=True) |
| 142 | out = F.adaptive_avg_pool2d(out, (1, 1)) |
| 143 | out = torch.flatten(out, 1) |
| 144 | out = self.classifier(out) |
| 145 | return out |
| 146 | |
| 147 | def densenet121(**kwargs): |
| 148 | return DenseNet(growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, **kwargs) |
nothing calls this directly
no outgoing calls
no test coverage detected