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  2. [200~DCGAN_G(
  3. (main): Sequential(
  4. (Start-ConvTranspose2d): ConvTranspose2d(128, 2048, kernel_size=(4, 4), stride=(1, 1), bias=False)
  5. (Start-BatchNorm2d): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  6. (Start-ReLU): ReLU()
  7. (Middle-UpSample [1]): Upsample(scale_factor=2, mode=nearest)
  8. (Middle-Conv2d [1]): Conv2d(2048, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  9. (Middle-BatchNorm2d [1]): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  10. (Middle-ReLU [1]): ReLU()
  11. (Middle-UpSample [2]): Upsample(scale_factor=2, mode=nearest)
  12. (Middle-Conv2d [2]): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  13. (Middle-BatchNorm2d [2]): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  14. (Middle-ReLU [2]): ReLU()
  15. (Middle-UpSample [3]): Upsample(scale_factor=2, mode=nearest)
  16. (Middle-Conv2d [3]): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  17. (Middle-BatchNorm2d [3]): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  18. (Middle-ReLU [3]): ReLU()
  19. (Middle-UpSample [4]): Upsample(scale_factor=2, mode=nearest)
  20. (Middle-Conv2d [4]): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  21. (Middle-BatchNorm2d [4]): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  22. (Middle-ReLU [4]): ReLU()
  23. (End-UpSample): Upsample(scale_factor=2, mode=nearest)
  24. (End-Conv2d): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  25. (End-Tanh): Tanh()
  26. )
  27. )
  28. DCGAN_D(
  29. (main): Sequential(
  30. (Start-Conv2d): Conv2d(3, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
  31. (Start-LeakyReLU): LeakyReLU(negative_slope=0.2, inplace)
  32. (Middle-Conv2d [0]): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
  33. (Middle-BatchNorm2d [0]): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  34. (Middle-LeakyReLU [0]): LeakyReLU(negative_slope=0.2, inplace)
  35. (Middle-Conv2d [1]): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
  36. (Middle-BatchNorm2d [1]): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  37. (Middle-LeakyReLU [1]): LeakyReLU(negative_slope=0.2, inplace)
  38. (Middle-Conv2d [2]): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
  39. (Middle-BatchNorm2d [2]): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  40. (Middle-LeakyReLU [2]): LeakyReLU(negative_slope=0.2, inplace)
  41. (Middle-Conv2d [3]): Conv2d(1024, 2048, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
  42. (Middle-BatchNorm2d [3]): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  43. (Middle-LeakyReLU [3]): LeakyReLU(negative_slope=0.2, inplace)
  44. (End-Conv2d): Conv2d(2048, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
  45. )
  46. )
  47.  
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