wire AnyUp: pixel-accurate segmentation masks via anyup.onnx + segm_head

README.md

@@ -9,8 +9,8 @@ license: apache-2.0
 short_description: Open-vocabulary detection in-browser via WebGPU
 ---
 
-# Falcon Perception — In-Browser Demo
+# Falcon Perception
 
-Interactive demo of [tiiuae/Falcon-Perception](https://huggingface.co/tiiuae/Falcon-Perception). Image / webcam / video modes, three render modes (Detection / Segment / Tracker). Multi-threaded WASM via coi-serviceworker; example images base64-inlined.
+Browser demo for [tiiuae/Falcon-Perception](https://huggingface.co/tiiuae/Falcon-Perception). Image, webcam, and video input with Detection / Segment / Tracker render modes. Multi-threaded WASM via coi-serviceworker. Pixel-accurate segmentation masks via the AnyUp upsampler running in ORT Web.
 
 Weights: [shreyask/falcon-perception-onnx-webgpu](https://huggingface.co/shreyask/falcon-perception-onnx-webgpu).

assets/index-BIGvobfH.css

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assets/inference.worker-BODVQac6.js

@@ -0,0 +1 @@
+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. 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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}};

index.html

@@ -10,8 +10,8 @@
          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-CG2tS2IC.js"></script>
-    <link rel="stylesheet" crossorigin href="/assets/index-DpjQuLKn.css">
+    <script type="module" crossorigin src="/assets/index-Cv8MqGH9.js"></script>
+    <link rel="stylesheet" crossorigin href="/assets/index-BIGvobfH.css">
   </head>
   <body class="bg-gray-950 text-gray-100 antialiased">
     <div id="root"></div>