205 lines
10 KiB
Plaintext
205 lines
10 KiB
Plaintext
nn.Sequential {
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[input -> (1) -> (2) -> (3) -> (4) -> output]
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(1): nn.Sequential {
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[input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
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(1): nn.Sequential {
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[input -> (1) -> (2) -> (3) -> (4) -> output]
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(1): nn.SpatialConvolutionMM(3 -> 32, 3x3)
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(2): nn.LeakyReLU(0.1)
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(3): nn.SpatialConvolutionMM(32 -> 64, 3x3)
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(4): nn.LeakyReLU(0.1)
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}
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(2): nn.Sequential {
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[input -> (1) -> (2) -> output]
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(1): nn.ConcatTable {
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input
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|`-> (1): nn.Sequential {
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| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
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| (1): nn.SpatialConvolutionMM(64 -> 64, 2x2, 2,2)
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| (2): nn.LeakyReLU(0.1)
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| (3): nn.Sequential {
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| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
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| (1): nn.SpatialConvolutionMM(64 -> 128, 3x3)
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| (2): nn.LeakyReLU(0.1)
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| (3): nn.SpatialConvolutionMM(128 -> 64, 3x3)
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| (4): nn.LeakyReLU(0.1)
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| (5): nn.ConcatTable {
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| input
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| |`-> (1): nn.Identity
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| `-> (2): nn.Sequential {
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| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
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| (1): nn.Sequential {
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| [input -> (1) -> (2) -> (3) -> output]
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| (1): nn.Mean
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| (2): nn.Mean
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| (3): nn.View(-1, 64, 1, 1)
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| }
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| (2): nn.SpatialConvolutionMM(64 -> 8, 1x1)
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| (3): nn.ReLU
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| (4): nn.SpatialConvolutionMM(8 -> 64, 1x1)
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| (5): nn.Sigmoid
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| }
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| ... -> output
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| }
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| (6): w2nn.ScaleTable
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| }
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| (4): nn.SpatialFullConvolution(64 -> 64, 2x2, 2,2)
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| (5): nn.LeakyReLU(0.1)
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| }
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`-> (2): nn.SpatialZeroPadding(l=-4, r=-4, t=-4, b=-4)
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... -> output
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}
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(2): nn.CAddTable
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}
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(3): nn.SpatialConvolutionMM(64 -> 64, 3x3)
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(4): nn.LeakyReLU(0.1)
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(5): nn.SpatialConvolutionMM(64 -> 3, 3x3)
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}
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(2): nn.ConcatTable {
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input
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|`-> (1): nn.Sequential {
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| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
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| (1): nn.Sequential {
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| [input -> (1) -> (2) -> (3) -> (4) -> output]
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| (1): nn.SpatialConvolutionMM(3 -> 32, 3x3)
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| (2): nn.LeakyReLU(0.1)
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| (3): nn.SpatialConvolutionMM(32 -> 64, 3x3)
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| (4): nn.LeakyReLU(0.1)
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| }
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| (2): nn.Sequential {
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| [input -> (1) -> (2) -> output]
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| (1): nn.ConcatTable {
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| input
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| |`-> (1): nn.Sequential {
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| | [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
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| | (1): nn.SpatialConvolutionMM(64 -> 64, 2x2, 2,2)
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| | (2): nn.LeakyReLU(0.1)
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| | (3): nn.Sequential {
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| | [input -> (1) -> (2) -> (3) -> output]
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| | (1): nn.Sequential {
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| | [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
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| | (1): nn.SpatialConvolutionMM(64 -> 64, 3x3)
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| | (2): nn.LeakyReLU(0.1)
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| | (3): nn.SpatialConvolutionMM(64 -> 128, 3x3)
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| | (4): nn.LeakyReLU(0.1)
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| | (5): nn.ConcatTable {
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| | input
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| | |`-> (1): nn.Identity
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| | `-> (2): nn.Sequential {
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| | [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
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| | (1): nn.Sequential {
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| | [input -> (1) -> (2) -> (3) -> output]
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| | (1): nn.Mean
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| | (2): nn.Mean
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| | (3): nn.View(-1, 128, 1, 1)
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| | }
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| | (2): nn.SpatialConvolutionMM(128 -> 16, 1x1)
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| | (3): nn.ReLU
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| | (4): nn.SpatialConvolutionMM(16 -> 128, 1x1)
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| | (5): nn.Sigmoid
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| | }
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| | ... -> output
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| | }
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| | (6): w2nn.ScaleTable
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| | }
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| | (2): nn.Sequential {
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| | [input -> (1) -> (2) -> output]
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| | (1): nn.ConcatTable {
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| | input
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| | |`-> (1): nn.Sequential {
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| | | [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
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| | | (1): nn.SpatialConvolutionMM(128 -> 128, 2x2, 2,2)
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| | | (2): nn.LeakyReLU(0.1)
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| | | (3): nn.Sequential {
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| | | [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
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| | | (1): nn.SpatialConvolutionMM(128 -> 256, 3x3)
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| | | (2): nn.LeakyReLU(0.1)
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| | | (3): nn.SpatialConvolutionMM(256 -> 128, 3x3)
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| | | (4): nn.LeakyReLU(0.1)
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| | | (5): nn.ConcatTable {
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| | | input
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| | | |`-> (1): nn.Identity
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| | | `-> (2): nn.Sequential {
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| | | [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
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| | | (1): nn.Sequential {
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| | | [input -> (1) -> (2) -> (3) -> output]
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| | | (1): nn.Mean
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| | | (2): nn.Mean
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| | | (3): nn.View(-1, 128, 1, 1)
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| | | }
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| | | (2): nn.SpatialConvolutionMM(128 -> 16, 1x1)
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| | | (3): nn.ReLU
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| | | (4): nn.SpatialConvolutionMM(16 -> 128, 1x1)
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| | | (5): nn.Sigmoid
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| | | }
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| | | ... -> output
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| | | }
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| | | (6): w2nn.ScaleTable
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| | | }
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| | | (4): nn.SpatialFullConvolution(128 -> 128, 2x2, 2,2)
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| | | (5): nn.LeakyReLU(0.1)
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| | | }
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| | `-> (2): nn.SpatialZeroPadding(l=-4, r=-4, t=-4, b=-4)
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| | ... -> output
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| | }
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| | (2): nn.CAddTable
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| | }
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| | (3): nn.Sequential {
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| | [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
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| | (1): nn.SpatialConvolutionMM(128 -> 64, 3x3)
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| | (2): nn.LeakyReLU(0.1)
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| | (3): nn.SpatialConvolutionMM(64 -> 64, 3x3)
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| | (4): nn.LeakyReLU(0.1)
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| | (5): nn.ConcatTable {
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| | input
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| | |`-> (1): nn.Identity
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| | `-> (2): nn.Sequential {
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| | [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
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| | (1): nn.Sequential {
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| | [input -> (1) -> (2) -> (3) -> output]
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| | (1): nn.Mean
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| | (2): nn.Mean
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| | (3): nn.View(-1, 64, 1, 1)
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| | }
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| | (2): nn.SpatialConvolutionMM(64 -> 8, 1x1)
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| | (3): nn.ReLU
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| | (4): nn.SpatialConvolutionMM(8 -> 64, 1x1)
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| | (5): nn.Sigmoid
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| | }
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| | ... -> output
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| | }
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| | (6): w2nn.ScaleTable
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| | }
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| | }
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| | (4): nn.SpatialFullConvolution(64 -> 64, 2x2, 2,2)
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| | (5): nn.LeakyReLU(0.1)
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| | }
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| `-> (2): nn.SpatialZeroPadding(l=-16, r=-16, t=-16, b=-16)
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| ... -> output
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| }
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| (2): nn.CAddTable
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| }
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| (3): nn.SpatialConvolutionMM(64 -> 64, 3x3)
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| (4): nn.LeakyReLU(0.1)
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| (5): nn.SpatialConvolutionMM(64 -> 3, 3x3)
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| }
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`-> (2): nn.SpatialZeroPadding(l=-20, r=-20, t=-20, b=-20)
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... -> output
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}
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(3): nn.ConcatTable {
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input
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|`-> (1): nn.Sequential {
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| [input -> (1) -> (2) -> output]
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| (1): nn.CAddTable
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| (2): w2nn.InplaceClip01
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| }
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`-> (2): nn.Sequential {
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[input -> (1) -> (2) -> output]
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(1): nn.SelectTable(2)
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(2): w2nn.InplaceClip01
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}
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... -> output
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}
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(4): w2nn.AuxiliaryLossTable
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}
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