(self, x)
| 356 | self.outconv = nn.Conv2d(6, out_ch, 1) |
| 357 | |
| 358 | def forward(self, x): |
| 359 | |
| 360 | hx = x |
| 361 | |
| 362 | # stage 1 |
| 363 | hx1 = self.stage1(hx) |
| 364 | hx = self.pool12(hx1) |
| 365 | |
| 366 | # stage 2 |
| 367 | hx2 = self.stage2(hx) |
| 368 | hx = self.pool23(hx2) |
| 369 | |
| 370 | # stage 3 |
| 371 | hx3 = self.stage3(hx) |
| 372 | hx = self.pool34(hx3) |
| 373 | |
| 374 | # stage 4 |
| 375 | hx4 = self.stage4(hx) |
| 376 | hx = self.pool45(hx4) |
| 377 | |
| 378 | # stage 5 |
| 379 | hx5 = self.stage5(hx) |
| 380 | hx = self.pool56(hx5) |
| 381 | |
| 382 | # stage 6 |
| 383 | hx6 = self.stage6(hx) |
| 384 | hx6up = _upsample_like(hx6, hx5) |
| 385 | |
| 386 | # -------------------- decoder -------------------- |
| 387 | hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) |
| 388 | hx5dup = _upsample_like(hx5d, hx4) |
| 389 | |
| 390 | hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) |
| 391 | hx4dup = _upsample_like(hx4d, hx3) |
| 392 | |
| 393 | hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) |
| 394 | hx3dup = _upsample_like(hx3d, hx2) |
| 395 | |
| 396 | hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) |
| 397 | hx2dup = _upsample_like(hx2d, hx1) |
| 398 | |
| 399 | hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) |
| 400 | |
| 401 | # side output |
| 402 | d1 = self.side1(hx1d) |
| 403 | |
| 404 | d2 = self.side2(hx2d) |
| 405 | d2 = _upsample_like(d2, d1) |
| 406 | |
| 407 | d3 = self.side3(hx3d) |
| 408 | d3 = _upsample_like(d3, d1) |
| 409 | |
| 410 | d4 = self.side4(hx4d) |
| 411 | d4 = _upsample_like(d4, d1) |
| 412 | |
| 413 | d5 = self.side5(hx5d) |
| 414 | d5 = _upsample_like(d5, d1) |
| 415 |
nothing calls this directly
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