mt/thesis/third_party/PlotNeuralNet/deepsad/arch_lenet_encoder.py

# subter_lenet_arch.py
# Requires running from inside the PlotNeuralNet repo, like: python3 ../subter_lenet_arch.py
import sys, argparse

sys.path.append("../")  # import pycore from repo root

from pycore.tikzeng import *

parser = argparse.ArgumentParser()
parser.add_argument("--rep_dim", type=int, default=1024, help="latent size for FC")
args = parser.parse_args()
REP = int(args.rep_dim)

# Visual scales so the huge width doesn't dominate the figure
H32, H16, H8 = 26, 18, 12
D2048, D1024, D512 = 52, 36, 24
W1, W4, W8 = 1, 2, 4


arch = [
    to_head(".."),
    to_cor(),
    to_begin(),
    # --------------------------- ENCODER ---------------------------
    # Input 1×32×2048  (caption carries H×W; s_filer is numeric)
    to_Conv(
        "input",
        s_filer="{{2048×32}}",
        zlabeloffset=0.15,
        n_filer=1,
        offset="(0,0,0)",
        to="(0,0,0)",
        height=H32,
        depth=D2048,
        width=W1,
        caption="Input",
    ),
    # Conv1 (5x5, same): 1->8, 32×2048
    to_Conv(
        "conv1",
        s_filer="{{2048×32}}",
        zlabeloffset=0.15,
        n_filer=8,
        offset="(2,0,0)",
        to="(input-east)",
        height=H32,
        depth=D2048,
        width=W8,
        caption="Conv1",
    ),
    # Pool1 2×2: 32×2048 -> 16×1024
    # to_connection("input", "conv1"),
    to_Pool(
        "pool1",
        offset="(0,0,0)",
        s_filer="{{1024×16}}",
        zlabeloffset=0.3,
        to="(conv1-east)",
        height=H16,
        depth=D1024,
        width=W8,
        caption="",
    ),
    # Conv2 (5x5, same): 8->4, stays 16×1024
    to_Conv(
        "conv2",
        s_filer="{{1024×16\hspace{2.5em}512×8}}",
        zlabeloffset=0.4,
        n_filer=4,
        offset="(2,0,0)",
        to="(pool1-east)",
        height=H16,
        depth=D1024,
        width=W4,
        caption="Conv2",
    ),
    # Pool2 2×2: 16×1024 -> 8×512
    # to_connection("pool1", "conv2"),
    to_Pool(
        "pool2",
        s_filer="{{}}",
        offset="(0,0,0)",
        zlabeloffset=0.3,
        to="(conv2-east)",
        height=H8,
        depth=D512,
        width=W4,
        caption="",
    ),
    # FC -> rep_dim (use numeric n_filer)
    to_fc(
        "fc1",
        n_filer="{{4×512×8}}",
        offset="(2,-.5,0)",
        zlabeloffset=0.5,
        to="(pool2-east)",
        height=1.3,
        depth=D512,
        width=W1,
        caption=f"FC",
        captionshift=20,
    ),
    # to_connection("pool2", "fc1"),
    # --------------------------- LATENT  ---------------------------
    to_Conv(
        "latent",
        n_filer="",
        s_filer="latent dim",
        offset="(2,0,0)",
        to="(fc1-east)",
        height=H8 * 1.6,
        depth=1.3,
        width=W1,
        caption=f"Latent Space",
    ),
    to_end(),
]


def main():
    name = "subter_lenet_arch"
    to_generate(arch, name + ".tex")


if __name__ == "__main__":
    main()