(self, planes, blocks, stride=1)
| 124 | self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim) |
| 125 | |
| 126 | def _make_layer(self, planes, blocks, stride=1): |
| 127 | layers = [Bottleneck(self._inplanes, planes, stride)] |
| 128 | |
| 129 | self._inplanes = planes * Bottleneck.expansion |
| 130 | for _ in range(1, blocks): |
| 131 | layers.append(Bottleneck(self._inplanes, planes)) |
| 132 | |
| 133 | return nn.Sequential(*layers) |
| 134 | |
| 135 | def forward(self, x): |
| 136 | def stem(x): |