(self, x)
| 225 | ) |
| 226 | |
| 227 | def forward(self, x): |
| 228 | # assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution) |
| 229 | |
| 230 | # timestep embedding |
| 231 | temb = None |
| 232 | |
| 233 | # downsampling |
| 234 | hs = [self.conv_in(x)] |
| 235 | for i_level in range(self.num_resolutions): |
| 236 | for i_block in range(self.num_res_blocks): |
| 237 | h = self.down[i_level].block[i_block](hs[-1], temb) |
| 238 | if len(self.down[i_level].attn) > 0: |
| 239 | h = self.down[i_level].attn[i_block](h) |
| 240 | hs.append(h) |
| 241 | if i_level != self.num_resolutions - 1: |
| 242 | hs.append(self.down[i_level].downsample(hs[-1])) |
| 243 | |
| 244 | # middle |
| 245 | h = hs[-1] |
| 246 | h = self.mid.block_1(h, temb) |
| 247 | h = self.mid.attn_1(h) |
| 248 | h = self.mid.block_2(h, temb) |
| 249 | |
| 250 | # end |
| 251 | h = self.norm_out(h) |
| 252 | h = nonlinearity(h) |
| 253 | h = self.conv_out(h) |
| 254 | return h |
| 255 | |
| 256 | def forward_with_features_output(self, x): |
| 257 | # assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution) |
nothing calls this directly
no test coverage detected