@tensorflow-models/deeplab - npm Package Compare versions

Comparing version 0.2.1 to 0.2.2

dist/deeplab.esm.js

 /**
     * @license
-    * Copyright 2021 Google LLC. All Rights Reserved.
+    * Copyright 2023 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
@@ -19,3 +19,3 @@ * you may not use this file except in compliance with the License.
     * @license
-    * Copyright 2021 Google LLC. All Rights Reserved.
+    * Copyright 2023 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
@@ -48,3 +48,3 @@ * you may not use this file except in compliance with the License.
 and limitations under the License.
-***************************************************************************** */function u(e,t,n,r){return new(n||(n=Promise))((function(o,a){function i(e){try{l(r.next(e))}catch(e){a(e)}}function s(e){try{l(r.throw(e))}catch(e){a(e)}}function l(e){e.done?o(e.value):new n((function(t){t(e.value)})).then(i,s)}l((r=r.apply(e,t||[])).next())}))}function f(e,t){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 7:a=i.ops.pop(),i.trys.pop();continue;default:if(!(o=(o=i.trys).length>0&&o[o.length-1])&&(6===a[0]||2===a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]<o[3])){i.label=a[1];break}if(6===a[0]&&i.label<o[1]){i.label=o[1],o=a;break}if(o&&i.label<o[2]){i.label=o[2],i.ops.push(a);break}o[2]&&i.ops.pop(),i.trys.pop();continue}a=t.call(e,i)}catch(e){a=[6,e],r=0}finally{n=o=0}if(5&a[0])throw a[1];return{value:a[0]?a[1]:void 0,done:!0}}([a,s])}}}
+***************************************************************************** */function u(e,t,n,r){return new(n||(n=Promise))((function(o,a){function i(e){try{l(r.next(e))}catch(e){a(e)}}function s(e){try{l(r.throw(e))}catch(e){a(e)}}function l(e){e.done?o(e.value):new n((function(t){t(e.value)})).then(i,s)}l((r=r.apply(e,t||[])).next())}))}function f(e,t){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 7:a=i.ops.pop(),i.trys.pop();continue;default:if(!(o=i.trys,(o=o.length>0&&o[o.length-1])||6!==a[0]&&2!==a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]<o[3])){i.label=a[1];break}if(6===a[0]&&i.label<o[1]){i.label=o[1],o=a;break}if(o&&i.label<o[2]){i.label=o[2],i.ops.push(a);break}o[2]&&i.ops.pop(),i.trys.pop();continue}a=t.call(e,i)}catch(e){a=[6,e],r=0}finally{n=o=0}if(5&a[0])throw a[1];return{value:a[0]?a[1]:void 0,done:!0}}([a,s])}}}
 /**
@@ -65,3 +65,3 @@ * @license
  * =============================================================================
- */var 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+ */var 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 /**
@@ -82,4 +82,4 @@ * @license
  * =============================================================================
- */function h(e,t){return""+d.BASE_PATH+"/"+(4===t?e+"/1/default/1/model.json":e+"/1/quantized/"+t+"/1/model.json")+"?tfjs-format=file"}function p(e){if("pascal"===e)return d.COLORMAPS.PASCAL;if("ade20k"===e)return d.COLORMAPS.ADE20K;if("cityscapes"===e)return d.COLORMAPS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function b(e){if("pascal"===e)return d.LABELS.PASCAL;if("ade20k"===e)return d.LABELS.ADE20K;if("cityscapes"===e)return d.LABELS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function m(e,t,n,o){return u(this,void 0,void 0,(function(){var a,l,c,u,d,h,p,b,m,w,g,y,v,S;return f(this,(function(f){switch(f.label){case 0:if(e.length<t.length)throw new Error("The colormap must be expansive enough to encode each label. Aborting, since the given colormap has length "+e.length+", but there are "+t.length+" labels.");return a=n.shape,l=a[0],c=a[1],u=i([l,c,3],"int32"),[4,n.array()];case 1:for(d=f.sent(),h=new Set,p=0;p<l;++p)for(b=0;b<c;++b)S=d[p][b],h.add(S),u.set(e[S][0],p,b,0),u.set(e[S][1],p,b,1),u.set(e[S][2],p,b,2);return m=u.toTensor(),[4,r.toPixels(m,o)];case 2:for(w=f.sent(),s(m),g={},y=0,v=Array.from(h);y<v.length;y++)S=v[y],g[t[S]]=e[S];return[2,{legend:g,segmentationMap:w}]}}))}))}
-/** @license See the LICENSE file. */var w="0.2.1";
+ */function m(e,t){return""+d+"/"+(4===t?e+"/1/default/1/model.json":e+"/1/quantized/"+t+"/1/model.json")+"?tfjs-format=file"}function w(e){if("pascal"===e)return p.PASCAL;if("ade20k"===e)return p.ADE20K;if("cityscapes"===e)return p.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function g(e){if("pascal"===e)return b.PASCAL;if("ade20k"===e)return b.ADE20K;if("cityscapes"===e)return b.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function y(e,t,n,o){return u(this,void 0,void 0,(function(){var a,l,c,u,d,h,p,b,m,w,g,y,v,k;return f(this,(function(f){switch(f.label){case 0:if(e.length<t.length)throw new Error("The colormap must be expansive enough to encode each label. Aborting, since the given colormap has length "+e.length+", but there are "+t.length+" labels.");return a=n.shape,l=a[0],c=a[1],u=i([l,c,3],"int32"),[4,n.array()];case 1:for(d=f.sent(),h=new Set,p=0;p<l;++p)for(b=0;b<c;++b)k=d[p][b],h.add(k),u.set(e[k][0],p,b,0),u.set(e[k][1],p,b,1),u.set(e[k][2],p,b,2);return m=u.toTensor(),[4,r.toPixels(m,o)];case 2:for(w=f.sent(),s(m),g={},y=0,v=Array.from(h);y<v.length;y++)k=v[y],g[t[k]]=e[k];return[2,{legend:g,segmentationMap:w}]}}))}))}
+/** @license See the LICENSE file. */var v="0.2.2";
 /**
@@ -100,2 +100,2 @@ * @license
  * =============================================================================
- */function g(t){return void 0===t&&(t={base:"pascal",quantizationBytes:2}),u(this,void 0,void 0,(function(){var n;return f(this,(function(r){switch(r.label){case 0:if(null==tf)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(t.base){if(-1===["pascal","cityscapes","ade20k"].indexOf(t.base))throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+t.base+". Try one of 'pascal', 'cityscapes' and 'ade20k'.");if(-1===[1,2,4].indexOf(t.quantizationBytes))throw new Error("Only quantization to 1, 2 or 4 bytes is supported.")}else if(!t.modelUrl)throw new Error("SemanticSegmentation can be constructed either by passing the weights URL or one of the supported base model names from 'pascal', 'cityscapes' and 'ade20k',together with the degree of quantization (either 1, 2 or 4).Aborting, since neither has been provided.");return[4,e(t.modelUrl||h(t.base,t.quantizationBytes))];case 1:return n=r.sent(),[2,new y(n,t.base)]}}))}))}var y=function(){function e(e,t){this.model=e,this.base=t}return e.prototype.predict=function(e){var i=this;return t((function(){var s=l(function(e){return t((function(){var t=e instanceof n?e:r.fromPixels(e),i=t.shape,s=i[0],l=i[1],c=d.CROP_SIZE/Math.max(l,s),u=Math.round(s*c),f=Math.round(l*c);return o(a.resizeBilinear(t,[u,f]))}))}(e),"int32");return c(i.model.execute(s))}))},e.prototype.segment=function(e,n){return void 0===n&&(n={}),u(this,void 0,void 0,(function(){var r,o,a,i,l,c,u,d,h,w,g=this;return f(this,(function(f){switch(f.label){case 0:if(!(n.colormap&&n.labels||this.base))throw new Error("Calling the 'segment' method requires either the 'base' attribute to be defined (e.g. 'pascal', 'cityscapes' or'ade20k'), or 'colormap' and 'labels' options to be set. Aborting, since neither has been provided.");return n.colormap&&n.labels||(n.colormap=p(this.base),n.labels=b(this.base)),r=n.colormap,o=n.labels,a=n.canvas,i=t((function(){return g.predict(e)})),l=i.shape,c=l[0],u=l[1],[4,m(r,o,i,a)];case 1:return d=f.sent(),h=d.legend,w=d.segmentationMap,s(i),[2,{legend:h,height:c,width:u,segmentationMap:w}]}}))}))},e.prototype.dispose=function(){return u(this,void 0,void 0,(function(){return f(this,(function(e){return this.model&&this.model.dispose(),[2]}))}))},e}();export{y as SemanticSegmentation,p as getColormap,b as getLabels,h as getURL,g as load,m as toSegmentationImage,w as version};
+ */function k(t){return void 0===t&&(t={base:"pascal",quantizationBytes:2}),u(this,void 0,void 0,(function(){var n;return f(this,(function(r){switch(r.label){case 0:if(null==tf)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(t.base){if(-1===["pascal","cityscapes","ade20k"].indexOf(t.base))throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+t.base+". Try one of 'pascal', 'cityscapes' and 'ade20k'.");if(-1===[1,2,4].indexOf(t.quantizationBytes))throw new Error("Only quantization to 1, 2 or 4 bytes is supported.")}else if(!t.modelUrl)throw new Error("SemanticSegmentation can be constructed either by passing the weights URL or one of the supported base model names from 'pascal', 'cityscapes' and 'ade20k',together with the degree of quantization (either 1, 2 or 4).Aborting, since neither has been provided.");return[4,e(t.modelUrl||m(t.base,t.quantizationBytes))];case 1:return n=r.sent(),[2,new S(n,t.base)]}}))}))}var S=function(){function e(e,t){this.model=e,this.base=t}return e.prototype.predict=function(e){var i=this;return t((function(){var s=l(function(e){return t((function(){var t=e instanceof n?e:r.fromPixels(e),i=t.shape,s=i[0],l=i[1],c=h/Math.max(l,s),u=Math.round(s*c),f=Math.round(l*c);return o(a.resizeBilinear(t,[u,f]))}))}(e),"int32");return c(i.model.execute(s))}))},e.prototype.segment=function(e,n){return void 0===n&&(n={}),u(this,void 0,void 0,(function(){var r,o,a,i,l,c,u,d,h,p,b=this;return f(this,(function(f){switch(f.label){case 0:if(!(n.colormap&&n.labels||this.base))throw new Error("Calling the 'segment' method requires either the 'base' attribute to be defined (e.g. 'pascal', 'cityscapes' or'ade20k'), or 'colormap' and 'labels' options to be set. Aborting, since neither has been provided.");return n.colormap&&n.labels||(n.colormap=w(this.base),n.labels=g(this.base)),r=n.colormap,o=n.labels,a=n.canvas,i=t((function(){return b.predict(e)})),l=i.shape,c=l[0],u=l[1],[4,y(r,o,i,a)];case 1:return d=f.sent(),h=d.legend,p=d.segmentationMap,s(i),[2,{legend:h,height:c,width:u,segmentationMap:p}]}}))}))},e.prototype.dispose=function(){return u(this,void 0,void 0,(function(){return f(this,(function(e){return this.model&&this.model.dispose(),[2]}))}))},e}();export{S as SemanticSegmentation,w as getColormap,g as getLabels,m as getURL,k as load,y as toSegmentationImage,v as version};

dist/deeplab.js

 /**
     * @license
-    * Copyright 2021 Google LLC. All Rights Reserved.
+    * Copyright 2023 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
@@ -455,3 +455,3 @@ * you may not use this file except in compliance with the License.
     // This code is auto-generated, do not modify this file!
-    var version = '0.2.1';
+    var version = '0.2.2';
 
@@ -458,0 +458,0 @@ /**

dist/deeplab.min.js

 /**
     * @license
-    * Copyright 2021 Google LLC. All Rights Reserved.
+    * Copyright 2023 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
@@ -19,3 +19,3 @@ * you may not use this file except in compliance with the License.
     * @license
-    * Copyright 2021 Google LLC. All Rights Reserved.
+    * Copyright 2023 Google LLC. All Rights Reserved.
     * Licensed under the Apache License, Version 2.0 (the "License");
@@ -34,3 +34,3 @@ * you may not use this file except in compliance with the License.
     */
-!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports,require("@tensorflow/tfjs-converter"),require("@tensorflow/tfjs-core")):"function"==typeof define&&define.amd?define(["exports","@tensorflow/tfjs-converter","@tensorflow/tfjs-core"],t):t((e=e||self).deeplab={},e.tf,e.tf)}(this,(function(e,t,tf){"use strict";
+var e,t;e=this,t=function(e,t,tf){
 /*! *****************************************************************************
@@ -49,3 +49,4 @@ Copyright (c) Microsoft Corporation. All rights reserved.
     and limitations under the License.
-    ***************************************************************************** */function n(e,t,n,r){return new(n||(n=Promise))((function(o,a){function i(e){try{l(r.next(e))}catch(e){a(e)}}function s(e){try{l(r.throw(e))}catch(e){a(e)}}function l(e){e.done?o(e.value):new n((function(t){t(e.value)})).then(i,s)}l((r=r.apply(e,t||[])).next())}))}function r(e,t){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 7:a=i.ops.pop(),i.trys.pop();continue;default:if(!(o=(o=i.trys).length>0&&o[o.length-1])&&(6===a[0]||2===a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]<o[3])){i.label=a[1];break}if(6===a[0]&&i.label<o[1]){i.label=o[1],o=a;break}if(o&&i.label<o[2]){i.label=o[2],i.ops.push(a);break}o[2]&&i.ops.pop(),i.trys.pop();continue}a=t.call(e,i)}catch(e){a=[6,e],r=0}finally{n=o=0}if(5&a[0])throw a[1];return{value:a[0]?a[1]:void 0,done:!0}}([a,s])}}}
+    ***************************************************************************** */
+function n(e,t,n,r){return new(n||(n=Promise))((function(o,a){function i(e){try{l(r.next(e))}catch(e){a(e)}}function s(e){try{l(r.throw(e))}catch(e){a(e)}}function l(e){e.done?o(e.value):new n((function(t){t(e.value)})).then(i,s)}l((r=r.apply(e,t||[])).next())}))}function r(e,t){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 7:a=i.ops.pop(),i.trys.pop();continue;default:if(!((o=(o=i.trys).length>0&&o[o.length-1])||6!==a[0]&&2!==a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]<o[3])){i.label=a[1];break}if(6===a[0]&&i.label<o[1]){i.label=o[1],o=a;break}if(o&&i.label<o[2]){i.label=o[2],i.ops.push(a);break}o[2]&&i.ops.pop(),i.trys.pop();continue}a=t.call(e,i)}catch(e){a=[6,e],r=0}finally{n=o=0}if(5&a[0])throw a[1];return{value:a[0]?a[1]:void 0,done:!0}}([a,s])}}}
 /**
@@ -66,3 +67,3 @@ * @license
      * =============================================================================
-     */var o={BASE_PATH:"https://tfhub.dev/tensorflow/tfjs-model/deeplab",CROP_SIZE:513,COLORMAPS:{ADE20K:[[0,0,0],[120,120,120],[180,120,120],[6,230,230],[80,50,50],[4,200,3],[120,120,80],[140,140,140],[204,5,255],[230,230,230],[4,250,7],[224,5,255],[235,255,7],[150,5,61],[120,120,70],[8,255,51],[255,6,82],[143,255,140],[204,255,4],[255,51,7],[204,70,3],[0,102,200],[61,230,250],[255,6,51],[11,102,255],[255,7,71],[255,9,224],[9,7,230],[220,220,220],[255,9,92],[112,9,255],[8,255,214],[7,255,224],[255,184,6],[10,255,71],[255,41,10],[7,255,255],[224,255,8],[102,8,255],[255,61,6],[255,194,7],[255,122,8],[0,255,20],[255,8,41],[255,5,153],[6,51,255],[235,12,255],[160,150,20],[0,163,255],[140,140,140],[250,10,15],[20,255,0],[31,255,0],[255,31,0],[255,224,0],[153,255,0],[0,0,255],[255,71,0],[0,235,255],[0,173,255],[31,0,255],[11,200,200],[255,82,0],[0,255,245],[0,61,255],[0,255,112],[0,255,133],[255,0,0],[255,163,0],[255,102,0],[194,255,0],[0,143,255],[51,255,0],[0,82,255],[0,255,41],[0,255,173],[10,0,255],[173,255,0],[0,255,153],[255,92,0],[255,0,255],[255,0,245],[255,0,102],[255,173,0],[255,0,20],[255,184,184],[0,31,255],[0,255,61],[0,71,255],[255,0,204],[0,255,194],[0,255,82],[0,10,255],[0,112,255],[51,0,255],[0,194,255],[0,122,255],[0,255,163],[255,153,0],[0,255,10],[255,112,0],[143,255,0],[82,0,255],[163,255,0],[255,235,0],[8,184,170],[133,0,255],[0,255,92],[184,0,255],[255,0,31],[0,184,255],[0,214,255],[255,0,112],[92,255,0],[0,224,255],[112,224,255],[70,184,160],[163,0,255],[153,0,255],[71,255,0],[255,0,163],[255,204,0],[255,0,143],[0,255,235],[133,255,0],[255,0,235],[245,0,255],[255,0,122],[255,245,0],[10,190,212],[214,255,0],[0,204,255],[20,0,255],[255,255,0],[0,153,255],[0,41,255],[0,255,204],[41,0,255],[41,255,0],[173,0,255],[0,245,255],[71,0,255],[122,0,255],[0,255,184],[0,92,255],[184,255,0],[0,133,255],[255,214,0],[25,194,194],[102,255,0],[92,0,255]],CITYSCAPES:[[128,64,128],[244,35,232],[70,70,70],[102,102,156],[190,153,153],[153,153,153],[250,170,30],[220,220,0],[107,142,35],[152,251,152],[70,130,180],[220,20,60],[255,0,0],[0,0,142],[0,0,70],[0,60,100],[0,80,100],[0,0,230],[119,11,32]],PASCAL:[[0,0,0],[128,0,0],[0,128,0],[128,128,0],[0,0,128],[128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0],[64,128,0],[192,128,0],[64,0,128],[192,0,128],[64,128,128],[192,128,128],[0,64,0],[128,64,0],[0,192,0],[128,192,0],[0,64,128],[128,64,128],[0,192,128],[128,192,128],[64,64,0],[192,64,0],[64,192,0],[192,192,0],[64,64,128],[192,64,128],[64,192,128],[192,192,128],[0,0,64],[128,0,64],[0,128,64],[128,128,64],[0,0,192],[128,0,192],[0,128,192],[128,128,192],[64,0,64],[192,0,64],[64,128,64],[192,128,64],[64,0,192],[192,0,192],[64,128,192],[192,128,192],[0,64,64],[128,64,64],[0,192,64],[128,192,64],[0,64,192],[128,64,192],[0,192,192],[128,192,192],[64,64,64],[192,64,64],[64,192,64],[192,192,64],[64,64,192],[192,64,192],[64,192,192],[192,192,192],[32,0,0],[160,0,0],[32,128,0],[160,128,0],[32,0,128],[160,0,128],[32,128,128],[160,128,128],[96,0,0],[224,0,0],[96,128,0],[224,128,0],[96,0,128],[224,0,128],[96,128,128],[224,128,128],[32,64,0],[160,64,0],[32,192,0],[160,192,0],[32,64,128],[160,64,128],[32,192,128],[160,192,128],[96,64,0],[224,64,0],[96,192,0],[224,192,0],[96,64,128],[224,64,128],[96,192,128],[224,192,128],[32,0,64],[160,0,64],[32,128,64],[160,128,64],[32,0,192],[160,0,192],[32,128,192],[160,128,192],[96,0,64],[224,0,64],[96,128,64],[224,128,64],[96,0,192],[224,0,192],[96,128,192],[224,128,192],[32,64,64],[160,64,64],[32,192,64],[160,192,64],[32,64,192],[160,64,192],[32,192,192],[160,192,192],[96,64,64],[224,64,64],[96,192,64],[224,192,64],[96,64,192],[224,64,192],[96,192,192],[224,192,192],[0,32,0],[128,32,0],[0,160,0],[128,160,0],[0,32,128],[128,32,128],[0,160,128],[128,160,128],[64,32,0],[192,32,0],[64,160,0],[192,160,0],[64,32,128],[192,32,128],[64,160,128],[192,160,128],[0,96,0],[128,96,0],[0,224,0],[128,224,0],[0,96,128],[128,96,128],[0,224,128],[128,224,128],[64,96,0],[192,96,0],[64,224,0],[192,224,0],[64,96,128],[192,96,128],[64,224,128],[192,224,128],[0,32,64],[128,32,64],[0,160,64],[128,160,64],[0,32,192],[128,32,192],[0,160,192],[128,160,192],[64,32,64],[192,32,64],[64,160,64],[192,160,64],[64,32,192],[192,32,192],[64,160,192],[192,160,192],[0,96,64],[128,96,64],[0,224,64],[128,224,64],[0,96,192],[128,96,192],[0,224,192],[128,224,192],[64,96,64],[192,96,64],[64,224,64],[192,224,64],[64,96,192],[192,96,192],[64,224,192],[192,224,192],[32,32,0],[160,32,0],[32,160,0],[160,160,0],[32,32,128],[160,32,128],[32,160,128],[160,160,128],[96,32,0],[224,32,0],[96,160,0],[224,160,0],[96,32,128],[224,32,128],[96,160,128],[224,160,128],[32,96,0],[160,96,0],[32,224,0],[160,224,0],[32,96,128],[160,96,128],[32,224,128],[160,224,128],[96,96,0],[224,96,0],[96,224,0],[224,224,0],[96,96,128],[224,96,128],[96,224,128],[224,224,128],[32,32,64],[160,32,64],[32,160,64],[160,160,64],[32,32,192],[160,32,192],[32,160,192],[160,160,192],[96,32,64],[224,32,64],[96,160,64],[224,160,64],[96,32,192],[224,32,192],[96,160,192],[224,160,192],[32,96,64],[160,96,64],[32,224,64],[160,224,64],[32,96,192],[160,96,192],[32,224,192],[160,224,192],[96,96,64],[224,96,64],[96,224,64],[224,224,64],[96,96,192],[224,96,192],[96,224,192],[224,224,192]]},DATASET_MAX_ENTRIES:{PASCAL:256,CITYSCAPES:256,ADE20K:151},LABELS:{PASCAL:["background","aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","dining table","dog","horse","motorbike","person","potted plant","sheep","sofa","train","TV"],CITYSCAPES:["road","sidewalk","building","wall","fence","pole","traffic light","traffic sign","vegetation","terrain","sky","person","rider","car","truck","bus","train","motorcycle","bicycle"],ADE20K:["background","wall","building","sky","floor","tree","ceiling","road","bed","windowpane","grass","cabinet","sidewalk","person","earth","door","table","mountain","plant","curtain","chair","car","water","painting","sofa","shelf","house","sea","mirror","rug","field","armchair","seat","fence","desk","rock","wardrobe","lamp","bathtub","railing","cushion","base","box","column","signboard","chest","counter","sand","sink","skyscraper","fireplace","refrigerator","grandstand","path","stairs","runway","case","pool","pillow","screen","stairway","river","bridge","bookcase","blind","coffee","toilet","flower","book","hill","bench","countertop","stove","palm","kitchen","computer","swivel","boat","bar","arcade","hovel","bus","towel","light","truck","tower","chandelier","awning","streetlight","booth","television","airplane","dirt","apparel","pole","land","bannister","escalator","ottoman","bottle","buffet","poster","stage","van","ship","fountain","conveyer","canopy","washer","plaything","swimming","stool","barrel","basket","waterfall","tent","bag","minibike","cradle","oven","ball","food","step","tank","trade","microwave","pot","animal","bicycle","lake","dishwasher","screen","blanket","sculpture","hood","sconce","vase","traffic","tray","ashcan","fan","pier","screen","plate","monitor","bulletin","shower","radiator","glass","clock","flag"]}};
+     */var o="https://tfhub.dev/tensorflow/tfjs-model/deeplab",a=513,i={ADE20K:[[0,0,0],[120,120,120],[180,120,120],[6,230,230],[80,50,50],[4,200,3],[120,120,80],[140,140,140],[204,5,255],[230,230,230],[4,250,7],[224,5,255],[235,255,7],[150,5,61],[120,120,70],[8,255,51],[255,6,82],[143,255,140],[204,255,4],[255,51,7],[204,70,3],[0,102,200],[61,230,250],[255,6,51],[11,102,255],[255,7,71],[255,9,224],[9,7,230],[220,220,220],[255,9,92],[112,9,255],[8,255,214],[7,255,224],[255,184,6],[10,255,71],[255,41,10],[7,255,255],[224,255,8],[102,8,255],[255,61,6],[255,194,7],[255,122,8],[0,255,20],[255,8,41],[255,5,153],[6,51,255],[235,12,255],[160,150,20],[0,163,255],[140,140,140],[250,10,15],[20,255,0],[31,255,0],[255,31,0],[255,224,0],[153,255,0],[0,0,255],[255,71,0],[0,235,255],[0,173,255],[31,0,255],[11,200,200],[255,82,0],[0,255,245],[0,61,255],[0,255,112],[0,255,133],[255,0,0],[255,163,0],[255,102,0],[194,255,0],[0,143,255],[51,255,0],[0,82,255],[0,255,41],[0,255,173],[10,0,255],[173,255,0],[0,255,153],[255,92,0],[255,0,255],[255,0,245],[255,0,102],[255,173,0],[255,0,20],[255,184,184],[0,31,255],[0,255,61],[0,71,255],[255,0,204],[0,255,194],[0,255,82],[0,10,255],[0,112,255],[51,0,255],[0,194,255],[0,122,255],[0,255,163],[255,153,0],[0,255,10],[255,112,0],[143,255,0],[82,0,255],[163,255,0],[255,235,0],[8,184,170],[133,0,255],[0,255,92],[184,0,255],[255,0,31],[0,184,255],[0,214,255],[255,0,112],[92,255,0],[0,224,255],[112,224,255],[70,184,160],[163,0,255],[153,0,255],[71,255,0],[255,0,163],[255,204,0],[255,0,143],[0,255,235],[133,255,0],[255,0,235],[245,0,255],[255,0,122],[255,245,0],[10,190,212],[214,255,0],[0,204,255],[20,0,255],[255,255,0],[0,153,255],[0,41,255],[0,255,204],[41,0,255],[41,255,0],[173,0,255],[0,245,255],[71,0,255],[122,0,255],[0,255,184],[0,92,255],[184,255,0],[0,133,255],[255,214,0],[25,194,194],[102,255,0],[92,0,255]],CITYSCAPES:[[128,64,128],[244,35,232],[70,70,70],[102,102,156],[190,153,153],[153,153,153],[250,170,30],[220,220,0],[107,142,35],[152,251,152],[70,130,180],[220,20,60],[255,0,0],[0,0,142],[0,0,70],[0,60,100],[0,80,100],[0,0,230],[119,11,32]],PASCAL:[[0,0,0],[128,0,0],[0,128,0],[128,128,0],[0,0,128],[128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0],[64,128,0],[192,128,0],[64,0,128],[192,0,128],[64,128,128],[192,128,128],[0,64,0],[128,64,0],[0,192,0],[128,192,0],[0,64,128],[128,64,128],[0,192,128],[128,192,128],[64,64,0],[192,64,0],[64,192,0],[192,192,0],[64,64,128],[192,64,128],[64,192,128],[192,192,128],[0,0,64],[128,0,64],[0,128,64],[128,128,64],[0,0,192],[128,0,192],[0,128,192],[128,128,192],[64,0,64],[192,0,64],[64,128,64],[192,128,64],[64,0,192],[192,0,192],[64,128,192],[192,128,192],[0,64,64],[128,64,64],[0,192,64],[128,192,64],[0,64,192],[128,64,192],[0,192,192],[128,192,192],[64,64,64],[192,64,64],[64,192,64],[192,192,64],[64,64,192],[192,64,192],[64,192,192],[192,192,192],[32,0,0],[160,0,0],[32,128,0],[160,128,0],[32,0,128],[160,0,128],[32,128,128],[160,128,128],[96,0,0],[224,0,0],[96,128,0],[224,128,0],[96,0,128],[224,0,128],[96,128,128],[224,128,128],[32,64,0],[160,64,0],[32,192,0],[160,192,0],[32,64,128],[160,64,128],[32,192,128],[160,192,128],[96,64,0],[224,64,0],[96,192,0],[224,192,0],[96,64,128],[224,64,128],[96,192,128],[224,192,128],[32,0,64],[160,0,64],[32,128,64],[160,128,64],[32,0,192],[160,0,192],[32,128,192],[160,128,192],[96,0,64],[224,0,64],[96,128,64],[224,128,64],[96,0,192],[224,0,192],[96,128,192],[224,128,192],[32,64,64],[160,64,64],[32,192,64],[160,192,64],[32,64,192],[160,64,192],[32,192,192],[160,192,192],[96,64,64],[224,64,64],[96,192,64],[224,192,64],[96,64,192],[224,64,192],[96,192,192],[224,192,192],[0,32,0],[128,32,0],[0,160,0],[128,160,0],[0,32,128],[128,32,128],[0,160,128],[128,160,128],[64,32,0],[192,32,0],[64,160,0],[192,160,0],[64,32,128],[192,32,128],[64,160,128],[192,160,128],[0,96,0],[128,96,0],[0,224,0],[128,224,0],[0,96,128],[128,96,128],[0,224,128],[128,224,128],[64,96,0],[192,96,0],[64,224,0],[192,224,0],[64,96,128],[192,96,128],[64,224,128],[192,224,128],[0,32,64],[128,32,64],[0,160,64],[128,160,64],[0,32,192],[128,32,192],[0,160,192],[128,160,192],[64,32,64],[192,32,64],[64,160,64],[192,160,64],[64,32,192],[192,32,192],[64,160,192],[192,160,192],[0,96,64],[128,96,64],[0,224,64],[128,224,64],[0,96,192],[128,96,192],[0,224,192],[128,224,192],[64,96,64],[192,96,64],[64,224,64],[192,224,64],[64,96,192],[192,96,192],[64,224,192],[192,224,192],[32,32,0],[160,32,0],[32,160,0],[160,160,0],[32,32,128],[160,32,128],[32,160,128],[160,160,128],[96,32,0],[224,32,0],[96,160,0],[224,160,0],[96,32,128],[224,32,128],[96,160,128],[224,160,128],[32,96,0],[160,96,0],[32,224,0],[160,224,0],[32,96,128],[160,96,128],[32,224,128],[160,224,128],[96,96,0],[224,96,0],[96,224,0],[224,224,0],[96,96,128],[224,96,128],[96,224,128],[224,224,128],[32,32,64],[160,32,64],[32,160,64],[160,160,64],[32,32,192],[160,32,192],[32,160,192],[160,160,192],[96,32,64],[224,32,64],[96,160,64],[224,160,64],[96,32,192],[224,32,192],[96,160,192],[224,160,192],[32,96,64],[160,96,64],[32,224,64],[160,224,64],[32,96,192],[160,96,192],[32,224,192],[160,224,192],[96,96,64],[224,96,64],[96,224,64],[224,224,64],[96,96,192],[224,96,192],[96,224,192],[224,224,192]]},s={PASCAL:["background","aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","dining table","dog","horse","motorbike","person","potted plant","sheep","sofa","train","TV"],CITYSCAPES:["road","sidewalk","building","wall","fence","pole","traffic light","traffic sign","vegetation","terrain","sky","person","rider","car","truck","bus","train","motorcycle","bicycle"],ADE20K:["background","wall","building","sky","floor","tree","ceiling","road","bed","windowpane","grass","cabinet","sidewalk","person","earth","door","table","mountain","plant","curtain","chair","car","water","painting","sofa","shelf","house","sea","mirror","rug","field","armchair","seat","fence","desk","rock","wardrobe","lamp","bathtub","railing","cushion","base","box","column","signboard","chest","counter","sand","sink","skyscraper","fireplace","refrigerator","grandstand","path","stairs","runway","case","pool","pillow","screen","stairway","river","bridge","bookcase","blind","coffee","toilet","flower","book","hill","bench","countertop","stove","palm","kitchen","computer","swivel","boat","bar","arcade","hovel","bus","towel","light","truck","tower","chandelier","awning","streetlight","booth","television","airplane","dirt","apparel","pole","land","bannister","escalator","ottoman","bottle","buffet","poster","stage","van","ship","fountain","conveyer","canopy","washer","plaything","swimming","stool","barrel","basket","waterfall","tent","bag","minibike","cradle","oven","ball","food","step","tank","trade","microwave","pot","animal","bicycle","lake","dishwasher","screen","blanket","sculpture","hood","sconce","vase","traffic","tray","ashcan","fan","pier","screen","plate","monitor","bulletin","shower","radiator","glass","clock","flag"]};
 /**
@@ -83,4 +84,4 @@ * @license
      * =============================================================================
-     */function a(e,t){return""+o.BASE_PATH+"/"+(4===t?e+"/1/default/1/model.json":e+"/1/quantized/"+t+"/1/model.json")+"?tfjs-format=file"}function i(e){if("pascal"===e)return o.COLORMAPS.PASCAL;if("ade20k"===e)return o.COLORMAPS.ADE20K;if("cityscapes"===e)return o.COLORMAPS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function s(e){if("pascal"===e)return o.LABELS.PASCAL;if("ade20k"===e)return o.LABELS.ADE20K;if("cityscapes"===e)return o.LABELS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function l(e,t,o,a){return n(this,void 0,void 0,(function(){var n,i,s,l,c,u,d,f,h,p,b,g,m,w;return r(this,(function(r){switch(r.label){case 0:if(e.length<t.length)throw new Error("The colormap must be expansive enough to encode each label. Aborting, since the given colormap has length "+e.length+", but there are "+t.length+" labels.");return n=o.shape,i=n[0],s=n[1],l=tf.buffer([i,s,3],"int32"),[4,o.array()];case 1:for(c=r.sent(),u=new Set,d=0;d<i;++d)for(f=0;f<s;++f)w=c[d][f],u.add(w),l.set(e[w][0],d,f,0),l.set(e[w][1],d,f,1),l.set(e[w][2],d,f,2);return h=l.toTensor(),[4,tf.browser.toPixels(h,a)];case 2:for(p=r.sent(),tf.dispose(h),b={},g=0,m=Array.from(u);g<m.length;g++)w=m[g],b[t[w]]=e[w];return[2,{legend:b,segmentationMap:p}]}}))}))}
-/** @license See the LICENSE file. */var c=function(){function e(e,t){this.model=e,this.base=t}return e.prototype.predict=function(e){var t=this;return tf.tidy((function(){var n=tf.cast(function(e){return tf.tidy((function(){var t=e instanceof tf.Tensor?e:tf.browser.fromPixels(e),n=t.shape,r=n[0],a=n[1],i=o.CROP_SIZE/Math.max(a,r),s=Math.round(r*i),l=Math.round(a*i);return tf.expandDims(tf.image.resizeBilinear(t,[s,l]))}))}(e),"int32");return tf.squeeze(t.model.execute(n))}))},e.prototype.segment=function(e,t){return void 0===t&&(t={}),n(this,void 0,void 0,(function(){var n,o,a,c,u,d,f,h,p,b,g=this;return r(this,(function(r){switch(r.label){case 0:if(!(t.colormap&&t.labels||this.base))throw new Error("Calling the 'segment' method requires either the 'base' attribute to be defined (e.g. 'pascal', 'cityscapes' or'ade20k'), or 'colormap' and 'labels' options to be set. Aborting, since neither has been provided.");return t.colormap&&t.labels||(t.colormap=i(this.base),t.labels=s(this.base)),n=t.colormap,o=t.labels,a=t.canvas,c=tf.tidy((function(){return g.predict(e)})),u=c.shape,d=u[0],f=u[1],[4,l(n,o,c,a)];case 1:return h=r.sent(),p=h.legend,b=h.segmentationMap,tf.dispose(c),[2,{legend:p,height:d,width:f,segmentationMap:b}]}}))}))},e.prototype.dispose=function(){return n(this,void 0,void 0,(function(){return r(this,(function(e){return this.model&&this.model.dispose(),[2]}))}))},e}();e.SemanticSegmentation=c,e.getColormap=i,e.getLabels=s,e.getURL=a,e.load=
+     */function l(e,t){return o+"/"+(4===t?e+"/1/default/1/model.json":e+"/1/quantized/"+t+"/1/model.json")+"?tfjs-format=file"}function c(e){if("pascal"===e)return i.PASCAL;if("ade20k"===e)return i.ADE20K;if("cityscapes"===e)return i.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function u(e){if("pascal"===e)return s.PASCAL;if("ade20k"===e)return s.ADE20K;if("cityscapes"===e)return s.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function d(e,t,o,a){return n(this,void 0,void 0,(function(){var n,i,s,l,c,u,d,f,h,p,b,g,m,w;return r(this,(function(r){switch(r.label){case 0:if(e.length<t.length)throw new Error("The colormap must be expansive enough to encode each label. Aborting, since the given colormap has length "+e.length+", but there are "+t.length+" labels.");return n=o.shape,i=n[0],s=n[1],l=tf.buffer([i,s,3],"int32"),[4,o.array()];case 1:for(c=r.sent(),u=new Set,d=0;d<i;++d)for(f=0;f<s;++f)w=c[d][f],u.add(w),l.set(e[w][0],d,f,0),l.set(e[w][1],d,f,1),l.set(e[w][2],d,f,2);return h=l.toTensor(),[4,tf.browser.toPixels(h,a)];case 2:for(p=r.sent(),tf.dispose(h),b={},g=0,m=Array.from(u);g<m.length;g++)w=m[g],b[t[w]]=e[w];return[2,{legend:b,segmentationMap:p}]}}))}))}
+/** @license See the LICENSE file. */var f=function(){function e(e,t){this.model=e,this.base=t}return e.prototype.predict=function(e){var t=this;return tf.tidy((function(){var n=tf.cast(function(e){return tf.tidy((function(){var t=e instanceof tf.Tensor?e:tf.browser.fromPixels(e),n=t.shape,r=n[0],o=n[1],i=a/Math.max(o,r),s=Math.round(r*i),l=Math.round(o*i);return tf.expandDims(tf.image.resizeBilinear(t,[s,l]))}))}(e),"int32");return tf.squeeze(t.model.execute(n))}))},e.prototype.segment=function(e,t){return void 0===t&&(t={}),n(this,void 0,void 0,(function(){var n,o,a,i,s,l,f,h,p,b,g=this;return r(this,(function(r){switch(r.label){case 0:if(!(t.colormap&&t.labels||this.base))throw new Error("Calling the 'segment' method requires either the 'base' attribute to be defined (e.g. 'pascal', 'cityscapes' or'ade20k'), or 'colormap' and 'labels' options to be set. Aborting, since neither has been provided.");return t.colormap&&t.labels||(t.colormap=c(this.base),t.labels=u(this.base)),n=t.colormap,o=t.labels,a=t.canvas,i=tf.tidy((function(){return g.predict(e)})),s=i.shape,l=s[0],f=s[1],[4,d(n,o,i,a)];case 1:return h=r.sent(),p=h.legend,b=h.segmentationMap,tf.dispose(i),[2,{legend:p,height:l,width:f,segmentationMap:b}]}}))}))},e.prototype.dispose=function(){return n(this,void 0,void 0,(function(){return r(this,(function(e){return this.model&&this.model.dispose(),[2]}))}))},e}();e.SemanticSegmentation=f,e.getColormap=c,e.getLabels=u,e.getURL=l,e.load=
 /**
@@ -102,2 +103,2 @@ * @license
      */
-function(e){return void 0===e&&(e={base:"pascal",quantizationBytes:2}),n(this,void 0,void 0,(function(){var n;return r(this,(function(r){switch(r.label){case 0:if(null==tf)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(e.base){if(-1===["pascal","cityscapes","ade20k"].indexOf(e.base))throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e.base+". Try one of 'pascal', 'cityscapes' and 'ade20k'.");if(-1===[1,2,4].indexOf(e.quantizationBytes))throw new Error("Only quantization to 1, 2 or 4 bytes is supported.")}else if(!e.modelUrl)throw new Error("SemanticSegmentation can be constructed either by passing the weights URL or one of the supported base model names from 'pascal', 'cityscapes' and 'ade20k',together with the degree of quantization (either 1, 2 or 4).Aborting, since neither has been provided.");return[4,t.loadGraphModel(e.modelUrl||a(e.base,e.quantizationBytes))];case 1:return n=r.sent(),[2,new c(n,e.base)]}}))}))},e.toSegmentationImage=l,e.version="0.2.1",Object.defineProperty(e,"__esModule",{value:!0})}));
+function(e){return void 0===e&&(e={base:"pascal",quantizationBytes:2}),n(this,void 0,void 0,(function(){var n;return r(this,(function(r){switch(r.label){case 0:if(null==tf)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(e.base){if(-1===["pascal","cityscapes","ade20k"].indexOf(e.base))throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e.base+". Try one of 'pascal', 'cityscapes' and 'ade20k'.");if(-1===[1,2,4].indexOf(e.quantizationBytes))throw new Error("Only quantization to 1, 2 or 4 bytes is supported.")}else if(!e.modelUrl)throw new Error("SemanticSegmentation can be constructed either by passing the weights URL or one of the supported base model names from 'pascal', 'cityscapes' and 'ade20k',together with the degree of quantization (either 1, 2 or 4).Aborting, since neither has been provided.");return[4,t.loadGraphModel(e.modelUrl||l(e.base,e.quantizationBytes))];case 1:return n=r.sent(),[2,new f(n,e.base)]}}))}))},e.toSegmentationImage=d,e.version="0.2.2",Object.defineProperty(e,"__esModule",{value:!0})},"object"==typeof exports&&"undefined"!=typeof module?t(exports,require("@tensorflow/tfjs-converter"),require("@tensorflow/tfjs-core")):"function"==typeof define&&define.amd?define(["exports","@tensorflow/tfjs-converter","@tensorflow/tfjs-core"],t):t((e=e||self).deeplab={},e.tf,e.tf);

dist/index.d.ts

@@ -22,3 +22,3 @@ /**
 export { version } from './version';
-export { getColormap, getLabels, getURL, toSegmentationImage };
+export { getColormap, getLabels, getURL, ModelConfig, PredictionConfig, toSegmentationImage };
 /**
@@ -25,0 +25,0 @@ * Initializes the DeepLab model and returns a `SemanticSegmentation` object.

dist/types.d.ts

@@ -23,13 +23,48 @@ /**
 }
+/**
+ * The model supports quantization to 1 and 2 bytes, leaving 4 for the
+ * non-quantized variant.
+ *
+ * @docinline
+ */
 export declare type QuantizationBytes = 1 | 2 | 4;
+/**
+ * Three types of pre-trained weights are available, trained on Pascal,
+ * Cityscapes and ADE20K datasets. Each dataset has its own colormap and
+ * labelling scheme.
+ *
+ * @docinline
+ */
 export declare type ModelArchitecture = 'pascal' | 'cityscapes' | 'ade20k';
 export declare type DeepLabInput = ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | tf.Tensor3D;
 export interface ModelConfig {
+    /**
+     * The degree to which weights are quantized (either 1, 2 or 4).
+     * Setting this attribute to 1 or 2 will load the model with int32 and
+     * float32 compressed to 1 or 2 bytes respectively.
+     * Set it to 4 to disable quantization.
+     */
     quantizationBytes?: QuantizationBytes;
+    /**
+     * The type of model to load (either `pascal`, `cityscapes` or `ade20k`).
+     */
     base?: ModelArchitecture;
+    /**
+     *
+     * The URL from which to load the TF.js GraphModel JSON.
+     * Inferred from `base` and `quantizationBytes` if undefined.
+     */
     modelUrl?: string;
 }
 export interface PredictionConfig {
+    /** The canvas where to draw the output. */
     canvas?: HTMLCanvasElement;
+    /** The array of RGB colors corresponding to labels. */
     colormap?: Color[];
+    /**
+     * The array of names corresponding to labels.
+     *
+     * By [default](./src/index.ts#L81), `colormap` and `labels` are set
+     * according to the `base` model attribute passed during initialization.
+     */
     labels?: string[];
@@ -36,0 +71,0 @@ }

dist/version.d.ts

 /** @license See the LICENSE file. */
-declare const version = "0.2.1";
+declare const version = "0.2.2";
 export { version };

dist/version.js

@@ -5,4 +5,4 @@ "use strict";
 // This code is auto-generated, do not modify this file!
-var version = '0.2.1';
+var version = '0.2.2';
 exports.version = version;
 //# sourceMappingURL=version.js.map

package.json

 {
   "name": "@tensorflow-models/deeplab",
-  "version": "0.2.1",
+  "version": "0.2.2",
   "description": "Semantic Segmentation in the Browser: DeepLab v3 Model",
@@ -5,0 +5,0 @@ "main": "dist/index.js",

New alerts

New author

Supply chain risk

A new npm collaborator published a version of the package for the first time. New collaborators are usually benign additions to a project, but do indicate a change to the security surface area of a package.

Found 1 instance in 1 package

Improved metrics

Lines of code

2439

increased by1.41%

Worsened metrics

Total package byte prevSize

1800984

decreased by-2.96%

Number of package files

35

decreased by-2.78%

Number of medium supply chain risk alerts

1

increased byInfinity%

No dependency changes