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net.txt
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net.txt
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modifying input layer to accept 11 channels
MattingModule(
(encoder): ResnetDilated(
(conv1): Conv2d(11, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): GroupNorm(32, 64, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 256, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): GroupNorm(32, 256, eps=1e-05, affine=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 256, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): GroupNorm(32, 64, eps=1e-05, affine=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 256, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 512, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): GroupNorm(32, 512, eps=1e-05, affine=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 512, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 512, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): GroupNorm(32, 128, eps=1e-05, affine=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 512, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 1024, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): GroupNorm(32, 1024, eps=1e-05, affine=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 1024, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 1024, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 1024, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 1024, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): GroupNorm(32, 256, eps=1e-05, affine=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 1024, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 512, eps=1e-05, affine=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): GroupNorm(32, 512, eps=1e-05, affine=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 2048, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): GroupNorm(32, 2048, eps=1e-05, affine=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 512, eps=1e-05, affine=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
(bn2): GroupNorm(32, 512, eps=1e-05, affine=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 2048, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): GroupNorm(32, 512, eps=1e-05, affine=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
(bn2): GroupNorm(32, 512, eps=1e-05, affine=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): GroupNorm(32, 2048, eps=1e-05, affine=True)
(relu): ReLU(inplace=True)
)
)
)
(decoder): fba_decoder(
(ppm): ModuleList(
(0): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(2): GroupNorm(32, 256, eps=1e-05, affine=True)
(3): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): AdaptiveAvgPool2d(output_size=2)
(1): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(2): GroupNorm(32, 256, eps=1e-05, affine=True)
(3): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): AdaptiveAvgPool2d(output_size=3)
(1): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(2): GroupNorm(32, 256, eps=1e-05, affine=True)
(3): LeakyReLU(negative_slope=0.01)
)
(3): Sequential(
(0): AdaptiveAvgPool2d(output_size=6)
(1): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(2): GroupNorm(32, 256, eps=1e-05, affine=True)
(3): LeakyReLU(negative_slope=0.01)
)
)
(conv_up1): Sequential(
(0): Conv2d(3072, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): GroupNorm(32, 256, eps=1e-05, affine=True)
(2): LeakyReLU(negative_slope=0.01)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): GroupNorm(32, 256, eps=1e-05, affine=True)
(5): LeakyReLU(negative_slope=0.01)
)
(conv_up2): Sequential(
(0): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): GroupNorm(32, 256, eps=1e-05, affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(conv_up3): Sequential(
(0): Conv2d(320, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): GroupNorm(32, 64, eps=1e-05, affine=True)
(2): LeakyReLU(negative_slope=0.01)
)
(unpool): MaxUnpool2d(kernel_size=(2, 2), stride=(2, 2), padding=(0, 0))
(conv_up4): Sequential(
(0): Conv2d(72, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.01)
(2): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): LeakyReLU(negative_slope=0.01)
(4): Conv2d(16, 7, kernel_size=(1, 1), stride=(1, 1))
)
)
)