Datasets:
File size: 13,123 Bytes
9126e0d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | """Publication vector figures. TikZ/PGFPlots only; RGB comes from experiment files."""
from pathlib import Path
import json,math
import numpy as np
from PIL import Image
ROOT=Path(__file__).resolve().parents[1];F=ROOT/'figures';A=ROOT/'results/analysis'
def esc(s):return str(s).replace('_',r'\_').replace('%',r'\%').replace('&',r'\&')
def write(name,body):
(F/(name+'.tex')).write_text(r'\documentclass[tikz,border=1.5pt]{standalone}'+'\n'+r'\input{style}'+'\n'+r'\begin{document}'+'\n'+body+'\n'+r'\end{document}'+'\n')
def synthetic():
examples=json.loads((F/'synthetic_examples.json').read_text());lines=[r'\begin{tikzpicture}[x=1mm,y=1mm]',r'\path[use as bounding box] (-6,-2) rectangle (155,133);']
for row,y,label in [(0,99,'RGB input'),(1,58,'Left of anchor'),(2,17,'Right of anchor')]:
lines.append(r'\node[rotate=90,anchor=south,font=\sffamily\fontsize{8}{10}\selectfont] at (-0.2,%s) {%s};'%(y,label))
for i,e in enumerate(examples):
x=19+39*i;obj=e['scene']['objects'][0];anc=e['scene']['objects'][3];sid=e['scene']['scene_id']
target=obj['color']+' '+obj['shape']+'s';anchor=anc['color']+' '+anc['shape']
lines.append(r'\node[align=center,text width=36mm,font=\sffamily\fontsize{8}{10}\selectfont] at (%s,125) {\textbf{%s}\quad %s\\anchor: %s};'%(x,chr(97+i),target,anchor))
for row,key,y in [(0,'rgb',99),(1,'left_gt',58),(2,'right_gt',17)]:
original=F/e['assets'][key]['file'];im=Image.open(original)
if row:
mask=np.asarray(im)>0;out=np.zeros((*mask.shape,3),np.uint8);out[:]=(247,248,249);out[mask]=(20,125,131);im=Image.fromarray(out)
# Lossless nearest-neighbour display enlargement preserves the exact
# 320-pixel experiment lattice; it does not create new observations.
display=F/'assets'/(sid+'_'+key+'_display.png');im.resize((1920,1920),Image.Resampling.NEAREST).save(display)
lines.append(r'\node[inner sep=0] at (%s,%s) {\includegraphics[width=35mm]{assets/%s}};'%(x,y,display.name))
lines.append(r'\draw[rule,line width=.35pt] (%s,%s) rectangle (%s,%s);'%(x-17.5,y-17.5,x+17.5,y+17.5))
if row:
count=e['records'][row-1]['target_count']
lines.append(r'\node[anchor=south east,fill=white,inner sep=1mm,font=\sffamily\fontsize{8}{9}\selectfont] at (%s,%s) {$n=%s$};'%(x+17,y-17,count))
lines.append(r'\node[font=\ttfamily\fontsize{8}{10}\selectfont,text=muted] at (%s,78) {%s};'%(x,esc(sid)))
lines.append(r'\end{tikzpicture}');write('Fig2_synthetic', '\n'.join(lines))
def budget():
alpha=.05/480;lines=[r'\begin{tikzpicture}',r'\begin{axis}[journal,width=56mm,height=36mm,xmode=log,xmin=50,xmax=2000,ymin=0,ymax=20,xlabel={Positive calibration images\\per stratum},xlabel style={align=center},ylabel={Zero-error upper bound (\%)},xtick={50,300,2000},xticklabels={50,300,2000},ytick={0,5,10,15,20},ymajorgrids]']
coords=' '.join('(%s,%.6f)'%(n,100*(1-alpha**(1/n))) for n in np.geomspace(50,2000,150))
lines.append(r'\addplot[teal,line width=1pt,no marks] coordinates {'+coords+'};')
for t,c in [(1,'muted'),(2.5,'muted'),(5,'ochre'),(10,'muted')]:lines.append(r'\addplot[%s,dashed,line width=.4pt] coordinates {(50,%s) (2000,%s)};'%(c,t,t))
risk=100*(1-alpha**(1/300));lines.append(r'\addplot[only marks,mark=*,mark size=2.3pt,teal] coordinates {(300,%.6f)};'%risk)
lines.append(r'\node[anchor=south west,align=left,font=\sffamily\fontsize{8}{10}\selectfont] at (axis cs:310,%.6f) {$n=300$\\%.2f\%%};'%(risk,risk))
lines.extend([r'\end{axis}',r'\end{tikzpicture}']);write('Fig8_calibration_budget','\n'.join(lines))
def get(summary,model,pop,metric,method):
return next(r for r in summary if (r['model'],r['population'],r['metric'],r['method'])==(model,pop,metric,method))
def forest(summary):
methods=[('global_direction','Global direction'),('target_only','Target phrases'),('anchor_gate','Anchor + gates'),('cacp','CACP'),('source_unconstrained_s11','Source abstention'),('constrained_0.05_s11','CACP + constrained')]
lines=[r'\begin{tikzpicture}[x=1mm,y=1mm]']
for i,(_,label) in enumerate(methods):lines.append(r'\node[anchor=east] at (37,%.4f) {%s};'%((i+.6)/6.2*50,label))
for panel,(model,name) in enumerate([('clipseg','CLIPSeg'),('groundedsam','Grounded SAM')]):
xpos=40+panel*62;lines.append(r'\node[anchor=west,font=\sffamily\bfseries\fontsize{8}{10}\selectfont] at (%s,58) {%s\quad %s};'%(xpos,chr(97+panel),name))
vals=[get(summary,model,'controlled_primary','PC50',m) for m,_ in methods]
xmin=min(-2,min(r['gain_lo']*100 for r in vals)-3);xmax=max(2,max(r['gain_hi']*100 for r in vals)+3)
labels=','.join('{'+label+'}' for _,label in methods)
lines.append(r'\begin{axis}[journal,at={(%s mm,0mm)},anchor=south west,width=48mm,height=50mm,xmin=%.3f,xmax=%.3f,ymin=-.6,ymax=5.6,ytick=\empty,xlabel={PC50 gain (percentage points)},xmajorgrids,axis y line=none,axis x line=bottom]'%(xpos,xmin,xmax))
lines.append(r'\addplot[muted,dashed,line width=.5pt] coordinates {(0,-.6) (0,5.6)};')
for i,r in enumerate(vals):
color='teal' if i==3 else 'ochre' if i==5 else 'ink'
lines.append(r'\addplot[%s,line width=.8pt,mark=*,mark size=1.6pt,error bars/.cd,x dir=both,x explicit] coordinates {(%.6f,%s) += (%.6f,0) -= (%.6f,0)};'%(color,r['gain']*100,i,(r['gain_hi']-r['gain'])*100,(r['gain']-r['gain_lo'])*100))
lines.append(r'\end{axis}')
lines.append(r'\end{tikzpicture}');write('Fig3_paired_gain','\n'.join(lines))
def capability(summary):
methods=[('frozen','Frozen'),('action_only','Action only'),('global_direction','Global direction'),('target_only','Target phrases'),('anchor_nogate','Anchor, no gates'),('anchor_gate','Anchor + gates'),('counterfactual_nogate','CF, no gates'),('cacp','CACP')]
families=['attribute','relation','quantifier','action','absence','paraphrase'];lines=[r'\begin{tikzpicture}[x=1mm,y=1mm]']
for panel,(model,name) in enumerate([('clipseg','CLIPSeg'),('groundedsam','Grounded SAM')]):
dy=-panel*70;lines.append(r'\node[anchor=west,font=\sffamily\bfseries\fontsize{8}{10}\selectfont] at (0,%s) {%s\quad %s};'%(dy+7,chr(97+panel),name))
for j,f in enumerate(families):lines.append(r'\node[font=\sffamily\fontsize{8}{10}\selectfont] at (%s,%s) {%s};'%(47+j*19,dy+7,f.title()))
for i,(m,label) in enumerate(methods):
y=dy-i*7
lines.append(r'\node[anchor=east,font=\sffamily\fontsize{8}{10}\selectfont] at (36,%s) {%s};'%(y,label))
for j,f in enumerate(families):
val=get(summary,model,'family_'+f,'PC50',m)['estimate']*100;x=47+j*19;strength=round(4+.74*val);text='white' if strength>55 else 'ink'
lines.append(r'\fill[teal!%s!white] (%s,%s) rectangle (%s,%s);'%(strength,x-9.1,y-3.2,x+9.1,y+3.2))
lines.append(r'\node[text=%s,font=\sffamily\fontsize{8}{10}\selectfont] at (%s,%s) {%.1f};'%(text,x,y,val))
lines.append(r'\end{tikzpicture}');write('Fig4_capability','\n'.join(lines))
def tradeoff(summary):
params=json.loads((A/'calibration.json').read_text());lines=[r'\begin{tikzpicture}[x=1mm,y=1mm]']
for panel,(model,name) in enumerate([('clipseg','CLIPSeg'),('groundedsam','Grounded SAM')]):
curves=json.loads((A/(model+'_test_curve.json')).read_text());dx=panel*83
lines.append(r'\node[anchor=west,font=\sffamily\bfseries\fontsize{8}{10}\selectfont] at (%s,110) {%s\quad %s};'%(dx,chr(97+panel),name))
coords=' '.join('(%.6f,%.6f)'%(r['additional_false_abstention']*100,r['no_target_accuracy']*100) for r in curves)
for row in [0,1]:
ymin,ymax=(65,85) if model=='clipseg' else (25,65)
if row==1:ymin,ymax=0,100
lines.append(r'\begin{axis}[journal,at={(%s mm,%s mm)},anchor=south west,width=61mm,height=35mm,xmin=0,xmax=%s,ymin=%s,ymax=%s,xlabel={Additional false abstention (\%%)},ylabel={No-target accuracy (\%%)},clip=true,xmajorgrids,ymajorgrids]'%(dx+13,60 if row==0 else 0,5 if row==0 else 100,ymin,ymax))
lines.append(r'\addplot[ink,line width=.8pt] coordinates {'+coords+'};')
for method,color,mark in [('target_unconstrained_s11','rose','triangle*'),('constrained_0.05_s11','teal','*')]:
x=get(summary,model,'natural_test','additional_false_abstention',method)['estimate']*100;y=get(summary,model,'natural_test','no_target_accuracy',method)['estimate']*100
lines.append(r'\addplot[only marks,%s,mark=%s,mark size=2.4pt] coordinates {(%.6f,%.6f)};'%(color,mark,x,y))
lines.append(r'\end{axis}')
lines.append(r'\node[anchor=west,font=\sffamily\fontsize{8}{10}\selectfont,text=muted] at (%s,100) {Low-intervention detail};'%(dx+13))
lines.append(r'\node[anchor=west,font=\sffamily\fontsize{8}{10}\selectfont,text=muted] at (%s,40) {Full threshold sweep};'%(dx+13))
lines.append(r'\node[anchor=west,font=\sffamily\fontsize{8}{10}\selectfont] at (12,-15) {\textcolor{rose}{$\blacktriangle$} Unconstrained\qquad\textcolor{teal}{$\bullet$} Calibration constraint, $\epsilon=5\%$};')
lines.append(r'\end{tikzpicture}');write('Fig5_abstention','\n'.join(lines))
def stress(summary):
lines=[r'\begin{tikzpicture}[x=1mm,y=1mm]']
for panel,(model,name) in enumerate([('clipseg','CLIPSeg'),('groundedsam','Grounded SAM')]):
dx=panel*82;lines.append(r'\node[anchor=west,font=\sffamily\bfseries\fontsize{8}{10}\selectfont] at (%s,65) {%s\quad %s};'%(dx,chr(97+panel),name))
lines.append(r'\begin{axis}[journal,at={(%s mm,0mm)},anchor=south west,width=62mm,height=48mm,ymin=0,ymax=60,xmin=-.3,xmax=3.3,xtick={0,1,2,3},xticklabels={Clean,Blur,Noise,Dim},ylabel={PC50 (\%%)},ymajorgrids,legend pos=north east]'%(dx+12))
for m,color,mark in [('frozen','muted','square*'),('target_only','ochre','triangle*'),('cacp','teal','*')]:
points=[]
for i,c in enumerate(['clean','blur','noise','dim']):
v=get(summary,model,'stress_'+c,'PC50',m);points.append('(%.2f,%.6f) += (0,%.6f) -= (0,%.6f)'%(i,v['estimate']*100,(v['hi']-v['estimate'])*100,(v['estimate']-v['lo'])*100))
lines.append(r'\addplot[%s,mark=%s,mark size=1.8pt,line width=.8pt,error bars/.cd,y dir=both,y explicit] coordinates {'%(color,mark)+' '.join(points)+'};')
if panel==1:lines.append(r'\legend{Frozen,Target phrases,CACP}')
lines.append(r'\end{axis}')
lines.append(r'\end{tikzpicture}');write('Fig6_stress','\n'.join(lines))
def qualitative():
meta=json.loads((F/'qualitative/qualitative_examples.json').read_text());lines=[r'\begin{tikzpicture}[x=1mm,y=1mm]']
for j,title in enumerate(['RGB input','Ground truth','Frozen errors','CACP errors']):lines.append(r'\node[font=\sffamily\bfseries\fontsize{8}{10}\selectfont] at (%s,13) {%s};'%(19+j*40,title))
for i,e in enumerate(meta['examples']):
y=-i*45.5;d=e['record'];name='CLIPSeg' if e['model']=='clipseg' else 'Grounded SAM'
lines.append(r'\node[anchor=west,text width=158mm,align=left,font=\sffamily\fontsize{8}{10}\selectfont] at (0,%s) {\textbf{%s\quad %s\quad %s}\enspace %s};'%(y+5,chr(97+i),name,e['kind'],esc(d['query'])))
gt=np.asarray(Image.open(F/'qualitative'/e['assets']['gt']))>0
for j,key in enumerate(['rgb','gt','frozen','cacp']):
im=Image.open(F/'qualitative'/e['assets'][key])
if key!='rgb':
mask=np.asarray(im)>0;out=np.zeros((*mask.shape,3),np.uint8);out[:]=(247,248,249)
if key=='gt':out[mask]=(20,125,131)
else:out[mask>]=(20,125,131);out[mask&~gt]=(182,120,34);out[~mask>]=(174,71,91)
im=Image.fromarray(out)
path=F/'qualitative'/(e['model']+'_'+e['kind']+'_'+key+'_display.png');im.resize((1920,1920),Image.Resampling.NEAREST).save(path)
x=19+j*40;lines.append(r'\node[inner sep=0] at (%s,%s) {\includegraphics[width=30mm]{qualitative/%s}};'%(x,y-17,path.name))
lines.append(r'\draw[rule,line width=.35pt] (%s,%s) rectangle (%s,%s);'%(x-15,y-32,x+15,y-2))
if key in ['frozen','cacp']:lines.append(r'\node[anchor=north,font=\sffamily\fontsize{8}{10}\selectfont] at (%s,%s) {IoU %.3f};'%(x,y-33,e['scores'][key]['iou']))
lines.append(r'\node[anchor=north west,font=\ttfamily\fontsize{8}{10}\selectfont,text=muted] at (2,%s) {%s};'%(y-33,esc(d['id'])))
lines.append(r'\node[anchor=west] at (2,-178) {\textcolor{teal}{$\blacksquare$} True positive\qquad\textcolor{ochre}{$\blacksquare$} False positive\qquad\textcolor{rose}{$\blacksquare$} False negative};')
lines.append(r'\end{tikzpicture}');write('Fig7_qualitative','\n'.join(lines))
def main():
synthetic();budget()
if (F/'qualitative/qualitative_examples.json').exists():qualitative()
if (A/'summary.json').exists():
s=json.loads((A/'summary.json').read_text())
if {'clipseg','groundedsam'}<={r['model'] for r in s}:forest(s);capability(s);tradeoff(s);stress(s)
if __name__=='__main__':main()
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