Ship MAX_DIM=512: match Python reference, better small-object detection

assets/index-BIGvobfH.css

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assets/inference.worker-BO6TvMbc.js

@@ -1 +0,0 @@
-const e=`shreyask/falcon-perception-onnx-webgpu`,t=`https://huggingface.co/${e}/resolve/main`,n={unified:{file:`unified_v2.onnx`,size:203e7},embed_tokens:{file:`embed_tokens.onnx`,size:256e6},coord_decoder:{file:`coord_decoder.onnx`,size:96e6},size_decoder:{file:`size_decoder.onnx`,size:96e6},segm_head:{file:`segm_head.onnx`,size:9e6},img_projector:{file:`img_projector.onnx`,size:3e6},coord_encoder:{file:`coord_encoder.onnx`,size:2e6},size_encoder:{file:`size_encoder.onnx`,size:2e6}},r={dim:1024,n_layers:28,n_heads:16,head_dim:128,n_kv_heads:8,vocab_size:65536,max_seq_len:8192,segm_out_dim:256,coord_out_dim:2048,size_out_dim:2048,coord_token_id:240,size_token_id:241,seg_token_id:262,eos_id:11,img_id:227,image_cls_token_id:244,img_end_id:230,spatial_patch_size:16,temporal_patch_size:1,channel_size:3,rope_theta:1e4,presence_token_id:268,absence_token_id:269,end_of_query_token_id:263,start_of_query_token_id:264,ref_seg_token_id:258};function i(e,t=.6){if(e.length<=1)return e;let n=e.map((e,t)=>({det:e,idx:t,area:o(e.box)}));n.sort((e,t)=>t.area-e.area);let r=new Set,i=[];for(let{det:e,idx:o}of n){let n=!1;for(let r of i)if(a(e.box,r)>t){n=!0;break}n||(r.add(o),i.push(e.box))}return e.filter((e,t)=>r.has(t))}function a(e,t){let n=Math.max(e.x1,t.x1),r=Math.max(e.y1,t.y1),i=Math.min(e.x2,t.x2),a=Math.min(e.y2,t.y2),s=i-n,c=a-r;if(s<=0||c<=0)return 0;let l=s*c,u=o(e)+o(t)-l;return u>0?l/u:0}function o(e){return Math.max(0,e.x2-e.x1)*Math.max(0,e.y2-e.y1)}let s,c=null,l=!1;const u={};function d(e){self.postMessage(e)}async function f(e,n){let r=`${t}/${e}`,i=await fetch(r);if(!i.ok)throw Error(`Failed to fetch ${e}: ${i.status}`);let a=i.body?.getReader();if(!a)return i.arrayBuffer();let o=[],s=0;for(;;){let{done:t,value:r}=await a.read();if(t)break;o.push(r),s+=r.length,d({type:`progress`,progress:Math.round(s/n*100),loaded:s,total:n,file:e})}let c=new Uint8Array(s),l=0;for(let e of o)c.set(e,l),l+=e.length;return c.buffer}async function p(e,t,n,r){let i=`${t}.data`,[a,o]=await Promise.all([f(t,n*.01),f(i,n*.99)]),c=await s.InferenceSession.create(a,{executionProviders:r,externalData:[{path:i,data:new Uint8Array(o)}]});return console.log(`Loaded ${e}: inputs=[${c.inputNames}] outputs=[${c.outputNames}]`),c}async function m(){try{s=await import(`./ort.bundle.min-BZrO1_VY.js`);let t=typeof SharedArrayBuffer<`u`;s.env.wasm.numThreads=t?Math.min(4,navigator.hardwareConcurrency||2):1;let r=import(`./transformers.web-DaZ4EEMB.js`).then(async({AutoTokenizer:t})=>{try{c=await t.from_pretrained(e),console.log(`Tokenizer loaded`)}catch(e){console.warn(`AutoTokenizer failed, will use fallback:`,e.message)}}),i=[`unified`,`embed_tokens`,`coord_decoder`,`size_decoder`,`segm_head`,`img_projector`,`coord_encoder`,`size_encoder`],a=0,o=Object.values(n).reduce((e,t)=>e+t.size,0),l=new Set([`unified`]);for(let e of i){let{file:t,size:r}=n[e];d({type:`progress`,progress:Math.round(a/o*100),loaded:a,total:o,file:t});let i=l.has(e)?[`wasm`]:[`webgpu`,`wasm`];try{u[e]=await p(e,t,r,i)}catch(n){console.warn(`${i[0]} failed for ${e}, trying WASM:`,n.message);try{u[e]=await p(e,t,r,[`wasm`])}catch(t){console.error(`Failed to load ${e}:`,t.message)}}a+=r}await r,c||console.log(`Using fallback tokenizer (hardcoded prompt tokens)`),d({type:`loaded`})}catch(e){d({type:`error`,message:e?.message??`Failed to load models`})}}function ee(e){let t=r.spatial_patch_size,n=e.width,i=e.height,a=Math.min(256/n,256/i,1);n=Math.floor(Math.round(n*a)/t)*t,i=Math.floor(Math.round(i*a)/t)*t,n=Math.max(n,t),i=Math.max(i,t);let o=new OffscreenCanvas(n,i).getContext(`2d`);o.drawImage(e,0,0,n,i);let s=o.getImageData(0,0,n,i),c=new Float32Array(i*n*3);for(let e=0;e<i*n;e++)c[e*3+0]=s.data[e*4+0]/255*2-1,c[e*3+1]=s.data[e*4+1]/255*2-1,c[e*3+2]=s.data[e*4+2]/255*2-1;return{pixels:c,width:n,height:i,patchH:i/t,patchW:n/t}}function te(e,t){let n=Math.sqrt(t/e),r=Math.sqrt(e/t),i=new Float32Array(e*t*2);for(let a=0;a<e;a++){let o=e>1?r*(2*a/(e-1)-1):0;for(let e=0;e<t;e++){let r=t>1?n*(2*e/(t-1)-1):0,s=a*t+e;i[s*2+0]=o,i[s*2+1]=r}}return i}async function h(e,t){if(l){console.log(`[detect] skipped — already running`);return}if(l=!0,console.log(`[detect] query="${t}" image=${e.width}x${e.height}`),!u.unified||!u.embed_tokens){d({type:`error`,message:`Not ready. 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u.embed_tokens.run({token_ids:new s.Tensor(`int64`,b,[1,y])}),C=new Float32Array(ae.embeddings.data),w=new Float32Array(_*l);for(let e=0;e<m;e++)for(let t=0;t<h;t++){let n=e*h+t;for(let r=0;r<o;r++)for(let i=0;i<o;i++){let a=e*o+r,s=t*o+i,c=(a*p+s)*3,u=n*l+(r*o+i)*3;w[u+0]=f[c+0],w[u+1]=f[c+1],w[u+2]=f[c+2]}}let oe=(await u.img_projector.run({pixel_patches:new s.Tensor(`float32`,w,[_,l])})).projected.data;for(let e=0;e<_;e++){let t=S*a+e*a,n=e*a;for(let e=0;e<a;e++)C[t+e]=oe[n+e]}let se=C.slice(0,5);console.log(`[detect] embedding[0][0:5]: [${Array.from(se).map(e=>e.toFixed(3)).join(`, `)}]`);let ce=C.slice(S*a,S*a+5);console.log(`[detect] embedding[imgStart][0:5]: [${Array.from(ce).map(e=>e.toFixed(3)).join(`, `)}]`);let le=C.slice((y-1)*a,(y-1)*a+5);console.log(`[detect] embedding[last][0:5]: [${Array.from(le).map(e=>e.toFixed(3)).join(`, `)}]`),console.log(`[detect] running encoder (seq_len=${y})...`);let T=new Float32Array(y),E=S+_+1,ue=new Set([245,246,247,248,r.img_id,r.img_end_id]),D=new Float32Array(y);for(let e=0;e<y;e++){let t=Number(b[e]);D[e]=ue.has(t)&&e<E?0:1}let O=0;for(let e=0;e<y;e++)O+=D[e],T[e]=O-1;let k=r.head_dim/4,A=r.rope_theta,j=new Float32Array(y*k*2);for(let e=0;e<y;e++){let t=T[e];for(let n=0;n<k;n++){let r=t/A**(2*n/(k*2));j[(e*k+n)*2+0]=Math.cos(r),j[(e*k+n)*2+1]=Math.sin(r)}}let M=new Uint8Array(y*y);for(let e=0;e<y;e++)for(let t=0;t<=e;t++)M[e*y+t]=1;for(let e=0;e<E;e++)for(let t=0;t<E;t++)M[e*y+t]=1;let N=r.vocab_size,P=r.coord_out_dim/2,F=r.n_layers,I=r.n_heads,L=r.head_dim,de=new Float32Array(F*2*1*I*0*L),R=new Float32Array(y*2);R.fill(NaN);let z=te(m,h);for(let e=0;e<_;e++){let t=(S+e)*2;R[t+0]=z[e*2+0],R[t+1]=z[e*2+1]}console.log(`[detect] prefill (seq_len=${y})...`);let B=await u.unified.run({token_embeddings:new s.Tensor(`float32`,C,[1,y,a]),freqs_cis:new s.Tensor(`float32`,j,[1,y,k,2]),pos_hw:new s.Tensor(`float32`,R,[1,y,2]),attention_mask:new s.Tensor(`bool`,M,[1,y,y]),past_kv:new 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assets/inference.worker-BODVQac6.js

@@ -1 +0,0 @@
-const e=`shreyask/falcon-perception-onnx-webgpu`,t=`https://huggingface.co/${e}/resolve/main`,n={unified:{file:`unified_v2.onnx`,size:203e7},embed_tokens:{file:`embed_tokens.onnx`,size:256e6},coord_decoder:{file:`coord_decoder.onnx`,size:96e6},size_decoder:{file:`size_decoder.onnx`,size:96e6},anyup:{file:`anyup.onnx`,size:81e6},segm_head:{file:`segm_head.onnx`,size:9e6},img_projector:{file:`img_projector.onnx`,size:3e6},coord_encoder:{file:`coord_encoder.onnx`,size:2e6},size_encoder:{file:`size_encoder.onnx`,size:2e6}},r={dim:1024,n_layers:28,n_heads:16,head_dim:128,n_kv_heads:8,vocab_size:65536,max_seq_len:8192,segm_out_dim:256,coord_out_dim:2048,size_out_dim:2048,coord_token_id:240,size_token_id:241,seg_token_id:262,eos_id:11,img_id:227,image_cls_token_id:244,img_end_id:230,spatial_patch_size:16,temporal_patch_size:1,channel_size:3,rope_theta:1e4,presence_token_id:268,absence_token_id:269,end_of_query_token_id:263,start_of_query_token_id:264,ref_seg_token_id:258};function i(e,t=.6){if(e.length<=1)return e;let n=e.map((e,t)=>({det:e,idx:t,area:o(e.box)}));n.sort((e,t)=>t.area-e.area);let r=new Set,i=[];for(let{det:e,idx:o}of n){let n=!1;for(let r of i)if(a(e.box,r)>t){n=!0;break}n||(r.add(o),i.push(e.box))}return e.filter((e,t)=>r.has(t))}function a(e,t){let n=Math.max(e.x1,t.x1),r=Math.max(e.y1,t.y1),i=Math.min(e.x2,t.x2),a=Math.min(e.y2,t.y2),s=i-n,c=a-r;if(s<=0||c<=0)return 0;let l=s*c,u=o(e)+o(t)-l;return u>0?l/u:0}function o(e){return Math.max(0,e.x2-e.x1)*Math.max(0,e.y2-e.y1)}let s,c=null,l=!1,u=null;const d={};function f(e){self.postMessage(e)}function p(e){let t=d[e];if(!t)throw Error(`ONNX session not loaded: ${e}`);return t}async function m(e,n){let r=`${t}/${e}`,i=await fetch(r);if(!i.ok)throw Error(`Failed to fetch ${e}: ${i.status}`);let a=i.body?.getReader();if(!a)return i.arrayBuffer();let o=[],s=0;for(;;){let{done:t,value:r}=await a.read();if(t)break;o.push(r),s+=r.length,f({type:`progress`,progress:Math.round(s/n*100),loaded:s,total:n,file:e})}let c=new Uint8Array(s),l=0;for(let e of o)c.set(e,l),l+=e.length;return c.buffer}async function h(e,t,n,r){let i=`${t}.data`,[a,o]=await Promise.all([m(t,n*.01),m(i,n*.99)]),c=await s.InferenceSession.create(a,{executionProviders:r,externalData:[{path:i,data:new Uint8Array(o)}]});return console.log(`Loaded ${e}: inputs=[${c.inputNames}] outputs=[${c.outputNames}]`),c}async function ee(){try{s=await import(`./ort.bundle.min-BZrO1_VY.js`);let t=typeof SharedArrayBuffer<`u`;s.env.wasm.numThreads=t?Math.min(4,navigator.hardwareConcurrency||2):1;let r=import(`./transformers.web-DaZ4EEMB.js`).then(async({AutoTokenizer:t})=>{try{c=await t.from_pretrained(e),console.log(`Tokenizer loaded`)}catch(e){console.warn(`AutoTokenizer failed, will use fallback:`,b(e,`unknown error`))}}),i=[`unified`,`embed_tokens`,`coord_decoder`,`size_decoder`,`segm_head`,`anyup`,`img_projector`,`coord_encoder`,`size_encoder`],a=0,o=Object.values(n).reduce((e,t)=>e+t.size,0),l=new Set([`unified`]);for(let e of i){let{file:t,size:r}=n[e];f({type:`progress`,progress:Math.round(a/o*100),loaded:a,total:o,file:t});let i=l.has(e)?[`wasm`]:[`webgpu`,`wasm`];try{d[e]=await h(e,t,r,i)}catch(n){console.warn(`${i[0]} failed for ${e}, trying WASM:`,b(n,`unknown error`));try{d[e]=await h(e,t,r,[`wasm`])}catch(t){console.error(`Failed to load ${e}:`,b(t,`unknown error`))}}a+=r}await r,c||console.log(`Using fallback tokenizer (hardcoded prompt tokens)`),f({type:`loaded`})}catch(e){f({type:`error`,message:b(e,`Failed to load models`)})}}function te(e){let t=r.spatial_patch_size,n=new OffscreenCanvas(256,256).getContext(`2d`);n.drawImage(e,0,0,256,256);let i=n.getImageData(0,0,256,256),a=new Float32Array(256*256*3),o=new Float32Array(3*256*256),s=256*256;for(let e=0;e<s;e++){let t=i.data[e*4+0]/255*2-1,n=i.data[e*4+1]/255*2-1,r=i.data[e*4+2]/255*2-1;a[e*3+0]=t,a[e*3+1]=n,a[e*3+2]=r,o[0*s+e]=t,o[1*s+e]=n,o[2*s+e]=r}return{pixels:a,pixelsChw:o,width:256,height:256,patchH:256/t,patchW:256/t}}function ne(e,t){let n=Math.sqrt(t/e),r=Math.sqrt(e/t),i=new Float32Array(e*t*2);for(let a=0;a<e;a++){let o=e>1?r*(2*a/(e-1)-1):0;for(let e=0;e<t;e++){let r=t>1?n*(2*e/(t-1)-1):0,s=a*t+e;i[s*2+0]=o,i[s*2+1]=r}}return i}function g(e){u?.image&&u.image!==e.image&&u.image.close(),u=e}function _(e){if(l){g(e),console.log(`[detect] queued latest frame while inference is running`);return}v(e)}async function v(e){l=!0;let{image:t,query:n,requestId:a,sourceTimestampMs:o,capturedAtMs:m}=e,h=(e,t)=>{f({type:`result`,detections:e,inferenceMs:t,query:n,requestId:a,sourceTimestampMs:o,capturedAtMs:m})};if(console.log(`[detect] query="${n}" image=${t.width}x${t.height}`),!d.unified||!d.embed_tokens){f({type:`error`,message:`Not ready. Sessions: ${Object.keys(d).join(`,`)}`}),l=!1;return}try{let e=performance.now(),a=r.dim,o=r.spatial_patch_size,l=o*o*r.channel_size,{pixels:u,pixelsChw:f,width:m,height:ee,patchH:g,patchW:_}=te(t),v=g*_;console.log(`[detect] image: ${m}px, ${g}x${_}=${v} patches`);let b=`Segment these expressions in the image:<|start_of_query|>${n}<|REF_SEG|>`,x;if(c){let e=c.encode(b,{add_special_tokens:!1});x=Array.from(e),console.log(`[detect] text tokens (${x.length}):`,x)}else x=[37462,1978,20528,821,790,6883,549,264,258],console.log(`[detect] using fallback tokens (no query text):`,x);let ie=[245,246,247,248],S=1+ie.length+v+1+x.length,C=new BigInt64Array(S),w=0;C[w++]=BigInt(r.image_cls_token_id);for(let e of ie)C[w++]=BigInt(e);let T=w;for(let e=0;e<v;e++)C[w++]=BigInt(r.img_id);C[w++]=BigInt(r.img_end_id);for(let e of x)C[w++]=BigInt(e);console.log(`[detect] total seq_len=${S}, token IDs: [${Array.from(C.slice(0,5)).map(Number)}...${Array.from(C.slice(-5)).map(Number)}]`);let ae=await p(`embed_tokens`).run({token_ids:new s.Tensor(`int64`,C,[1,S])}),E=new Float32Array(ae.embeddings.data),D=new Float32Array(v*l);for(let e=0;e<g;e++)for(let t=0;t<_;t++){let n=e*_+t;for(let r=0;r<o;r++)for(let i=0;i<o;i++){let a=e*o+r,s=t*o+i,c=(a*m+s)*3,d=n*l+(r*o+i)*3;D[d+0]=u[c+0],D[d+1]=u[c+1],D[d+2]=u[c+2]}}let oe=(await p(`img_projector`).run({pixel_patches:new s.Tensor(`float32`,D,[v,l])})).projected.data;for(let e=0;e<v;e++){let t=T*a+e*a,n=e*a;for(let e=0;e<a;e++)E[t+e]=oe[n+e]}let se=E.slice(0,5);console.log(`[detect] embedding[0][0:5]: [${Array.from(se).map(e=>e.toFixed(3)).join(`, `)}]`);let ce=E.slice(T*a,T*a+5);console.log(`[detect] embedding[imgStart][0:5]: [${Array.from(ce).map(e=>e.toFixed(3)).join(`, `)}]`);let le=E.slice((S-1)*a,(S-1)*a+5);console.log(`[detect] embedding[last][0:5]: [${Array.from(le).map(e=>e.toFixed(3)).join(`, `)}]`),console.log(`[detect] running encoder (seq_len=${S})...`);let O=new Float32Array(S),k=T+v+1,ue=new Set([245,246,247,248,r.img_id,r.img_end_id]),A=new Float32Array(S);for(let e=0;e<S;e++){let t=Number(C[e]);A[e]=ue.has(t)&&e<k?0:1}let de=0;for(let e=0;e<S;e++)de+=A[e],O[e]=de-1;let j=r.head_dim/4,M=r.rope_theta,N=new Float32Array(S*j*2);for(let e=0;e<S;e++){let t=O[e];for(let n=0;n<j;n++){let r=t/M**(2*n/(j*2));N[(e*j+n)*2+0]=Math.cos(r),N[(e*j+n)*2+1]=Math.sin(r)}}let P=new Uint8Array(S*S);for(let e=0;e<S;e++)for(let t=0;t<=e;t++)P[e*S+t]=1;for(let e=0;e<k;e++)for(let t=0;t<k;t++)P[e*S+t]=1;let F=r.vocab_size,I=r.coord_out_dim/2,L=r.n_layers,R=r.n_heads,z=r.head_dim,fe=new Float32Array(L*2*1*R*0*z),B=new Float32Array(S*2);B.fill(NaN);let V=ne(g,_);for(let e=0;e<v;e++){let t=(T+e)*2;B[t+0]=V[e*2+0],B[t+1]=V[e*2+1]}console.log(`[detect] prefill (seq_len=${S})...`);let H=await p(`unified`).run({token_embeddings:new s.Tensor(`float32`,E,[1,S,a]),freqs_cis:new s.Tensor(`float32`,N,[1,S,j,2]),pos_hw:new s.Tensor(`float32`,B,[1,S,2]),attention_mask:new s.Tensor(`bool`,P,[1,S,S]),past_kv:new s.Tensor(`float32`,fe,[L,2,1,R,0,z]),past_len:new s.Tensor(`int64`,new BigInt64Array([0n]),[])}),U=H.logits.data,pe=H.new_kv,W=S,me=(S-1)*F,G=U[me+r.presence_token_id],K=U[me+r.absence_token_id];console.log(`[detect] prefill done: presence=${G.toFixed(2)}, absence=${K.toFixed(2)}`);let q=[];if(G<=K&&G<0){console.log(`[detect] object "${n}" not found`),h(q,performance.now()-e);return}let he=H.hidden_states.data,J=null,ge=d.anyup;if(ge){let e=new Float32Array(a*g*_),t=g*_;for(let n=0;n<v;n++){let r=(T+n)*a,i=Math.floor(n/_),o=n%_,s=i*_+o;for(let n=0;n<a;n++)e[n*t+s]=he[r+n]}let n=performance.now();try{J=(await ge.run({images:new s.Tensor(`float32`,f,[1,3,ee,m]),lr_tokens:new s.Tensor(`float32`,e,[1,a,g,_])})).hr_features,console.log(`[detect] anyup done in ${(performance.now()-n).toFixed(0)}ms, hr_features=${J.dims.join(`x`)}`)}catch(e){console.warn(`[detect] anyup failed; segmentation will fall back:`,e),J=null}}let Y=new Float32Array(he.subarray((S-1)*a,S*a)),X=U,_e=O[S-1]+1,Z=null,Q=null,ve=new Set([r.eos_id,r.end_of_query_token_id]);for(let t=0;t<510;t++){let i=X.length,o=y(X,i-F,F);if(ve.has(o)){console.log(`[detect] stop token ${o} (${re(o)}) at step ${t}`);break}if(o===r.absence_token_id){console.log(`[detect] <absence> at step ${t}, stopping`);break}if(o===r.coord_token_id){let e=await p(`coord_decoder`).run({hidden:new s.Tensor(`float32`,Y,[1,a])}),n=new Float32Array(e.logits.data),r=.01,i=0,o=0,c=0,l=0;for(let e=0;e<100&&(i=y(n,0,I),o=y(n,I,I),c=i/(I-1),l=o/(I-1),q.some(e=>{let t=(e.box.x1+e.box.x2)/2,n=(e.box.y1+e.box.y2)/2;return Math.abs(t-c)<r&&Math.abs(n-l)<r}));e++)n[i]=-1/0,n[I+o]=-1/0;Z=[c,l],console.log(`[detect] step ${t}: <coord> → (${c.toFixed(3)}, ${l.toFixed(3)})`)}else if(o===r.size_token_id){let e=(await p(`size_decoder`).run({hidden:new s.Tensor(`float32`,Y,[1,a])})).logits.data,n=y(e,0,I),r=y(e,I,I),i=Math.log2(1/I),o=2**(n/(I-1)*-i+i),c=2**(r/(I-1)*-i+i);Q=[o,c],console.log(`[detect] step ${t}: <size> → (h=${o.toFixed(3)}, w=${c.toFixed(3)})`)}else if(o===r.seg_token_id){if(Z&&Q){let[r,i]=Z,[o,c]=Q,l;if(J&&d.segm_head)try{let e=await p(`segm_head`).run({seg_token_hidden:new s.Tensor(`float32`,Y,[1,a]),hr_features:J}),t=e.masks.data,[,n,r]=e.masks.dims,i=new Uint8Array(n*r);for(let e=0;e<i.length;e++)i[e]=t[e]>0?255:0;l={data:i,width:r,height:n}}catch(e){console.warn(`[detect] segm_head failed for object #${q.length+1}:`,e)}if(q.push({label:n,score:1/(1+Math.exp(-(G-K))),box:{x1:Math.max(0,r-c/2),y1:Math.max(0,i-o/2),x2:Math.min(1,r+c/2),y2:Math.min(1,i+o/2)},mask:l}),console.log(`[detect] step ${t}: <seg> → object #${q.length} xy=(${r.toFixed(2)},${i.toFixed(2)}) hw=(${o.toFixed(2)},${c.toFixed(2)})${l?` +mask`:``}`),h([...q],performance.now()-e),q.length>=100){console.log(`[detect] reached max 100 objects`);break}Z=null,Q=null}}else console.log(`[detect] step ${t}: ${re(o)} (${o})`);let c;if(o===r.coord_token_id&&Z){let e=await p(`coord_encoder`).run({coords:new s.Tensor(`float32`,new Float32Array(Z),[1,2])});c=new Float32Array(e.encoded.data)}else if(o===r.size_token_id&&Q){let e=await p(`size_encoder`).run({sizes:new s.Tensor(`float32`,new Float32Array(Q),[1,2])});c=new Float32Array(e.encoded.data)}else{let e=await p(`embed_tokens`).run({token_ids:new s.Tensor(`int64`,new BigInt64Array([BigInt(o)]),[1,1])});c=new Float32Array(e.embeddings.data)}let l=new Float32Array(j*2);for(let e=0;e<j;e++){let t=_e/M**(2*e/(j*2));l[e*2+0]=Math.cos(t),l[e*2+1]=Math.sin(t)}let u=new Uint8Array(W+1);u.fill(1);let f=new Float32Array([NaN,NaN]),m=await p(`unified`).run({token_embeddings:new s.Tensor(`float32`,c,[1,1,a]),freqs_cis:new s.Tensor(`float32`,l,[1,1,j,2]),pos_hw:new s.Tensor(`float32`,f,[1,1,2]),attention_mask:new s.Tensor(`bool`,u,[1,1,W+1]),past_kv:pe,past_len:new s.Tensor(`int64`,new BigInt64Array([BigInt(W)]),[])});Y=new Float32Array(m.hidden_states.data),X=m.logits.data,pe=m.new_kv,W+=1,_e+=1}let ye=q.length,$=i(q,.6),be=performance.now()-e;console.log(`[detect] done in ${be.toFixed(0)}ms, ${$.length} detections (${ye} raw, ${ye-$.length} merged by bbox-IoU NMS)`),h($,be)}catch(e){console.error(`[detect] error:`,e),f({type:`error`,message:b(e,`Inference failed`)})}finally{t.close(),l=!1;let e=u;u=null,e&&_(e)}}function y(e,t,n){let r=0,i=-1/0;for(let a=0;a<n;a++)e[t+a]>i&&(i=e[t+a],r=a);return r}function b(e,t){return e instanceof Error?e.message:t}function re(e){return{[r.coord_token_id]:`<coord>`,[r.size_token_id]:`<size>`,[r.seg_token_id]:`<seg>`,[r.eos_id]:`<eos>`,[r.presence_token_id]:`<presence>`,[r.absence_token_id]:`<absence>`,[r.end_of_query_token_id]:`<end_of_query>`}[e]??`token_${e}`}self.onmessage=async e=>{switch(e.data.type){case`load`:await ee();break;case`detect`:_(e.data);break}};

assets/inference.worker-Bn4qPWdm.js

@@ -1 +0,0 @@
-const e=`shreyask/falcon-perception-onnx-webgpu`,t=`https://huggingface.co/${e}/resolve/main`,n={unified:{file:`unified_v2.onnx`,size:203e7},embed_tokens:{file:`embed_tokens.onnx`,size:256e6},coord_decoder:{file:`coord_decoder.onnx`,size:96e6},size_decoder:{file:`size_decoder.onnx`,size:96e6},segm_head:{file:`segm_head.onnx`,size:9e6},img_projector:{file:`img_projector.onnx`,size:3e6},coord_encoder:{file:`coord_encoder.onnx`,size:2e6},size_encoder:{file:`size_encoder.onnx`,size:2e6}},r={dim:1024,n_layers:28,n_heads:16,head_dim:128,n_kv_heads:8,vocab_size:65536,max_seq_len:8192,segm_out_dim:256,coord_out_dim:2048,size_out_dim:2048,coord_token_id:240,size_token_id:241,seg_token_id:262,eos_id:11,img_id:227,image_cls_token_id:244,img_end_id:230,spatial_patch_size:16,temporal_patch_size:1,channel_size:3,rope_theta:1e4,presence_token_id:268,absence_token_id:269,end_of_query_token_id:263,start_of_query_token_id:264,ref_seg_token_id:258};function i(e,t=.6){if(e.length<=1)return e;let n=e.map((e,t)=>({det:e,idx:t,area:o(e.box)}));n.sort((e,t)=>t.area-e.area);let r=new Set,i=[];for(let{det:e,idx:o}of n){let n=!1;for(let r of i)if(a(e.box,r)>t){n=!0;break}n||(r.add(o),i.push(e.box))}return e.filter((e,t)=>r.has(t))}function a(e,t){let n=Math.max(e.x1,t.x1),r=Math.max(e.y1,t.y1),i=Math.min(e.x2,t.x2),a=Math.min(e.y2,t.y2),s=i-n,c=a-r;if(s<=0||c<=0)return 0;let l=s*c,u=o(e)+o(t)-l;return u>0?l/u:0}function o(e){return Math.max(0,e.x2-e.x1)*Math.max(0,e.y2-e.y1)}let s,c=null,l=!1;const u={};function d(e){self.postMessage(e)}async function f(e,n){let r=`${t}/${e}`,i=await fetch(r);if(!i.ok)throw Error(`Failed to fetch ${e}: ${i.status}`);let a=i.body?.getReader();if(!a)return i.arrayBuffer();let o=[],s=0;for(;;){let{done:t,value:r}=await a.read();if(t)break;o.push(r),s+=r.length,d({type:`progress`,progress:Math.round(s/n*100),loaded:s,total:n,file:e})}let c=new Uint8Array(s),l=0;for(let e of o)c.set(e,l),l+=e.length;return c.buffer}async function p(e,t,n,r){let i=`${t}.data`,[a,o]=await Promise.all([f(t,n*.01),f(i,n*.99)]),c=await s.InferenceSession.create(a,{executionProviders:r,externalData:[{path:i,data:new Uint8Array(o)}]});return console.log(`Loaded ${e}: inputs=[${c.inputNames}] outputs=[${c.outputNames}]`),c}async function m(){try{s=await import(`./ort.bundle.min-BZrO1_VY.js`);let t=typeof SharedArrayBuffer<`u`;s.env.wasm.numThreads=t?Math.min(4,navigator.hardwareConcurrency||2):1;let r=import(`./transformers.web-DaZ4EEMB.js`).then(async({AutoTokenizer:t})=>{try{c=await t.from_pretrained(e),console.log(`Tokenizer loaded`)}catch(e){console.warn(`AutoTokenizer failed, will use fallback:`,e.message)}}),i=[`unified`,`embed_tokens`,`coord_decoder`,`size_decoder`,`segm_head`,`img_projector`,`coord_encoder`,`size_encoder`],a=0,o=Object.values(n).reduce((e,t)=>e+t.size,0),l=new Set([`unified`]);for(let e of i){let{file:t,size:r}=n[e];d({type:`progress`,progress:Math.round(a/o*100),loaded:a,total:o,file:t});let i=l.has(e)?[`wasm`]:[`webgpu`,`wasm`];try{u[e]=await p(e,t,r,i)}catch(n){console.warn(`${i[0]} failed for ${e}, trying WASM:`,n.message);try{u[e]=await p(e,t,r,[`wasm`])}catch(t){console.error(`Failed to load ${e}:`,t.message)}}a+=r}await r,c||console.log(`Using fallback tokenizer (hardcoded prompt tokens)`),d({type:`loaded`})}catch(e){d({type:`error`,message:e?.message??`Failed to load models`})}}function ee(e){let t=r.spatial_patch_size,n=e.width,i=e.height,a=Math.min(160/n,160/i,1);n=Math.floor(Math.round(n*a)/t)*t,i=Math.floor(Math.round(i*a)/t)*t,n=Math.max(n,t),i=Math.max(i,t);let o=new OffscreenCanvas(n,i).getContext(`2d`);o.drawImage(e,0,0,n,i);let s=o.getImageData(0,0,n,i),c=new Float32Array(i*n*3);for(let e=0;e<i*n;e++)c[e*3+0]=s.data[e*4+0]/255*2-1,c[e*3+1]=s.data[e*4+1]/255*2-1,c[e*3+2]=s.data[e*4+2]/255*2-1;return{pixels:c,width:n,height:i,patchH:i/t,patchW:n/t}}function te(e,t){let n=Math.sqrt(t/e),r=Math.sqrt(e/t),i=new Float32Array(e*t*2);for(let a=0;a<e;a++){let o=e>1?r*(2*a/(e-1)-1):0;for(let e=0;e<t;e++){let r=t>1?n*(2*e/(t-1)-1):0,s=a*t+e;i[s*2+0]=o,i[s*2+1]=r}}return i}async function h(e,t){if(l){console.log(`[detect] skipped — already running`);return}if(l=!0,console.log(`[detect] query="${t}" image=${e.width}x${e.height}`),!u.unified||!u.embed_tokens){d({type:`error`,message:`Not ready. Sessions: ${Object.keys(u).join(`,`)}`}),l=!1;return}try{let n=performance.now(),a=r.dim,o=r.spatial_patch_size,l=o*o*r.channel_size,{pixels:f,width:p,patchH:m,patchW:h}=ee(e),_=m*h;console.log(`[detect] image: ${p}px, ${m}x${h}=${_} patches`);let re=`Segment these expressions in the image:<|start_of_query|>${t}<|REF_SEG|>`,v;if(c){let e=c.encode(re,{add_special_tokens:!1});v=Array.from(e),console.log(`[detect] text tokens (${v.length}):`,v)}else v=[37462,1978,20528,821,790,6883,549,264,258],console.log(`[detect] using fallback tokens (no query text):`,v);let ie=[245,246,247,248],y=1+ie.length+_+1+v.length,b=new BigInt64Array(y),x=0;b[x++]=BigInt(r.image_cls_token_id);for(let e of ie)b[x++]=BigInt(e);let S=x;for(let e=0;e<_;e++)b[x++]=BigInt(r.img_id);b[x++]=BigInt(r.img_end_id);for(let e of v)b[x++]=BigInt(e);console.log(`[detect] total seq_len=${y}, token IDs: [${Array.from(b.slice(0,5)).map(Number)}...${Array.from(b.slice(-5)).map(Number)}]`);let ae=await u.embed_tokens.run({token_ids:new s.Tensor(`int64`,b,[1,y])}),C=new Float32Array(ae.embeddings.data),w=new Float32Array(_*l);for(let e=0;e<m;e++)for(let t=0;t<h;t++){let n=e*h+t;for(let r=0;r<o;r++)for(let i=0;i<o;i++){let a=e*o+r,s=t*o+i,c=(a*p+s)*3,u=n*l+(r*o+i)*3;w[u+0]=f[c+0],w[u+1]=f[c+1],w[u+2]=f[c+2]}}let oe=(await u.img_projector.run({pixel_patches:new s.Tensor(`float32`,w,[_,l])})).projected.data;for(let e=0;e<_;e++){let t=S*a+e*a,n=e*a;for(let e=0;e<a;e++)C[t+e]=oe[n+e]}let se=C.slice(0,5);console.log(`[detect] embedding[0][0:5]: [${Array.from(se).map(e=>e.toFixed(3)).join(`, `)}]`);let ce=C.slice(S*a,S*a+5);console.log(`[detect] embedding[imgStart][0:5]: [${Array.from(ce).map(e=>e.toFixed(3)).join(`, `)}]`);let le=C.slice((y-1)*a,(y-1)*a+5);console.log(`[detect] embedding[last][0:5]: [${Array.from(le).map(e=>e.toFixed(3)).join(`, `)}]`),console.log(`[detect] running encoder (seq_len=${y})...`);let T=new Float32Array(y),E=S+_+1,ue=new Set([245,246,247,248,r.img_id,r.img_end_id]),D=new Float32Array(y);for(let e=0;e<y;e++){let t=Number(b[e]);D[e]=ue.has(t)&&e<E?0:1}let O=0;for(let e=0;e<y;e++)O+=D[e],T[e]=O-1;let k=r.head_dim/4,A=r.rope_theta,j=new Float32Array(y*k*2);for(let e=0;e<y;e++){let t=T[e];for(let n=0;n<k;n++){let r=t/A**(2*n/(k*2));j[(e*k+n)*2+0]=Math.cos(r),j[(e*k+n)*2+1]=Math.sin(r)}}let M=new Uint8Array(y*y);for(let e=0;e<y;e++)for(let t=0;t<=e;t++)M[e*y+t]=1;for(let e=0;e<E;e++)for(let t=0;t<E;t++)M[e*y+t]=1;let N=r.vocab_size,P=r.coord_out_dim/2,F=r.n_layers,I=r.n_heads,L=r.head_dim,de=new Float32Array(F*2*1*I*0*L),R=new Float32Array(y*2);R.fill(NaN);let z=te(m,h);for(let e=0;e<_;e++){let t=(S+e)*2;R[t+0]=z[e*2+0],R[t+1]=z[e*2+1]}console.log(`[detect] prefill (seq_len=${y})...`);let B=await u.unified.run({token_embeddings:new s.Tensor(`float32`,C,[1,y,a]),freqs_cis:new s.Tensor(`float32`,j,[1,y,k,2]),pos_hw:new s.Tensor(`float32`,R,[1,y,2]),attention_mask:new s.Tensor(`bool`,M,[1,y,y]),past_kv:new s.Tensor(`float32`,de,[F,2,1,I,0,L]),past_len:new s.Tensor(`int64`,new BigInt64Array([0n]),[])}),V=B.logits.data,H=B.new_kv,U=y,W=(y-1)*N,G=V[W+r.presence_token_id],K=V[W+r.absence_token_id];console.log(`[detect] prefill done: presence=${G.toFixed(2)}, absence=${K.toFixed(2)}`);let q=[];if(G<=K&&G<0){console.log(`[detect] object "${t}" not found`),d({type:`result`,detections:q,inferenceMs:performance.now()-n});return}let fe=B.hidden_states.data,J=new Float32Array(fe.subarray((y-1)*a,y*a)),Y=V,X=T[y-1]+1,Z=null,Q=null,pe=new Set([r.eos_id,r.end_of_query_token_id]);for(let e=0;e<510;e++){let i=Y.length,o=g(Y,i-N,N);if(pe.has(o)){console.log(`[detect] stop token ${o} (${ne(o)}) at step ${e}`);break}if(o===r.absence_token_id){console.log(`[detect] <absence> at step ${e}, stopping`);break}if(o===r.coord_token_id){let t=await u.coord_decoder.run({hidden:new s.Tensor(`float32`,J,[1,a])}),n=new Float32Array(t.logits.data),r=.01,i=0,o=0,c=0,l=0;for(let e=0;e<100&&(i=g(n,0,P),o=g(n,P,P),c=i/(P-1),l=o/(P-1),q.some(e=>{let t=(e.box.x1+e.box.x2)/2,n=(e.box.y1+e.box.y2)/2;return Math.abs(t-c)<r&&Math.abs(n-l)<r}));e++)n[i]=-1/0,n[P+o]=-1/0;Z=[c,l],console.log(`[detect] step ${e}: <coord> → (${c.toFixed(3)}, ${l.toFixed(3)})`)}else if(o===r.size_token_id){let t=(await u.size_decoder.run({hidden:new s.Tensor(`float32`,J,[1,a])})).logits.data,n=g(t,0,P),r=g(t,P,P),i=Math.log2(1/P),o=2**(n/(P-1)*-i+i),c=2**(r/(P-1)*-i+i);Q=[o,c],console.log(`[detect] step ${e}: <size> → (h=${o.toFixed(3)}, w=${c.toFixed(3)})`)}else if(o===r.seg_token_id){if(Z&&Q){let[r,i]=Z,[a,o]=Q;if(q.push({label:t,score:1/(1+Math.exp(-(G-K))),box:{x1:Math.max(0,r-o/2),y1:Math.max(0,i-a/2),x2:Math.min(1,r+o/2),y2:Math.min(1,i+a/2)}}),console.log(`[detect] step ${e}: <seg> → object #${q.length} xy=(${r.toFixed(2)},${i.toFixed(2)}) hw=(${a.toFixed(2)},${o.toFixed(2)})`),d({type:`result`,detections:[...q],inferenceMs:performance.now()-n}),q.length>=100){console.log(`[detect] reached max 100 objects`);break}Z=null,Q=null}}else console.log(`[detect] step ${e}: ${ne(o)} (${o})`);let c;if(o===r.coord_token_id&&Z){let e=await u.coord_encoder.run({coords:new s.Tensor(`float32`,new Float32Array(Z),[1,2])});c=new Float32Array(e.encoded.data)}else if(o===r.size_token_id&&Q){let e=await u.size_encoder.run({sizes:new s.Tensor(`float32`,new Float32Array(Q),[1,2])});c=new Float32Array(e.encoded.data)}else{let e=await u.embed_tokens.run({token_ids:new s.Tensor(`int64`,new BigInt64Array([BigInt(o)]),[1,1])});c=new Float32Array(e.embeddings.data)}let l=new Float32Array(k*2);for(let e=0;e<k;e++){let t=X/A**(2*e/(k*2));l[e*2+0]=Math.cos(t),l[e*2+1]=Math.sin(t)}let f=new Uint8Array(U+1);f.fill(1);let p=new Float32Array([NaN,NaN]),m=await u.unified.run({token_embeddings:new s.Tensor(`float32`,c,[1,1,a]),freqs_cis:new s.Tensor(`float32`,l,[1,1,k,2]),pos_hw:new s.Tensor(`float32`,p,[1,1,2]),attention_mask:new s.Tensor(`bool`,f,[1,1,U+1]),past_kv:H,past_len:new s.Tensor(`int64`,new BigInt64Array([BigInt(U)]),[])});J=new Float32Array(m.hidden_states.data),Y=m.logits.data,H=m.new_kv,U+=1,X+=1}let me=q.length,$=i(q,.6),he=performance.now()-n;console.log(`[detect] done in ${he.toFixed(0)}ms, ${$.length} detections (${me} raw, ${me-$.length} merged by bbox-IoU NMS)`),d({type:`result`,detections:$,inferenceMs:he})}catch(e){console.error(`[detect] error:`,e),d({type:`error`,message:e?.message??`Inference failed`})}finally{l=!1}}function g(e,t,n){let r=0,i=-1/0;for(let a=0;a<n;a++)e[t+a]>i&&(i=e[t+a],r=a);return r}function ne(e){return{[r.coord_token_id]:`<coord>`,[r.size_token_id]:`<size>`,[r.seg_token_id]:`<seg>`,[r.eos_id]:`<eos>`,[r.presence_token_id]:`<presence>`,[r.absence_token_id]:`<absence>`,[r.end_of_query_token_id]:`<end_of_query>`}[e]??`token_${e}`}self.onmessage=async e=>{switch(e.data.type){case`load`:await m();break;case`detect`:await h(e.data.image,e.data.query);break}};

assets/inference.worker-CFimk-18.js

@@ -1 +0,0 @@
-const e=`shreyask/falcon-perception-onnx-webgpu`,t=`https://huggingface.co/${e}/resolve/main`,n={unified:{file:`unified_v2.onnx`,size:203e7},embed_tokens:{file:`embed_tokens.onnx`,size:256e6},coord_decoder:{file:`coord_decoder.onnx`,size:96e6},size_decoder:{file:`size_decoder.onnx`,size:96e6},segm_head:{file:`segm_head.onnx`,size:9e6},img_projector:{file:`img_projector.onnx`,size:3e6},coord_encoder:{file:`coord_encoder.onnx`,size:2e6},size_encoder:{file:`size_encoder.onnx`,size:2e6}},r={dim:1024,n_layers:28,n_heads:16,head_dim:128,n_kv_heads:8,vocab_size:65536,max_seq_len:8192,segm_out_dim:256,coord_out_dim:2048,size_out_dim:2048,coord_token_id:240,size_token_id:241,seg_token_id:262,eos_id:11,img_id:227,image_cls_token_id:244,img_end_id:230,spatial_patch_size:16,temporal_patch_size:1,channel_size:3,rope_theta:1e4,presence_token_id:268,absence_token_id:269,end_of_query_token_id:263,start_of_query_token_id:264,ref_seg_token_id:258};function i(e,t=.6){if(e.length<=1)return e;let n=e.map((e,t)=>({det:e,idx:t,area:o(e.box)}));n.sort((e,t)=>t.area-e.area);let r=new Set,i=[];for(let{det:e,idx:o}of n){let n=!1;for(let r of i)if(a(e.box,r)>t){n=!0;break}n||(r.add(o),i.push(e.box))}return e.filter((e,t)=>r.has(t))}function a(e,t){let n=Math.max(e.x1,t.x1),r=Math.max(e.y1,t.y1),i=Math.min(e.x2,t.x2),a=Math.min(e.y2,t.y2),s=i-n,c=a-r;if(s<=0||c<=0)return 0;let l=s*c,u=o(e)+o(t)-l;return u>0?l/u:0}function o(e){return Math.max(0,e.x2-e.x1)*Math.max(0,e.y2-e.y1)}let s,c=null,l=!1;const u={};function d(e){self.postMessage(e)}async function f(e,n){let r=`${t}/${e}`,i=await fetch(r);if(!i.ok)throw Error(`Failed to fetch ${e}: ${i.status}`);let a=i.body?.getReader();if(!a)return i.arrayBuffer();let o=[],s=0;for(;;){let{done:t,value:r}=await a.read();if(t)break;o.push(r),s+=r.length,d({type:`progress`,progress:Math.round(s/n*100),loaded:s,total:n,file:e})}let c=new Uint8Array(s),l=0;for(let e of o)c.set(e,l),l+=e.length;return c.buffer}async function p(e,t,n,r){let i=`${t}.data`,[a,o]=await Promise.all([f(t,n*.01),f(i,n*.99)]),c=await s.InferenceSession.create(a,{executionProviders:r,externalData:[{path:i,data:new Uint8Array(o)}]});return console.log(`Loaded ${e}: inputs=[${c.inputNames}] outputs=[${c.outputNames}]`),c}async function m(){try{s=await import(`./ort.bundle.min-BZrO1_VY.js`),s.env.wasm.numThreads=Math.min(4,navigator.hardwareConcurrency||2);let t=import(`./transformers.web-DaZ4EEMB.js`).then(async({AutoTokenizer:t})=>{try{c=await t.from_pretrained(e),console.log(`Tokenizer loaded`)}catch(e){console.warn(`AutoTokenizer failed, will use fallback:`,e.message)}}),r=[`unified`,`embed_tokens`,`coord_decoder`,`size_decoder`,`segm_head`,`img_projector`,`coord_encoder`,`size_encoder`],i=0,a=Object.values(n).reduce((e,t)=>e+t.size,0),o=new Set([`unified`]);for(let e of r){let{file:t,size:r}=n[e];d({type:`progress`,progress:Math.round(i/a*100),loaded:i,total:a,file:t});let s=o.has(e)?[`wasm`]:[`webgpu`,`wasm`];try{u[e]=await p(e,t,r,s)}catch(n){console.warn(`${s[0]} failed for ${e}, trying WASM:`,n.message);try{u[e]=await p(e,t,r,[`wasm`])}catch(t){console.error(`Failed to load ${e}:`,t.message)}}i+=r}await t,c||console.log(`Using fallback tokenizer (hardcoded prompt tokens)`),d({type:`loaded`})}catch(e){d({type:`error`,message:e?.message??`Failed to load models`})}}function ee(e){let t=r.spatial_patch_size,n=e.width,i=e.height,a=Math.min(256/n,256/i,1);n=Math.floor(Math.round(n*a)/t)*t,i=Math.floor(Math.round(i*a)/t)*t,n=Math.max(n,t),i=Math.max(i,t);let o=new OffscreenCanvas(n,i).getContext(`2d`);o.drawImage(e,0,0,n,i);let s=o.getImageData(0,0,n,i),c=new Float32Array(i*n*3);for(let e=0;e<i*n;e++)c[e*3+0]=s.data[e*4+0]/255*2-1,c[e*3+1]=s.data[e*4+1]/255*2-1,c[e*3+2]=s.data[e*4+2]/255*2-1;return{pixels:c,width:n,height:i,patchH:i/t,patchW:n/t}}function te(e,t){let n=Math.sqrt(t/e),r=Math.sqrt(e/t),i=new Float32Array(e*t*2);for(let a=0;a<e;a++){let o=e>1?r*(2*a/(e-1)-1):0;for(let e=0;e<t;e++){let r=t>1?n*(2*e/(t-1)-1):0,s=a*t+e;i[s*2+0]=o,i[s*2+1]=r}}return i}async function h(e,t){if(l){console.log(`[detect] skipped — already running`);return}if(l=!0,console.log(`[detect] query="${t}" image=${e.width}x${e.height}`),!u.unified||!u.embed_tokens){d({type:`error`,message:`Not ready. Sessions: ${Object.keys(u).join(`,`)}`}),l=!1;return}try{let n=performance.now(),a=r.dim,o=r.spatial_patch_size,l=o*o*r.channel_size,{pixels:f,width:p,patchH:m,patchW:h}=ee(e),_=m*h;console.log(`[detect] image: ${p}px, ${m}x${h}=${_} patches`);let re=`Segment these expressions in the image:<|start_of_query|>${t}<|REF_SEG|>`,v;if(c){let e=c.encode(re,{add_special_tokens:!1});v=Array.from(e),console.log(`[detect] text tokens (${v.length}):`,v)}else v=[37462,1978,20528,821,790,6883,549,264,258],console.log(`[detect] using fallback tokens (no query text):`,v);let ie=[245,246,247,248],y=1+ie.length+_+1+v.length,b=new BigInt64Array(y),x=0;b[x++]=BigInt(r.image_cls_token_id);for(let e of ie)b[x++]=BigInt(e);let S=x;for(let e=0;e<_;e++)b[x++]=BigInt(r.img_id);b[x++]=BigInt(r.img_end_id);for(let e of v)b[x++]=BigInt(e);console.log(`[detect] total seq_len=${y}, token IDs: [${Array.from(b.slice(0,5)).map(Number)}...${Array.from(b.slice(-5)).map(Number)}]`);let ae=await u.embed_tokens.run({token_ids:new s.Tensor(`int64`,b,[1,y])}),C=new Float32Array(ae.embeddings.data),w=new Float32Array(_*l);for(let e=0;e<m;e++)for(let t=0;t<h;t++){let n=e*h+t;for(let r=0;r<o;r++)for(let i=0;i<o;i++){let a=e*o+r,s=t*o+i,c=(a*p+s)*3,u=n*l+(r*o+i)*3;w[u+0]=f[c+0],w[u+1]=f[c+1],w[u+2]=f[c+2]}}let oe=(await u.img_projector.run({pixel_patches:new s.Tensor(`float32`,w,[_,l])})).projected.data;for(let e=0;e<_;e++){let t=S*a+e*a,n=e*a;for(let e=0;e<a;e++)C[t+e]=oe[n+e]}let se=C.slice(0,5);console.log(`[detect] embedding[0][0:5]: [${Array.from(se).map(e=>e.toFixed(3)).join(`, `)}]`);let ce=C.slice(S*a,S*a+5);console.log(`[detect] embedding[imgStart][0:5]: [${Array.from(ce).map(e=>e.toFixed(3)).join(`, `)}]`);let le=C.slice((y-1)*a,(y-1)*a+5);console.log(`[detect] embedding[last][0:5]: [${Array.from(le).map(e=>e.toFixed(3)).join(`, `)}]`),console.log(`[detect] running encoder (seq_len=${y})...`);let T=new Float32Array(y),E=S+_+1,ue=new Set([245,246,247,248,r.img_id,r.img_end_id]),D=new Float32Array(y);for(let e=0;e<y;e++){let t=Number(b[e]);D[e]=ue.has(t)&&e<E?0:1}let O=0;for(let e=0;e<y;e++)O+=D[e],T[e]=O-1;let k=r.head_dim/4,A=r.rope_theta,j=new Float32Array(y*k*2);for(let e=0;e<y;e++){let t=T[e];for(let n=0;n<k;n++){let r=t/A**(2*n/(k*2));j[(e*k+n)*2+0]=Math.cos(r),j[(e*k+n)*2+1]=Math.sin(r)}}let M=new Uint8Array(y*y);for(let e=0;e<y;e++)for(let t=0;t<=e;t++)M[e*y+t]=1;for(let e=0;e<E;e++)for(let t=0;t<E;t++)M[e*y+t]=1;let N=r.vocab_size,P=r.coord_out_dim/2,F=r.n_layers,I=r.n_heads,L=r.head_dim,de=new Float32Array(F*2*1*I*0*L),R=new Float32Array(y*2);R.fill(NaN);let z=te(m,h);for(let e=0;e<_;e++){let t=(S+e)*2;R[t+0]=z[e*2+0],R[t+1]=z[e*2+1]}console.log(`[detect] prefill (seq_len=${y})...`);let B=await u.unified.run({token_embeddings:new s.Tensor(`float32`,C,[1,y,a]),freqs_cis:new s.Tensor(`float32`,j,[1,y,k,2]),pos_hw:new s.Tensor(`float32`,R,[1,y,2]),attention_mask:new s.Tensor(`bool`,M,[1,y,y]),past_kv:new s.Tensor(`float32`,de,[F,2,1,I,0,L]),past_len:new s.Tensor(`int64`,new BigInt64Array([0n]),[])}),V=B.logits.data,H=B.new_kv,U=y,W=(y-1)*N,G=V[W+r.presence_token_id],K=V[W+r.absence_token_id];console.log(`[detect] prefill done: presence=${G.toFixed(2)}, absence=${K.toFixed(2)}`);let q=[];if(G<=K&&G<0){console.log(`[detect] object "${t}" not found`),d({type:`result`,detections:q,inferenceMs:performance.now()-n});return}let fe=B.hidden_states.data,J=new Float32Array(fe.subarray((y-1)*a,y*a)),Y=V,X=T[y-1]+1,Z=null,Q=null,pe=new Set([r.eos_id,r.end_of_query_token_id]);for(let e=0;e<510;e++){let i=Y.length,o=g(Y,i-N,N);if(pe.has(o)){console.log(`[detect] stop token ${o} (${ne(o)}) at step ${e}`);break}if(o===r.absence_token_id){console.log(`[detect] <absence> at step ${e}, stopping`);break}if(o===r.coord_token_id){let t=await u.coord_decoder.run({hidden:new s.Tensor(`float32`,J,[1,a])}),n=new Float32Array(t.logits.data),r=.01,i=0,o=0,c=0,l=0;for(let e=0;e<100&&(i=g(n,0,P),o=g(n,P,P),c=i/(P-1),l=o/(P-1),q.some(e=>{let t=(e.box.x1+e.box.x2)/2,n=(e.box.y1+e.box.y2)/2;return Math.abs(t-c)<r&&Math.abs(n-l)<r}));e++)n[i]=-1/0,n[P+o]=-1/0;Z=[c,l],console.log(`[detect] step ${e}: <coord> → (${c.toFixed(3)}, ${l.toFixed(3)})`)}else if(o===r.size_token_id){let t=(await u.size_decoder.run({hidden:new s.Tensor(`float32`,J,[1,a])})).logits.data,n=g(t,0,P),r=g(t,P,P),i=Math.log2(1/P),o=2**(n/(P-1)*-i+i),c=2**(r/(P-1)*-i+i);Q=[o,c],console.log(`[detect] step ${e}: <size> → (h=${o.toFixed(3)}, w=${c.toFixed(3)})`)}else if(o===r.seg_token_id){if(Z&&Q){let[r,i]=Z,[a,o]=Q;if(q.push({label:t,score:1/(1+Math.exp(-(G-K))),box:{x1:Math.max(0,r-o/2),y1:Math.max(0,i-a/2),x2:Math.min(1,r+o/2),y2:Math.min(1,i+a/2)}}),console.log(`[detect] step ${e}: <seg> → object #${q.length} xy=(${r.toFixed(2)},${i.toFixed(2)}) hw=(${a.toFixed(2)},${o.toFixed(2)})`),d({type:`result`,detections:[...q],inferenceMs:performance.now()-n}),q.length>=100){console.log(`[detect] reached max 100 objects`);break}Z=null,Q=null}}else console.log(`[detect] step ${e}: ${ne(o)} (${o})`);let c;if(o===r.coord_token_id&&Z){let e=await u.coord_encoder.run({coords:new s.Tensor(`float32`,new Float32Array(Z),[1,2])});c=new Float32Array(e.encoded.data)}else if(o===r.size_token_id&&Q){let e=await u.size_encoder.run({sizes:new s.Tensor(`float32`,new Float32Array(Q),[1,2])});c=new Float32Array(e.encoded.data)}else{let e=await u.embed_tokens.run({token_ids:new s.Tensor(`int64`,new BigInt64Array([BigInt(o)]),[1,1])});c=new Float32Array(e.embeddings.data)}let l=new Float32Array(k*2);for(let e=0;e<k;e++){let t=X/A**(2*e/(k*2));l[e*2+0]=Math.cos(t),l[e*2+1]=Math.sin(t)}let f=new Uint8Array(U+1);f.fill(1);let p=new Float32Array([NaN,NaN]),m=await u.unified.run({token_embeddings:new s.Tensor(`float32`,c,[1,1,a]),freqs_cis:new s.Tensor(`float32`,l,[1,1,k,2]),pos_hw:new s.Tensor(`float32`,p,[1,1,2]),attention_mask:new s.Tensor(`bool`,f,[1,1,U+1]),past_kv:H,past_len:new s.Tensor(`int64`,new BigInt64Array([BigInt(U)]),[])});J=new Float32Array(m.hidden_states.data),Y=m.logits.data,H=m.new_kv,U+=1,X+=1}let me=q.length,$=i(q,.6),he=performance.now()-n;console.log(`[detect] done in ${he.toFixed(0)}ms, ${$.length} detections (${me} raw, ${me-$.length} merged by bbox-IoU NMS)`),d({type:`result`,detections:$,inferenceMs:he})}catch(e){console.error(`[detect] error:`,e),d({type:`error`,message:e?.message??`Inference failed`})}finally{l=!1}}function g(e,t,n){let r=0,i=-1/0;for(let a=0;a<n;a++)e[t+a]>i&&(i=e[t+a],r=a);return r}function ne(e){return{[r.coord_token_id]:`<coord>`,[r.size_token_id]:`<size>`,[r.seg_token_id]:`<seg>`,[r.eos_id]:`<eos>`,[r.presence_token_id]:`<presence>`,[r.absence_token_id]:`<absence>`,[r.end_of_query_token_id]:`<end_of_query>`}[e]??`token_${e}`}self.onmessage=async e=>{switch(e.data.type){case`load`:await m();break;case`detect`:await h(e.data.image,e.data.query);break}};

assets/inference.worker-CKN52DNs.js

@@ -1 +0,0 @@
-const e=`shreyask/falcon-perception-onnx-webgpu`,t=`https://huggingface.co/${e}/resolve/main`,n={unified:{file:`unified_v2.onnx`,size:203e7},embed_tokens:{file:`embed_tokens.onnx`,size:256e6},coord_decoder:{file:`coord_decoder.onnx`,size:96e6},size_decoder:{file:`size_decoder.onnx`,size:96e6},segm_head:{file:`segm_head.onnx`,size:9e6},img_projector:{file:`img_projector.onnx`,size:3e6},coord_encoder:{file:`coord_encoder.onnx`,size:2e6},size_encoder:{file:`size_encoder.onnx`,size:2e6}},r={dim:1024,n_layers:28,n_heads:16,head_dim:128,n_kv_heads:8,vocab_size:65536,max_seq_len:8192,segm_out_dim:256,coord_out_dim:2048,size_out_dim:2048,coord_token_id:240,size_token_id:241,seg_token_id:262,eos_id:11,img_id:227,image_cls_token_id:244,img_end_id:230,spatial_patch_size:16,temporal_patch_size:1,channel_size:3,rope_theta:1e4,presence_token_id:268,absence_token_id:269,end_of_query_token_id:263,start_of_query_token_id:264,ref_seg_token_id:258};function i(e,t=.6){if(e.length<=1)return e;let n=e.map((e,t)=>({det:e,idx:t,area:o(e.box)}));n.sort((e,t)=>t.area-e.area);let r=new Set,i=[];for(let{det:e,idx:o}of n){let n=!1;for(let r of i)if(a(e.box,r)>t){n=!0;break}n||(r.add(o),i.push(e.box))}return e.filter((e,t)=>r.has(t))}function a(e,t){let n=Math.max(e.x1,t.x1),r=Math.max(e.y1,t.y1),i=Math.min(e.x2,t.x2),a=Math.min(e.y2,t.y2),s=i-n,c=a-r;if(s<=0||c<=0)return 0;let l=s*c,u=o(e)+o(t)-l;return u>0?l/u:0}function o(e){return Math.max(0,e.x2-e.x1)*Math.max(0,e.y2-e.y1)}let s,c=null,l=!1,u=null;const d={};function f(e){self.postMessage(e)}function p(e){let t=d[e];if(!t)throw Error(`ONNX session not loaded: ${e}`);return t}async function m(e,n){let r=`${t}/${e}`,i=await fetch(r);if(!i.ok)throw Error(`Failed to fetch ${e}: ${i.status}`);let a=i.body?.getReader();if(!a)return i.arrayBuffer();let o=[],s=0;for(;;){let{done:t,value:r}=await a.read();if(t)break;o.push(r),s+=r.length,f({type:`progress`,progress:Math.round(s/n*100),loaded:s,total:n,file:e})}let c=new Uint8Array(s),l=0;for(let e of o)c.set(e,l),l+=e.length;return c.buffer}async function h(e,t,n,r){let i=`${t}.data`,[a,o]=await Promise.all([m(t,n*.01),m(i,n*.99)]),c=await s.InferenceSession.create(a,{executionProviders:r,externalData:[{path:i,data:new Uint8Array(o)}]});return console.log(`Loaded ${e}: inputs=[${c.inputNames}] outputs=[${c.outputNames}]`),c}async function g(){try{s=await import(`./ort.bundle.min-BZrO1_VY.js`);let t=typeof SharedArrayBuffer<`u`;s.env.wasm.numThreads=t?Math.min(4,navigator.hardwareConcurrency||2):1;let r=import(`./transformers.web-DaZ4EEMB.js`).then(async({AutoTokenizer:t})=>{try{c=await t.from_pretrained(e),console.log(`Tokenizer loaded`)}catch(e){console.warn(`AutoTokenizer failed, will use fallback:`,x(e,`unknown error`))}}),i=[`unified`,`embed_tokens`,`coord_decoder`,`size_decoder`,`segm_head`,`img_projector`,`coord_encoder`,`size_encoder`],a=0,o=Object.values(n).reduce((e,t)=>e+t.size,0),l=new Set([`unified`]);for(let e of i){let{file:t,size:r}=n[e];f({type:`progress`,progress:Math.round(a/o*100),loaded:a,total:o,file:t});let i=l.has(e)?[`wasm`]:[`webgpu`,`wasm`];try{d[e]=await h(e,t,r,i)}catch(n){console.warn(`${i[0]} failed for ${e}, trying WASM:`,x(n,`unknown error`));try{d[e]=await h(e,t,r,[`wasm`])}catch(t){console.error(`Failed to load ${e}:`,x(t,`unknown error`))}}a+=r}await r,c||console.log(`Using fallback tokenizer (hardcoded prompt tokens)`),f({type:`loaded`})}catch(e){f({type:`error`,message:x(e,`Failed to load models`)})}}function ee(e){let t=r.spatial_patch_size,n=e.width,i=e.height,a=Math.min(256/n,256/i,1);n=Math.floor(Math.round(n*a)/t)*t,i=Math.floor(Math.round(i*a)/t)*t,n=Math.max(n,t),i=Math.max(i,t);let o=new OffscreenCanvas(n,i).getContext(`2d`);o.drawImage(e,0,0,n,i);let s=o.getImageData(0,0,n,i),c=new Float32Array(i*n*3);for(let e=0;e<i*n;e++)c[e*3+0]=s.data[e*4+0]/255*2-1,c[e*3+1]=s.data[e*4+1]/255*2-1,c[e*3+2]=s.data[e*4+2]/255*2-1;return{pixels:c,width:n,height:i,patchH:i/t,patchW:n/t}}function te(e,t){let n=Math.sqrt(t/e),r=Math.sqrt(e/t),i=new Float32Array(e*t*2);for(let a=0;a<e;a++){let o=e>1?r*(2*a/(e-1)-1):0;for(let e=0;e<t;e++){let r=t>1?n*(2*e/(t-1)-1):0,s=a*t+e;i[s*2+0]=o,i[s*2+1]=r}}return i}function _(e){u?.image&&u.image!==e.image&&u.image.close(),u=e}function v(e){if(l){_(e),console.log(`[detect] queued latest frame while inference is running`);return}y(e)}async function y(e){l=!0;let{image:t,query:n,requestId:a,sourceTimestampMs:o,capturedAtMs:m}=e,h=(e,t)=>{f({type:`result`,detections:e,inferenceMs:t,query:n,requestId:a,sourceTimestampMs:o,capturedAtMs:m})};if(console.log(`[detect] query="${n}" image=${t.width}x${t.height}`),!d.unified||!d.embed_tokens){f({type:`error`,message:`Not ready. Sessions: ${Object.keys(d).join(`,`)}`}),l=!1;return}try{let e=performance.now(),a=r.dim,o=r.spatial_patch_size,l=o*o*r.channel_size,{pixels:u,width:d,patchH:f,patchW:m}=ee(t),g=f*m;console.log(`[detect] image: ${d}px, ${f}x${m}=${g} patches`);let _=`Segment these expressions in the image:<|start_of_query|>${n}<|REF_SEG|>`,v;if(c){let e=c.encode(_,{add_special_tokens:!1});v=Array.from(e),console.log(`[detect] text tokens (${v.length}):`,v)}else v=[37462,1978,20528,821,790,6883,549,264,258],console.log(`[detect] using fallback tokens (no query text):`,v);let y=[245,246,247,248],x=1+y.length+g+1+v.length,S=new BigInt64Array(x),C=0;S[C++]=BigInt(r.image_cls_token_id);for(let e of y)S[C++]=BigInt(e);let w=C;for(let e=0;e<g;e++)S[C++]=BigInt(r.img_id);S[C++]=BigInt(r.img_end_id);for(let e of v)S[C++]=BigInt(e);console.log(`[detect] total seq_len=${x}, token IDs: [${Array.from(S.slice(0,5)).map(Number)}...${Array.from(S.slice(-5)).map(Number)}]`);let re=await p(`embed_tokens`).run({token_ids:new s.Tensor(`int64`,S,[1,x])}),T=new Float32Array(re.embeddings.data),E=new Float32Array(g*l);for(let e=0;e<f;e++)for(let t=0;t<m;t++){let n=e*m+t;for(let r=0;r<o;r++)for(let i=0;i<o;i++){let a=e*o+r,s=t*o+i,c=(a*d+s)*3,f=n*l+(r*o+i)*3;E[f+0]=u[c+0],E[f+1]=u[c+1],E[f+2]=u[c+2]}}let ie=(await p(`img_projector`).run({pixel_patches:new s.Tensor(`float32`,E,[g,l])})).projected.data;for(let e=0;e<g;e++){let t=w*a+e*a,n=e*a;for(let e=0;e<a;e++)T[t+e]=ie[n+e]}let ae=T.slice(0,5);console.log(`[detect] embedding[0][0:5]: [${Array.from(ae).map(e=>e.toFixed(3)).join(`, `)}]`);let oe=T.slice(w*a,w*a+5);console.log(`[detect] embedding[imgStart][0:5]: [${Array.from(oe).map(e=>e.toFixed(3)).join(`, `)}]`);let se=T.slice((x-1)*a,(x-1)*a+5);console.log(`[detect] embedding[last][0:5]: [${Array.from(se).map(e=>e.toFixed(3)).join(`, `)}]`),console.log(`[detect] running encoder (seq_len=${x})...`);let D=new Float32Array(x),O=w+g+1,ce=new Set([245,246,247,248,r.img_id,r.img_end_id]),k=new Float32Array(x);for(let e=0;e<x;e++){let t=Number(S[e]);k[e]=ce.has(t)&&e<O?0:1}let A=0;for(let e=0;e<x;e++)A+=k[e],D[e]=A-1;let j=r.head_dim/4,le=r.rope_theta,M=new Float32Array(x*j*2);for(let e=0;e<x;e++){let t=D[e];for(let n=0;n<j;n++){let r=t/le**(2*n/(j*2));M[(e*j+n)*2+0]=Math.cos(r),M[(e*j+n)*2+1]=Math.sin(r)}}let N=new Uint8Array(x*x);for(let e=0;e<x;e++)for(let t=0;t<=e;t++)N[e*x+t]=1;for(let e=0;e<O;e++)for(let t=0;t<O;t++)N[e*x+t]=1;let P=r.vocab_size,F=r.coord_out_dim/2,I=r.n_layers,L=r.n_heads,R=r.head_dim,ue=new Float32Array(I*2*1*L*0*R),z=new Float32Array(x*2);z.fill(NaN);let B=te(f,m);for(let e=0;e<g;e++){let t=(w+e)*2;z[t+0]=B[e*2+0],z[t+1]=B[e*2+1]}console.log(`[detect] prefill (seq_len=${x})...`);let V=await p(`unified`).run({token_embeddings:new s.Tensor(`float32`,T,[1,x,a]),freqs_cis:new s.Tensor(`float32`,M,[1,x,j,2]),pos_hw:new s.Tensor(`float32`,z,[1,x,2]),attention_mask:new s.Tensor(`bool`,N,[1,x,x]),past_kv:new s.Tensor(`float32`,ue,[I,2,1,L,0,R]),past_len:new s.Tensor(`int64`,new BigInt64Array([0n]),[])}),H=V.logits.data,U=V.new_kv,W=x,G=(x-1)*P,K=H[G+r.presence_token_id],q=H[G+r.absence_token_id];console.log(`[detect] prefill done: presence=${K.toFixed(2)}, absence=${q.toFixed(2)}`);let J=[];if(K<=q&&K<0){console.log(`[detect] object "${n}" not found`),h(J,performance.now()-e);return}let de=V.hidden_states.data,Y=new Float32Array(de.subarray((x-1)*a,x*a)),X=H,fe=D[x-1]+1,Z=null,Q=null,pe=new Set([r.eos_id,r.end_of_query_token_id]);for(let t=0;t<510;t++){let i=X.length,o=b(X,i-P,P);if(pe.has(o)){console.log(`[detect] stop token ${o} (${ne(o)}) at step ${t}`);break}if(o===r.absence_token_id){console.log(`[detect] <absence> at step ${t}, stopping`);break}if(o===r.coord_token_id){let e=await p(`coord_decoder`).run({hidden:new s.Tensor(`float32`,Y,[1,a])}),n=new Float32Array(e.logits.data),r=.01,i=0,o=0,c=0,l=0;for(let e=0;e<100&&(i=b(n,0,F),o=b(n,F,F),c=i/(F-1),l=o/(F-1),J.some(e=>{let t=(e.box.x1+e.box.x2)/2,n=(e.box.y1+e.box.y2)/2;return Math.abs(t-c)<r&&Math.abs(n-l)<r}));e++)n[i]=-1/0,n[F+o]=-1/0;Z=[c,l],console.log(`[detect] step ${t}: <coord> → (${c.toFixed(3)}, ${l.toFixed(3)})`)}else if(o===r.size_token_id){let e=(await p(`size_decoder`).run({hidden:new s.Tensor(`float32`,Y,[1,a])})).logits.data,n=b(e,0,F),r=b(e,F,F),i=Math.log2(1/F),o=2**(n/(F-1)*-i+i),c=2**(r/(F-1)*-i+i);Q=[o,c],console.log(`[detect] step ${t}: <size> → (h=${o.toFixed(3)}, w=${c.toFixed(3)})`)}else if(o===r.seg_token_id){if(Z&&Q){let[r,i]=Z,[a,o]=Q;if(J.push({label:n,score:1/(1+Math.exp(-(K-q))),box:{x1:Math.max(0,r-o/2),y1:Math.max(0,i-a/2),x2:Math.min(1,r+o/2),y2:Math.min(1,i+a/2)}}),console.log(`[detect] step ${t}: <seg> → object #${J.length} xy=(${r.toFixed(2)},${i.toFixed(2)}) hw=(${a.toFixed(2)},${o.toFixed(2)})`),h([...J],performance.now()-e),J.length>=100){console.log(`[detect] reached max 100 objects`);break}Z=null,Q=null}}else console.log(`[detect] step ${t}: ${ne(o)} (${o})`);let c;if(o===r.coord_token_id&&Z){let e=await p(`coord_encoder`).run({coords:new s.Tensor(`float32`,new Float32Array(Z),[1,2])});c=new Float32Array(e.encoded.data)}else if(o===r.size_token_id&&Q){let e=await p(`size_encoder`).run({sizes:new s.Tensor(`float32`,new Float32Array(Q),[1,2])});c=new Float32Array(e.encoded.data)}else{let e=await p(`embed_tokens`).run({token_ids:new s.Tensor(`int64`,new BigInt64Array([BigInt(o)]),[1,1])});c=new Float32Array(e.embeddings.data)}let l=new Float32Array(j*2);for(let e=0;e<j;e++){let t=fe/le**(2*e/(j*2));l[e*2+0]=Math.cos(t),l[e*2+1]=Math.sin(t)}let u=new Uint8Array(W+1);u.fill(1);let d=new Float32Array([NaN,NaN]),f=await p(`unified`).run({token_embeddings:new s.Tensor(`float32`,c,[1,1,a]),freqs_cis:new s.Tensor(`float32`,l,[1,1,j,2]),pos_hw:new s.Tensor(`float32`,d,[1,1,2]),attention_mask:new s.Tensor(`bool`,u,[1,1,W+1]),past_kv:U,past_len:new s.Tensor(`int64`,new BigInt64Array([BigInt(W)]),[])});Y=new Float32Array(f.hidden_states.data),X=f.logits.data,U=f.new_kv,W+=1,fe+=1}let me=J.length,$=i(J,.6),he=performance.now()-e;console.log(`[detect] done in ${he.toFixed(0)}ms, ${$.length} detections (${me} raw, ${me-$.length} merged by bbox-IoU NMS)`),h($,he)}catch(e){console.error(`[detect] error:`,e),f({type:`error`,message:x(e,`Inference failed`)})}finally{t.close(),l=!1;let e=u;u=null,e&&v(e)}}function b(e,t,n){let r=0,i=-1/0;for(let a=0;a<n;a++)e[t+a]>i&&(i=e[t+a],r=a);return r}function x(e,t){return e instanceof Error?e.message:t}function ne(e){return{[r.coord_token_id]:`<coord>`,[r.size_token_id]:`<size>`,[r.seg_token_id]:`<seg>`,[r.eos_id]:`<eos>`,[r.presence_token_id]:`<presence>`,[r.absence_token_id]:`<absence>`,[r.end_of_query_token_id]:`<end_of_query>`}[e]??`token_${e}`}self.onmessage=async e=>{switch(e.data.type){case`load`:await g();break;case`detect`:v(e.data);break}};

assets/{inference.worker-CgJ4R6cj.js → inference.worker-Gfj_BSLk.js}

@@ -1 +1 @@
-const e=`onnx-community/falcon-perception-onnx-webgpu`,t=`https://huggingface.co/${e}/resolve/main`,n={unified:{file:`unified_v2.onnx`,size:203e7},embed_tokens:{file:`embed_tokens.onnx`,size:256e6},coord_decoder:{file:`coord_decoder.onnx`,size:96e6},size_decoder:{file:`size_decoder.onnx`,size:96e6},anyup:{file:`anyup.onnx`,size:81e6},segm_head:{file:`segm_head.onnx`,size:9e6},img_projector:{file:`img_projector.onnx`,size:3e6},coord_encoder:{file:`coord_encoder.onnx`,size:2e6},size_encoder:{file:`size_encoder.onnx`,size:2e6}},r={dim:1024,n_layers:28,n_heads:16,head_dim:128,n_kv_heads:8,vocab_size:65536,max_seq_len:8192,segm_out_dim:256,coord_out_dim:2048,size_out_dim:2048,coord_token_id:240,size_token_id:241,seg_token_id:262,eos_id:11,img_id:227,image_cls_token_id:244,img_end_id:230,spatial_patch_size:16,temporal_patch_size:1,channel_size:3,rope_theta:1e4,presence_token_id:268,absence_token_id:269,end_of_query_token_id:263,start_of_query_token_id:264,ref_seg_token_id:258};function i(e,t=.6){if(e.length<=1)return e;let n=e.map((e,t)=>({det:e,idx:t,area:o(e.box)}));n.sort((e,t)=>t.area-e.area);let r=new Set,i=[];for(let{det:e,idx:o}of n){let n=!1;for(let r of i)if(a(e.box,r)>t){n=!0;break}n||(r.add(o),i.push(e.box))}return e.filter((e,t)=>r.has(t))}function a(e,t){let n=Math.max(e.x1,t.x1),r=Math.max(e.y1,t.y1),i=Math.min(e.x2,t.x2),a=Math.min(e.y2,t.y2),s=i-n,c=a-r;if(s<=0||c<=0)return 0;let l=s*c,u=o(e)+o(t)-l;return u>0?l/u:0}function o(e){return Math.max(0,e.x2-e.x1)*Math.max(0,e.y2-e.y1)}let s,c=null,l=!1,u=null;const d={};function f(e){self.postMessage(e)}function p(e){let t=d[e];if(!t)throw Error(`ONNX session not loaded: ${e}`);return t}async function m(e,n){let r=`${t}/${e}`,i=await fetch(r);if(!i.ok)throw Error(`Failed to fetch ${e}: ${i.status}`);let a=i.body?.getReader();if(!a)return i.arrayBuffer();let o=[],s=0;for(;;){let{done:t,value:r}=await a.read();if(t)break;o.push(r),s+=r.length,f({type:`progress`,progress:Math.round(s/n*100),loaded:s,total:n,file:e})}let c=new Uint8Array(s),l=0;for(let e of o)c.set(e,l),l+=e.length;return c.buffer}async function h(e,t,n,r){let i=`${t}.data`,[a,o]=await Promise.all([m(t,n*.01),m(i,n*.99)]),c=await s.InferenceSession.create(a,{executionProviders:r,externalData:[{path:i,data:new Uint8Array(o)}]});return console.log(`Loaded ${e}: inputs=[${c.inputNames}] outputs=[${c.outputNames}]`),c}async function g(){try{s=await import(`./ort.bundle.min-BZrO1_VY.js`);let t=typeof SharedArrayBuffer<`u`;s.env.wasm.numThreads=t?Math.min(4,navigator.hardwareConcurrency||2):1;let r=import(`./transformers.web-DaZ4EEMB.js`).then(async({AutoTokenizer:t})=>{try{c=await t.from_pretrained(e),console.log(`Tokenizer loaded`)}catch(e){console.warn(`AutoTokenizer failed, will use fallback:`,x(e,`unknown error`))}}),i=[`unified`,`embed_tokens`,`coord_decoder`,`size_decoder`,`segm_head`,`anyup`,`img_projector`,`coord_encoder`,`size_encoder`],a=0,o=Object.values(n).reduce((e,t)=>e+t.size,0),l=new Set([`unified`]);for(let e of i){let{file:t,size:r}=n[e];f({type:`progress`,progress:Math.round(a/o*100),loaded:a,total:o,file:t});let i=l.has(e)?[`wasm`]:[`webgpu`,`wasm`];try{d[e]=await h(e,t,r,i)}catch(n){console.warn(`${i[0]} failed for ${e}, trying WASM:`,x(n,`unknown error`));try{d[e]=await h(e,t,r,[`wasm`])}catch(t){console.error(`Failed to load ${e}:`,x(t,`unknown error`))}}a+=r}await r,c||console.log(`Using fallback tokenizer (hardcoded prompt tokens)`),f({type:`loaded`})}catch(e){f({type:`error`,message:x(e,`Failed to load models`)})}}function ee(e){let t=r.spatial_patch_size,n=e.width,i=e.height,a=Math.min(256/n,256/i,1),o=Math.max(t,Math.round(n*a/t)*t),s=Math.max(t,Math.round(i*a/t)*t),c=new OffscreenCanvas(o,s).getContext(`2d`);c.drawImage(e,0,0,o,s);let l=c.getImageData(0,0,o,s),u=new Float32Array(s*o*3),d=new Float32Array(3*s*o),f=s*o;for(let e=0;e<f;e++){let t=l.data[e*4+0]/255*2-1,n=l.data[e*4+1]/255*2-1,r=l.data[e*4+2]/255*2-1;u[e*3+0]=t,u[e*3+1]=n,u[e*3+2]=r,d[0*f+e]=t,d[1*f+e]=n,d[2*f+e]=r}return{pixels:u,pixelsChw:d,width:o,height:s,patchH:s/t,patchW:o/t}}function te(e,t){let n=Math.sqrt(t/e),r=Math.sqrt(e/t),i=new Float32Array(e*t*2);for(let a=0;a<e;a++){let o=e>1?r*(2*a/(e-1)-1):0;for(let e=0;e<t;e++){let r=t>1?n*(2*e/(t-1)-1):0,s=a*t+e;i[s*2+0]=o,i[s*2+1]=r}}return i}function _(e){u?.image&&u.image!==e.image&&u.image.close(),u=e}function v(e){if(l){_(e),console.log(`[detect] queued latest frame while inference is running`);return}y(e)}async function y(e){l=!0;let{image:t,query:n,requestId:a,sourceTimestampMs:o,capturedAtMs:m}=e,h=(e,t)=>{f({type:`result`,detections:e,inferenceMs:t,query:n,requestId:a,sourceTimestampMs:o,capturedAtMs:m})};if(console.log(`[detect] query="${n}" image=${t.width}x${t.height}`),!d.unified||!d.embed_tokens){f({type:`error`,message:`Not ready. Sessions: ${Object.keys(d).join(`,`)}`}),l=!1;return}try{let e=performance.now(),a=r.dim,o=r.spatial_patch_size,l=o*o*r.channel_size,{pixels:u,pixelsChw:f,width:m,height:g,patchH:_,patchW:v}=ee(t),y=_*v;console.log(`[detect] image: ${m}px, ${_}x${v}=${y} patches`);let x=`Segment these expressions in the image:<|start_of_query|>${n}<|REF_SEG|>`,S;if(c){let e=c.encode(x,{add_special_tokens:!1});S=Array.from(e),console.log(`[detect] text tokens (${S.length}):`,S)}else S=[37462,1978,20528,821,790,6883,549,264,258],console.log(`[detect] using fallback tokens (no query text):`,S);let re=[245,246,247,248],C=1+re.length+y+1+S.length,w=new BigInt64Array(C),T=0;w[T++]=BigInt(r.image_cls_token_id);for(let e of re)w[T++]=BigInt(e);let E=T;for(let e=0;e<y;e++)w[T++]=BigInt(r.img_id);w[T++]=BigInt(r.img_end_id);for(let e of S)w[T++]=BigInt(e);console.log(`[detect] total seq_len=${C}, token IDs: [${Array.from(w.slice(0,5)).map(Number)}...${Array.from(w.slice(-5)).map(Number)}]`);let ie=await p(`embed_tokens`).run({token_ids:new s.Tensor(`int64`,w,[1,C])}),D=new Float32Array(ie.embeddings.data),O=new Float32Array(y*l);for(let e=0;e<_;e++)for(let t=0;t<v;t++){let n=e*v+t;for(let r=0;r<o;r++)for(let i=0;i<o;i++){let a=e*o+r,s=t*o+i,c=(a*m+s)*3,d=n*l+(r*o+i)*3;O[d+0]=u[c+0],O[d+1]=u[c+1],O[d+2]=u[c+2]}}let ae=(await p(`img_projector`).run({pixel_patches:new s.Tensor(`float32`,O,[y,l])})).projected.data;for(let e=0;e<y;e++){let t=E*a+e*a,n=e*a;for(let e=0;e<a;e++)D[t+e]=ae[n+e]}let oe=D.slice(0,5);console.log(`[detect] embedding[0][0:5]: [${Array.from(oe).map(e=>e.toFixed(3)).join(`, `)}]`);let se=D.slice(E*a,E*a+5);console.log(`[detect] embedding[imgStart][0:5]: [${Array.from(se).map(e=>e.toFixed(3)).join(`, `)}]`);let ce=D.slice((C-1)*a,(C-1)*a+5);console.log(`[detect] embedding[last][0:5]: [${Array.from(ce).map(e=>e.toFixed(3)).join(`, `)}]`),console.log(`[detect] running encoder (seq_len=${C})...`);let k=new Float32Array(C),A=E+y+1,le=new Set([245,246,247,248,r.img_id,r.img_end_id]),j=new Float32Array(C);for(let e=0;e<C;e++){let t=Number(w[e]);j[e]=le.has(t)&&e<A?0:1}let ue=0;for(let e=0;e<C;e++)ue+=j[e],k[e]=ue-1;let M=r.head_dim/4,N=r.rope_theta,P=new Float32Array(C*M*2);for(let e=0;e<C;e++){let t=k[e];for(let n=0;n<M;n++){let r=t/N**(2*n/(M*2));P[(e*M+n)*2+0]=Math.cos(r),P[(e*M+n)*2+1]=Math.sin(r)}}let F=new Uint8Array(C*C);for(let e=0;e<C;e++)for(let t=0;t<=e;t++)F[e*C+t]=1;for(let e=0;e<A;e++)for(let t=0;t<A;t++)F[e*C+t]=1;let I=r.vocab_size,L=r.coord_out_dim/2,de=r.n_layers,R=r.n_heads,z=r.head_dim,fe=new Float32Array(de*2*1*R*0*z),B=new Float32Array(C*2);B.fill(NaN);let V=te(_,v);for(let e=0;e<y;e++){let t=(E+e)*2;B[t+0]=V[e*2+0],B[t+1]=V[e*2+1]}console.log(`[detect] prefill (seq_len=${C})...`);let H=await p(`unified`).run({token_embeddings:new s.Tensor(`float32`,D,[1,C,a]),freqs_cis:new s.Tensor(`float32`,P,[1,C,M,2]),pos_hw:new s.Tensor(`float32`,B,[1,C,2]),attention_mask:new s.Tensor(`bool`,F,[1,C,C]),past_kv:new s.Tensor(`float32`,fe,[de,2,1,R,0,z]),past_len:new s.Tensor(`int64`,new BigInt64Array([0n]),[])}),U=H.logits.data,pe=H.new_kv,W=C,me=(C-1)*I,G=U[me+r.presence_token_id],K=U[me+r.absence_token_id];console.log(`[detect] prefill done: presence=${G.toFixed(2)}, absence=${K.toFixed(2)}`);let q=[];if(G<=K&&G<0){console.log(`[detect] object "${n}" not found`),h(q,performance.now()-e);return}let he=H.hidden_states.data,J=null,ge=d.anyup;if(ge&&_===16&&v===16&&m===256&&g===256){let e=new Float32Array(a*_*v),t=_*v;for(let n=0;n<y;n++){let r=(E+n)*a,i=Math.floor(n/v),o=n%v,s=i*v+o;for(let n=0;n<a;n++)e[n*t+s]=he[r+n]}let n=performance.now();try{J=(await ge.run({images:new s.Tensor(`float32`,f,[1,3,g,m]),lr_tokens:new s.Tensor(`float32`,e,[1,a,_,v])})).hr_features,console.log(`[detect] anyup done in ${(performance.now()-n).toFixed(0)}ms, hr_features=${J.dims.join(`x`)}`)}catch(e){console.warn(`[detect] anyup failed; segmentation will fall back:`,e),J=null}}let Y=new Float32Array(he.subarray((C-1)*a,C*a)),X=U,_e=k[C-1]+1,Z=null,Q=null,ve=new Set([r.eos_id,r.end_of_query_token_id]);for(let t=0;t<510;t++){let i=X.length,o=b(X,i-I,I);if(ve.has(o)){console.log(`[detect] stop token ${o} (${ne(o)}) at step ${t}`);break}if(o===r.absence_token_id){console.log(`[detect] <absence> at step ${t}, stopping`);break}if(o===r.coord_token_id){let e=await p(`coord_decoder`).run({hidden:new s.Tensor(`float32`,Y,[1,a])}),n=new Float32Array(e.logits.data),r=.01,i=0,o=0,c=0,l=0;for(let e=0;e<100&&(i=b(n,0,L),o=b(n,L,L),c=i/(L-1),l=o/(L-1),q.some(e=>{let t=(e.box.x1+e.box.x2)/2,n=(e.box.y1+e.box.y2)/2;return Math.abs(t-c)<r&&Math.abs(n-l)<r}));e++)n[i]=-1/0,n[L+o]=-1/0;Z=[c,l],console.log(`[detect] step ${t}: <coord> → (${c.toFixed(3)}, ${l.toFixed(3)})`)}else if(o===r.size_token_id){let e=(await p(`size_decoder`).run({hidden:new s.Tensor(`float32`,Y,[1,a])})).logits.data,n=b(e,0,L),r=b(e,L,L),i=Math.log2(1/L),o=2**(n/(L-1)*-i+i),c=2**(r/(L-1)*-i+i);Q=[o,c],console.log(`[detect] step ${t}: <size> → (h=${o.toFixed(3)}, w=${c.toFixed(3)})`)}else if(o===r.seg_token_id){if(Z&&Q){let[r,i]=Z,[o,c]=Q,l;if(J&&d.segm_head)try{let e=await p(`segm_head`).run({seg_token_hidden:new s.Tensor(`float32`,Y,[1,a]),hr_features:J}),t=e.masks.data,[,n,r]=e.masks.dims,i=new Uint8Array(n*r);for(let e=0;e<i.length;e++)i[e]=t[e]>0?255:0;l={data:i,width:r,height:n}}catch(e){console.warn(`[detect] segm_head failed for object #${q.length+1}:`,e)}if(q.push({label:n,score:1/(1+Math.exp(-(G-K))),box:{x1:Math.max(0,r-c/2),y1:Math.max(0,i-o/2),x2:Math.min(1,r+c/2),y2:Math.min(1,i+o/2)},mask:l}),console.log(`[detect] step ${t}: <seg> → object #${q.length} xy=(${r.toFixed(2)},${i.toFixed(2)}) hw=(${o.toFixed(2)},${c.toFixed(2)})${l?` +mask`:``}`),h([...q],performance.now()-e),q.length>=100){console.log(`[detect] reached max 100 objects`);break}Z=null,Q=null}}else console.log(`[detect] step ${t}: ${ne(o)} (${o})`);let c;if(o===r.coord_token_id&&Z){let e=await p(`coord_encoder`).run({coords:new s.Tensor(`float32`,new Float32Array(Z),[1,2])});c=new Float32Array(e.encoded.data)}else if(o===r.size_token_id&&Q){let e=await p(`size_encoder`).run({sizes:new s.Tensor(`float32`,new Float32Array(Q),[1,2])});c=new Float32Array(e.encoded.data)}else{let e=await p(`embed_tokens`).run({token_ids:new s.Tensor(`int64`,new BigInt64Array([BigInt(o)]),[1,1])});c=new Float32Array(e.embeddings.data)}let l=new Float32Array(M*2);for(let e=0;e<M;e++){let t=_e/N**(2*e/(M*2));l[e*2+0]=Math.cos(t),l[e*2+1]=Math.sin(t)}let u=new Uint8Array(W+1);u.fill(1);let f=new Float32Array([NaN,NaN]),m=await p(`unified`).run({token_embeddings:new s.Tensor(`float32`,c,[1,1,a]),freqs_cis:new s.Tensor(`float32`,l,[1,1,M,2]),pos_hw:new s.Tensor(`float32`,f,[1,1,2]),attention_mask:new s.Tensor(`bool`,u,[1,1,W+1]),past_kv:pe,past_len:new s.Tensor(`int64`,new BigInt64Array([BigInt(W)]),[])});Y=new Float32Array(m.hidden_states.data),X=m.logits.data,pe=m.new_kv,W+=1,_e+=1}let ye=q.length,$=i(q,.6),be=performance.now()-e;console.log(`[detect] done in ${be.toFixed(0)}ms, ${$.length} detections (${ye} raw, ${ye-$.length} merged by bbox-IoU NMS)`),h($,be)}catch(e){console.error(`[detect] error:`,e),f({type:`error`,message:x(e,`Inference failed`)})}finally{t.close(),l=!1;let e=u;u=null,e&&v(e)}}function b(e,t,n){let r=0,i=-1/0;for(let a=0;a<n;a++)e[t+a]>i&&(i=e[t+a],r=a);return r}function x(e,t){return e instanceof Error?e.message:t}function ne(e){return{[r.coord_token_id]:`<coord>`,[r.size_token_id]:`<size>`,[r.seg_token_id]:`<seg>`,[r.eos_id]:`<eos>`,[r.presence_token_id]:`<presence>`,[r.absence_token_id]:`<absence>`,[r.end_of_query_token_id]:`<end_of_query>`}[e]??`token_${e}`}self.onmessage=async e=>{switch(e.data.type){case`load`:await g();break;case`detect`:v(e.data);break}};
+const e=`onnx-community/falcon-perception-onnx-webgpu`,t=`https://huggingface.co/${e}/resolve/main`,n={unified:{file:`unified_v2.onnx`,size:203e7},embed_tokens:{file:`embed_tokens.onnx`,size:256e6},coord_decoder:{file:`coord_decoder.onnx`,size:96e6},size_decoder:{file:`size_decoder.onnx`,size:96e6},anyup:{file:`anyup.onnx`,size:81e6},segm_head:{file:`segm_head.onnx`,size:9e6},img_projector:{file:`img_projector.onnx`,size:3e6},coord_encoder:{file:`coord_encoder.onnx`,size:2e6},size_encoder:{file:`size_encoder.onnx`,size:2e6}},r={dim:1024,n_layers:28,n_heads:16,head_dim:128,n_kv_heads:8,vocab_size:65536,max_seq_len:8192,segm_out_dim:256,coord_out_dim:2048,size_out_dim:2048,coord_token_id:240,size_token_id:241,seg_token_id:262,eos_id:11,img_id:227,image_cls_token_id:244,img_end_id:230,spatial_patch_size:16,temporal_patch_size:1,channel_size:3,rope_theta:1e4,presence_token_id:268,absence_token_id:269,end_of_query_token_id:263,start_of_query_token_id:264,ref_seg_token_id:258};function i(e,t=.6){if(e.length<=1)return e;let n=e.map((e,t)=>({det:e,idx:t,area:o(e.box)}));n.sort((e,t)=>t.area-e.area);let r=new Set,i=[];for(let{det:e,idx:o}of n){let n=!1;for(let r of i)if(a(e.box,r)>t){n=!0;break}n||(r.add(o),i.push(e.box))}return e.filter((e,t)=>r.has(t))}function a(e,t){let n=Math.max(e.x1,t.x1),r=Math.max(e.y1,t.y1),i=Math.min(e.x2,t.x2),a=Math.min(e.y2,t.y2),s=i-n,c=a-r;if(s<=0||c<=0)return 0;let l=s*c,u=o(e)+o(t)-l;return u>0?l/u:0}function o(e){return Math.max(0,e.x2-e.x1)*Math.max(0,e.y2-e.y1)}let s,c=null,l=!1,u=null;const d={};function f(e){self.postMessage(e)}function p(e){let t=d[e];if(!t)throw Error(`ONNX session not loaded: ${e}`);return t}async function m(e,n){let r=`${t}/${e}`,i=await fetch(r);if(!i.ok)throw Error(`Failed to fetch ${e}: ${i.status}`);let a=i.body?.getReader();if(!a)return i.arrayBuffer();let o=[],s=0;for(;;){let{done:t,value:r}=await a.read();if(t)break;o.push(r),s+=r.length,f({type:`progress`,progress:Math.round(s/n*100),loaded:s,total:n,file:e})}let c=new Uint8Array(s),l=0;for(let e of o)c.set(e,l),l+=e.length;return c.buffer}async function h(e,t,n,r){let i=`${t}.data`,[a,o]=await Promise.all([m(t,n*.01),m(i,n*.99)]),c=await s.InferenceSession.create(a,{executionProviders:r,externalData:[{path:i,data:new Uint8Array(o)}]});return console.log(`Loaded ${e}: inputs=[${c.inputNames}] outputs=[${c.outputNames}]`),c}async function g(){try{s=await import(`./ort.bundle.min-BZrO1_VY.js`);let t=typeof SharedArrayBuffer<`u`;s.env.wasm.numThreads=t?Math.min(4,navigator.hardwareConcurrency||2):1;let r=import(`./transformers.web-DaZ4EEMB.js`).then(async({AutoTokenizer:t})=>{try{c=await t.from_pretrained(e),console.log(`Tokenizer loaded`)}catch(e){console.warn(`AutoTokenizer failed, will use fallback:`,x(e,`unknown error`))}}),i=[`unified`,`embed_tokens`,`coord_decoder`,`size_decoder`,`segm_head`,`anyup`,`img_projector`,`coord_encoder`,`size_encoder`],a=0,o=Object.values(n).reduce((e,t)=>e+t.size,0),l=new Set([`unified`]);for(let e of i){let{file:t,size:r}=n[e];f({type:`progress`,progress:Math.round(a/o*100),loaded:a,total:o,file:t});let i=l.has(e)?[`wasm`]:[`webgpu`,`wasm`];try{d[e]=await h(e,t,r,i)}catch(n){console.warn(`${i[0]} failed for ${e}, trying WASM:`,x(n,`unknown error`));try{d[e]=await h(e,t,r,[`wasm`])}catch(t){console.error(`Failed to load ${e}:`,x(t,`unknown error`))}}a+=r}await r,c||console.log(`Using fallback tokenizer (hardcoded prompt tokens)`),f({type:`loaded`})}catch(e){f({type:`error`,message:x(e,`Failed to load models`)})}}function ee(e){let t=r.spatial_patch_size,n=e.width,i=e.height,a=Math.min(512/n,512/i,1),o=Math.max(t,Math.round(n*a/t)*t),s=Math.max(t,Math.round(i*a/t)*t),c=new OffscreenCanvas(o,s).getContext(`2d`);c.drawImage(e,0,0,o,s);let l=c.getImageData(0,0,o,s),u=new Float32Array(s*o*3),d=new Float32Array(3*s*o),f=s*o;for(let e=0;e<f;e++){let t=l.data[e*4+0]/255*2-1,n=l.data[e*4+1]/255*2-1,r=l.data[e*4+2]/255*2-1;u[e*3+0]=t,u[e*3+1]=n,u[e*3+2]=r,d[0*f+e]=t,d[1*f+e]=n,d[2*f+e]=r}return{pixels:u,pixelsChw:d,width:o,height:s,patchH:s/t,patchW:o/t}}function te(e,t){let n=Math.sqrt(t/e),r=Math.sqrt(e/t),i=new Float32Array(e*t*2);for(let a=0;a<e;a++){let o=e>1?r*(2*a/(e-1)-1):0;for(let e=0;e<t;e++){let r=t>1?n*(2*e/(t-1)-1):0,s=a*t+e;i[s*2+0]=o,i[s*2+1]=r}}return i}function _(e){u?.image&&u.image!==e.image&&u.image.close(),u=e}function v(e){if(l){_(e),console.log(`[detect] queued latest frame while inference is running`);return}y(e)}async function y(e){l=!0;let{image:t,query:n,requestId:a,sourceTimestampMs:o,capturedAtMs:m}=e,h=(e,t)=>{f({type:`result`,detections:e,inferenceMs:t,query:n,requestId:a,sourceTimestampMs:o,capturedAtMs:m})};if(console.log(`[detect] query="${n}" image=${t.width}x${t.height}`),!d.unified||!d.embed_tokens){f({type:`error`,message:`Not ready. Sessions: ${Object.keys(d).join(`,`)}`}),l=!1;return}try{let e=performance.now(),a=r.dim,o=r.spatial_patch_size,l=o*o*r.channel_size,{pixels:u,pixelsChw:f,width:m,height:g,patchH:_,patchW:v}=ee(t),y=_*v;console.log(`[detect] image: ${m}x${g}px, ${_}x${v}=${y} patches`);let x=`Segment these expressions in the image:<|start_of_query|>${n}<|REF_SEG|>`,S;if(c){let e=c.encode(x,{add_special_tokens:!1});S=Array.from(e),console.log(`[detect] text tokens (${S.length}):`,S)}else S=[37462,1978,20528,821,790,6883,549,264,258],console.log(`[detect] using fallback tokens (no query text):`,S);let re=[245,246,247,248],C=1+re.length+y+1+S.length,w=new BigInt64Array(C),T=0;w[T++]=BigInt(r.image_cls_token_id);for(let e of re)w[T++]=BigInt(e);let E=T;for(let e=0;e<y;e++)w[T++]=BigInt(r.img_id);w[T++]=BigInt(r.img_end_id);for(let e of S)w[T++]=BigInt(e);console.log(`[detect] total seq_len=${C}, token IDs: [${Array.from(w.slice(0,5)).map(Number)}...${Array.from(w.slice(-5)).map(Number)}]`);let ie=await p(`embed_tokens`).run({token_ids:new s.Tensor(`int64`,w,[1,C])}),D=new Float32Array(ie.embeddings.data),O=new Float32Array(y*l);for(let e=0;e<_;e++)for(let t=0;t<v;t++){let n=e*v+t;for(let r=0;r<o;r++)for(let i=0;i<o;i++){let a=e*o+r,s=t*o+i,c=(a*m+s)*3,d=n*l+(r*o+i)*3;O[d+0]=u[c+0],O[d+1]=u[c+1],O[d+2]=u[c+2]}}let ae=(await p(`img_projector`).run({pixel_patches:new s.Tensor(`float32`,O,[y,l])})).projected.data;for(let e=0;e<y;e++){let t=E*a+e*a,n=e*a;for(let e=0;e<a;e++)D[t+e]=ae[n+e]}let oe=D.slice(0,5);console.log(`[detect] embedding[0][0:5]: [${Array.from(oe).map(e=>e.toFixed(3)).join(`, `)}]`);let se=D.slice(E*a,E*a+5);console.log(`[detect] embedding[imgStart][0:5]: [${Array.from(se).map(e=>e.toFixed(3)).join(`, `)}]`);let ce=D.slice((C-1)*a,(C-1)*a+5);console.log(`[detect] embedding[last][0:5]: [${Array.from(ce).map(e=>e.toFixed(3)).join(`, `)}]`),console.log(`[detect] running encoder (seq_len=${C})...`);let k=new Float32Array(C),A=E+y+1,le=new Set([245,246,247,248,r.img_id,r.img_end_id]),j=new Float32Array(C);for(let e=0;e<C;e++){let t=Number(w[e]);j[e]=le.has(t)&&e<A?0:1}let M=0;for(let e=0;e<C;e++)M+=j[e],k[e]=M-1;let N=r.head_dim/4,ue=r.rope_theta,P=new Float32Array(C*N*2);for(let e=0;e<C;e++){let t=k[e];for(let n=0;n<N;n++){let r=t/ue**(2*n/(N*2));P[(e*N+n)*2+0]=Math.cos(r),P[(e*N+n)*2+1]=Math.sin(r)}}let F=new Uint8Array(C*C);for(let e=0;e<C;e++)for(let t=0;t<=e;t++)F[e*C+t]=1;for(let e=0;e<A;e++)for(let t=0;t<A;t++)F[e*C+t]=1;let I=r.vocab_size,L=r.coord_out_dim/2,de=r.n_layers,fe=r.n_heads,R=r.head_dim,pe=new Float32Array(de*2*1*fe*0*R),z=new Float32Array(C*2);z.fill(NaN);let B=te(_,v);for(let e=0;e<y;e++){let t=(E+e)*2;z[t+0]=B[e*2+0],z[t+1]=B[e*2+1]}console.log(`[detect] prefill (seq_len=${C})...`);let V=await p(`unified`).run({token_embeddings:new s.Tensor(`float32`,D,[1,C,a]),freqs_cis:new s.Tensor(`float32`,P,[1,C,N,2]),pos_hw:new s.Tensor(`float32`,z,[1,C,2]),attention_mask:new s.Tensor(`bool`,F,[1,C,C]),past_kv:new s.Tensor(`float32`,pe,[de,2,1,fe,0,R]),past_len:new s.Tensor(`int64`,new BigInt64Array([0n]),[])}),H=V.logits.data,me=V.new_kv,U=C,he=(C-1)*I,W=H[he+r.presence_token_id],G=H[he+r.absence_token_id];console.log(`[detect] prefill done: presence=${W.toFixed(2)}, absence=${G.toFixed(2)}`);let K=[];if(W<=G&&W<0){console.log(`[detect] object "${n}" not found`),h(K,performance.now()-e);return}let ge=V.hidden_states.data,q=null,_e=d.anyup;if(_e&&_===16&&v===16&&m===256&&g===256){let e=new Float32Array(a*_*v),t=_*v;for(let n=0;n<y;n++){let r=(E+n)*a,i=Math.floor(n/v),o=n%v,s=i*v+o;for(let n=0;n<a;n++)e[n*t+s]=ge[r+n]}let n=performance.now();try{q=(await _e.run({images:new s.Tensor(`float32`,f,[1,3,g,m]),lr_tokens:new s.Tensor(`float32`,e,[1,a,_,v])})).hr_features,console.log(`[detect] anyup done in ${(performance.now()-n).toFixed(0)}ms, hr_features=${q.dims.join(`x`)}`)}catch(e){console.warn(`[detect] anyup failed; segmentation will fall back:`,e),q=null}}let J=new Float32Array(ge.subarray((C-1)*a,C*a)),Y=H,X=k[C-1]+1,Z=null,Q=null,ve=new Set([r.eos_id,r.end_of_query_token_id]);for(let t=0;t<510;t++){let i=Y.length,o=b(Y,i-I,I);if(ve.has(o)){console.log(`[detect] stop token ${o} (${ne(o)}) at step ${t}`);break}if(o===r.absence_token_id){console.log(`[detect] <absence> at step ${t}, stopping`);break}if(o===r.coord_token_id){let e=await p(`coord_decoder`).run({hidden:new s.Tensor(`float32`,J,[1,a])}),n=new Float32Array(e.logits.data),r=.01,i=0,o=0,c=0,l=0;for(let e=0;e<100&&(i=b(n,0,L),o=b(n,L,L),c=i/(L-1),l=o/(L-1),K.some(e=>{let t=(e.box.x1+e.box.x2)/2,n=(e.box.y1+e.box.y2)/2;return Math.abs(t-c)<r&&Math.abs(n-l)<r}));e++)n[i]=-1/0,n[L+o]=-1/0;Z=[c,l],console.log(`[detect] step ${t}: <coord> → (${c.toFixed(3)}, ${l.toFixed(3)})`)}else if(o===r.size_token_id){let e=(await p(`size_decoder`).run({hidden:new s.Tensor(`float32`,J,[1,a])})).logits.data,n=b(e,0,L),r=b(e,L,L),i=Math.log2(1/L),o=2**(n/(L-1)*-i+i),c=2**(r/(L-1)*-i+i);Q=[o,c],console.log(`[detect] step ${t}: <size> → (h=${o.toFixed(3)}, w=${c.toFixed(3)})`)}else if(o===r.seg_token_id){if(Z&&Q){let[r,i]=Z,[o,c]=Q,l;if(q&&d.segm_head)try{let e=await p(`segm_head`).run({seg_token_hidden:new s.Tensor(`float32`,J,[1,a]),hr_features:q}),t=e.masks.data,[,n,r]=e.masks.dims,i=new Uint8Array(n*r);for(let e=0;e<i.length;e++)i[e]=t[e]>0?255:0;l={data:i,width:r,height:n}}catch(e){console.warn(`[detect] segm_head failed for object #${K.length+1}:`,e)}if(K.push({label:n,score:1/(1+Math.exp(-(W-G))),box:{x1:Math.max(0,r-c/2),y1:Math.max(0,i-o/2),x2:Math.min(1,r+c/2),y2:Math.min(1,i+o/2)},mask:l}),console.log(`[detect] step ${t}: <seg> → object #${K.length} xy=(${r.toFixed(2)},${i.toFixed(2)}) hw=(${o.toFixed(2)},${c.toFixed(2)})${l?` +mask`:``}`),h([...K],performance.now()-e),K.length>=100){console.log(`[detect] reached max 100 objects`);break}Z=null,Q=null}}else console.log(`[detect] step ${t}: ${ne(o)} (${o})`);let c;if(o===r.coord_token_id&&Z){let e=await p(`coord_encoder`).run({coords:new s.Tensor(`float32`,new Float32Array(Z),[1,2])});c=new Float32Array(e.encoded.data)}else if(o===r.size_token_id&&Q){let e=await p(`size_encoder`).run({sizes:new s.Tensor(`float32`,new Float32Array(Q),[1,2])});c=new Float32Array(e.encoded.data)}else{let e=await p(`embed_tokens`).run({token_ids:new s.Tensor(`int64`,new BigInt64Array([BigInt(o)]),[1,1])});c=new Float32Array(e.embeddings.data)}let l=new Float32Array(N*2);for(let e=0;e<N;e++){let t=X/ue**(2*e/(N*2));l[e*2+0]=Math.cos(t),l[e*2+1]=Math.sin(t)}let u=new Uint8Array(U+1);u.fill(1);let f=new Float32Array([NaN,NaN]),m=await p(`unified`).run({token_embeddings:new s.Tensor(`float32`,c,[1,1,a]),freqs_cis:new s.Tensor(`float32`,l,[1,1,N,2]),pos_hw:new s.Tensor(`float32`,f,[1,1,2]),attention_mask:new s.Tensor(`bool`,u,[1,1,U+1]),past_kv:me,past_len:new s.Tensor(`int64`,new BigInt64Array([BigInt(U)]),[])});J=new Float32Array(m.hidden_states.data),Y=m.logits.data,me=m.new_kv,U+=1,X+=1}let ye=K.length,$=i(K,.6),be=performance.now()-e;console.log(`[detect] done in ${be.toFixed(0)}ms, ${$.length} detections (${ye} raw, ${ye-$.length} merged by bbox-IoU NMS)`),h($,be)}catch(e){console.error(`[detect] error:`,e),f({type:`error`,message:x(e,`Inference failed`)})}finally{t.close(),l=!1;let e=u;u=null,e&&v(e)}}function b(e,t,n){let r=0,i=-1/0;for(let a=0;a<n;a++)e[t+a]>i&&(i=e[t+a],r=a);return r}function x(e,t){return e instanceof Error?e.message:t}function ne(e){return{[r.coord_token_id]:`<coord>`,[r.size_token_id]:`<size>`,[r.seg_token_id]:`<seg>`,[r.eos_id]:`<eos>`,[r.presence_token_id]:`<presence>`,[r.absence_token_id]:`<absence>`,[r.end_of_query_token_id]:`<end_of_query>`}[e]??`token_${e}`}self.onmessage=async e=>{switch(e.data.type){case`load`:await g();break;case`detect`:v(e.data);break}};

index.html

@@ -10,7 +10,7 @@
          auto-handles the one-time reload required to enter a cross-origin
          isolated context. Must run before anything else. -->
     <script src="/coi-serviceworker.js"></script>
-    <script type="module" crossorigin src="/assets/index-DYvuiVzw.js"></script>
+    <script type="module" crossorigin src="/assets/index-B4Fc5cIK.js"></script>
     <link rel="stylesheet" crossorigin href="/assets/index-DBYhI3p4.css">
   </head>
   <body class="bg-gray-950 text-gray-100 antialiased">