Add new regression and ensemble experiment configurations for V2-M and OrthoBridge
- Introduced multiple regression experiment configurations targeting vf_md, including: - cd_solo_reg_set.json: CD tower only regression setup. - img_solo_reg_set.json: Image tower only regression setup. - reg_head_epoch_sweep.json: Baseline regression sweeps at different epochs (50, 75, 100). - reg_head_set.json: Various regression setups including baseline and OrthoBridge configurations. - single_eye_reg.json: Single-eye regression setup for worst-eye aggregation analysis. - Added ensemble configurations for OrthoBridge with different inner bridges: - ortho_alts_ensemble.json: Ensemble tests with ConcatBridge, PairwiseAdditiveBridge, and GatedAdditiveBridge. - ortho_alts_tritower.json: Tritower tests with the same inner bridges. - Created V2-M specific configurations: - baseline_reg_nt50.json: Regression baseline with V2-M backbone. - geom_vec_gt.json and geom_vec_unet.json: Geometry vector injection experiments with V2-M. - single_l1_bridges.json: Single-eye ensemble experiments with various bridge types. - tritower_geom_gt.json: Tritower setup with GT contour-rasterized masks. - Promoted existing experiments to higher repetitions for robustness.
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rpotter6298
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"""F1 — System architecture diagram.
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Bilateral multimodal fusion architecture. Modelled on v3's architecture_fused_head
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but adapted for v4 + manuscript terminology:
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* "OD HyperTower" / "OS HyperTower" -> "OD Fusion" / "OS Fusion"
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* Bridge boxes show their math explicitly (image projection, clinical
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projection, fusion operation), no longer abbreviated "Bridge"
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* Title drops the HyperTower brand
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Re-run anytime:
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python -m v4.figures.F1_architecture
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"""
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from __future__ import annotations
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from matplotlib.patches import FancyBboxPatch
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OUT = Path(__file__).parent / "output" / "F1_architecture.png"
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# ── Palette (matches v3 plot_architecture.py for visual consistency) ────────
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C_IMG = "#4e8d3a" # green — image / CNN
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C_MD = "#4c72b0" # blue — clinical network
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C_BRIDGE = "#c44e52" # red — fusion bridge
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C_HEAD = "#d4a017" # gold — patient-level head
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C_OUT = "#8c6bb1" # purple — output classes
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C_INPUT = "#a0a0a0" # grey — raw inputs
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C_BG = "#e8e8e8"
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C_ARROW = "#444444"
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FONT = "DejaVu Sans"
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# ── Primitives ───────────────────────────────────────────────────────────────
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def _box(ax, cx, cy, w, h, color, text="", fontsize=9, text_color="white",
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bold=False, alpha=0.92, radius=0.12, lw=1.5):
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patch = FancyBboxPatch(
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(cx - w / 2, cy - h / 2), w, h,
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boxstyle=f"round,pad=0,rounding_size={radius}",
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facecolor=color, edgecolor="white", linewidth=lw, alpha=alpha, zorder=3,
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transform=ax.transData,
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)
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ax.add_patch(patch)
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if text:
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ax.text(cx, cy, text, ha="center", va="center",
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fontsize=fontsize, color=text_color,
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fontweight="bold" if bold else "normal",
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fontfamily=FONT, zorder=4)
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return patch
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def _arrow(ax, x0, y0, x1, y1, lw=1.4, color=C_ARROW, style="-|>"):
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ax.annotate("", xy=(x1, y1), xytext=(x0, y0),
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arrowprops=dict(arrowstyle=style, color=color, lw=lw),
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zorder=2)
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def _text(ax, x, y, s, fontsize=9, color="#333", ha="center", va="center", bold=False):
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ax.text(x, y, s, ha=ha, va=va, fontsize=fontsize, color=color,
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fontfamily=FONT, fontweight="bold" if bold else "normal", zorder=5)
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def _bracket(ax, x, y0, y1, text="", pad=0.20, fontsize=9, badge_color="#555"):
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mid = (y0 + y1) / 2
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ax.plot([x, x + pad, x + pad, x], [y1, y1, y0, y0],
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color=badge_color, lw=1.4, solid_capstyle="round", zorder=2)
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if text:
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ax.text(x + pad * 1.4, mid, text, ha="left", va="center",
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fontsize=fontsize, color="white", fontfamily=FONT, fontweight="bold",
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zorder=6,
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bbox=dict(facecolor=badge_color, edgecolor="none", pad=3.5,
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boxstyle="round,pad=0.3"))
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def _draw_output(ax, x, y, classes=("Glaucoma", "Normal")):
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bw, bh, gap = 1.10, 0.38, 0.08
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n = len(classes)
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total = n * bh + (n - 1) * gap
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y_top = y + total / 2 - bh / 2
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for i, cls in enumerate(classes):
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cy = y_top - i * (bh + gap)
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_box(ax, x + bw / 2, cy, bw, bh, C_OUT, cls, fontsize=8.5, radius=0.08)
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_arrow(ax, x, y, x, cy, lw=1.1, style="-|>")
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_text(ax, x + bw / 2, y - total / 2 - 0.20, "Softmax",
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fontsize=7.5, color=C_OUT)
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def _draw_eye_fusion(ax, x_left, y_img, y_md, eye_label):
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"""One eye's row: Image Network box + Clinical Network box -> Fusion bridge box.
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Returns (x_right_of_bridge, y_bridge_center).
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"""
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bw_img, bh_img = 1.45, 0.72
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bw_md, bh_md = 1.48, 0.66
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bw_br, bh_br = 1.75, 1.30
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# Image network box
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_box(ax, x_left + bw_img / 2, y_img, bw_img, bh_img, C_IMG,
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f"{eye_label}\nImage Network", fontsize=8.5, radius=0.08)
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# Clinical network box
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_box(ax, x_left + bw_md / 2, y_md, bw_md, bh_md, C_MD,
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f"{eye_label}\nClinical Network", fontsize=8.5, radius=0.08)
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# Fusion bridge — with math detail (replaces compact "Bridge" label)
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br_x = x_left + max(bw_img, bw_md) + 1.40
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cy_br = (y_img + y_md) / 2
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_arrow(ax, x_left + bw_img, y_img, br_x - bw_br / 2, cy_br, lw=1.2, style="-|>")
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_arrow(ax, x_left + bw_md, y_md, br_x - bw_br / 2, cy_br, lw=1.2, style="-|>")
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_box(ax, br_x, cy_br, bw_br, bh_br, C_BRIDGE,
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"Fusion Bridge\nFC(img → 256)\nFC(md → 256)\nHadamard product",
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fontsize=8, radius=0.10)
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return br_x + bw_br / 2, cy_br
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# ── Main figure ──────────────────────────────────────────────────────────────
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def main() -> None:
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W, H = 13.0, 7.8
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fig, ax = plt.subplots(figsize=(W, H))
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ax.set_xlim(0, W); ax.set_ylim(0, H); ax.axis("off")
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ax.set_facecolor(C_BG); fig.patch.set_facecolor(C_BG)
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ax.set_title("Bilateral Multimodal Fusion Architecture",
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fontsize=13, fontweight="bold", fontfamily=FONT, pad=10, color="#222")
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x_left = 3.2
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inp_cx = 1.85
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inp_w = 0.95
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inp_h = 0.55
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# OD (top)
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od_y_img, od_y_md = 6.10, 4.80
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br_od_x, cy_od = _draw_eye_fusion(ax, x_left, od_y_img, od_y_md, "OD")
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_text(ax, 0.50, (od_y_img + od_y_md) / 2, "OD\n(Right Eye)",
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fontsize=9.5, color="#444", bold=True)
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_box(ax, inp_cx, od_y_img, inp_w, inp_h, C_INPUT, "Fundus\nImage",
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fontsize=8.5, radius=0.08, alpha=0.78, text_color="#333")
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_box(ax, inp_cx, od_y_md, inp_w, inp_h, C_INPUT, "Clinical\nData",
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fontsize=8.5, radius=0.08, alpha=0.78, text_color="#333")
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_arrow(ax, inp_cx + inp_w / 2, od_y_img, x_left, od_y_img, lw=1.2, style="-|>")
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_arrow(ax, inp_cx + inp_w / 2, od_y_md, x_left, od_y_md, lw=1.2, style="-|>")
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# OS (bottom)
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os_y_img, os_y_md = 2.95, 1.65
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br_os_x, cy_os = _draw_eye_fusion(ax, x_left, os_y_img, os_y_md, "OS")
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_text(ax, 0.50, (os_y_img + os_y_md) / 2, "OS\n(Left Eye)",
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fontsize=9.5, color="#444", bold=True)
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_box(ax, inp_cx, os_y_img, inp_w, inp_h, C_INPUT, "Fundus\nImage",
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fontsize=8.5, radius=0.08, alpha=0.78, text_color="#333")
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_box(ax, inp_cx, os_y_md, inp_w, inp_h, C_INPUT, "Clinical\nData",
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fontsize=8.5, radius=0.08, alpha=0.78, text_color="#333")
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_arrow(ax, inp_cx + inp_w / 2, os_y_img, x_left, os_y_img, lw=1.2, style="-|>")
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_arrow(ax, inp_cx + inp_w / 2, os_y_md, x_left, os_y_md, lw=1.2, style="-|>")
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# Side brackets — re-labelled "OD Fusion" / "OS Fusion"
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_bracket(ax, x=br_od_x + 0.05,
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y0=od_y_md - 0.50, y1=od_y_img + 0.50,
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text="OD Fusion", pad=0.22, fontsize=9, badge_color="#555")
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_bracket(ax, x=br_os_x + 0.05,
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y0=os_y_md - 0.50, y1=os_y_img + 0.50,
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text="OS Fusion", pad=0.22, fontsize=9, badge_color="#555")
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# Patient-level head (Fused Head)
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head_y = (cy_od + cy_os) / 2
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head_x = max(br_od_x, br_os_x) + 2.55
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head_w, head_h = 1.95, 1.30
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_arrow(ax, br_od_x + 0.05, cy_od, head_x - head_w / 2, head_y, lw=1.4, style="-|>")
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_arrow(ax, br_os_x + 0.05, cy_os, head_x - head_w / 2, head_y, lw=1.4, style="-|>")
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_box(ax, head_x, head_y, head_w, head_h, C_HEAD,
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"Patient Head\ncat(z_OD, z_OS)\n→ FC(256) → FC(2)",
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fontsize=8.5, radius=0.10)
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# Output nodes
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out_x = head_x + head_w / 2 + 0.55
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_arrow(ax, head_x + head_w / 2, head_y, out_x, head_y, lw=1.5, style="-|>")
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_draw_output(ax, out_x, head_y)
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OUT.parent.mkdir(parents=True, exist_ok=True)
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fig.savefig(OUT, dpi=180, bbox_inches="tight")
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plt.close(fig)
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print(f"saved {OUT}")
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if __name__ == "__main__":
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main()
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