118 lines
2.6 KiB
Python
118 lines
2.6 KiB
Python
# subter_lenet_arch.py
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# Requires running from inside the PlotNeuralNet repo, like: python3 ../subter_lenet_arch.py
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import sys, argparse
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sys.path.append("../") # import pycore from repo root
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from pycore.tikzeng import *
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parser = argparse.ArgumentParser()
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parser.add_argument("--rep_dim", type=int, default=1024, help="latent size for FC")
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args = parser.parse_args()
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REP = int(args.rep_dim)
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# Visual scales so the huge width doesn't dominate the figure
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H32, H16, H8 = 26, 18, 12
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D2048, D1024, D512 = 52, 36, 24
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W1, W4, W8 = 1, 2, 4
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arch = [
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to_head(".."),
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to_cor(),
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to_begin(),
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# --------------------------- ENCODER ---------------------------
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# Input 1×32×2048 (caption carries H×W; s_filer is numeric)
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to_Conv(
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"input",
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s_filer="{{2048×32}}",
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n_filer=1,
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offset="(0,0,0)",
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to="(0,0,0)",
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height=H32,
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depth=D2048,
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width=W1,
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caption="Input",
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),
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# Conv1 (5x5, same): 1->8, 32×2048
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to_Conv(
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"conv1",
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s_filer="{{1024×16}}",
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n_filer=8,
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offset="(2,0,0)",
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to="(input-east)",
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height=H32,
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depth=D2048,
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width=W8,
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caption="Conv1",
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),
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# Pool1 2×2: 32×2048 -> 16×1024
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# to_connection("input", "conv1"),
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to_Pool(
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"pool1",
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offset="(0,0,0)",
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to="(conv1-east)",
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height=H16,
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depth=D1024,
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width=W8,
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caption="",
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),
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# Conv2 (5x5, same): 8->4, stays 16×1024
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to_Conv(
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"conv2",
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s_filer="{{512×8}}",
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n_filer=4,
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offset="(2,0,0)",
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to="(pool1-east)",
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height=H16,
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depth=D1024,
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width=W4,
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caption="Conv2",
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),
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# Pool2 2×2: 16×1024 -> 8×512
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# to_connection("pool1", "conv2"),
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to_Pool(
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"pool2",
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offset="(0,0,0)",
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to="(conv2-east)",
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height=H8,
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depth=D512,
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width=W4,
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caption="",
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),
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# FC -> rep_dim (use numeric n_filer)
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to_fc(
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"fc1",
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n_filer="{{4×512×8}}",
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offset="(2,0,0)",
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to="(pool2-east)",
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height=1.3,
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depth=D512,
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width=W1,
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caption=f"FC",
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),
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# to_connection("pool2", "fc1"),
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# --------------------------- LATENT ---------------------------
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to_Conv(
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"latent",
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n_filer="",
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s_filer="latent dim",
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offset="(2,0,0)",
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to="(fc1-east)",
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height=H8 * 1.6,
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depth=1.3,
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width=W1,
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caption=f"Latent Space",
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),
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to_end(),
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]
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def main():
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name = "subter_lenet_arch"
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to_generate(arch, name + ".tex")
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if __name__ == "__main__":
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main()
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