As the model refers to a lot of stuff, I printed the final one here, using print(netG)
G_NET(
(ca_net): CA_NET(
(fc): Linear(in_features=256, out_features=400, bias=True)
(relu): GLU()
)
(h_net1): INIT_STAGE_G(
(fc): Sequential(
(0): Linear(in_features=200, out_features=16384, bias=False)
(1): BatchNorm1d(16384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): GLU()
)
(upsample1): Sequential(
(0): Upsample(scale_factor=2, mode=nearest)
(1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): GLU()
)
(upsample2): Sequential(
(0): Upsample(scale_factor=2, mode=nearest)
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): GLU()
)
(upsample3): Sequential(
(0): Upsample(scale_factor=2, mode=nearest)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): GLU()
)
(upsample4): Sequential(
(0): Upsample(scale_factor=2, mode=nearest)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): GLU()
)
)
(img_net1): GET_IMAGE_G(
(img): Sequential(
(0): Conv2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Tanh()
)
)
(h_net2): NEXT_STAGE_G(
(att): GlobalAttentionGeneral(
(conv_context): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(sm): Softmax()
)
(residual): Sequential(
(0): ResBlock(
(block): Sequential(
(0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): GLU()
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): ResBlock(
(block): Sequential(
(0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): GLU()
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(upsample): Sequential(
(0): Upsample(scale_factor=2, mode=nearest)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): GLU()
)
)
(img_net2): GET_IMAGE_G(
(img): Sequential(
(0): Conv2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Tanh()
)
)
(h_net3): NEXT_STAGE_G(
(att): GlobalAttentionGeneral(
(conv_context): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(sm): Softmax()
)
(residual): Sequential(
(0): ResBlock(
(block): Sequential(
(0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): GLU()
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): ResBlock(
(block): Sequential(
(0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): GLU()
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(upsample): Sequential(
(0): Upsample(scale_factor=2, mode=nearest)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): GLU()
)
)
(img_net3): GET_IMAGE_G(
(img): Sequential(
(0): Conv2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): Tanh()
)
)
)