From 6c0781793f39ebec9efb31d623208e72454e0754 Mon Sep 17 00:00:00 2001 From: vecna Date: Mon, 27 Jul 2026 17:44:59 +0200 Subject: [PATCH 1/5] large vendor pre-fetching --- README.md | 3 +- ghostyle-transfer.html | 2 +- lab.html | 2 +- loader.html | 2 +- playwright.config.js | 13 + realtime.html | 2 +- scripts/config.js | 39 +- scripts/mediapipe-loop.js | 2 +- scripts/transfer.js | 2 +- scripts/vendor/README.md | 47 + scripts/vendor/age_gender_model-shard1 | Bin 0 -> 429708 bytes .../age_gender_model-weights_manifest.json | 1 + scripts/vendor/face-api.js | 5009 ++++++++++ scripts/vendor/face_expression_model-shard1 | Bin 0 -> 329468 bytes ...ace_expression_model-weights_manifest.json | 1 + scripts/vendor/face_landmark_68_model-shard1 | Bin 0 -> 356840 bytes ...ce_landmark_68_model-weights_manifest.json | 1 + scripts/vendor/face_landmarker.task | Bin 0 -> 3758596 bytes scripts/vendor/face_recognition_model-shard1 | Bin 0 -> 4194304 bytes scripts/vendor/face_recognition_model-shard2 | 6 + ...ce_recognition_model-weights_manifest.json | 1 + scripts/vendor/foca.sh | 39 + scripts/vendor/mobilenet_v3_small.tflite | Bin 0 -> 4117670 bytes scripts/vendor/tasks-vision@0.10.35.js | 8 + .../vendor/tiny_face_detector_model-shard1 | Bin 0 -> 193321 bytes ..._face_detector_model-weights_manifest.json | 1 + scripts/vendor/wasm/vision_wasm_internal.js | 8827 +++++++++++++++++ scripts/vendor/wasm/vision_wasm_internal.wasm | Bin 0 -> 11153617 bytes styles/lab.css | 2 + styles/pages.css | 2 + .../CSR54zpSlumSWj9CGVsoBZdeWNReuQ.woff2 | Bin 0 -> 25276 bytes styles/vendor/QGYvz_MVcBeNP4NJtEtq.woff2 | Bin 0 -> 32292 bytes styles/vendor/QGYvz_MVcBeNP4NJuktqQ4E.woff2 | Bin 0 -> 14808 bytes styles/vendor/README.md | 25 + styles/vendor/UcCo3FwrK3iLTcvhYwYL8g.woff2 | Bin 0 -> 30200 bytes styles/vendor/UcCo3FwrK3iLTcviYwY.woff2 | Bin 0 -> 72920 bytes styles/vendor/UcCo3FwrK3iLTcvmYwYL8g.woff2 | Bin 0 -> 29636 bytes styles/vendor/UcCo3FwrK3iLTcvsYwYL8g.woff2 | Bin 0 -> 133336 bytes styles/vendor/UcCo3FwrK3iLTcvtYwYL8g.woff2 | Bin 0 -> 15268 bytes styles/vendor/UcCo3FwrK3iLTcvuYwYL8g.woff2 | Bin 0 -> 17376 bytes styles/vendor/UcCo3FwrK3iLTcvvYwYL8g.woff2 | Bin 0 -> 41520 bytes styles/vendor/google-fonts.css | 153 + ...0PnT8RD8yKwBNntkaToggR7BYRbKPx3cwhsk.woff2 | Bin 0 -> 1640 bytes ...0PnT8RD8yKwBNntkaToggR7BYRbKPx7cwhsk.woff2 | Bin 0 -> 11624 bytes ...BX0PnT8RD8yKwBNntkaToggR7BYRbKPxDcwg.woff2 | Bin 0 -> 31432 bytes ...0PnT8RD8yKwBNntkaToggR7BYRbKPxPcwhsk.woff2 | Bin 0 -> 6836 bytes ...0PnT8RD8yKwBNntkaToggR7BYRbKPxTcwhsk.woff2 | Bin 0 -> 8872 bytes ...0PnT8RD8yKwBNntkaToggR7BYRbKPx_cwhsk.woff2 | Bin 0 -> 5888 bytes tests/e2e/loader.spec.js | 8 +- 49 files changed, 14165 insertions(+), 33 deletions(-) create mode 100644 scripts/vendor/README.md create mode 100644 scripts/vendor/age_gender_model-shard1 create mode 100644 scripts/vendor/age_gender_model-weights_manifest.json create mode 100644 scripts/vendor/face-api.js create mode 100644 scripts/vendor/face_expression_model-shard1 create mode 100644 scripts/vendor/face_expression_model-weights_manifest.json create mode 100644 scripts/vendor/face_landmark_68_model-shard1 create mode 100644 scripts/vendor/face_landmark_68_model-weights_manifest.json create mode 100644 scripts/vendor/face_landmarker.task create mode 100644 scripts/vendor/face_recognition_model-shard1 create mode 100644 scripts/vendor/face_recognition_model-shard2 create mode 100644 scripts/vendor/face_recognition_model-weights_manifest.json create mode 100755 scripts/vendor/foca.sh create mode 100644 scripts/vendor/mobilenet_v3_small.tflite create mode 100644 scripts/vendor/tasks-vision@0.10.35.js create mode 100644 scripts/vendor/tiny_face_detector_model-shard1 create mode 100644 scripts/vendor/tiny_face_detector_model-weights_manifest.json create mode 100644 scripts/vendor/wasm/vision_wasm_internal.js create mode 100644 scripts/vendor/wasm/vision_wasm_internal.wasm create mode 100644 styles/vendor/CSR54zpSlumSWj9CGVsoBZdeWNReuQ.woff2 create mode 100644 styles/vendor/QGYvz_MVcBeNP4NJtEtq.woff2 create mode 100644 styles/vendor/QGYvz_MVcBeNP4NJuktqQ4E.woff2 create mode 100644 styles/vendor/README.md create mode 100644 styles/vendor/UcCo3FwrK3iLTcvhYwYL8g.woff2 create mode 100644 styles/vendor/UcCo3FwrK3iLTcviYwY.woff2 create mode 100644 styles/vendor/UcCo3FwrK3iLTcvmYwYL8g.woff2 create mode 100644 styles/vendor/UcCo3FwrK3iLTcvsYwYL8g.woff2 create mode 100644 styles/vendor/UcCo3FwrK3iLTcvtYwYL8g.woff2 create mode 100644 styles/vendor/UcCo3FwrK3iLTcvuYwYL8g.woff2 create mode 100644 styles/vendor/UcCo3FwrK3iLTcvvYwYL8g.woff2 create mode 100644 styles/vendor/google-fonts.css create mode 100644 styles/vendor/tDbv2o-flEEny0FZhsfKu5WU4zr3E_BX0PnT8RD8yKwBNntkaToggR7BYRbKPx3cwhsk.woff2 create mode 100644 styles/vendor/tDbv2o-flEEny0FZhsfKu5WU4zr3E_BX0PnT8RD8yKwBNntkaToggR7BYRbKPx7cwhsk.woff2 create mode 100644 styles/vendor/tDbv2o-flEEny0FZhsfKu5WU4zr3E_BX0PnT8RD8yKwBNntkaToggR7BYRbKPxDcwg.woff2 create mode 100644 styles/vendor/tDbv2o-flEEny0FZhsfKu5WU4zr3E_BX0PnT8RD8yKwBNntkaToggR7BYRbKPxPcwhsk.woff2 create mode 100644 styles/vendor/tDbv2o-flEEny0FZhsfKu5WU4zr3E_BX0PnT8RD8yKwBNntkaToggR7BYRbKPxTcwhsk.woff2 create mode 100644 styles/vendor/tDbv2o-flEEny0FZhsfKu5WU4zr3E_BX0PnT8RD8yKwBNntkaToggR7BYRbKPx_cwhsk.woff2 diff --git a/README.md b/README.md index 3cee061..526e825 100644 --- a/README.md +++ b/README.md @@ -97,8 +97,7 @@ Other static pages such as [`about.html`](about.html), [`report.html`](report.ht Runtime external dependencies visible from the HTML/config layer: -- [Google Fonts](https://fonts.googleapis.com) / [Google Fonts static assets](https://fonts.gstatic.com) -- [Landing-page Google Fonts CSS](https://fonts.googleapis.com/css2?family=League+Script&family=Outfit:wght@400;600;700;800;900&family=JetBrains+Mono:wght@400;700&display=swap) +- Local vendored Google Fonts CSS/assets in `styles/vendor/` (`Inter`, `JetBrains Mono`, `League Script`, `Outfit`) - [jsDelivr CDN](https://cdn.jsdelivr.net) - [`@vladmandic/face-api`](https://cdn.jsdelivr.net/npm/@vladmandic/face-api/dist/face-api.js) - [face-api.js model weights](https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/) diff --git a/ghostyle-transfer.html b/ghostyle-transfer.html index 5c681af..67c71a8 100644 --- a/ghostyle-transfer.html +++ b/ghostyle-transfer.html @@ -19,7 +19,7 @@ - + diff --git a/lab.html b/lab.html index dbcf34e..5eb0794 100644 --- a/lab.html +++ b/lab.html @@ -302,7 +302,7 @@

Ghostyle

- + diff --git a/loader.html b/loader.html index 823e934..a125e16 100644 --- a/loader.html +++ b/loader.html @@ -94,7 +94,7 @@

Activity log

- + diff --git a/playwright.config.js b/playwright.config.js index 1e822d3..2f31cf5 100644 --- a/playwright.config.js +++ b/playwright.config.js @@ -1,4 +1,16 @@ import { defineConfig, devices } from '@playwright/test'; +import { existsSync } from 'node:fs'; + +const isLinux = process.platform === 'linux'; +const linuxChromeCandidates = [ + '/usr/bin/google-chrome', + '/usr/bin/google-chrome-stable', + '/usr/bin/chromium', + '/usr/bin/chromium-browser', +]; +const systemChromePath = isLinux + ? linuxChromeCandidates.find((candidate) => existsSync(candidate)) + : undefined; export default defineConfig({ testDir: './tests/e2e', @@ -18,6 +30,7 @@ export default defineConfig({ use: { ...devices['Desktop Chrome'], launchOptions: { + executablePath: systemChromePath, args: [ '--use-fake-ui-for-media-stream', '--use-fake-device-for-media-stream', diff --git a/realtime.html b/realtime.html index 69ce2c7..ddf6842 100644 --- a/realtime.html +++ b/realtime.html @@ -56,7 +56,7 @@ - + diff --git a/scripts/config.js b/scripts/config.js index d41b907..58cd294 100644 --- a/scripts/config.js +++ b/scripts/config.js @@ -11,20 +11,21 @@ * file so the upgrade path to npm + bundler is mechanical. */ +const VENDOR_ROOT_URL = new URL('./vendor/', import.meta.url).href; + /** - * Public CDN URLs for the face-api.js model shards (TinyFaceDetector + the - * landmarks / recognition / age-gender heads). These point at the canonical - * `face-api.js-models` mirror, which serves the same weights for both the - * unmaintained original library and the @vladmandic fork in use here. + * Local URLs for vendored face-api model shards (TinyFaceDetector + landmarks / + * recognition / age-gender / expressions). Every network fetch stays inside + * `/scripts/vendor` so pages can run without third-party CDNs. * * @see scripts/main.js – `loadModels()` reads these and calls `faceapi.nets.*.loadFromUri()`. */ export const MODEL_URLS = { - tiny: 'https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/tiny_face_detector', - landmarks: 'https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_landmark_68', - recognition: 'https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_recognition', - ageGender: 'https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/age_gender_model', - expressions: 'https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_expression' + tiny: VENDOR_ROOT_URL, + landmarks: VENDOR_ROOT_URL, + recognition: VENDOR_ROOT_URL, + ageGender: VENDOR_ROOT_URL, + expressions: VENDOR_ROOT_URL }; export const ANALYZE_PANEL_MAX_WIDTH_DESKTOP = 900; @@ -53,39 +54,35 @@ export const THUMBNAIL_OUTPUT_SIZE = 160; export const THUMBNAIL_JPEG_QUALITY = 0.8; /** - * Root CDN URL for the @mediapipe/tasks-vision package. Used as a base for - * dynamic ES-module imports of `FaceLandmarker` and `ImageEmbedder`. + * Vendored tasks-vision ES module path used for dynamic imports. * * @see scripts/engine-3d.js – `loadMobileNet()` does `await import(MEDIAPIPE_TASKS_VISION_URL)`. * @see scripts/mediapipe-loop.js – uses the same package for face landmarks. */ -export const MEDIAPIPE_TASKS_VISION_URL = 'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35'; +export const MEDIAPIPE_TASKS_VISION_URL = new URL('./vendor/tasks-vision@0.10.35.js', import.meta.url).href; /** - * URL of the MediaPipe WASM blob loaded by `FilesetResolver.forVisionTasks()`. - * The path must match the package version of `MEDIAPIPE_TASKS_VISION_URL` to - * avoid runtime version mismatch errors. + * Local URL of the MediaPipe WASM directory loaded by `FilesetResolver.forVisionTasks()`. * * @see scripts/engine-3d.js * @see scripts/mediapipe-loop.js */ -export const MEDIAPIPE_WASM_URL = 'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/wasm'; +export const MEDIAPIPE_WASM_URL = new URL('./vendor/wasm', import.meta.url).href; /** - * URL of the MediaPipe FaceLandmarker `.task` bundle (float16, 478 landmarks). - * Hosted by Google's `mediapipe-models` storage. + * Local URL of the MediaPipe FaceLandmarker `.task` bundle (float16, 478 landmarks). * * @see scripts/mediapipe-loop.js – passed to `FaceLandmarker.createFromOptions()`. */ -export const MEDIAPIPE_FACE_LANDMARKER_URL = 'https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task'; +export const MEDIAPIPE_FACE_LANDMARKER_URL = new URL('./vendor/face_landmarker.task', import.meta.url).href; /** - * URL of the MediaPipe ImageEmbedder model bundle (MobileNetV3 Small, float32). + * Local URL of the MediaPipe ImageEmbedder model bundle (MobileNetV3 Small, float32). * Produces the embedding vector consumed by the 3D recognition pipeline. * * @see scripts/engine-3d.js – passed to `ImageEmbedder.createFromOptions()`. */ -export const MEDIAPIPE_IMAGE_EMBEDDER_URL = 'https://storage.googleapis.com/mediapipe-models/image_embedder/mobilenet_v3_small/float32/1/mobilenet_v3_small.tflite'; +export const MEDIAPIPE_IMAGE_EMBEDDER_URL = new URL('./vendor/mobilenet_v3_small.tflite', import.meta.url).href; /** * Pre-built TinyFaceDetector options reused across every face-api detection diff --git a/scripts/mediapipe-loop.js b/scripts/mediapipe-loop.js index e415e62..1455350 100644 --- a/scripts/mediapipe-loop.js +++ b/scripts/mediapipe-loop.js @@ -11,7 +11,7 @@ * changing the 2D recognition engine. */ -import { FaceLandmarker, FilesetResolver } from "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35"; +import { FaceLandmarker, FilesetResolver } from './vendor/tasks-vision@0.10.35.js'; import { MEDIAPIPE_WASM_URL, MEDIAPIPE_FACE_LANDMARKER_URL } from './config.js'; import { t } from './i18n.js'; diff --git a/scripts/transfer.js b/scripts/transfer.js index 843aa40..0067998 100644 --- a/scripts/transfer.js +++ b/scripts/transfer.js @@ -44,7 +44,7 @@ import { applyI18n, initI18n, setupLocaleSelect, t } from './i18n.js'; const CANON = 512; const RESULT_MAX = 900; -const MODEL_ROOT = 'https://cdn.jsdelivr.net/npm/@vladmandic/face-api/model/'; +const MODEL_ROOT = new URL('./vendor/', import.meta.url).href; /** * Two-dimensional point in image or canvas coordinates. diff --git a/scripts/vendor/README.md b/scripts/vendor/README.md new file mode 100644 index 0000000..4b97470 --- /dev/null +++ b/scripts/vendor/README.md @@ -0,0 +1,47 @@ +Vendor asset inventory for third-party runtime files. + +Use `scripts/vendor/foca.sh` to refresh the assets it manages. + +## Library bundles + +| Local file | Source URL | Upstream version/reference | Notes | +|---|---|---|---| +| `face-api.js` | `https://cdn.jsdelivr.net/npm/@vladmandic/face-api/dist/face-api.js` | floating (no `@x.y.z` in URL) | Main `faceapi` browser bundle used by pages and transfer tool. | +| `tasks-vision@0.10.35.js` | `https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35` | `0.10.35` | ESM bundle imported by 3D/embedder paths. | + +## URLs synced by `foca.sh` + +### face-api.js model assets + +Source reference for this block is currently `justadudewhohacks/face-api.js-models@master`. + +| Local file | Source URL | +|---|---| +| `face_landmark_68_model-weights_manifest.json` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_landmark_68/face_landmark_68_model-weights_manifest.json` | +| `face_recognition_model-weights_manifest.json` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_recognition/face_recognition_model-weights_manifest.json` | +| `age_gender_model-weights_manifest.json` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/age_gender_model/age_gender_model-weights_manifest.json` | +| `face_expression_model-weights_manifest.json` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_expression/face_expression_model-weights_manifest.json` | +| `tiny_face_detector_model-shard1` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/tiny_face_detector/tiny_face_detector_model-shard1` | +| `face_landmark_68_model-shard1` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_landmark_68/face_landmark_68_model-shard1` | +| `face_recognition_model-shard1` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_recognition/face_recognition_model-shard1` | +| `face_recognition_model-shard2` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_recognition/face_recognition_model-shard2` | +| `face_expression_model-shard1` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_expression/face_expression_model-shard1` | +| `age_gender_model-shard1` | `https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/age_gender_model/age_gender_model-shard1` | + +### MediaPipe Tasks Vision runtime (`0.10.35`) + +| Local file | Source URL | +|---|---| +| `wasm/vision_wasm_internal.js` | `https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/wasm/vision_wasm_internal.js` | +| `wasm/vision_wasm_internal.wasm` | `https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/wasm/vision_wasm_internal.wasm` | + +### MediaPipe model files + +| Local file | Source URL | Version segment | +|---|---|---| +| `face_landmarker.task` | `https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task` | `float16/1` | +| `mobilenet_v3_small.tflite` | `https://storage.googleapis.com/mediapipe-models/image_embedder/mobilenet_v3_small/float32/1/mobilenet_v3_small.tflite` | `float32/1` | + +## Update rule + +When any source URL or version segment above changes, re-run `scripts/vendor/foca.sh` 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p={x:l,filter:o},d={strides:n,pad:a,dilations:r},c=P.runKernel(ji,p,d);return u?W(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var mw=L({dilation2d_:u3}),cp={};_e(cp,{assertAndGetBroadcastShape:()=>ct,getBroadcastDims:()=>ZN,getReductionAxes:()=>Bt});function ZN(e,t){let n=e.length,a=[];for(let r=0;r1&&i===1&&a.unshift(s)}return a}function Bt(e,t){let n=[];for(let a=0;a1)&&n.unshift(s)}return n}function ct(e,t){let n=Math.max(e.length,t.length),a=new Array(n);for(let r=0;r`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${a.rank}.`);let r=n.rank===1?n.size:n.shape[1],s=a.rank===1?a.size:a.shape[0];if(A(r===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${s}.`),n.rank===1&&a.rank===1){let i=W(n,[1,-1]),o=W(a,[-1,1]),l=$e(i,o);return W(l,[])}else if(n.rank===1&&a.rank===2){let i=W(n,[1,-1]),o=W(a,[a.shape[0],a.shape[1]]),l=$e(i,o);return W(l,[l.size])}else if(n.rank===2&&a.rank===1){let i=W(a,[-1,1]),o=$e(n,i);return W(o,[o.size])}else{let i=W(a,[a.shape[0],a.shape[1]]);return $e(n,i)}}var gw=L({dot_:m3});function f3(e,...t){let n=t.map((r,s)=>E(r,`tensors${s}`,"einsum")),a={equation:e};return P.runKernel(Wm,n,a)}var Qs=L({einsum_:f3});function g3(e){let t={x:E(e,"x","elu","float32")};return P.runKernel(Ki,t)}var dp=L({elu_:g3});function b3(e,t){let n=E(e,"x","ensureShape","string_or_numeric");if(!nN(n.shape,t))throw new Error(`EnsureShape: Shape of tensor ${n.shape} is not compatible with expected shape ${t}`);return e}var JN=L({ensureShape_:b3});function y3(e){let t=E(e,"x","erf");A(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=re(t,"float32"));let n={x:t};return P.runKernel(Xi,n)}var af=L({erf_:y3});function bw(e,t){for(let n=0;ne[s]);return[n,r]}function gi(e,t){let n=t.map(a=>1);return QN(e,n,t)}function x3(e,t,n){A(bw(t,n),()=>`${e} supports only inner-most axes for now. 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d=E(e,"x","conv2d","float32"),c=E(t,"filter","conv2d","float32"),h=d,m=!1;d.rank===3&&(m=!0,h=W(d,[1,d.shape[0],d.shape[1],d.shape[2]])),A(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),A(c.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${c.rank}.`),Tn("fused conv2d",a,i);let f=r==="NHWC"?h.shape[3]:h.shape[1];A(c.shape[2]===f,()=>`Error in conv2d: depth of input (${f}) must match input depth for filter ${c.shape[2]}.`),A(mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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p=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=a?u.shape[u.rank-1]:u.shape[u.rank-2],c=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?u.shape[u.rank-2]:u.shape[u.rank-1],m=l.shape.slice(0,-2),f=u.shape.slice(0,-2),g=ot(m),b=ot(f);A(p===d,()=>`Error in fused matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${a} must match.`);let y=ct(l.shape.slice(0,-2),u.shape.slice(0,-2)).concat([c,h]),x=n?W(l,[g,p,c]):W(l,[g,c,p]),v=a?W(u,[b,h,d]):W(u,[b,d,h]),I;r!=null&&(I=E(r,"bias","fused matMul"),[I]=At(I,l),ct(y,I.shape));let N;i!=null&&(N=E(i,"prelu weights","fused matMul"));let C=(D,$)=>{let[S,M,B,U]=$,H=Nf(W(D,B.shape),B,s),q,K;if(!n&&!a?(q=$e(H,M,!1,!0),K=$e(S,H,!0,!1)):!n&&a?(q=$e(H,M,!1,!1),K=$e(H,S,!0,!1)):n&&!a?(q=$e(M,H,!1,!0),K=$e(S,H,!1,!1)):(q=$e(M,H,!0,!0),K=$e(H,S,!0,!0)),r!=null){let Z=Tf(U,H);return[q,K,Z]}else return[q,K]},_={a:x,b:v,bias:I,preluActivationWeights:N},F={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?dr((D,$,S)=>{let M=P.runKernel(li,_,F);return S([D,$,M]),{value:W(M,y),gradFunc:C}})(x,v):dr((D,$,S,M)=>{let B=P.runKernel(li,_,F);return M([D,$,B,S]),{value:W(B,y),gradFunc:C}})(x,v,I)}var sW=L({fusedMatMul_:rW});function iW(e){return Sf(e,.54,.46)}var oW=L({hammingWindow_:iW});function lW(e){return Sf(e,.5,.5)}var GT=L({hannWindow_:lW});function uW(e,t,n,a=!1,r=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Ve(e,s,t)),s+=n;if(a)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),A(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),A(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`),A(a.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length 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t=E(e,"image","RGBToGrayscale"),n=t.rank-1,a=t.shape[n];A(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),A(a===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${a}.`);let r=t.dtype,s=re(t,"float32"),i=je([.2989,.587,.114]),o;switch(t.rank){case 2:o=Qs("ij,j->i",s,i);break;case 3:o=Qs("ijk,k->ij",s,i);break;case 4:o=Qs("ijkl,l->ijk",s,i);break;case 5:o=Qs("ijklm,m->ijkl",s,i);break;case 6:o=Qs("ijklmn,n->ijklm",s,i);break;default:throw new Error("Not a valid tensor rank.")}return o=Gt(o,-1),re(o,r)}var xW=L({rgbToGrayscale_:yW});function vW(e,t,n=0,a=.5){let r=E(e,"image","rotateWithOffset","float32");A(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return P.runKernel(pp,s,i)}var wW=L({rotateWithOffset_:vW});function gp(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),A(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),A(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),A(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),A(t.rank===1,()=>"scores must be a 1D tensor"),A(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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g=0;gr&&u.push({score:t[g],boxIndex:g,suppressBeginIndex:0});u.sort(II);let p=s>0?-.5/s:0,d=[],c=[];for(;d.length0;){let g=u.pop(),{score:b,boxIndex:y,suppressBeginIndex:x}=g;if(b=x;--I){let N=EW(e,y,d[I]);if(N>=a){v=!0;break}if(g.score=g.score*_W(a,p,N),g.score<=r)break}g.suppressBeginIndex=d.length,v||(g.score===b?(d.push(y),c.push(g.score)):g.score>r&&SW(u,g,II))}let h=d.length,m=n-h;o&&m>0&&(d.push(...new Array(m).fill(0)),c.push(...new Array(m).fill(0)));let f={selectedIndices:d};return i&&(f.selectedScores=c),l&&(f.validOutputs=h),f}function EW(e,t,n){let a=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),s=Math.min(a[0],a[2]),i=Math.min(a[1],a[3]),o=Math.max(a[0],a[2]),l=Math.max(a[1],a[3]),u=Math.min(r[0],r[2]),p=Math.min(r[1],r[3]),d=Math.max(r[0],r[2]),c=Math.max(r[1],r[3]),h=(o-s)*(l-i),m=(d-u)*(c-p);if(h<=0||m<=0)return 0;let f=Math.max(s,u),g=Math.max(i,p),b=Math.min(o,d),y=Math.min(l,c),x=Math.max(b-f,0)*Math.max(y-g,0);return x/(h+m-x)}function _W(e,t,n){let a=Math.exp(t*n*n);return n<=e?a:0}function II(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function AW(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=E(e,"boxes","nonMaxSuppressionAsync"),i=E(t,"scores","nonMaxSuppressionAsync"),o=gp(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],p=l[1],{selectedIndices:d}=jT(u,p,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),je(d,"int32")}var FW=AW;function $W(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=E(e,"boxes","nonMaxSuppression"),o=E(t,"scores","nonMaxSuppression"),l=gp(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},p={maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s},d=P.runKernel(Gu,u,p);return{selectedIndices:d[0],selectedScores:d[1]}}var DW=L({nonMaxSuppressionWithScore_:$W});async function RW(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=E(e,"boxes","nonMaxSuppressionAsync"),o=E(t,"scores","nonMaxSuppressionAsync"),l=gp(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),p=u[0],d=u[1],{selectedIndices:c,selectedScores:h}=KT(p,d,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:je(c,"int32"),selectedScores:je(h)}}var MW=RW;function OW(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=E(e,"boxes","nonMaxSuppression"),o=E(t,"scores","nonMaxSuppression"),l=gp(i,o,n,a,r,null),u=l.maxOutputSize,p=l.iouThreshold,d=l.scoreThreshold,c={boxes:i,scores:o},h={maxOutputSize:u,iouThreshold:p,scoreThreshold:d,padToMaxOutputSize:s},m=P.runKernel(Uu,c,h);return{selectedIndices:m[0],validOutputs:m[1]}}var PW=L({nonMaxSuppressionPadded_:OW});async function LW(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let 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className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:O(()=>yn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;O(()=>{let i=X(s,pt(r));s.assign(i);let o=X(z(he(r,rn(X(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Jw=class extends Or{static get className(){return"Adam"}constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],O(()=>{this.accBeta1=xe(t).variable(),this.accBeta2=xe(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=pe(1,this.accBeta1),a=pe(1,this.accBeta2);t.forEach((r,s)=>{let 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f=X(z(he(h,X(rn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),O(()=>{this.accBeta1.assign(cr(this.beta1,this.iterations_+1)),this.accBeta2.assign(cr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},Qw=class extends Or{static get className(){return"Adamax"}constructor(e,t,n,a=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],O(()=>{this.iteration=xe(0).variable(),this.accBeta1=xe(t).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=pe(1,this.accBeta1),a=he(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let 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compiled before being used.");return this.model.evaluate(t,n,a)}async evaluateDataset(t,n){if(!this.built)throw new sr("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,n)}predict(t,n={}){return this.model==null&&this.build(),this.model.predict(t,n)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(t){this.model.optimizer=t}async fit(t,n,a={}){if(!this.built)throw new sr("The model needs to be compiled before being used.");return this.model.fit(t,n,a)}async fitDataset(t,n){if(!this.built)throw new sr("The model needs to be compiled before being used.");return this.model.fitDataset(t,n)}async trainOnBatch(t,n){return this.model.trainOnBatch(t,n)}static fromConfig(t,n,a={},r=!1){let s,i={};if(n instanceof Array){if(n[0].className==null||n[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");s=n}else w.assert(n.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=n.layers,delete n.layers,i=n;let o=new t(i);if(!(o instanceof nv))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let l of s){let u=Ba(l,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(t){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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xn{apply(e,t=1){return O(()=>z(ma(z(e,t)),e))}};_C.className="swish";ne.registerClass(_C);function ys(e){return e.getClassName()}function Ix(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function xs(e){if(e==null){let t={};return t.className="linear",t.config={},Ix(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ix(t)}else return e instanceof xn?e:Ix(e)}function A0(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var AC=class extends ne.Serializable{},Fd=class extends AC{constructor(e){super(),A0(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return O(()=>{let t=It([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,Ed(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Fd.className="L1L2";ne.registerClass(Fd);function Sj(e){return A0(e),new Fd({l1:e!=null?e.l1:null,l2:0})}function Nj(e){return A0(e),new Fd({l2:e!=null?e.l2:null,l1:0})}var QI={l1l2:"L1L2"};function ft(e){return o0(e)}function eS(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in QI?QI[e]:e,config:{}};return eS(t)}else return e instanceof AC?e:eS(e)}var F0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};F0.className="ReLU";ne.registerClass(F0);var $0=class extends We{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Te(e);return fd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$0.className="LeakyReLU";ne.registerClass($0);var D0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(Pn(n.shape),re(a,n.dtype)),xe(-1e9));n=X(n,r)}return this.axis instanceof Array?this.axis.length>1?mn(pe(n,bd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};O0.className="Softmax";ne.registerClass(O0);function Gl(e,t,n){if(typeof e=="number")return xi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. 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V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=P0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=tu.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function Cj(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=FC(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=cw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var $C=class DC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gl(n.kernelSize,t,"kernelSize"),this.strides=Gl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,wa(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=xs(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Gl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(ar("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ys(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},Uf=class RC extends $C{constructor(t,n){super(t,n),this.kernel=null,RC.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${t[n]}`);let a=t[n],r=this.kernelSize.concat([a,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[n]:a}}],this.built=!0}call(t,n){return O(()=>{t=Te(t);let a,r=this.bias==null?null:this.bias.read(),s=O2(this.activation.getClassName());if(s!=null&&this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)a=Tj(t,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)a=Cj(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(a=this.activation.apply(a))}return a})}computeOutputShape(t){t=Je(t);let n=[],a=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Gf=class MC extends Uf{constructor(t){super(2,t),MC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Gf.className="Conv2D";ne.registerClass(Gf);var Hf=class OC extends Uf{constructor(t){super(3,t),OC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hf.className="Conv3D";ne.registerClass(Hf);var L0=class extends Gf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=rr(o,d,u,this.padding),m=rr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,1]));let g=ef(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=De(g,[0,3,1,2])),this.bias!=null&&(g=Ka(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=rr(t[a],o,s,this.padding),t[r]=rr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};L0.className="Conv2DTranspose";ne.registerClass(L0);var z0=class extends Hf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=rr(l,m,d,this.padding),y=rr(u,f,c,this.padding),x=rr(p,g,h,this.padding),v=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=dw(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ka(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=rr(t[a],u,i,this.padding),t[r]=rr(t[r],p,o,this.padding),t[s]=rr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};z0.className="Conv3DTranspose";ne.registerClass(z0);var PC=class extends Uf{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=Ds(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};PC.className="SeparableConv";var W0=class extends PC{constructor(e){super(2,e)}};W0.className="SeparableConv2D";ne.registerClass(W0);var B0=class LC extends Uf{constructor(t){super(1,t),LC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};B0.className="Conv1D";ne.registerClass(B0);var V0=class extends We{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ph(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ph(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ph(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ph(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};V0.className="Cropping2D";ne.registerClass(V0);var U0=class extends We{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,MG(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};U0.className="UpSampling2D";ne.registerClass(U0);function Ej(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=P0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=As(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var G0=class extends $C{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{e=Te(e);let n=Ej(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};G0.className="DepthwiseConv2D";ne.registerClass(G0);function zC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function WC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ya(t,0),r!=null&&(r=ya(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(na(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Ft(p,1)),[d,g,c]})}var Pr=class BC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Kf({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Yx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=zC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=WC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=Cd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?Kx(n,[1,a]):n):this.cell.stateSize>1?[Kx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===BC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Pr.className="RNN";ne.registerClass(Pr);var $d=class extends We{},jf=class extends $d{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0na(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0na(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,ur(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};jf.className="SimpleRNNCell";ne.registerClass(jf);var H0=class extends Pr{constructor(e){e.cell=new jf(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};H0.className="SimpleRNN";ne.registerClass(H0);var qf=class extends $d{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0na(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};j0.className="GRU";ne.registerClass(j0);var Dd=class extends $d{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Ra{apply(i,o){let l=r.apply([s]),u=new $f().apply([s]),p=r.apply([s*2]);return zI(zI(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0na(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};q0.className="LSTM";ne.registerClass(q0);var Kf=class extends $d{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ii(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ba(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Zx(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):U2(t(),n),o=()=>_d(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var _j=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_=[It(r)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_[0]=It(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let s=0;sHt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],p=Va(l,a[0],r,s[0],i[0]),d=Va(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};VC.className="ConvRNN2D";var Xf=class extends Dd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,tn(this.filters,"filters"),this.kernelSize=Gl(n,2,"kernelSize"),this.kernelSize.forEach(o=>tn(o,"kernelSize")),this.strides=Gl(a||1,2,"strides"),this.strides.forEach(o=>tn(o,"strides")),this.padding=r||"valid",wa(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=Gl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>tn(o,"dilationRate"))}build(e){var t;e=Je(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Ra{apply(p,d){let c=l.apply([u]),h=Pn([u]),m=l.apply([u*2]);return u0([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0na(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0na(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Ln(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Ln(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),q=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=_j(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xf.className="ConvLSTM2DCell";ne.registerClass(Xf);var K0=class extends VC{constructor(e){let t=new Xf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};K0.className="ConvLSTM2D";ne.registerClass(K0);var Yf=class extends We{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Te(e);if(0U2(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Yf.className="Dropout";ne.registerClass(Yf);var X0=class extends Yf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};X0.className="SpatialDropout1D";ne.registerClass(X0);var Y0=class extends We{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=O2(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Y0.className="Dense";ne.registerClass(Y0);var Z0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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s==="max"?i=Dt(e,t,n,o):i=xa(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}function UC(e,t,n,a,r,s){return O(()=>{Rt(r),L2(s),wa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ga()),s==null&&(s="max"),e=FC(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Cw(e,t,n,o):i=ew(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,4,1,2,3])),i})}var GC=class extends We{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(tn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},x1=class extends HC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Zf(e,t,n,a,r,"max")}};x1.className="MaxPooling2D";ne.registerClass(x1);var v1=class extends HC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Zf(e,t,n,a,r,"avg")}};v1.className="AveragePooling2D";ne.registerClass(v1);var jC=class extends We{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),wa(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),a=Va(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},w1=class extends jC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),UC(e,t,n,a,r,"max")}};w1.className="MaxPooling3D";ne.registerClass(w1);var k1=class extends jC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),UC(e,t,n,a,r,"avg")}};k1.className="AveragePooling3D";ne.registerClass(k1);var qC=class extends We{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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e=Je(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return O(()=>{e=Te(e),e.dtype!=="int32"&&(e=lr(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. + Received countWeights=${t.countWeights}`);n=Te(t.countWeights)}let a=fa(e),r=Xl(e),s=Cn(this.numTokens,a).bufferSync().get(0),i=Rr(r,0).bufferSync().get(0);if(!(s&&i))throw new V(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return zj(e,this.outputMode,this.numTokens,n)})}};F1.className="CategoryEncoding";ne.registerClass(F1);var Wj=["bilinear","nearest"],nS=new Set(Wj),$1=class extends We{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation)if(nS.has(e.interpolation))this.interpolation=e.interpolation;else throw new V(`Invalid interpolation parameter: ${e.interpolation} 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r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n),o=k("includeBatchInIndex",e,t,n),{result:l,indexes:u}=a.maxPoolWithArgmax(k("x",e,t,n),[i[1],i[2]],[r[1],r[2]],s,o);return[l,u]}case"AvgPool3D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.avgPool3d(k("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"MaxPool3D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.maxPool3d(k("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"Dilation2D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("dilations",e,t,n),o=r[1],l=r[2],u=i[1],p=i[2];return[a.dilation2d(k("x",e,t,n),k("filter",e,t,n),[o,l],s,[u,p],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},a5=(e,t,n,a=on)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,n),s=k("dtype",e,t,n),i=k("value",e,t,n);return[a.fill(r,i,s)]}case"LinSpace":{let r=k("start",e,t,n),s=k("stop",e,t,n),i=k("num",e,t,n);return[a.linspace(r,s,i)]}case"Multinomial":{let 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implemented`)}},c5=(e,t,n,a=on)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[a.matMul(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Einsum":return[a.einsum(k("equation",e,t,n),...k("tensors",e,t,n))];case"Transpose":return[a.transpose(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,n),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,n),u=k("leakyreluAlpha",e,t,n);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,d]=k("args",e,t,n);return[a.fused.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:p,activation:s,preluActivationWeights:d,leakyreluAlpha:u})];case"MatrixBandPart":return[a.linalg.bandPart(k("a",e,t,n),k("numLower",e,t,n),k("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},d5=(e,t,n,a=on)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>Kq(i,o,l));case"basic_math":return r(()=>Xq(i,o,l));case"control":return t5(i,o,l);case"convolution":return r(()=>n5(i,o,l));case"creation":return r(()=>a5(i,o,l));case"dynamic":return r5(i,o,l);case"evaluation":return r(()=>s5(i,o,l));case"image":return r(()=>u5(i,o,l));case"graph":return r(()=>i5(i,o,l));case"logical":return r(()=>p5(i,o,l));case"matrices":return r(()=>c5(i,o,l));case"normalization":return r(()=>d5(i,o,l));case"ragged":return r(()=>h5(i,o,l));case"reduction":return r(()=>m5(i,o,l));case"slice_join":return r(()=>f5(i,o,l));case"sparse":return r(()=>g5(i,o,l));case"spectral":return r(()=>b5(i,o,l));case"string":return r(()=>y5(i,o,l));case"transformation":return r(()=>x5(i,o,l));case"hash_table":return l5(i,o,l,a);case"custom":let u=oE(i.op);if(u&&u.customExecutor)return u.customExecutor(new qq(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. 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t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function hS(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Zn(c)[0]));a=a||[];let p=new Set(a.map(c=>Zn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((ti(c)||C5(c)||E5(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function v5(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Zn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=w5(m,l);return k5(f,l),f}function w5(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Wh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function k5(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Wh(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Wh(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Wh(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Wh(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function I5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>ti(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new _E(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=hS(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. 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Missing the following inputs: [${r}]`)}let o=v5(this.graph,a),l=I5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Zn(c)[0]]),s=n.map(c=>Zn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new dS(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Zn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=cS(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. 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p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Tr(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Zn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Zn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Zn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},_5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return 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sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},J5=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},Q5=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},e8=class extends sn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},fS=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},B1=class extends sn{constructor(){super(),this.outputQueue=new ME,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},t8=class extends B1{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return!0}},LE=class extends sn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},rs;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(rs||(rs={}));var n8=class extends sn{constructor(e,t=rs.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof sn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await DE(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case rs.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case rs.SHORTEST:return{value:null,done:!0};case rs.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},zE=class extends sn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new RE(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},a8=class extends zE{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=P5.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},xp=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Yn(async()=>(await n.iterator()).columnMajorBatch(e,t,i8),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Yn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Yn(async()=>(await t.iterator()).filter(a=>O(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Yn(async()=>(await t.iterator()).map(n=>O(()=>e(n))),this.size)}mapAsync(e){let t=this;return Yn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Yn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Yn(async()=>{let a=W1(async()=>({value:await t.iterator(),done:!1}));return G5(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=O5.alea(t||w.now().toString());return Yn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Yn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};xp.MAX_BUFFER_SIZE=1e4;function Yn(e,t=null){return new class extends xp{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function r8(e){return Yn(async()=>PE(e),e.length)}function s8(e){if(!ru(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await DE(e,a=>{if(a instanceof xp)return{value:a.iterator(),recurse:!1};if(ru(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return H5(n,rs.SHORTEST)},t)}function i8(e){if(e===null)return null;let t=e[0];return W5(t)?{value:o8(e),recurse:!1}:{value:null,recurse:!0}}function o8(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ce?Ft(e):bn(e)}var WE=class extends xp{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Bh='"',rc=Symbol("out"),gS=Symbol("field"),Vh=Symbol("quote"),Nx=Symbol("quoteafterquote"),bS=Symbol("quoteinquote"),BE=class extends xp{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new WE(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(n))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new VE(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(a){throw new Error(`Error thrown while initializing video stream: ${a.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,n,a=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(a.freqDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(a.timeDataQueue);n=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],a=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++a===this.numFrames&&(clearInterval(s),r({freqDataQueue:t,timeDataQueue:n}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let n=t[0].length,a=new Float32Array(t.length*n);return t.forEach((r,s)=>a.set(r,s*n)),a}getTensorFromAudioDataArray(t,n){let a=new Float32Array(w.sizeFromShape(n));return a.set(t,a.length-t.length),bn(a,n)}},u8=class UE extends sn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let a=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-a)/2,i=(1-r)/2,o=s+a,l=r+i;this.cropBox=Aa([i,s,l,o],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let a=new UE(t,n);return await a.start(),a}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Xo.fromPixels(this.webcamVideoElement)}catch(n){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(n)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(n){throw new Error(`Error thrown cropping the video: ${n.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return O(()=>{let n=Gt(re(t,"float32"),0),a;a=Qn.cropAndResize(n,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=a.shape;return W(a,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},GE=class{},HE=class extends sn{split(e){return new p8(this,e)}},p8=class extends HE{constructor(e,t){super(),this.upstream=e,this.impl=new c8(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},c8=class extends B1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},d8=class extends sn{decodeUTF8(){return new h8(this)}},h8=class extends HE{constructor(e){super(),this.upstream=e,this.impl=new m8(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},m8=class extends B1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=QS();this.decoder=new 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p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,N=p.effectiveFilterWidth,C=v-1-p.padInfo.front,_=N-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Pe(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let se=0;se=p.outHeight||Math.floor(ie)!==ie))for(let ve=0;ve=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,ie,ue,B);ee+=ye}}}D.set(ee*$,M,U,H,q,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var SX={kernelName:Wc,backendName:"cpu",kernelFunc:IX};function NX(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,v=b-1-p.padInfo.top,I=Pe(i.shape,"float32"),N=1/(h*m),C=n.data.get(r.dataId).values,_=Pe(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(q)!==q))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=_.get(F,q,Z,D);U+=J}}I.set(U*N,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var TX={kernelName:zc,backendName:"cpu",kernelFunc:NX};function CX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((b,y)=>b*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=wi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var AX={kernelName:fu,backendName:"cpu",kernelFunc:_X};function FX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=H1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var $X={kernelName:gu,backendName:"cpu",kernelFunc:FX};function DX(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var RX={kernelName:Bc,backendName:"cpu",kernelFunc:DX},MX=lt(Ns,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.sizeFromShape(f.shape)>0);if(l.length===1)return hr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(v=>vi({inputs:{input:v},backend:n})),g=l.map(v=>iu({inputs:{input:v},backend:n})),b=ou({inputs:f,backend:n,attrs:{axis:s}}),y=ou({inputs:g,backend:n,attrs:{axis:s}}),x=Jn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=T.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=j1(p,o,t[0].dtype,d),h=T.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var WX={kernelName:yu,backendName:"cpu",kernelFunc:ou};function j_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,q=v.values;for(let K=0;K=c.inHeight)continue;let ve=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=ve+Le*N[1],st=ue+Ge*F,at=mt;for(let rt=0;rt=u.inDepth)continue;let K=H*F[0],Z=$+q*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ve=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ge=ie+Se*F[2],mt=ve+Le*u.inChannels,st=Ge;for(let at=0;atMath.cos(e)),QX={kernelName:Vi,backendName:"cpu",kernelFunc:JX},eY=lt(Ui,e=>Math.cosh(e)),tY={kernelName:Ui,backendName:"cpu",kernelFunc:eY};function nY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Pe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(q<0||q>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ve=0;ve1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,v=y.top,I=h.outChannels/h.inChannels,N=new Wt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,F=N.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+q*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],ve=Z+se*h.inChannels,ue=ee,ye=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:N,filterWidth:C,dilationHeight:_,dilationWidth:F,outShape:D}=T.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=ye)}}}let ee=w.locToIndex([B,U,q,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},vY={kernelName:ql,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${ql}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ee,J=te)}}}$[Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},wY={kernelName:jl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${jl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ae,J=se)}}}$[S][Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function kY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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255.0; + } else if(v < -FLOAT_MAX) { + return vec4(0.0, 0.0, 128.0, 255.0) / 255.0; + } + + highp vec4 c = vec4(0,0,0,0); + + highp float e = floor(log2(av)); + highp float m = exp2(fract(log2(av))) - 1.0; + + c[2] = floor(128.0 * m); + m -= c[2] / 128.0; + c[1] = floor(32768.0 * m); + m -= c[1] / 32768.0; + c[0] = floor(8388608.0 * m); + + highp float ebias = e + 127.0; + c[3] = floor(ebias / 2.0); + ebias -= c[3] * 2.0; + c[2] += floor(ebias) * 128.0; + + c[3] += 128.0 * step(0.0, -v); + + return c / 255.0; + } +`,{getBroadcastDims:CA}=T;function g9(e,t,n){let a=[];if(e.forEach(c=>{let h=w.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=uk(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform 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t.isPacked?(u=y9(t.logicalShape,i,n.enableShapeUniforms),p=k9(o)):(u=x9(t.logicalShape,i,n.enableShapeUniforms),p=w9(o)),n.packedInputs&&(d+=C9),[d,l,p,r,u,s,n.userCode].join(` +`)}function kp(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return z9(e,t);case 1:return B9(e,t);case 2:return U9(e,t);case 3:return H9(e,t);case 4:return q9(e,t);case 5:return K9(e);case 6:return X9(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function EA(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return L9(e);case 1:return W9(e,t);case 2:return V9(e,t);case 3:return G9(e,t);default:return j9(e,t)}}function b9(e,t,n=!1,a){let r="";n?r+=EA(e,a):r+=kp(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=Y9(e,t):r+=Z9(e,t)),r}function y9(e,t,n){switch(e.length){case 0:return _A();case 1:return E9(e,t,n);case 2:return O9(e,t,n);case 3:return A9(e,t,n);default:return $9(e,t,n)}}function x9(e,t,n){switch(e.length){case 0:return _A();case 1:return _9(e,t,n);case 2:return P9(e,t,n);case 3:return F9(e,t,n);case 4:return D9(e,t,n);case 5:return R9(e,t);case 6:return M9(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function v9(e){return` + float sampleTexture(sampler2D textureSampler, vec2 uv) { + return ${e.texture2D}(textureSampler, uv).r; + } + `}function w9(e){return` + void setOutput(float val) { + ${e.output} = vec4(val, 0, 0, 0); + } + `}function k9(e){return` + void setOutput(vec4 val) { + ${e.output} = val; + } + `}function I9(e){return`${e.version} + precision highp float; + precision highp int; + precision highp sampler2D; + ${e.varyingFs} vec2 resultUV; + ${e.defineOutput} + const vec2 halfCR = vec2(0.5, 0.5); + + struct ivec5 + { + int x; + int y; + int z; + int w; + int u; + }; + + struct ivec6 + { + int x; + int y; + int z; + int w; + int u; + int v; + }; + + uniform float NAN; + ${e.defineSpecialNaN} + ${e.defineSpecialInf} + ${e.defineRound} + + int imod(int x, int y) { + return x - y * (x / y); + } + + int idiv(int a, int b, float sign) { + int res = a / b; + int mod = imod(a, b); + if (sign < 0. && mod != 0) { + res -= 1; + } + return res; + } + + //Based on the work of Dave Hoskins + //https://www.shadertoy.com/view/4djSRW + #define HASHSCALE1 443.8975 + float random(float seed){ + vec2 p = resultUV * seed; + vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1); + p3 += dot(p3, p3.yzx + 19.19); + return fract((p3.x + p3.y) * p3.z); + } + + ${S9} + ${N9} + ${T9} + `}var S9=` +vec2 uvFromFlat(int texNumR, int texNumC, int index) { + int texR = index / texNumC; + int texC = index - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); +} +vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { + int texelIndex = index / 2; + int texR = texelIndex / texNumC; + int texC = texelIndex - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); +} +`,N9=` +vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, + int texNumC, int row, int col) { + int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); + int texR = texelIndex / texNumC; + int texC = texelIndex - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); +} +`,T9=` +vec2 packedUVfrom3D(int texNumR, int texNumC, + int texelsInBatch, int texelsInLogicalRow, int b, + int row, int col) { + int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2); + int texR = index / texNumC; + int texC = index - texR * texNumC; + return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); +} +`,C9=` + float getChannel(vec4 frag, vec2 innerDims) { + vec2 modCoord = mod(innerDims, 2.); + return modCoord.x == 0. ? + (modCoord.y == 0. ? frag.r : frag.g) : + (modCoord.y == 0. ? frag.b : frag.a); + } + float getChannel(vec4 frag, int dim) { + float modCoord = mod(float(dim), 2.); + return modCoord == 0. ? frag.r : frag.g; + } +`;function _A(){return` + int getOutputCoords() { + return 0; + } + `}function E9(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` + int getOutputCoords() { + return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); + } + `:` + int getOutputCoords() { + return 2 * int(resultUV.x * ${a[1]}.0); + } + `:a[1]===1?n?` + int getOutputCoords() { + return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0)); + } + `:` + int getOutputCoords() { + return 2 * int(resultUV.y * ${a[0]}.0); + } + `:n?` + int getOutputCoords() { + ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(packedTexShape[0], packedTexShape[1])); + return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y); + } + `:` + int getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(${a[0]}, ${a[1]})); + return 2 * (resTexRC.x * ${a[1]} + resTexRC.y); + } + `}function _9(e,t,n){return t[0]===1?n?` + int getOutputCoords() { + return int(resultUV.x * float(outTexShape[1])); + } + `:` + int getOutputCoords() { + return int(resultUV.x * ${t[1]}.0); + } + `:t[1]===1?n?` + int getOutputCoords() { + return int(resultUV.y * float(outTexShape[0])); + } + `:` + int getOutputCoords() { + return int(resultUV.y * ${t[0]}.0); + } + `:n?` + int getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(outTexShape[0], outTexShape[1])); + return resTexRC.x * outTexShape[1] + resTexRC.y; + } + `:` + int getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(${t[0]}, ${t[1]})); + return resTexRC.x * ${t[1]} + resTexRC.y; + } + `}function A9(e,t,n){if(n)return` + ivec3 getOutputCoords() { + ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); + int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); + int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0)); + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(packedTexShape[0], packedTexShape[1])); + int index = resTexRC.x * packedTexShape[1] + resTexRC.y; + + int b = index / texelsInBatch; + index -= b * texelsInBatch; + + int r = 2 * (index / texelsInLogicalRow); + int c = imod(index, texelsInLogicalRow) * 2; + + return ivec3(b, r, c); + } + `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return` + ivec3 getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(${a[0]}, ${a[1]})); + int index = resTexRC.x * ${a[1]} + resTexRC.y; + + int b = index / ${s}; + index -= b * ${s}; + + int r = 2 * (index / ${r}); + int c = imod(index, ${r}) * 2; + + return ivec3(b, r, c); + } + `}function F9(e,t,n){if(n)return` + ivec3 getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(outTexShape[0], outTexShape[1])); + int index = resTexRC.x * outTexShape[1] + resTexRC.y; + ${eg(["r","c","d"],e)} + return ivec3(r, c, d); + } +`;let a=Qo(["r","c","d"],e);return` + ivec3 getOutputCoords() { + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(${t[0]}, ${t[1]})); + int index = resTexRC.x * ${t[1]} + resTexRC.y; + ${a} + return ivec3(r, c, d); + } + `}function $9(e,t,n){if(n)return` + ivec4 getOutputCoords() { + ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); + ivec2 resTexRC = ivec2(resultUV.yx * + vec2(packedTexShape[0], packedTexShape[1])); + int index = resTexRC.x * packedTexShape[1] + resTexRC.y; + + int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0)); + int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0)); + int texelsInBatchN = texelsInBatch * outShape[1]; + + int b2 = index / texelsInBatchN; + index -= b2 * texelsInBatchN; + + int b = index / texelsInBatch; + index -= b * texelsInBatch; + + int r = 2 * (index / texelsInLogicalRow); + int c = imod(index, texelsInLogicalRow) * 2; + + return ivec4(b2, b, r, c); + } + `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` +`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,b)=>`coords.${d[b+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,f=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` + return vec4(outputValue.xy, outputValue.xy); + `;else if(m&&!f)i===1?h=` + return vec4(outputValue.x, outputValue.x, 0., 0.); + `:h=` + return vec4(outputValue.x); + `;else if(o.length){let g=s-2,b=s-1;o.indexOf(g)>-1&&o.indexOf(b)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(b)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return` + vec4 ${r}() { + ${l} coords = getOutputCoords(); + ${p} + vec4 outputValue = get${a}(${c}); + ${h} + } + `}function Z9(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return` + float ${r}() { + return sampleTexture(${n}, resultUV); + } + `;let u=ht(l),p=CA(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` +`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),` + float ${r}() { + ${u} coords = getOutputCoords(); + ${c} + return get${a}(${m}); + } + `}function ht(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function uk(e,t,n){let{newShape:a,keptDims:r}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!w.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function J9(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=g9(r,i,t),l=sA(e.gl,o),u=e.createProgram(l);return 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c={name:d,uniform:e.getUniformLocation(n,d,p),offset:e.getUniformLocation(n,`offset${d}`,p)};t.enableShapeUniforms&&(c.shape=e.getUniformLocation(n,`${d}Shape`,p),c.texShape=e.getUniformLocation(n,`${d}TexShape`,p)),a.push(c)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(n,"outShape",p),o=e.getUniformLocation(n,"outShapeStrides",p),i=e.getUniformLocation(n,"outTexShape",p)),t.customUniforms)for(let d of t.customUniforms)r.push(e.getUniformLocation(n,d.name,p));return{variablesLocations:a,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function SS(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${o} and ${l} must match`)})}function Q9(e,t,n,a,r){t.program.enableShapeUniforms||(SS(t.inShapeInfos,n),SS([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),G().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=uk(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=w.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&w.arraysEqual(i.shape,l),b=w.sizeFromShape(i.shape)===1,y=T.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&w.arraysEqual(l,n.texData.texShape),v=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${b}_${y}_${g}_${c}_${h}_${m}_${v}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${G().getNumber("WEBGL_VERSION")}`,s}function vn(e){return G().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var tQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Fc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + ivec3 outCoordsFromFlatIndex(int index) { + ${this.enableShapeUniforms?eg(["r","c","d"],e):Qo(["r","c","d"],e)} + return ivec3(r, c, d); + } + + void main() { + ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1])); + int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y); + + vec4 result = vec4(0.); + + for (int i=0; i<4; i++) { + int flatIndex = index + i; + ivec3 rc = outCoordsFromFlatIndex(flatIndex); + result[i] = getA(rc.x, rc.y, rc.z); + } + + ${t.output} = result; + } + `}},nQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Fc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + ivec3 outCoordsFromFlatIndex(int index) { + ${this.enableShapeUniforms?eg(["r","c","d"],e):Qo(["r","c","d"],e)} + return ivec3(r, c, d); + } + + void main() { + ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1])); + int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y); + + vec4 result = vec4(0.); + + for (int i=0; i<4; i++) { + int flatIndex = index + i; + ivec3 rc = outCoordsFromFlatIndex(flatIndex); + result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z)); + } + + ${t.output} = result; + } + `}},aQ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=da.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` + ${TA} + + void main() { + float x = getAAtOutCoords(); + ${t.output} = encode_float(x); + } + `}},rQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=da.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` + ${TA} + + void main() { + ivec3 coords = getOutputCoords(); + float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); + ${t.output} = encode_float(x); + } + `}},sQ={R:0,G:1,B:2,A:3},NS=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;iWA,createBufferFromOutputTexture:()=>UA,createFloat16MatrixTexture:()=>OA,createFloat16PackedMatrixTexture:()=>zA,createFloat32MatrixTexture:()=>MA,createIndexBuffer:()=>RA,createPackedMatrixTexture:()=>LA,createUnsignedBytesMatrixTexture:()=>PA,createVertexBuffer:()=>DA,createVertexShader:()=>$A,downloadByteEncodedFloatMatrixFromOutputTexture:()=>HA,downloadFloat32MatrixFromBuffer:()=>GA,downloadMatrixFromPackedOutputTexture:()=>qA,downloadPackedMatrixFromBuffer:()=>jA,getInternalFormatForFloat16MatrixTexture:()=>ck,getInternalFormatForFloat16PackedMatrixTexture:()=>mk,getInternalFormatForFloat32MatrixTexture:()=>pk,getInternalFormatForPackedMatrixTexture:()=>hk,getInternalFormatForUnsignedBytesMatrixTexture:()=>dk,uploadDenseMatrixToTexture:()=>BA,uploadPixelDataToTexture:()=>VA});function $A(e){let t=En(),n=`${t.version} + precision highp float; + ${t.attribute} vec3 clipSpacePos; + ${t.attribute} vec2 uv; + ${t.varyingVs} vec2 resultUV; + + void main() { + gl_Position = vec4(clipSpacePos, 1); + resultUV = uv; + }`;return rA(e,n)}function DA(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return lA(e,t)}function RA(e){let t=new Uint16Array([0,1,2,2,1,3]);return uA(e,t)}function Od(e,t,n,a,r,s){cA(t,n);let i=pA(e),o=e.TEXTURE_2D;return de(e,()=>e.bindTexture(o,i)),de(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),de(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),de(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),de(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),G().getNumber("WEBGL_VERSION")===1?de(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)):de(e,()=>e.texStorage2D(o,1,a,t,n)),de(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function pk(e){return e.internalFormatFloat}function MA(e,t,n,a){let[r,s]=Md(t,n);return 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t=this.gl;de(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),WA(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(de(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Yh(this.gl,this.program),de(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?mA(this.gl,e,t):fA(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),de(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return 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this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Zh(a,e,this.framebuffer),this.debug&&cc(a),this.outputTexture=e,de(a,()=>a.viewport(0,0,t,n)),de(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),de(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function oQ(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:ZA(e,t)}function QQ(e,t){if(e===1)return"rc";let n="";for(let a=0;a ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n}; + bool rEdge = rp1 >= ${a}; + `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), + cEdge ? 0. : getA(${t[1]}), + rEdge ? 0. : getA(${t[2]}), + rEdge || cEdge ? 0. : getA(${t[3]})`}},JA=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` + ${r} + ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""} + int flatIndex = getFlatIndex(thisRC); + + ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); + vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); + + result[${a}] = + getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); + ${a>0?"}":""} + `}this.userCode=` + ${tee(t,this.enableShapeUniforms)} + ${this.enableShapeUniforms?lk():ok(e)} + + void main() { + ivec3 rc = getOutputCoords(); + + vec4 result = vec4(0.); + + ivec3 thisRC; + int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]}; + int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]}; + + ${n} + + setOutput(result); + } + `}};function tee(e,t){return` + ivec3 inputCoordsFromReshapedOutCoords(int index) { + ${t?f9(["r","c","d"],"inputShape"):Qo(["r","c","d"],e)} + return ivec3(r, c, d); + } + `}var nee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=CS(t,n),r=ES(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=TS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===hn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===hn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===hn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=CS(n,a),s=ES(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=TS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function aee(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function TS(e,t,n,a,r){let s=ree(t,a),i;if(r){let[l,u]=vp(e[0],e[1]);i=l*u}else{let[l,u]=Md(e[0],e[1]);i=l*u}let o=aee(n,s);return i*o}function ree(e,t){switch(e){case hn.PACKED_2X2_FLOAT32:return hk(t);case hn.PACKED_2X2_FLOAT16:return mk(t);case hn.UNPACKED_FLOAT32:return pk(t);case hn.UNPACKED_FLOAT16:return ck(t);case hn.PACKED_4X1_UNSIGNED_BYTE:return dk(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function see(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?hn.PACKED_2X2_FLOAT32:hn.UNPACKED_FLOAT32:e?hn.PACKED_2X2_FLOAT16:hn.UNPACKED_FLOAT16}function CS(e,t){if(e===da.UPLOAD)return hn.PACKED_2X2_FLOAT32;if(e===da.RENDER||e==null)return see(t);if(e===da.DOWNLOAD||e===da.PIXELS)return hn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function ES(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ir=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + float unaryOperation(float x) { + ${t} + } + + void main() { + float x = getAAtOutCoords(); + float y = unaryOperation(x); + + setOutput(y); + } + `}},Ma="if (isnan(x)) return x;",iee="return x;",_S="return abs(x);",oee="return (x >= 0.0) ? x : (exp(x) - 1.0);",lee=Ma+` + return (x < 0.0) ? 0.0 : x; +`,uee=Ma+` + return (x < 0.0) ? 0.0 : min(6.0, x); +`,es="return x;",pee="return 1.0 / (1.0 + exp(-1.0 * x));",cee="return x;",dee=` + vec4 result; + + result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); + result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); + result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); + result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); + + return result; +`,hee=` + vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); + bvec4 isNaN = isnan(x); + + result.r = isNaN.r ? x.r : result.r; + result.g = isNaN.g ? x.g : result.g; + result.b = isNaN.b ? x.b : result.b; + result.a = isNaN.a ? x.a : result.a; + + return result; +`,mee=` + vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); + bvec4 isNaN = isnan(x); + + result.r = isNaN.r ? x.r : result.r; + result.g = isNaN.g ? x.g : result.g; + result.b = isNaN.b ? x.b : result.b; + result.a = isNaN.a ? x.a : result.a; + + return result; +`,fee="return 1.0 / (1.0 + exp(-1.0 * x));",ss=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + vec4 unaryOperation(vec4 x) { + ${t} + } + + void main() { + vec4 x = getAAtOutCoords(); + vec4 y = unaryOperation(x); + + setOutput(y); + } + `}},gee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=In("rc",t),a=ht(t),r=QQ(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + void main() { + ${a} rc = getOutputCoords(); + vec4 packedInput = getA(${r}); + + setOutput(getChannel(packedInput, ${i})); + } + `}},bee=gr.whereImpl,yee=1e-7,xee=1e-4,Tx={};function vee(e){return e in Tx||(Tx[e]={}),Tx[e]}var wee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),kee=600;function Iee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*kee/1024/1024}var gk=class QA extends Pc{nextDataId(){return QA.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof em)n=t;else{let a=ja(G().getNumber("WEBGL_VERSION"),t);n=new em(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=ja(G().getNumber("WEBGL_VERSION"));n=new em(a),this.binaryCache=vee(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new nee(this.gpgpu),this.numMBBeforeWarning=Iee(),this.texData=new $m(this,Ea())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,a,r,s,i){let o=this.makeTensorInfo(n,a),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=dc(n),p=new NS(u,!1,i),d=this.runWebGLProgram(p,[o],a,[[r,s]]);return d.shape=n,l.texture=null,this.disposeIntermediateTensorInfo(o),d.dataId}write(t,n,a){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),a==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:n,dtype:a,values:t,usage:da.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,a,r,s){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:a,dtype:r,values:n,usage:da.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:a,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=n;if(i!=null){let c;l?c=new ss(o,es):c=new ir(o,es);let h=this.runWebGLProgram(c,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(a!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return a;let u=this.activeTimers!=null,p;u&&(p=w.now());let d;if(r==="complex64"){let c=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);d=T.mergeRealAndImagArrays(c,h)}else d=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=w.now()-p),this.convertAndCacheOnCPU(t,d)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ss(r,es):m=new ir(r,es);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Gh(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ea().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ss(s,es):h=new ir(s,es);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ea().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Pe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=wee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ea().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new gee(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new eee(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[ki(t.shape),...Ii(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[ki(n),...Ii(n)],i=new JA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=dc(s),l;r?l=new nQ(o):l=new tQ(o);let u=!0,p=[n!=null?n:Gh(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Fc.DENSE){let b=i!=null?i:Gh(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!$c(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=eQ(t,p,d),h=this.getAndSaveBinary(c,()=>J9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||Q9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?yee:xee}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=xA(a,l),n.texShape=d),s!=null){let c=dc(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=vp(d[0],d[1])),l?h=new iQ(c,g):h=new NS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=da.PIXELS:x.usage=da.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=See(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await s0(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(ik(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=AA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ea().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. 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NAN : result.g; + result.b = isNaN.b ? NAN : result.b; + result.a = isNaN.a ? NAN : result.a; +`,Tp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=vn(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=` + result.y = 0.; + result.z = 0.; + result.w = 0.; + `;else if(s=` + ${ht(r)} coords = getOutputCoords(); + `,r===1)this.enableShapeUniforms?s+=` + result.y = (coords + 1) >= outShape ? 0. : result.y; + result.z = 0.; + result.w = 0.; + `:s+=` + result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; + result.z = 0.; + result.w = 0.; + `;else{let i=In("coords",r);this.enableShapeUniforms?s+=` + bool nextRowOutOfBounds = + (${i[r-2]} + 1) >= outShape[${r} - 2]; + bool nextColOutOfBounds = + (${i[r-1]} + 1) >= outShape[${r} - 1]; + result.y = nextColOutOfBounds ? 0. : result.y; + result.z = nextRowOutOfBounds ? 0. : result.z; + result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; + `:s+=` + bool nextRowOutOfBounds = + (${i[r-2]} + 1) >= ${this.outputShape[r-2]}; + bool nextColOutOfBounds = + (${i[r-1]} + 1) >= ${this.outputShape[r-1]}; + result.y = nextColOutOfBounds ? 0. : result.y; + result.z = nextRowOutOfBounds ? 0. : result.z; + result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; + `}this.userCode=` + vec4 binaryOperation(vec4 a, vec4 b) { + ${e} + } + + void main() { + vec4 a = getAAtOutCoords(); + vec4 b = getBAtOutCoords(); + + vec4 result = binaryOperation(a, b); + ${s} + + setOutput(result); + } + `}};function aa(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var Cee={kernelName:no,backendName:"webgl",kernelFunc:aa};function Os(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=aa({inputs:{x:a},backend:n}),l=aa({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var Eee={kernelName:Mm,backendName:"webgl",kernelFunc:Os},tF="return (a < 0.) ? b * a : a;",nF=` + vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); + return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); +`;function _ee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(nF,r.shape,i.shape):new Si(tF,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var Aee={kernelName:io,backendName:"webgl",kernelFunc:_ee},aF="return (a < 0.) ? b * a : a;",rF=` + vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); + return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); +`;function Fee(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(rF,a.shape,r.shape):new Si(aF,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var $ee={kernelName:Io,backendName:"webgl",kernelFunc:Fee},Cp="if (isnan(x)) return x;";function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new ss(i.shape,t):p=new ir(i.shape,e),o.runWebGLProgram(p,[i],l)}}function fn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,b]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,I]=x,N={dataId:v.dataId,dtype:v.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},_=new Si(e,l.shape,u.shape);return p.runWebGLProgram(_,[N,C],ga(v.dtype,I.dtype))}),y=Os({inputs:{real:g,imag:b},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(b),y}let d=s||ga(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(m):m,b=l.dtype==="string"?T.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,b,d),v=p.makeTensorInfo(x,d),I=p.texData.get(v.dataId);return I.values=y,v}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Tp(t,l.shape,u.shape,n):h=new Si(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Dc(e,t=!1){if(e==="linear")return t?cee:iee;if(e==="relu")return t?hee:lee;if(e==="elu")return t?dee:oee;if(e==="relu6")return t?mee:uee;if(e==="prelu")return t?rF:aF;if(e==="leakyrelu")return t?nF:tF;if(e==="sigmoid")return t?fee:pee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var sF=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=vn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { + vec4 b = getPreluActivationWeightsAtOutCoords(); + ${i} + }`:l?f=`vec4 activation(vec4 a) { + vec4 b = getLeakyreluAlphaAtOutCoords(); + ${i} + }`:f=`vec4 activation(vec4 x) { + ${i} + }`,g="result = activation(result);");let b=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let y="rc.x",x="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. 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+ vec4 values = vec4( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), + getValue(batch, inIdx + 3) + ); + + ${l} + } + + int inIdx = inOffset + ${i}; + if (${o===1}) { + vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0); + + ${l} + } else if (${o===2}) { + vec4 values = vec4( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), 0.0, 0.0); + + ${l} + } else if (${o===3}) { + vec4 values = vec4( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), 0.0); + + ${l} + } + setOutput(sumValue); + } + `}},Oee=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,p=n%4,d=` + if (${t==="sum"}) { + sumValue += dot(values, ones); + } else if (${t==="prod"}) { + vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]); + prodValue *= tmp[0] * tmp[1]; + } else { + minMaxValue = ${o}(values, minMaxValue); + if (${t==="min"} || ${t==="max"}) { + minMaxValue = ${o}(values, minMaxValue); + bvec4 isNaN = isnan(values); + if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) { + minMaxValue = vec4(NAN); + } + } + } + `,c="vec4";t==="all"?(i="1.0",d=` + bool reducedAllValue = all(values); + float floatedReducedAllValue = float(reducedAllValue); + allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); + `,c="bvec4"):t==="any"&&(i="0.0",d=` + bool reducedAnyValue = any(values); + float floatedReducedAnyValue = float(reducedAnyValue); + anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); + `,c="bvec4");let h="";r%n>0&&(h=` + if (inIdx < 0 || inIdx >= ${r}) { + return initializationValue; + } + `),this.userCode=` + const float initializationValue = ${i}; + const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); + + float getValue(int batch, int inIdx) { + ${h} + return getX(batch, inIdx); + } + + void main() { + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + int outIdx = coords[1]; + int inOffset = outIdx * ${n}; + + vec4 minMaxValue = vec4(${i}); + float prodValue = 1.0; + float sumValue = 0.0; + float allValue = 1.0; + float anyValue = 0.0; + + for (int i = 0; i < ${u}; i += 4) { + int inIdx = inOffset + i; + ${c} values = ${c}( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), + getValue(batch, inIdx + 3) + ); + + ${d} + } + + int inIdx = inOffset + ${u}; + if (${p===1}) { + ${c} values = ${c}( + getValue(batch, inIdx), + initializationValue, + initializationValue, + initializationValue + ); + + ${d} + } else if (${p===2}) { + ${c} values = ${c}( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + initializationValue, + initializationValue + ); + + ${d} + } else if (${p===3}) { + ${c} values = ${c}( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), + initializationValue + ); + + ${d} + } + setOutput(${l}); + } + `}};function Pee(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=T.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function nl(e,t,n,a){let r=Pee(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ht(this.rank),r=ZA("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],N=ce({inputs:{x:e},backend:r,attrs:{shape:v}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),_=[N,C],F=Math.max(b,y),D=n?N.shape[1]:N.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?Dc(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>iF&&U===!1){let K=N,Z=C;n&&(K=Sn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),_.push(K)),a&&(Z=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),_.push(ae));let te=m===1?2:1,se=Z;ee&&(se=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),_.push(se));let ie=yk({inputs:{a:ae,b:se},backend:r});H=ng({inputs:{x:ie},backend:r,attrs:{axis:te,keepDims:!0}}),_.push(ie)}else{let K=ga(e.dtype,t.dtype),Z=new sF(v,I,[F,h,m],n,a,$,B,S,M),J=[N,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));J.push(ee),_.push(ee)}H=r.runWebGLProgram(Z,J,K)}let q=ce({inputs:{x:H},backend:r,attrs:{shape:x}});_.push(H);for(let K of _)r.disposeIntermediateTensorInfo(K);return q}function Gee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return Cm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var Hee={kernelName:li,backendName:"webgl",kernelFunc:Gee},RS="return abs(x);";function jee(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=XA(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,RS):r=new ir(a.shape,RS),n.runWebGLProgram(r,[a],a.dtype)}var qee={kernelName:uu,backendName:"webgl",kernelFunc:jee},Kee=Ma+` + if (abs(x) > 1.) { + return NAN; + } + return acos(x); +`,Xee=Ze({opSnippet:Kee}),Yee={kernelName:Ci,backendName:"webgl",kernelFunc:Xee},Zee=Ma+` + if (x < 1.0) return NAN; +return log(x + sqrt(x * x - 1.0));`,Jee=Ze({opSnippet:Zee}),Qee={kernelName:Ei,backendName:"webgl",kernelFunc:Jee},MS="return a + b;",ete=fn({opSnippet:MS,packedOpSnippet:MS,supportsComplex:!0,cpuKernelImpl:lQ}),tte={kernelName:Ss,backendName:"webgl",kernelFunc:ete},nte=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + void main() { + ${n.join(` + `)} + + float result = ${a}; + setOutput(result); + } + `}},ate=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + void main() { + ${n.join(` + `)} + + vec4 result = ${a}; + setOutput(result); + } + `}};function tm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return aa({inputs:{x:a[0]},backend:n});if(a.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=tm({inputs:a.slice(0,o),backend:n}),u=tm({inputs:a.slice(o),backend:n});return tm({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ga(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new ate(a[0].shape,s):new nte(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var rte={kernelName:_i,backendName:"webgl",kernelFunc:tm};function ste(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"all",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var ite={kernelName:pu,backendName:"webgl",kernelFunc:ste};function ote(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"any",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var lte={kernelName:cu,backendName:"webgl",kernelFunc:ote},ute=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + void main() { + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + int outIdx = coords[1]; + int inOffset = outIdx * ${a}; + + int bestIndex = inOffset; + float bestValue = getA(batch, bestIndex); + + for (int i = 0; i < ${a}; i++) { + int inIdx = ${o}; + float candidate = getA(batch, inIdx); + if (candidate ${i} bestValue) { + bestValue = candidate; + bestIndex = inIdx; + } + } + setOutput(float(bestIndex)); + } + `}},pte=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ht(o),u=In("coords",o),p,d;if(s===1){d=o+1;let C=ht(d);p=` + ${C} sourceLocR = ${C}(${u.join()}, 0); + ++${u[o-1]}; + ${C} sourceLocG = ${C}(${u.join()}, 0); + ++${u[o-2]}; + ${C} sourceLocA = ${C}(${u.join()}, 0); + --${u[o-1]}; + ${C} sourceLocB = ${C}(${u.join()}, 0); + --${u[o-2]};`}else d=o,p=` + ${l} sourceLocR = coords; + ++${u[o-1]}; + ${l} sourceLocG = coords; + ++${u[o-2]}; + ${l} sourceLocA = coords; + --${u[o-1]}; + ${l} sourceLocB = coords; + --${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,d),h="."+c[d-1],m=c.map(C=>"int "+C),f=In("sourceLocR",d-1).concat("inIdx.r"),g=In("sourceLocG",d-1).concat("inIdx.g"),b=In("sourceLocB",d-1).concat("inIdx.b"),y=In("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":` + inIdx = round(vec4(getBestIndicesAChannel(${f.join()}), + getBestIndicesAChannel(${g.join()}), + getBestIndicesAChannel(${b.join()}), + getBestIndicesAChannel(${y.join()})));`,I=`vec4( + getAChannel(${f.join()}), + hasNextCol ? getAChannel(${g.join()}) : 0., + hasNextRow ? getAChannel(${b.join()}) : 0., + hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,N=a?"":` + float getBestIndicesAChannel(${m.join()}) { + return getChannel(getBestIndicesA(${c.join()}), + vec2(${c.slice(-2).join()})); + }`;this.userCode=` + float getAChannel(${m.join()}) { + return getChannel(getA(${c.join()}), + vec2(${c.slice(-2).join()})); + } + ${N} + void main() { + ${l} coords = getOutputCoords(); + bool hasNextCol = ${u[o-1]} < ${i[o-1]-1}; + bool hasNextRow = ${u[o-2]} < ${i[o-2]-1}; + ${p} + ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h}, + sourceLocB${h}, sourceLocA${h}) * ${t}; + ivec4 inIdx = srcIdx; + vec4 bestIndex = vec4(inIdx); + vec4 bestValue = ${I}; + + for (int i = 0; i < ${t}; i++) { + inIdx = srcIdx; + ${v} + vec4 candidate = ${I}; + bvec4 nan = isnan(candidate); + bvec4 replace = bvec4( + vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); + + bestValue = vec4(replace.x ? candidate.x : bestValue.x, + replace.y ? candidate.y : bestValue.y, + replace.z ? candidate.z : bestValue.z, + replace.w ? candidate.w : bestValue.w); + bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace)); + srcIdx++; + } + setOutput(bestIndex); + } + `}};function oF(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new ute(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=oF(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function lF(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new pte(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=lF(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function uF(e,t,n,a){let r=[n];if(T.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=T.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(p),c=ce({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=oF(e,c,a);s.push(h);let m=ce({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return lF(e,t,a)}function cte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=uF(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var dte={kernelName:du,backendName:"webgl",kernelFunc:cte};function hte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=uF(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var mte={kernelName:hu,backendName:"webgl",kernelFunc:hte},fte=Ma+` + if (abs(x) > 1.) { + return NAN; + } + return asin(x); +`,gte=Ze({opSnippet:fte}),bte={kernelName:Ai,backendName:"webgl",kernelFunc:gte},yte=Ma+"return log(x + sqrt(x * x + 1.0));",xte=Ze({opSnippet:yte}),vte={kernelName:Fi,backendName:"webgl",kernelFunc:xte},wte=Ma+` + return atan(x); +`,kte=Ze({opSnippet:wte}),Ite={kernelName:$i,backendName:"webgl",kernelFunc:kte},Ste=bk+` + return atan(a, b); +`,Nte=` + vec4 result = atan(a, b); + bvec4 isNaNA = isnan(a); + bvec4 isNaNB = isnan(b); + bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); + `+tl+` + return result; +`,Tte=fn({opSnippet:Ste,packedOpSnippet:Nte}),Cte={kernelName:Ri,backendName:"webgl",kernelFunc:Tte},Ete=Ma+` + if ((x < -1.0) || (x > 1.0)) return NAN; +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_te=Ze({opSnippet:Ete}),Ate={kernelName:Di,backendName:"webgl",kernelFunc:_te},Rc=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(m||(b="-1.0 / 1e-20"),n){let C=">=";this.userCode=` + const ivec2 strides = ivec2(${i}, ${o}); + const ivec2 pads = ivec2(${c}, ${h}); + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d = coords[3]; + + ivec2 xRCCorner = coords.yz * strides - pads; + int xRCorner = xRCCorner.x; + int xCCorner = xRCCorner.y; + + // max/min x(?, ?, d) to get y(yR, yC, d). + // ? = to be determined + float minMaxValue = 0.0; + float minMaxValueFound = 0.0; + int minMaxPosition = 0; + float avgValue = 0.0; + + for (int wR = 0; wR < ${p}; + wR += ${l}) { + int xR = xRCorner + wR; + + if (xR < 0 || xR >= ${e.inHeight}) { + continue; + } + + for (int wC = 0; wC < ${d}; + wC += ${u}) { + int xC = xCCorner + wC; + + if (xC < 0 || xC >= ${e.inWidth}) { + continue; + } + + float value = getX(batch, xR, xC, d); + + // If a min / max value has already been found, use it. If not, + // use the current value. + float currMinMaxValue = mix( + value, minMaxValue, minMaxValueFound); + if (value ${C} currMinMaxValue) { + minMaxValue = value; + minMaxValueFound = 1.0; + minMaxPosition = ${a?r?f:g:`wR * ${d} + wC`}; + } + } + } + setOutput(float(minMaxPosition)); + } + `;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / max(count, 1.0)");let v=Math.floor(s/4)*4,I=s%4,N=` + if (${m}) { + avgValue += dot(values, ones); + } else { + minMaxValue = ${y}(values, minMaxValue); + } + `;this.userCode=` + const ivec2 strides = ivec2(${i}, ${o}); + const ivec2 pads = ivec2(${c}, ${h}); + const float initializationValue = ${b}; + const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); + + float count = 0.0; + + float getValue(int batch, int xR, int xC, int d) { + if (xC < 0 || xC >= ${e.inWidth}) { + return initializationValue; + } + count += 1.0; + return getX(batch, xR, xC, d); + } + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d = coords[3]; + + ivec2 xRCCorner = coords.yz * strides - pads; + int xRCorner = xRCCorner.x; + int xCCorner = xRCCorner.y; + + // max/min x(?, ?, d) to get y(yR, yC, d). + // ? = to be determined + vec4 minMaxValue = vec4(${b}); + float avgValue = 0.0; + count = 0.0; + + for (int wR = 0; wR < ${p}; + wR += ${l}) { + int xR = xRCorner + wR; + + if (xR < 0 || xR >= ${e.inHeight}) { + continue; + } + + for (int wC = 0; wC < ${v}; wC += 4) { + int xC = xCCorner + wC * ${u}; + + vec4 values = vec4( + getValue(batch, xR, xC, d), + getValue(batch, xR, xC + ${u}, d), + getValue(batch, xR, xC + 2 * ${u}, d), + getValue(batch, xR, xC + 3 * ${u}, d) + ); + + ${N} + } + + int xC = xCCorner + ${v}; + if (${I===1}) { + vec4 values = vec4( + getValue(batch, xR, xC, d), + initializationValue, + initializationValue, + initializationValue + ); + + ${N} + } else if (${I===2}) { + vec4 values = vec4( + getValue(batch, xR, xC, d), + getValue(batch, xR, xC + ${u}, d), + initializationValue, + initializationValue + ); + + ${N} + } else if (${I===3}) { + vec4 values = vec4( + getValue(batch, xR, xC, d), + getValue(batch, xR, xC + ${u}, d), + getValue(batch, xR, xC + 2 * ${u}, d), + initializationValue + ); + + ${N} + } + } + setOutput(${x}); + } + `}},xk=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let F=">=";this.userCode=` + const ivec3 strides = + ivec3(${i}, ${o}, ${l}); + const ivec3 pads = ivec3(${f}, ${g}, ${b}); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int ch = coords.u; + + ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; + int xDCorner = xCorner.x; + int xRCorner = xCorner.y; + int xCCorner = xCorner.z; + + // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). + // ? = to be determined + float minMaxValue = 0.0; + float minMaxValueFound = 0.0; + int minMaxPosition = 0; + + for (int wD = 0; wD < ${c}; + wD += ${u}) { + int xD = xDCorner + wD; + + if (xD < 0 || xD >= ${e.inDepth}) { + continue; + } + + for (int wR = 0; wR < ${h}; + wR += ${p}) { + int xR = xRCorner + wR; + + if (xR < 0 || xR >= ${e.inHeight}) { + continue; + } + + for (int wC = 0; wC < ${m}; + wC += ${d}) { + int xC = xCCorner + wC; + + if (xC < 0 || xC >= ${e.inWidth}) { + continue; + } + + float value = getX(batch, xD, xR, xC, ch); + + // If a min / max value has already been found, use it. If not, + // use the current value. + float currMinMaxValue = mix( + value, minMaxValue, minMaxValueFound); + if (value ${F} currMinMaxValue) { + minMaxValue = value; + minMaxValueFound = 1.0; + minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} + + wR * ${m} + wC`}; + } + } + } + } + setOutput(float(minMaxPosition)); + } + `;return}let v="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / max(count, 1.0)");let N=Math.floor(s/4)*4,C=s%4,_=` + if (${y}) { + avgValue += dot(values, ones); + } else { + minMaxValue = ${v}(values, minMaxValue); + } + `;this.userCode=` + const ivec3 strides = + ivec3(${i}, ${o}, ${l}); + const ivec3 pads = ivec3(${f}, ${g}, ${b}); + const float initializationValue = ${x}; + const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); + + float count = 0.0; + + float getValue(int batch, int xD, int xR, int xC, int ch) { + if (xC < 0 || xC >= ${e.inWidth}) { + return initializationValue; + } + count += 1.0; + return getX(batch, xD, xR, xC, ch); + } + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int ch = coords.u; + + ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; + int xDCorner = xCorner.x; + int xRCorner = xCorner.y; + int xCCorner = xCorner.z; + + // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). + // ? = to be determined + vec4 minMaxValue = vec4(${x}); + float avgValue = 0.0; + count = 0.0; + + for (int wD = 0; wD < ${c}; + wD += ${u}) { + int xD = xDCorner + wD; + + if (xD < 0 || xD >= ${e.inDepth}) { + continue; + } + + for (int wR = 0; wR < ${h}; + wR += ${p}) { + int xR = xRCorner + wR; + + if (xR < 0 || xR >= ${e.inHeight}) { + continue; + } + + for (int wC = 0; wC < ${N}; wC += 4) { + int xC = xCCorner + wC * ${d}; + + vec4 values = vec4( + getValue(batch, xD, xR, xC, ch), + getValue(batch, xD, xR, xC + ${d}, ch), + getValue(batch, xD, xR, xC + 2 * ${d}, ch), + getValue(batch, xD, xR, xC + 3 * ${d}, ch) + ); + + ${_} + } + + int xC = xCCorner + ${N}; + if (${C===1}) { + vec4 values = vec4( + getValue(batch, xD, xR, xC, ch), + initializationValue, + initializationValue, + initializationValue + ); + + ${_} + } else if (${C===2}) { + vec4 values = vec4( + getValue(batch, xD, xR, xC, ch), + getValue(batch, xD, xR, xC + ${d}, ch), + initializationValue, + initializationValue + ); + + ${_} + } else if (${C===3}) { + vec4 values = vec4( + getValue(batch, xD, xR, xC, ch), + getValue(batch, xD, xR, xC + ${d}, ch), + getValue(batch, xD, xR, xC + 2 * ${d}, ch), + initializationValue + ); + + ${_} + } + } + } + setOutput(${I}); + } + `}};function Fte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;wp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Rc(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var $te={kernelName:Mi,backendName:"webgl",kernelFunc:Fte};function Dte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new xk(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Rte={kernelName:mu,backendName:"webgl",kernelFunc:Dte},Mte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` + const ivec2 pads = ivec2(${u}, ${p}); + const float avgMultiplier = float(${d}); + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + + ivec2 dyRCCorner = coords.yz - pads; + int dyRCorner = dyRCCorner.x; + int dyCCorner = dyRCCorner.y; + + // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + for (int wR = 0; wR < ${o}; + wR += ${s}) { + float dyR = float(dyRCorner + wR) / ${a}.0; + + if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + for (int wC = 0; wC < ${l}; + wC+= ${i}) { + float dyC = float(dyCCorner + wC) / ${r}.0; + + if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + float dyValue = getDy(b, idyR, idyC, d); + + dotProd += dyValue * avgMultiplier; + } + } + setOutput(dotProd); + } + `}},Ote=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=` + const ivec3 pads = ivec3(${h}, ${m}, ${f}); + const float avgMultiplier = float(${g}); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int ch = coords.u; + + ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; + int dyDCorner = dyCorner.x; + int dyRCorner = dyCorner.y; + int dyCCorner = dyCorner.z; + + // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get + // dx(xD, xR, xC, ch). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + + for (int wD = 0; wD < ${p}; + wD += ${o}) { + float dyD = float(dyDCorner + wD) / ${r}.0; + + if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { + continue; + } + int idyD = int(dyD); + + for (int wR = 0; wR < ${d}; + wR += ${l}) { + float dyR = float(dyRCorner + wR) / ${s}.0; + + if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || + fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + for (int wC = 0; wC < ${c}; + wC += ${u}) { + float dyC = float(dyCCorner + wC) / ${i}.0; + + if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + float dyValue = getDy(batch, idyD, idyR, idyC, ch); + + dotProd += dyValue * avgMultiplier; + } + } + } + setOutput(dotProd); + } + `}};function Pte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new Ote(c);return n.runWebGLProgram(h,[r],i.dtype)}var Lte={kernelName:Wc,backendName:"webgl",kernelFunc:Pte};function zte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;wp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=new Mte(p);return n.runWebGLProgram(d,[r],i.dtype)}var Wte={kernelName:zc,backendName:"webgl",kernelFunc:zte};function Bte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Cm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Vte={kernelName:Oi,backendName:"webgl",kernelFunc:Bte},Ute=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + void main() { + float x = getXAtOutCoords(); + float mean = getMeanAtOutCoords(); + float variance = getVarianceAtOutCoords(); + float offset = ${i}; + float scale = ${o}; + float inv = scale * inversesqrt(variance + float(${s})); + setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); + } + `}},Gte=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + void main() { + vec4 offset = ${i}; + vec4 scale = ${o}; + + vec4 x = getXAtOutCoords(); + vec4 mean = getMeanAtOutCoords(); + vec4 variance = getVarianceAtOutCoords(); + + vec4 inv = scale * inversesqrt(variance + vec4(${s})); + + setOutput((x - mean) * inv + offset); + } + `}},Hte=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new Gte(a.shape,r.shape,s.shape,p,d,l):new Ute(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},jte={kernelName:eo,backendName:"webgl",kernelFunc:Hte},qte=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Kte(this.rank),a,r=e.map((s,i)=>`sourceLoc.${kv[i]} = start[${i}] + coords.${kv[i]};`);a=` + ${t} sourceLoc; + ${t} coords = getOutputCoords(); + ${r.join(` +`)} + `,this.userCode=` + void main() { + ${a} + setOutput(getSource(${n})); + } + `}},kv=["x","y","z","w","u","v"];function Kte(e){if(e===1)return"sourceLoc";if(e<=6)return kv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Xte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ht(this.rank),n=In("coords",this.rank),a=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` + result.x = ${s}; + if (++${n[this.rank-1]} < ${e[this.rank-1]}) { + ++${a[this.rank-1]}; + result.y = ${s}; + --${a[this.rank-1]}; + } + `,o=this.rank===1?"":` + --${n[this.rank-1]}; + if (++${n[this.rank-2]} < ${e[this.rank-2]}) { + ++${a[this.rank-2]}; + result.z = ${s}; + if (++${n[this.rank-1]} < ${e[this.rank-1]}) { + ++${a[this.rank-1]}; + result.w = ${s}; + } + } + `,l=this.rank<=4?`sourceLoc = coords + + ${t}(${e.map((u,p)=>`start[${p}]`).join()});`:e.map((u,p)=>`${a[p]} = ${n[p]} + start[${p}];`).join(` +`);this.userCode=` + void main() { + ${t} coords = getOutputCoords(); + ${t} sourceLoc; + ${l} + vec4 result = vec4(0.); + ${i} + ${o} + setOutput(result); + } + `}};function Yte(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=Kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Ep(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=WQ(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xte(l):new qte(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),Yte(r,o,l,n)}var Zte={kernelName:ep,backendName:"webgl",kernelFunc:Ep},Jte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=[],m=ce({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=ce({inputs:{x:f},backend:n,attrs:{shape:p}}),b=Ep({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},Qte={kernelName:fu,backendName:"webgl",kernelFunc:Jte};function ene(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=KA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var tne={kernelName:gu,backendName:"webgl",kernelFunc:ene},nne=` + int r = int(a.r) & int(b.r); + int g = int(a.g) & int(b.g); + int rb = int(a.b) & int(b.b); + int ra = int(a.a) & int(b.a); + return vec4(r, g, rb, ra); +`,ane=` + return float(int(a.r) & int(b.r)); +`;function rne(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=G().getNumber("WEBGL_VERSION");if(n.shouldExecuteOnCPU([a,r])||i===1){let l=n.texData.get(a.dataId).values,u=n.texData.get(r.dataId).values,[p,d]=pQ(a.shape,r.shape,l,u,a.dtype),c=n.makeTensorInfo(d,a.dtype),h=n.texData.get(c.dataId);return h.values=p,c}let o;return s?o=new Tp(nne,a.shape,r.shape,!1):o=new Si(ane,a.shape,r.shape),n.runWebGLProgram(o,[a,r],a.dtype)}var sne={kernelName:bu,backendName:"webgl",kernelFunc:rne};function ine(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var one={kernelName:Bc,backendName:"webgl",kernelFunc:ine},lne="return float(a != b);",pF=fn({opSnippet:lne,cpuKernelImpl:FQ,dtype:"bool"}),une={kernelName:Bu,backendName:"webgl",kernelFunc:pF};function Pd(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return aa({inputs:{x:r.complexTensorInfos.real},backend:n})}var pne={kernelName:qm,backendName:"webgl",kernelFunc:Pd},cne="return float(int(x));";function dne(e,t){let n=new ir(e.shape,cne),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Iv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return aa({inputs:{x:r},backend:n});let i=It(r.shape),o=Iv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Os({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Pd({inputs:{input:r},backend:n}),o=Iv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,s)){let i=aa({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=cQ(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return dne(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=pF({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var hne={kernelName:Pi,backendName:"webgl",kernelFunc:Iv},OS="return ceil(x);",mne=Ze({opSnippet:OS,packedOpSnippet:OS,cpuKernelImpl:dQ}),fne={kernelName:Li,backendName:"webgl",kernelFunc:mne},gne=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + + void main() { + float value = getAAtOutCoords(); + if (isnan(value)) { + setOutput(value); + return; + } + + setOutput(clamp(value, minVal, maxVal)); + } + `}},bne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + void main() { + vec4 value = getAAtOutCoords(); + + if (any(isnan(value))) { + setOutput(value); + return; + } + + setOutput(clamp(value, vec4(minVal), vec4(maxVal))); + } + `}};function yne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;G().getBool("WEBGL_PACK_CLIP")?o=new bne(r.shape):o=new gne(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var xne={kernelName:Ns,backendName:"webgl",kernelFunc:yne},vne=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + void main() { + float re = abs(getRealAtOutCoords()); + float im = abs(getImagAtOutCoords()); + float mx = max(re, im); + + // sadly the length function in glsl is not underflow-safe + // (at least not on Intel GPUs). So the safe solution is + // to ensure underflow-safety in all cases. + setOutput( + mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) + ); + } + `}};function PS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function wne(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new vne(a.shape),i=[PS(a,r.complexTensorInfos.real),PS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var kne={kernelName:Vc,backendName:"webgl",kernelFunc:wne},Ine=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m= ${o[m-1]}) { + return getChannel( + getT${m}(${jh(i,l,f)}), + vec2(${jh(u,l,f)})); + }`}let c=o.length,h=o[o.length-1];d+=` + return getChannel( + getT${c}(${jh(i,l,h)}), + vec2(${jh(u,l,h)}));`,this.userCode=` + float getValue(${i.map(m=>"int "+m)}) { + ${d} + } + + void main() { + ${r} coords = getOutputCoords(); + vec4 result = vec4(getValue(${s}), 0., 0., 0.); + + ${s[a-1]} = ${s[a-1]} + 1; + if (${s[a-1]} < ${n[a-1]}) { + result.g = getValue(${s}); + } + + ${s[a-2]} = ${s[a-2]} + 1; + if (${s[a-2]} < ${n[a-2]}) { + result.a = getValue(${s}); + } + + ${s[a-1]} = ${s[a-1]} - 1; + if (${s[a-2]} < ${n[a-2]} && + ${s[a-1]} < ${n[a-1]}) { + result.b = getValue(${s}); + } + setOutput(result); + } + `}};function jh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function ag(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return aa({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Nne={kernelName:Um,backendName:"webgl",kernelFunc:ag};function hc(e,t,n){let a=e[0].dtype;if(a==="complex64"){let h=e.map(y=>Pd({inputs:{input:y},backend:n})),m=e.map(y=>ag({inputs:{input:y},backend:n})),f=hc(h,t,n),g=hc(m,t,n),b=Os({inputs:{real:f,imag:g},backend:n});return h.forEach(y=>n.disposeIntermediateTensorInfo(y)),m.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),b}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let h=e.map(v=>{let I=[-1,w.sizeFromShape(v.shape.slice(t))];return ce({inputs:{x:v},backend:n,attrs:{shape:I}})}),m=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape})),f=T.computeOutShape(h.map(v=>v.shape),1),g=h[0].shape[0]===1,b=hQ(m,f,a,g),y=T.computeOutShape(e.map(v=>v.shape),t),x=n.makeTensorInfo(y,a,b);return h.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}let s=e.filter(h=>w.sizeFromShape(h.shape)>0),i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new ir(e[0].shape,es):new ss(e[0].shape,es);return n.runWebGLProgram(h,e,a)}let o=G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return n.runWebGLProgram(h,s,a)}let{tensors2D:l,outShape:u}=Tne(s,t,n),p=new Ine(l.map(h=>h.shape)),d=n.runWebGLProgram(p,l,a);l.forEach(h=>n.disposeIntermediateTensorInfo(h));let c=ce({inputs:{x:d},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(d),c}function Tne(e,t,n){let a=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function cF(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>w.sizeFromShape(u.shape)>0);return l.length===1?aa({inputs:{x:l[0]},backend:n}):hc(l,s,n)}var Cne={kernelName:yu,backendName:"webgl",kernelFunc:cF},dF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,b=f?2:3,y=f?3:1,x="",v="";n&&(a?x=`float activation(float a) { + float b = getPreluActivationWeightsAtOutCoords(); + ${n} + }`:r?x=`float activation(float a) { + float b = getLeakyreluAlphaAtOutCoords(); + ${n} + }`:x=` + float activation(float x) { + ${n} + } + `,v="result = activation(result);");let I=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` + ${x} + + const ivec2 strides = ivec2(${o}, ${l}); + const ivec2 pads = ivec2(${s}, ${i}); + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d2 = coords[${y}]; + + ivec2 xRCCorner = + ivec2(coords[${g}], coords[${b}]) * strides - pads; + int xRCorner = xRCCorner.x; + int xCCorner = xRCCorner.y; + + // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + for (int wR = 0; wR < ${d}; wR++) { + int xR = xRCorner + wR * ${u}; + + if (xR < 0 || xR >= ${e.inHeight}) { + continue; + } + + for (int wC = 0; wC < ${c}; wC++) { + int xC = xCCorner + wC * ${p}; + + if (xC < 0 || xC >= ${e.inWidth}) { + continue; + } + + for (int d1 = 0; d1 < ${h}; d1 += 4) { + vec4 wValues = vec4( + getW(wR, wC, d1, d2), + getW(wR, wC, d1 + 1, d2), + getW(wR, wC, d1 + 2, d2), + getW(wR, wC, d1 + 3, d2) + ); + + if (${f}) { + vec4 xValues = vec4( + getX(batch, xR, xC, d1), + getX(batch, xR, xC, d1 + 1), + getX(batch, xR, xC, d1 + 2), + getX(batch, xR, xC, d1 + 3) + ); + dotProd += dot(xValues, wValues); + } else { + vec4 xValues = vec4( + getX(batch, d1, xR, xC), + getX(batch, d1 + 1, xR, xC), + getX(batch, d1 + 2, xR, xC), + getX(batch, d1 + 3, xR, xC) + ); + dotProd += dot(xValues, wValues); + } + } + + if (${m===1}) { + + if (${f}) { + dotProd += + getX(batch, xR, xC, ${h}) * + getW(wR, wC, ${h}, d2); + } else { + dotProd += + getX(batch, ${h}, xR, xC) * + getW(wR, wC, ${h}, d2); + } + + } else if (${m===2}) { + vec2 wValues = vec2( + getW(wR, wC, ${h}, d2), + getW(wR, wC, ${h} + 1, d2) + ); + + if (${f}) { + vec2 xValues = vec2( + getX(batch, xR, xC, ${h}), + getX(batch, xR, xC, ${h} + 1) + ); + dotProd += dot(xValues, wValues); + } else { + vec2 xValues = vec2( + getX(batch, ${h}, xR, xC), + getX(batch, ${h} + 1, xR, xC) + ); + dotProd += dot(xValues, wValues); + } + + } else if (${m===3}) { + vec3 wValues = vec3( + getW(wR, wC, ${h}, d2), + getW(wR, wC, ${h} + 1, d2), + getW(wR, wC, ${h} + 2, d2) + ); + + if (${f}) { + vec3 xValues = vec3( + getX(batch, xR, xC, ${h}), + getX(batch, xR, xC, ${h} + 1), + getX(batch, xR, xC, ${h} + 2) + ); + dotProd += dot(xValues, wValues); + } else { + vec3 xValues = vec3( + getX(batch, ${h}, xR, xC), + getX(batch, ${h} + 1, xR, xC), + getX(batch, ${h} + 2, xR, xC) + ); + dotProd += dot(xValues, wValues); + } + + } + } + } + + float result = dotProd; + ${I} + ${v} + setOutput(result); + } + `}},Ene=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` + const ivec3 strides = ivec3(${r}, ${s}, ${i}); + const ivec3 pads = ivec3(${t}, ${n}, ${a}); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int d2 = coords.u; + + ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; + int xFCorner = xFRCCorner.x; + int xRCorner = xFRCCorner.y; + int xCCorner = xFRCCorner.z; + + // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get + // y(yF, yR, yC, d2). ? = to be determined. : = across all + // values in that axis. + float dotProd = 0.0; + for (int wF = 0; wF < ${p}; wF++) { + int xF = xFCorner + wF * ${o}; + + if (xF < 0 || xF >= ${e.inDepth}) { + continue; + } + + for (int wR = 0; wR < ${d}; wR++) { + int xR = xRCorner + wR * ${l}; + + if (xR < 0 || xR >= ${e.inHeight}) { + continue; + } + + for (int wC = 0; wC < ${c}; wC++) { + int xC = xCCorner + wC * ${u}; + + if (xC < 0 || xC >= ${e.inWidth}) { + continue; + } + + for (int d1 = 0; d1 < ${h}; d1 += 4) { + vec4 xValues = vec4( + getX(batch, xF, xR, xC, d1), + getX(batch, xF, xR, xC, d1 + 1), + getX(batch, xF, xR, xC, d1 + 2), + getX(batch, xF, xR, xC, d1 + 3) + ); + vec4 wValues = vec4( + getW(wF, wR, wC, d1, d2), + getW(wF, wR, wC, d1 + 1, d2), + getW(wF, wR, wC, d1 + 2, d2), + getW(wF, wR, wC, d1 + 3, d2) + ); + + dotProd += dot(xValues, wValues); + } + + if (${m===1}) { + dotProd += + getX(batch, xF, xR, xC, ${h}) * + getW(wF, wR, wC, ${h}, d2); + } else if (${m===2}) { + vec2 xValues = vec2( + getX(batch, xF, xR, xC, ${h}), + getX(batch, xF, xR, xC, ${h} + 1) + ); + vec2 wValues = vec2( + getW(wF, wR, wC, ${h}, d2), + getW(wF, wR, wC, ${h} + 1, d2) + ); + dotProd += dot(xValues, wValues); + } else if (${m===3}) { + vec3 xValues = vec3( + getX(batch, xF, xR, xC, ${h}), + getX(batch, xF, xR, xC, ${h} + 1), + getX(batch, xF, xR, xC, ${h} + 2) + ); + vec3 wValues = vec3( + getW(wF, wR, wC, ${h}, d2), + getW(wF, wR, wC, ${h} + 1, d2), + getW(wF, wR, wC, ${h} + 2, d2) + ); + dotProd += dot(xValues, wValues); + } + } + } + } + setOutput(dotProd); + } + `}},hF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` + int xR; int xC; int xCOffset; + vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=0 && xR < inDims[0]) { + `;for(let f=0;f<(p+1)/2;f++){let g=f*2;if(d+=` + xC = xCCorner + ${g*o}; + `,i===1){if(g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { + xTexelC${g} = getX(batch, xR, xCOffset, d1); + + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xCOffset + 1 >= inDims[1]) { + xTexelC${g}.zw = vec2(0.0); + } + xTexelC${g}Ready = 1; + } + `,o===1&&g>0?d+=` + xC${g} = vec4(xTexelC${g-2}.zw, xTexelC${g}.xy); + `:d+=` + xCOffset = xC + 1 - 2; + + if (xCOffset >= 0 && xCOffset < inDims[1]) { + previous = getX(batch, xR, xCOffset, d1); + + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xCOffset + 1 >= inDims[1]) { + previous.zw = vec2(0.0); + } + + xC${g} = vec4(previous.zw, xTexelC${g}.xy); + } else { + xC${g} = vec4(0.0, 0.0, xTexelC${g}.xy); + } + `):d+=` + if (xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { + xTexelC${g} = getX(batch, xR, xC, d1); + if (xC + 1 >= inDims[1]) { + xTexelC${g}.zw = vec2(0.0); + } + xTexelC${g}Ready = 1; + } + + xC${g} = xTexelC${g}; + `,g+1= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { + xTexelC${g+1} = getX(batch, xR, xCOffset, d1); + + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xCOffset + 1 >= inDims[1]) { + xTexelC${g+1}.zw = vec2(0.0); + } + xTexelC${g+1}Ready = 1; + } + `,o>1?d+=` + xCOffset -= 2; + if (xCOffset >= 0 && xCOffset < inDims[1]) { + previous = getX(batch, xR, xCOffset, d1); + xC${g+1} = vec4(previous.zw, xTexelC${g+1}.xy); + } else { + xC${g+1} = vec4(0.0, 0.0, xTexelC${g+1}.xy); + } + `:d+=` + xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.xy); + `):b===1?d+=` + xC${g+1} = xTexelC${g}; + `:d+=` + xCOffset = xC + ${b}; + + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { + xTexelC${g+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset + 1 >= inDims[1]) { + xTexelC${g+1}.zw = vec2(0.0); + } + xTexelC${g+1}Ready = 1; + } + + xC${g+1} = xTexelC${g+1}; + `}}else g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { + xTexelC${g} = getX(batch, xR, xCOffset, d1); + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xCOffset + 1 >= inDims[1]) { + xTexelC${g}.zw = vec2(0.0); + } + xTexelC${g}Ready = 1; + } + + if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${g+1}Ready == 0) { + xTexelC${g+1} = getX(batch, xR, xC + 1, d1); + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xC + 2 >= inDims[1]) { + xTexelC${g+1}.zw = vec2(0.0); + } + xTexelC${g+1}Ready = 1; + } + + xC${g} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw); + `,g+1= 0 && xCOffset < inDims[1]) { + final = getX(batch, xR, xCOffset, d1); + } + xC${g+1} = vec4(xTexelC${g+1}.xy, final.xy); + `)):(d+=` + if(xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { + xTexelC${g} = getX(batch, xR, xC, d1); + if (xC + 1 >= inDims[1]) { + xTexelC${g}.zw = vec2(0.0); + } + xTexelC${g}Ready = 1; + } + + xCOffset = xC + strides[1]; + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { + xTexelC${g+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset + 1 >= inDims[1]) { + xTexelC${g+1}.zw = vec2(0.); + } + xTexelC${g+1}Ready = 1; + } + + xC${g} = vec4( + xTexelC${g}.xy, xTexelC${g+1}.xy); + `,g+1= 0) { + // Use custom imod instead mod. On Intel GPU, mod may generate + // unexpected value. + // https://github.com/tensorflow/tfjs/issues/5447 + offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1]; + d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / + inChannels); + + if(d1 < inputShape[${i}] && d1 >= 0) { + + ch = imod(pos, inChannels); + + if (${r}) { + innerDims = vec2(d1, ch); + result[${u*2+p}] = getChannel( + getA(rc.x, d0, int(innerDims.x), + int(innerDims.y)), innerDims); + } else { + innerDims = vec2(d0, d1); + result[${u*2+p}] = getChannel( + getA(rc.x, ch, int(innerDims.x), + int(innerDims.y)), innerDims); + } + } + } + } + `;this.userCode=` + void main() { + ivec3 rc = getOutputCoords(); + + vec4 result = vec4(0); + + int blockIndex, pos, offsetY, d0, offsetX, d1, ch; + vec2 innerDims; + + ${l} + + ${a.output} = result; + } + `}};function Em(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function mF({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=Em(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=Em(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>iF)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert($c(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let N=Cm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(N.dataId);w.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=aa({inputs:{x:N},backend:a}),g.shape=n.outShape,b.push(N)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),v=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Cm({a:h?x:v,b:h?v:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(v),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function fF({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,v=[];if(s!=null){let K=Em(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),v.push(s))}if(r!=null){let K=Em(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),v.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(I);let N=new _ne(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],_=a.runWebGLProgram(N,[e],"float32",C),F=ce({inputs:{x:_},backend:a,attrs:{shape:b}});v.push(_),v.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?Dc(o,!0):null,B=new sF(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));U.push(K),v.push(K)}let H=a.runWebGLProgram(B,U,"float32"),q=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});v.push(H);for(let K of v)a.disposeIntermediateTensorInfo(K);return q}function Ane(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=mF({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new hF(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=fF({x:r,filter:s,convInfo:c,backend:n});else{let f=new dF(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var Fne={kernelName:zi,backendName:"webgl",kernelFunc:Ane},$ne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int wR = coords.x; + int wC = coords.y; + int d1 = coords.z; + int d2 = coords.w; + + // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + + for (int b = 0; b < ${e.batchSize}; b++) { + for (int yR = 0; yR < ${e.outHeight}; yR++) { + int xR = wR + yR * ${t} - ${a}; + + if (xR < 0 || xR >= ${e.inHeight}) { + continue; + } + + for (int yC = 0; yC < ${e.outWidth}; yC++) { + int xC = wC + yC * ${n} - ${r}; + + if (xC < 0 || xC >= ${e.inWidth}) { + continue; + } + + ${s?`float dyValue = getDy(b, yR, yC, d2); + float xValue = getX(b, xR, xC, d1); + dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC); + float xValue = getX(b, d1, xR, xC); + dotProd += (xValue * dyValue);`} + } + } + } + setOutput(dotProd); + } + `}},Dne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` + const ivec2 pads = ivec2(${i}, ${o}); + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d1 = coords[${p}]; + + ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads; + int dyRCorner = dyCorner.x; + int dyCCorner = dyCorner.y; + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + for (int wR = 0; wR < ${t}; wR++) { + float dyR = float(dyRCorner + wR) / ${a}.0; + + if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + int wRPerm = ${t} - 1 - wR; + + for (int wC = 0; wC < ${n}; wC++) { + float dyC = float(dyCCorner + wC) / ${r}.0; + + if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + int wCPerm = ${n} - 1 - wC; + + for (int d2 = 0; d2 < ${e.outChannels}; d2++) { + + if (${s}) { + float xValue = getDy(batch, idyR, idyC, d2); + float wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd += xValue * wValue; + } else { + float xValue = getDy(batch, d2, idyR, idyC); + float wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd += xValue * wValue; + } + + } + } + } + setOutput(dotProd); + } + `}},Rne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + void main() { + ivec5 coords = getOutputCoords(); + int wF = coords.x; + int wR = coords.y; + int wC = coords.z; + int d1 = coords.w; + int d2 = coords.u; + + float dotProd = 0.0; + + for (int b = 0; b < ${e.batchSize}; b++) { + for (int yF = 0; yF < ${e.outDepth}; yF++) { + int xF = wF + yF * ${t} - ${r}; + + if (xF < 0 || xF >= ${e.inDepth}) { + continue; + } + + for (int yR = 0; yR < ${e.outHeight}; yR++) { + int xR = wR + yR * ${n} - ${s}; + + if (xR < 0 || xR >= ${e.inHeight}) { + continue; + } + + for (int yC = 0; yC < ${e.outWidth}; yC++) { + int xC = wC + yC * ${a} - ${i}; + + if (xC < 0 || xC >= ${e.inWidth}) { + continue; + } + + float dyValue = getDy(b, yF, yR, yC, d2); + float xValue = getX(b, xF, xR, xC, d1); + dotProd += (xValue * dyValue); + } + } + } + } + setOutput(dotProd); + } + `}},Mne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` + const ivec3 pads = ivec3(${o}, ${l}, ${u}); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int d1 = coords.u; + + + ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; + int dyFCorner = dyCorner.x; + int dyRCorner = dyCorner.y; + int dyCCorner = dyCorner.z; + + float dotProd = 0.0; + for (int wF = 0; wF < ${t}; wF++) { + float dyF = float(dyFCorner + wF) / ${r}.0; + + if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { + continue; + } + int idyF = int(dyF); + + int wFPerm = ${t} - 1 - wF; + + for (int wR = 0; wR < ${n}; wR++) { + float dyR = float(dyRCorner + wR) / ${s}.0; + + if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || + fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + int wRPerm = ${n} - 1 - wR; + + for (int wC = 0; wC < ${a}; wC++) { + float dyC = float(dyCCorner + wC) / ${i}.0; + + if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + int wCPerm = ${a} - 1 - wC; + + for (int d2 = 0; d2 < ${e.outChannels}; d2++) { + float xValue = getDy(batch, idyF, idyR, idyC, d2); + float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); + dotProd += xValue * wValue; + } + } + } + } + setOutput(dotProd); + } + `}};function One(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new $ne(c);return n.runWebGLProgram(h,[r,s],"float32")}var Pne={kernelName:Om,backendName:"webgl",kernelFunc:One},Lne=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` + const ivec2 pads = ivec2(${a}, ${r}); + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d1 = coords[3]; + + ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads; + int dyRCorner = dyCorner.x; + int dyCCorner = dyCorner.y; + + vec4 result = vec4(0.); + for (int wR = 0; wR < ${t}; wR++) { + float dyR = float(dyRCorner + wR) / strides[0]; + if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + int wRPerm = ${t} - 1 - wR; + + for (int wC = 0; wC < ${n}; wC++) { + int wCPerm = ${n} - 1 - wC; + + float dyC = float(dyCCorner + wC) / strides[1]; + bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0) + && (fract(dyC) == 0.0); + int idyC = int(dyC); + + float dyC2 = float(dyCCorner + wC + 1) / strides[1]; + bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0) + && (fract(dyC2) == 0.0); + int idyC2 = int(dyC2); + + if (idyCVal && idyCVal2) { + for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) { + vec4 wValue = getW(wRPerm, wCPerm, d1, d2); + vec4 dySample = getDy(batch, idyR, idyC, d2); + vec4 dySample2 = (idyC / 2 == idyC2 / 2) ? + dySample : getDy(batch, idyR, idyC2, d2); + + vec2 dyValue = mod(float(idyC), 2.) == 0. ? + dySample.xy : dySample.zw; + result.xy += vec2(dot(dyValue, wValue.xy), + dot(dyValue, wValue.zw)); + + dyValue = mod(float(idyC2), 2.) == 0. ? + dySample2.xy : dySample2.zw; + result.zw += vec2(dot(dyValue, wValue.xy), + dot(dyValue, wValue.zw)); + } + } else if (idyCVal) { + for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) { + vec4 wValue = getW(wRPerm, wCPerm, d1, d2); + vec4 dySample = getDy(batch, idyR, idyC, d2); + vec2 dyValue = mod(float(idyC), 2.) == 0. ? + dySample.xy : dySample.zw; + result.xy += vec2(dot(dyValue, wValue.xy), + dot(dyValue, wValue.zw)); + } + } else if (idyCVal2) { + for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) { + vec4 wValue = getW(wRPerm, wCPerm, d1, d2); + vec4 dySample = getDy(batch, idyR, idyC2, d2); + vec2 dyValue = mod(float(idyC2), 2.) == 0. ? + dySample.xy : dySample.zw; + result.zw += vec2(dot(dyValue, wValue.xy), + dot(dyValue, wValue.zw)); + } + } + } + } + setOutput(result); + } + `}};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(u),c=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new Lne(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new Dne(c);return n.runWebGLProgram(h,[r,s],"float32")}}var Wne={kernelName:Wi,backendName:"webgl",kernelFunc:zne};function Bne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new Ene(u);return n.runWebGLProgram(p,[r,s],"float32")}var Vne={kernelName:Bi,backendName:"webgl",kernelFunc:Bne};function Une(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new Rne(u);return n.runWebGLProgram(p,[r,s],"float32")}var Gne={kernelName:xu,backendName:"webgl",kernelFunc:Une};function Hne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Mne(u);return n.runWebGLProgram(p,[r,s],"float32")}var jne={kernelName:vu,backendName:"webgl",kernelFunc:Hne},qne=Cp+` + return cos(x); +`,Kne=` + vec4 result = cos(x); + bvec4 isNaN = isnan(x); + ${tl} + return result; +`,Xne=Ze({opSnippet:qne,packedOpSnippet:Kne}),Yne={kernelName:Vi,backendName:"webgl",kernelFunc:Xne},Zne=` + float e2x = exp(-x); + return (e2x + 1.0 / e2x) / 2.0; +`,Jne=Ze({opSnippet:Zne}),Qne={kernelName:Ui,backendName:"webgl",kernelFunc:Jne},eae=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,v]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` + const float height_ratio = float(${f}); + const float width_ratio = float(${y}); + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int y = coords[1]; + int x = coords[2]; + int d = coords[3]; + + // get box vals + float y1 = getBoxes(b,0); + float x1 = getBoxes(b,1); + float y2 = getBoxes(b,2); + float x2 = getBoxes(b,3); + + // get image in batch index + int bInd = round(getBoxInd(b)); + if(bInd < 0 || bInd >= ${s}) { + return; + } + + float height_scale = ${g}; + float width_scale = ${x}; + + float in_y = ${b}; + if( in_y < 0.0 || in_y > ${h} ) { + setOutput(float(${r})); + return; + } + float in_x = ${v}; + if( in_x < 0.0 || in_x > ${m} ) { + setOutput(float(${r})); + return; + } + + vec2 sourceFracIndexCR = vec2(in_x,in_y); + if(${c} == 1) { + // Compute the four integer indices. + ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); + ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); + + float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); + float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); + float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); + float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); + + vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); + + float top = topLeft + (topRight - topLeft) * fracCR.x; + float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; + float newValue = top + (bottom - top) * fracCR.y; + setOutput(newValue); + } else { + // Compute the coordinators of nearest neighbor point. + ivec2 sourceNearestCR = ivec2(floor( + sourceFracIndexCR + vec2(0.5,0.5))); + float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); + setOutput(newValue); + } + } + `}},tae=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new eae(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},nae={kernelName:ku,backendName:"webgl",kernelFunc:tae},Mc;(function(e){e.Prod="*",e.Sum="+"})(Mc||(Mc={}));var LS=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Mc.Prod?"1.0":"0.0",i=n?s:`getX(${zS(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` + void main() { + ${ht(r)} coords = getOutputCoords(); + int end = ${WS(r,"coords",this.op)}; + float val = ${i}; + int pow2 = int(pow(2.0, index)); + if (${l}) { + int idx = ${u}; + ${WS(r,"coords",this.op)} = idx; + val ${this.op}= getX(${zS(r,"coords",this.op)}); + } + setOutput(val); + } + `}};function zS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function WS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function gF(e,t,n,a,r,s){let i=t.shape.length,o=T.getAxesPermutation([a],i),l=t;o!=null&&(l=Sn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=aa({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new LS(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new LS(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=T.getUndoAxesPermutation(o),h=Sn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function aae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return gF(Mc.Prod,r,n,s,i,o)}var rae={kernelName:wu,backendName:"webgl",kernelFunc:aae};function sae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return gF(Mc.Sum,r,n,s,i,o)}var iae={kernelName:Gi,backendName:"webgl",kernelFunc:sae};function oae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=KA(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=uQ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var lae={kernelName:Uc,backendName:"webgl",kernelFunc:oae},uae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int h = ${this.getHeightCoordString()}; + int w = ${this.getWidthCoordString()}; + int d = ${this.getDepthCoordString()}; + + int in_h = h / ${t}; + int offset_h = imod(h, ${t}); + int in_w = w / ${t}; + int offset_w = imod(w, ${t}); + int offset_d = (offset_h * ${t} + offset_w) * + ${this.getOutputDepthSize()}; + int in_d = d + offset_d; + + float result = ${this.getInputSamplingString()}; + setOutput(result); + } + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function pae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new uae(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var cae={kernelName:Iu,backendName:"webgl",kernelFunc:pae},bF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { + float b = getPreluActivationWeightsAtOutCoords(); + ${n} + }`:r?l=`float activation(float a) { + float b = getLeakyreluAlphaAtOutCoords(); + ${n} + }`:l=` + float activation(float x) { + ${n} + } + `,u="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` + ${l} + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords.x; + ivec2 xRCCorner = coords.yz * strides - pads; + int d2 = coords.w; + int d1 = d2 / ${o}; + int q = d2 - d1 * ${o}; + + int xRCorner = xRCCorner.x; + int xCCorner = xRCCorner.y; + + // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. + for (int wR = 0; wR < ${s}; wR++) { + int xR = xRCorner + wR * dilations[0]; + + if (xR < 0 || xR >= inDims[0]) { + continue; + } + + for (int wC = 0; wC < ${i}; wC++) { + int xC = xCCorner + wC * dilations[1]; + + if (xC < 0 || xC >= inDims[1]) { + continue; + } + + float xVal = getX(batch, xR, xC, d1); + float wVal = getW(wR, wC, d1, q); + dotProd += xVal * wVal; + } + } + + float result = dotProd; + ${p} + ${u} + setOutput(result); + } + `}},yF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` + int xR; int xC; int xCOffset; + vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { + `;for(let g=0;g<(d+1)/2;g++){let b=g*2;if(c+=` + xC = xCCorner + ${b*l}; + `,o===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { + xTexelC${b} = getX(batch, xR, xCOffset, d1); + + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xCOffset + 1 >= inDims[1]) { + xTexelC${b}.zw = vec2(0.0); + } + xTexelC${b}Ready = 1; + } + `,l===1&&b>0?c+=` + xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy); + `:c+=` + xCOffset = xC + 1 - 2; + + if (xCOffset >= 0 && xCOffset < inDims[1]) { + previous = getX(batch, xR, xCOffset, d1); + + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xCOffset + 1 >= inDims[1]) { + previous.zw = vec2(0.0); + } + + xC${b} = vec4(previous.zw, xTexelC${b}.xy); + } else { + xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy); + } + `):c+=` + if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { + xTexelC${b} = getX(batch, xR, xC, d1); + if (xC + 1 >= inDims[1]) { + xTexelC${b}.zw = vec2(0.0); + } + xTexelC${b}Ready = 1; + } + + xC${b} = xTexelC${b}; + `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { + xTexelC${b+1} = getX(batch, xR, xCOffset, d1); + + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xCOffset + 1 >= inDims[1]) { + xTexelC${b+1}.zw = vec2(0.0); + } + xTexelC${b+1}Ready = 1; + } + `,l>1?c+=` + xCOffset -= 2; + if (xCOffset >= 0 && xCOffset < inDims[1]) { + previous = getX(batch, xR, xCOffset, d1); + xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy); + } else { + xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy); + } + `:c+=` + xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy); + `):y===1?c+=` + xC${b+1} = xTexelC${b}; + `:c+=` + xCOffset = xC + ${y}; + + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { + xTexelC${b+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset + 1 >= inDims[1]) { + xTexelC${b+1}.zw = vec2(0.0); + } + xTexelC${b+1}Ready = 1; + } + + xC${b+1} = xTexelC${b+1}; + `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { + xTexelC${b} = getX(batch, xR, xCOffset, d1); + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xCOffset + 1 >= inDims[1]) { + xTexelC${b}.zw = vec2(0.0); + } + xTexelC${b}Ready = 1; + } + + if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) { + xTexelC${b+1} = getX(batch, xR, xC + 1, d1); + // Need to manually clear unused channels in case + // we're reading from recycled texture. + if (xC + 2 >= inDims[1]) { + xTexelC${b+1}.zw = vec2(0.0); + } + xTexelC${b+1}Ready = 1; + } + + xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw); + `,b+1= 0 && xCOffset < inDims[1]) { + final = getX(batch, xR, xCOffset, d1); + } + xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy); + `)):(c+=` + if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { + xTexelC${b} = getX(batch, xR, xC, d1); + if (xC + 1 >= inDims[1]) { + xTexelC${b}.zw = vec2(0.0); + } + xTexelC${b}Ready = 1; + } + + xCOffset = xC + strides[1]; + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { + xTexelC${b+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset + 1 >= inDims[1]) { + xTexelC${b+1}.zw = vec2(0.); + } + xTexelC${b+1}Ready = 1; + } + + xC${b} = vec4( + xTexelC${b}.xy, xTexelC${b+1}.xy); + `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new yF(d):c=new bF(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var hae={kernelName:Hi,backendName:"webgl",kernelFunc:dae},mae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int wR = coords.x; + int wC = coords.y; + int d1 = coords.z; + int dm = coords.w; + int d2 = d1 * ${s} + dm; + + float dotProd = 0.0; + + // TO DO: Vec4 over the batch size + for (int b = 0; b < ${e.batchSize}; b++) { + for (int yR = 0; yR < ${e.outHeight}; yR++) { + int xR = wR + yR * ${t} - ${a}; + + if (xR < 0 || xR >= ${e.inHeight}) { + continue; + } + + for (int yC = 0; yC < ${e.outWidth}; yC++) { + int xC = wC + yC * ${n} - ${r}; + + if (xC < 0 || xC >= ${e.inWidth}) { + continue; + } + + float dyValue = getDy(b, yR, yC, d2); + float xValue = getX(b, xR, xC, d1); + dotProd += (xValue * dyValue); + } + } + } + setOutput(dotProd); + } + `}},fae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` + const ivec2 pads = ivec2(${s}, ${i}); + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords[0]; + int d1 = coords[3]; + ivec2 dyCorner = coords.yz - pads; + int dyRCorner = dyCorner.x; + int dyCCorner = dyCorner.y; + + float dotProd = 0.0; + + for (int wR = 0; wR < ${t}; wR++) { + float dyR = float(dyRCorner + wR) / ${a}.0; + + if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + int wRPerm = ${t} - 1 - wR; + + for (int wC = 0; wC < ${n}; wC++) { + float dyC = float(dyCCorner + wC) / ${r}.0; + + if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + int wCPerm = ${n} - 1 - wC; + + // TO DO: Vec4 over the channelMul + for (int dm = 0; dm < ${o}; dm++) { + int d2 = d1 * ${o} + dm; + float xValue = getDy(batch, idyR, idyC, d2); + float wValue = getW(wRPerm, wCPerm, d1, dm); + dotProd += xValue * wValue; + } + } + } + setOutput(dotProd); + } + `}};function gae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new mae(d);return n.runWebGLProgram(c,[r,s],"float32")}var bae={kernelName:Pm,backendName:"webgl",kernelFunc:gae};function yae(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new fae(d);return n.runWebGLProgram(c,[r,s],"float32")}var xae={kernelName:Lm,backendName:"webgl",kernelFunc:yae},vae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + void main() { + ivec2 coords = getOutputCoords(); + float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; + setOutput(val); + } + `}};function wae(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new vae(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var kae={kernelName:Gc,backendName:"webgl",kernelFunc:wae},Iae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=` + const ivec2 strides = ivec2(${r}, ${s}); + const ivec2 pads = ivec2(${p}, ${d}); + const float neg_infinity = -3.4e38; + + void main() { + ivec4 coords = getOutputCoords(); + int batch = coords.x; + int d1 = coords.w; + ivec2 outTopLeftCorner = + coords.yz * strides - pads; + int hBeg = outTopLeftCorner.x; + int wBeg = outTopLeftCorner.y; + + float curVal = neg_infinity; + for (int h = 0; h < ${i}; h++) { + int hIn = hBeg + h * ${l}; + + if (hIn >= 0 && hIn < ${t}) { + for (int w = 0; w < ${o}; w++) { + int wIn = wBeg + w * ${u}; + + if (wIn >= 0 && wIn < ${n}) { + float xVal = getX(batch, hIn, wIn, d1); + float wVal = getW(h, w, d1); + + float val = xVal + wVal; + if (val > curVal) { + curVal = val; + } + } + } + } + } + + float result = curVal; + setOutput(result); + } + `}};function Sae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new Iae(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var Nae={kernelName:ji,backendName:"webgl",kernelFunc:Sae};function Tae(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=ng({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Cae={kernelName:Wm,backendName:"webgl",kernelFunc:Tae},Eae="return (x >= 0.0) ? x : (exp(x) - 1.0);",_ae=` + vec4 result; + + result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); + result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); + result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); + result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); + + return result; +`,Aae=Ze({opSnippet:Eae,packedOpSnippet:_ae}),Fae={kernelName:Ki,backendName:"webgl",kernelFunc:Aae},$ae="return (b >= 0.0) ? a : a * (b + 1.0);",Dae=` + vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); + return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); +`,Rae=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(Dae,a.shape,r.shape):new Si($ae,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Mae={kernelName:Su,backendName:"webgl",kernelFunc:Rae},Oae=` + return vec4(equal(a, b)); +`,Pae="return float(a == b);",Lae=fn({opSnippet:Pae,packedOpSnippet:Oae,dtype:"bool",cpuKernelImpl:mQ}),zae={kernelName:Nu,backendName:"webgl",kernelFunc:Lae},Wae=` + // Error function is calculated approximately with elementary function. + // See "Handbook of Mathematical Functions with Formulas, + // Graphs, and Mathematical Tables", Abramowitz and Stegun. + float p = ${T.ERF_P}; + float a1 = ${T.ERF_A1}; + float a2 = ${T.ERF_A2}; + float a3 = ${T.ERF_A3}; + float a4 = ${T.ERF_A4}; + float a5 = ${T.ERF_A5}; + + float sign = sign(x); + x = abs(x); + float t = 1.0 / (1.0 + p * x); + return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); +`,Bae=Ze({opSnippet:Wae}),Vae={kernelName:Xi,backendName:"webgl",kernelFunc:Bae},Uae=Cp+` + return exp(x); +`,Gae=` + vec4 result = exp(x); + bvec4 isNaN = isnan(x); + result.r = isNaN.r ? x.r : result.r; + result.g = isNaN.g ? x.g : result.g; + result.b = isNaN.b ? x.b : result.b; + result.a = isNaN.a ? x.a : result.a; + + return result; +`,xF=Ze({opSnippet:Uae,packedOpSnippet:Gae,cpuKernelImpl:fQ,dtype:"float32"}),Hae={kernelName:Yi,backendName:"webgl",kernelFunc:xF};function Sv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var jae={kernelName:Tu,backendName:"webgl",kernelFunc:Sv},BS="return exp(x) - 1.0;",qae=Ze({opSnippet:BS,packedOpSnippet:BS,cpuKernelImpl:gQ}),Kae={kernelName:Zi,backendName:"webgl",kernelFunc:qae},VS=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` + const float exponentMultiplier = ${r}; + + float unaryOpComplex(float real, float expR, float imag, float expI) { + ${i} + } + + float mulMatDFT(int batch, int index) { + float indexRatio = float(index) / float(${a}); + float exponentMultiplierTimesIndexRatio = + exponentMultiplier * indexRatio; + + float result = 0.0; + + for (int i = 0; i < ${a}; i++) { + // x = (-2|2 * PI / N) * index * i; + float x = exponentMultiplierTimesIndexRatio * float(i); + float expR = cos(x); + float expI = sin(x); + float real = getReal(batch, i); + float imag = getImag(batch, i); + + result += + unaryOpComplex(real, expR, imag, expI) / ${s}; + } + + return result; + } + + void main() { + ivec2 coords = getOutputCoords(); + setOutput(mulMatDFT(coords[0], coords[1])); + } + `}};function vF(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new VS("real",l,t),p=new VS("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Os({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Xae(e){let{inputs:t,backend:n}=e,{input:a}=t;return vF(a,!1,n)}var Yae={kernelName:Bm,backendName:"webgl",kernelFunc:Xae},Zae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + void main() { + // Input can be obtained from uniform value. + setOutput(value); + } + `}};function Ld(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Zae(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Jae={kernelName:Hc,backendName:"webgl",kernelFunc:Ld},Qae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int x = coords[2]; + + int coordX = ${t} - x - 1; + float outputValue; + if(coordX >= 0 && coordX < ${t}) { + outputValue = getImage(coords[0], coords[1], coordX, coords[3]); + } else { + outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); + } + setOutput(outputValue); + } + `}},ere={kernelName:Cu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Qae(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},US="return floor(x);",tre=Ze({opSnippet:US,packedOpSnippet:US,cpuKernelImpl:bQ}),nre={kernelName:Ji,backendName:"webgl",kernelFunc:tre},are=` + float s = sign(a) * sign(b); + int ia = round(a); + int ib = round(b); + if (ib != 0) { + // Windows (D3D) wants guaranteed non-zero int division at compile-time. + return float(idiv(ia, ib, s)); + } else { + return NAN; + } +`,rre=` + ivec4 ia = round(a); + ivec4 ib = round(b); + bvec4 cond = notEqual(ib, ivec4(0)); + ivec4 result = ivec4(0); + vec4 s = sign(a) * sign(b); + + // Windows (D3D) wants guaranteed non-zero int division at compile-time. + if (cond[0]) { + result[0] = idiv(ia[0], ib[0], s[0]); + } + if (cond[1]) { + result[1] = idiv(ia[1], ib[1], s[1]); + } + if (cond[2]) { + result[2] = idiv(ia[2], ib[2], s[2]); + } + if (cond[3]) { + result[3] = idiv(ia[3], ib[3], s[3]); + } + return vec4(result); +`,sre=fn({opSnippet:are,packedOpSnippet:rre,dtype:"int32"}),ire={kernelName:Qi,backendName:"webgl",kernelFunc:sre},ore=class{constructor(e){this.variableNames=["A"];let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` + void main() { + ivec3 coords = getOutputCoords(); + int texR = coords[0]; + int texC = coords[1]; + int depth = coords[2]; + vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0); + + vec4 values = ${t.texture2D}(A, uv); + float value; + if (depth == 0) { + value = values.r; + } else if (depth == 1) { + value = values.g; + } else if (depth == 2) { + value = values.b; + } else if (depth == 3) { + value = values.a; + } + + setOutput(floor(value * 255.0 + 0.5)); + } + `}},lre=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` + void main() { + ivec3 coords = getOutputCoords(); + int texR = coords[0]; + int texC = coords[1]; + int depth = coords[2]; + + vec4 result = vec4(0.); + + for(int row=0; row<=1; row++) { + for(int col=0; col<=1; col++) { + texC = coords[1] + row; + depth = coords[2] + col; + + vec2 uv = (vec2(texC, texR) + halfCR) / + vec2(${a}.0, ${n}.0); + vec4 values = ${t.texture2D}(A, uv); + float value; + if (depth == 0) { + value = values.r; + } else if (depth == 1) { + value = values.g; + } else if (depth == 2) { + value = values.b; + } else if (depth == 3) { + value = values.a; + } + + result[row * 2 + col] = floor(value * 255.0 + 0.5); + } + } + + ${t.output} = result; + } + `}},ure={kernelName:sm,backendName:"webgl",kernelFunc:pre},Ol,Cx=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function pre(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],d=[u,l,s];if(o||i){let f=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ol==null||f!==Cx)&&(Cx=f,Ol=document.createElement("canvas").getContext("2d",{willReadFrequently:Cx})),Ol.canvas.width=l,Ol.canvas.height=u,Ol.drawImage(r,0,0,l,u),r=Ol.canvas}let c=n.makeTensorInfo(p,"int32");n.texData.get(c.dataId).usage=da.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=G().getBool("WEBGL_PACK")?new lre(d):new ore(d),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function cre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=T.convertConv2DDataFormat(p),g=T.computeConv2DInfo(r.shape,s.shape,l,d,u,c,!1,f),b,y=[],x=i!=null,v=o!=null,I=h==="leakyrelu",N=()=>{let _=[r,s],F=(D,$)=>{if($==="NCHW"&&D.shape.length===1&&D.shape[0]!==1){let S=ce({inputs:{x:D},backend:n,attrs:{shape:[D.shape[0],1,1]}});return y.push(S),S}return D};if(x&&_.push(F(i,p)),v&&_.push(F(o,p)),I){let D=n.makeTensorInfo([],"float32",w.createScalarValue(m,"float32"));_.push(D),y.push(D)}return _};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=mF({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(g.strideWidth<=2&&f==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let _=h?Dc(h,!0):null,F=new hF(g,x,_,v,I),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=N();b=n.runWebGLProgram(F,$,"float32",D)}else if(G().getBool("WEBGL_CONV_IM2COL"))b=fF({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let _=h?Dc(h,!1):null,F=new dF(g,x,_,v,I),D=N();b=n.runWebGLProgram(F,D,"float32")}let C=ce({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(_=>n.disposeIntermediateTensorInfo(_)),C}var dre={kernelName:ui,backendName:"webgl",kernelFunc:cre};function hre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:d,activation:c,leakyreluAlpha:h}=a,m=[],f=p;f==null&&(f=[1,1]),w.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=T.computeConv2DInfo(r.shape,s.shape,l,f,u,d,!0),b=G().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=c?Dc(c,b):null,x=[r,s],v=i!=null,I=o!=null,N=c==="leakyrelu";if(v&&x.push(i),I&&x.push(o),N){let D=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));x.push(D),m.push(D)}let C;b?C=new yF(g,v,y,I,N):C=new bF(g,v,y,I,N);let _=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=n.runWebGLProgram(C,x,"float32",_);return m.forEach(D=>n.disposeIntermediateTensorInfo(D)),F}var mre={kernelName:pi,backendName:"webgl",kernelFunc:hre},fre=class{constructor(e,t,n,a){this.sliceDim=e,this.strides=t,this.paramsShape=a,this.variableNames=["x","indices"],this.outputShape=n;let r=ht(n.length),s=` + int index;`;for(let i=0;i= ${this.paramsShape[i]}; + flattenIndex += index * ${this.strides[i]};`;this.userCode=` + void main() { + ${r} coords = getOutputCoords(); + int flattenIndex = 0; + bool out_of_bounds = false; + + ${s} + + setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1])); + } + `}};function gre(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=w.sizeFromShape(a.shape),[l,u,p,d]=T.prepareAndValidate(a,r),c=ce({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),h=ce({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape)/p,p]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(a),x=yQ(b,y,a.dtype,u,i,p,d,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new fre(i,d,[u,p],a.shape),f=n.runWebGLProgram(m,[h,c],h.dtype),g=ce({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var bre={kernelName:_u,backendName:"webgl",kernelFunc:gre},yre=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ht(this.rank),a=xre(e,2);this.userCode=` + void main() { + ${n} resRC = getOutputCoords(); + int index = int(getIndices(resRC.x, resRC.z)); + float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0; + setOutput(inBounds * getA(${a})); + } + `}};function xre(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r=0,()=>`GatherV2: the index value ${I} is not in [0, ${x-1}]`)}}let u=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=w.sizeFromShape(s.shape),d=[],c=ce({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=ce({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,p/u.batchSize]}});d.push(c),d.push(h);let m=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let y=n.bufferSync(h),x=n.bufferSync(c),v=xQ(x,y,m);return d.forEach(I=>n.disposeIntermediateTensorInfo(I)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new yre(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);d.push(g);let b=ce({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var vre={kernelName:Eu,backendName:"webgl",kernelFunc:wF},wre="return float(a > b);",kre=` + return vec4(greaterThan(a, b)); +`,Ire=fn({opSnippet:wre,packedOpSnippet:kre,cpuKernelImpl:vQ,dtype:"bool"}),Sre={kernelName:Au,backendName:"webgl",kernelFunc:Ire},Nre="return float(a >= b);",Tre=` + return vec4(greaterThanEqual(a, b)); +`,Cre=fn({opSnippet:Nre,packedOpSnippet:Tre,dtype:"bool",cpuKernelImpl:wQ}),Ere={kernelName:to,backendName:"webgl",kernelFunc:Cre};function _re(e){let{inputs:t,backend:n}=e,{input:a}=t;return vF(a,!0,n)}var Are={kernelName:Vm,backendName:"webgl",kernelFunc:_re},Fre="return float(!isnan(x) && !isinf(x));",$re=Ze({opSnippet:Fre,dtype:"bool"}),Dre={kernelName:ao,backendName:"webgl",kernelFunc:$re},Rre="return float(isinf(x));",Mre=Ze({opSnippet:Rre,dtype:"bool"}),Ore={kernelName:ro,backendName:"webgl",kernelFunc:Mre},Pre="return float(isnan(x));",Lre=Ze({opSnippet:Pre,dtype:"bool"}),zre={kernelName:so,backendName:"webgl",kernelFunc:Lre},Wre="return float(a < b);",Bre=` + return vec4(lessThan(a, b)); +`,Vre=fn({opSnippet:Wre,packedOpSnippet:Bre,cpuKernelImpl:kQ,dtype:"bool"}),Ure={kernelName:Fu,backendName:"webgl",kernelFunc:Vre},Gre="return float(a <= b);",Hre=` + return vec4(lessThanEqual(a, b)); +`,jre=fn({opSnippet:Gre,packedOpSnippet:Hre,cpuKernelImpl:IQ,dtype:"bool"}),qre={kernelName:$u,backendName:"webgl",kernelFunc:jre};function Kre(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=SQ(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Xre={kernelName:Du,backendName:"webgl",kernelFunc:Kre},Yre=Cp+` + return x < 0.0 ? 0./0. : log(x); +`,Zre=` + vec4 result = log(x); + bvec4 isNaN = isnan(x); + result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); + result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g); + result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); + result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); + return result; +`,Jre=Ze({opSnippet:Yre,packedOpSnippet:Zre,cpuKernelImpl:NQ}),Qre={kernelName:oo,backendName:"webgl",kernelFunc:Jre},ese=Cp+` + return log(1.0 + x); +`,tse=Ze({opSnippet:ese}),nse={kernelName:lo,backendName:"webgl",kernelFunc:tse},ase="return float(a >= 1.0 && b >= 1.0);",rse=` + return vec4( + vec4(greaterThanEqual(a, vec4(1.0))) * + vec4(greaterThanEqual(b, vec4(1.0)))); +`,sse=fn({opSnippet:ase,packedOpSnippet:rse,dtype:"bool"}),ise={kernelName:Ru,backendName:"webgl",kernelFunc:sse},ose="return float(!(x >= 1.0));",lse=Ze({opSnippet:ose}),use={kernelName:Mu,backendName:"webgl",kernelFunc:lse},pse="return float(a >= 1.0 || b >= 1.0);",cse=` + return min( + vec4(greaterThanEqual(a, vec4(1.0))) + + vec4(greaterThanEqual(b, vec4(1.0))), + vec4(1.0)); +`,dse=fn({opSnippet:pse,packedOpSnippet:cse,dtype:"bool"}),hse={kernelName:Ou,backendName:"webgl",kernelFunc:dse},mse=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int r = coords[1]; + int c = coords[2]; + int d = coords[3]; + float x = getX(b, r, c, d); + float sum = 0.0; + for (int j = -${s}; j <= ${s}; j++) { + int idx = d + j; + if (idx >= 0 && idx <= ${i}) { + float z = getX(b, r, c, idx); + sum += z * z; + } + } + float val = x * ${o}; + setOutput(val); + } + `}},fse=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords.x; + int r = coords.y; + int c = coords.z; + int d = coords.w; + + bool hasNextCol = d < ${this.outputShape[3]}; + bool hasNextRow = c < ${this.outputShape[2]}; + + vec4 sum = vec4(0.); + vec4 xFragAtOutputCoords = getX(b, r, c, d); + + vec4 xAtOutputCoords = vec4( + getChannel(xFragAtOutputCoords, vec2(c, d)), + hasNextCol ? + getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, + hasNextRow ? + getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, + (hasNextRow && hasNextCol) ? + getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 + ); + + int firstChannel = d - ${s}; + vec2 cache = vec2(0.); + if(firstChannel >= 0){ + vec4 firstChannelFrag = getX(b, r, c, firstChannel); + cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel)); + if(hasNextRow){ + cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel)); + } + } + + ivec2 depth = ivec2(d, d + 1); + for (int j = - ${s}; j <= ${s}; j++) { + ivec2 idx = depth + j; + bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); + bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); + + bool depthInRange = aboveLowerBound.x && belowUpperBound.x; + bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; + + if(depthInRange || depthPlusOneInRange){ + vec4 z = vec4(0.); + vec4 xFragAtCurrentDepth; + z.xz = cache.xy; + if(depthPlusOneInRange && hasNextCol){ + xFragAtCurrentDepth = idx.y != d ? + getX(b, r, c, idx.y) : xFragAtOutputCoords; + z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y)); + if(hasNextRow){ + z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y)); + } + } + cache.xy = z.yw; + sum += z * z; + } + } + vec4 result = xAtOutputCoords * ${o}; + setOutput(result); + } + `}},gse=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new fse(r.shape,s,i,o,l):new mse(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},bse={kernelName:uo,backendName:"webgl",kernelFunc:gse},yse=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int r = coords[1]; + int c = coords[2]; + + float result = 0.0; + for (int d = 0; d < ${this.depth}; ++d) { + int depthBegin = int(max(0.0, float(d - ${t}))); + int depthEnd = int(min(float(${this.depth}), + float(d + ${t} + 1))); + + const int MIN_DEPTH_BEGIN = 0; + const int MAX_DEPTH_END = ${this.depth}; + + float norm = 0.0; + for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { + if (k < depthBegin){ + continue; + } + else if (k >= depthBegin && k < depthEnd) { + norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); + } + else { + break; + } + } + + norm = float(${a}) * norm + float(${n}); + + for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ + if (k < depthBegin){ + continue; + } + else if (k >= depthBegin && k < depthEnd){ + float dyi = -2.0 * float(${a}) + * float(${r}) + * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) + / norm; + if (k == d) { + dyi += pow(norm, -1.0 * ${r}); + } + if (k == coords[3]) { + dyi *= getDy(b, r, c, d); + result += dyi; + } + } + else { + break; + } + } + } + setOutput(result); + } + `}},xse=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new yse(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},vse={kernelName:Pu,backendName:"webgl",kernelFunc:xse};function wse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=nl(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function kF(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Rc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Ese={kernelName:ho,backendName:"webgl",kernelFunc:Cse};function _se(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new xk(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Ase={kernelName:Lu,backendName:"webgl",kernelFunc:_se},Fse=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` + const ivec2 pads = ivec2(${i}, ${o}); + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + + ivec2 dyRCCorner = coords.yz - pads; + int dyRCorner = dyRCCorner.x; + int dyCCorner = dyRCCorner.y; + + // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + for (int wR = 0; wR < ${r}; + wR += ${a}) { + float dyR = float(dyRCorner + wR) / ${t}.0; + + if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + for (int wC = 0; wC < ${s}; wC++) { + float dyC = float(dyCCorner + wC) / ${n}.0; + + if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + float dyValue = getDy(b, idyR, idyC, d); + int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d)); + + // Get the current value, check it against the value from the + // position matrix. + int curPosValue = wR * ${s} + wC; + float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); + + dotProd += dyValue * mask; + } + } + setOutput(dotProd); + } + `}},$se=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` + const ivec3 pads = ivec3(${p}, ${d}, ${c}); + + void main() { + ivec5 coords = getOutputCoords(); + int batch = coords.x; + int ch = coords.u; + + ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; + int dyDCorner = dyCorner.x; + int dyRCorner = dyCorner.y; + int dyCCorner = dyCorner.z; + + // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get + // dx(xD, xR, xC, ch). + // ? = to be determined. : = across all values in that axis. + float dotProd = 0.0; + + for (int wD = 0; wD < ${o}; + wD += ${r}) { + float dyD = float(dyDCorner + wD) / ${t}.0; + + if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { + continue; + } + int idyD = int(dyD); + + for (int wR = 0; wR < ${l}; + wR += ${s}) { + float dyR = float(dyRCorner + wR) / ${n}.0; + + if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || + fract(dyR) > 0.0) { + continue; + } + int idyR = int(dyR); + + for (int wC = 0; wC < ${u}; + wC += ${i}) { + float dyC = float(dyCCorner + wC) / ${a}.0; + + if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + fract(dyC) > 0.0) { + continue; + } + int idyC = int(dyC); + + float dyValue = getDy(batch, idyD, idyR, idyC, ch); + int maxPosValue = ${h} - + int(getMaxPos(batch, idyD, idyR, idyC, ch)); + + // Get the current value, check it against the value from the + // position matrix. + int curPosValue = + wD * ${l} * ${u} + + wR * ${u} + wC; + float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); + + dotProd += dyValue * mask; + } + } + } + setOutput(dotProd); + } + `}};function Dse(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new xk(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new $se(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Rse={kernelName:qc,backendName:"webgl",kernelFunc:Dse};function Mse(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;wp([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=T.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new Rc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Fse(c),b=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),b}var Ose={kernelName:jc,backendName:"webgl",kernelFunc:Mse};function Pse(e,t,n,a){let r=new Rc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Rc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Lse={kernelName:Kc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(a.shape,r,s,u,i),[d,c]=Pse(a,o,p,l);return[d,c]}};function zse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=nl(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Wse={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let C=0;Cu[0]+e[p]+u[1]);let a=e.length,r=ht(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` + int start = ${s}; + int end = ${i}; + + void main() { + int outC = getOutputCoords(); + if (outC < start) { + outC = start * 2 - outC - ${l}; + } else if(outC >= end) { + outC = (end - 1) * 2 - outC + ${l}; + } + setOutput(getX(outC - start)); + } + `;return}this.userCode=` + ${r} start = ${r}(${s}); + ${r} end = ${r}(${i}); + + void main() { + ${r} outC = getOutputCoords(); + for (int i = 0; i < ${a}; i++) { + if (outC[i] < start[i]) { + outC[i] = start[i] * 2 - outC[i] - ${l}; + } else if(outC[i] >= end[i]) { + outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; + } + } + ${r} coords = outC - start; + setOutput(getX(${o})); + } + `}},Kse=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ht(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` + ${r} source = rc; + if (source < start) { + source = start * 2 - source - ${d}; + } else if (source >= end) { + source = (end - 1) * 2 - source + ${d}; + } + source -= start; + `;c=` + ${r} rc = outputLoc; + ${h} + result[0] = getChannel(getX(${l.join()}), ${p}); + ${o[a-1]} += 1; + if(${u}) { + ${h} + result[1] = getChannel(getX(${l.join()}), ${p}); + } + `}else{let h=` + ${r} source = rc; + ${r} lt = ${r}(lessThan(source, start)); + ${r} gte = ${r}(greaterThanEqual(source, end)); + ${r} orig = 1 - (lt + gte); + source = orig * source + + lt * (start * 2 - source - ${d}) + + gte * ((end - 1) * 2 - source + ${d}); + source -= start; + `;c=` + ${r} rc = outputLoc; + ${h} + result[0] = getChannel(getX(${l.join()}), ${p}); + ${o[a-1]} += 1; + if(${u}) { + ${h} + result[1] = getChannel(getX(${l.join()}), ${p}); + } + rc = outputLoc; + ${o[a-2]} += 1; + if(${o[a-2]} < ${this.outputShape[a-2]}) { + ${h} + result[2] = getChannel(getX(${l.join()}), ${p}); + ${o[a-1]} += 1; + if(${u}) { + ${h} + result[3] = getChannel(getX(${l.join()}), ${p}); + } + } + `}this.userCode=` + const ${r} start = ${r}(${s}); + const ${r} end = ${r}(${i}); + + void main() { + ${r} outputLoc = getOutputCoords(); + vec4 result = vec4(0.); + ${c} + setOutput(result); + } + `}},Xse=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kse(a.shape,r,s):new qse(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Yse={kernelName:bo,backendName:"webgl",kernelFunc:Xse},Zse=`if (b == 0.0) return NAN; + return mod(a, b);`,Jse=` + vec4 result = mod(a, b); + bvec4 isNaN = equal(b, vec4(0.0)); + `+tl+` + return result; +`,Qse=fn({opSnippet:Zse,packedOpSnippet:Jse}),eie={kernelName:yo,backendName:"webgl",kernelFunc:Qse},tie=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` + void main() { + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + + float r = random(seed); + float cdf = 0.0; + + for (int i = 0; i < ${t-1}; i++) { + cdf += getProbs(batch, i); + + if (r < cdf) { + setOutput(float(i)); + return; + } + } + + // If no other event happened, last event happened. + setOutput(float(${t-1})); + } + `}},nie=` +if (a == b) { + return 1.0; +}; +return a / b;`,aie=` + // vec4 one = vec4(equal(a, b)); + // return one + (vec4(1.0) - one) * a / b; + vec4 result = a / b; + if(a.x == b.x) { + result.x = 1.; + } + if(a.y == b.y) { + result.y = 1.; + } + if(a.z == b.z) { + result.z = 1.; + } + if(a.w == b.w) { + result.w = 1.; + } + + return result; +`,IF=fn({opSnippet:nie,packedOpSnippet:aie,checkOutOfBounds:!0}),rie={kernelName:qi,backendName:"webgl",kernelFunc:IF},GS="return a - b;",SF=fn({opSnippet:GS,packedOpSnippet:GS,supportsComplex:!0,cpuKernelImpl:XQ}),sie={kernelName:Uo,backendName:"webgl",kernelFunc:SF};function NF(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=kF({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=SF({inputs:{a:r,b:u},backend:n}),d=xF({inputs:{x:p},backend:n}),c=ng({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=IF({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var iie={kernelName:Bo,backendName:"webgl",kernelFunc:NF};function oie(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:NF({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new tie(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var lie={kernelName:zu,backendName:"webgl",kernelFunc:oie},uie=Ma+` + return -x; +`,pie=` + vec4 result = -x; + bvec4 isNaN = isnan(x); + + result.r = isNaN.r ? x.r : result.r; + result.g = isNaN.g ? x.g : result.g; + result.b = isNaN.b ? x.b : result.b; + result.a = isNaN.a ? x.a : result.a; + + return result; +`;function cie(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=AQ(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,pie):r=new ir(a.shape,uie),n.runWebGLProgram(r,[a],a.dtype)}var die={kernelName:Wu,backendName:"webgl",kernelFunc:cie},hie=gr.nonMaxSuppressionV3Impl;function mie(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=hie(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var fie={kernelName:Vu,backendName:"webgl",kernelFunc:mie},gie=gr.nonMaxSuppressionV4Impl;function bie(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=gie(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var yie={kernelName:Uu,backendName:"webgl",kernelFunc:bie},xie=gr.nonMaxSuppressionV5Impl;function vie(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=xie(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var wie={kernelName:Gu,backendName:"webgl",kernelFunc:vie},kie=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` + void main() { + ivec2 coords = getOutputCoords(); + int index = round(getIndices(coords.x)); + setOutput(mix(float(${a}), float(${n}), + float(index == coords.y))); + } + `}},Iie=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=w.sizeFromShape(r.shape),p=new kie(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},Sie={kernelName:vo,backendName:"webgl",kernelFunc:Iie};function _m(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Pd({inputs:{input:a},backend:n}),s=_m({inputs:{x:r},backend:n}),i=ag({inputs:{input:a},backend:n}),o=_m({inputs:{x:i},backend:n}),l=Os({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var Nie={kernelName:up,backendName:"webgl",kernelFunc:_m};function TF(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Pd({inputs:{input:a},backend:n}),s=TF({inputs:{x:r},backend:n}),i=ag({inputs:{input:a},backend:n}),o=_m({inputs:{x:i},backend:n}),l=Os({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var Tie={kernelName:Hu,backendName:"webgl",kernelFunc:TF};function Cie(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Sv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=Sv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=cF({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Eie={kernelName:ju,backendName:"webgl",kernelFunc:Cie},_ie=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ht(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` + int start = ${s}; + int end = ${i}; + + void main() { + int outC = getOutputCoords(); + if (outC < start || outC >= end) { + setOutput(value); + } else { + setOutput(getX(outC - start)); + } + } + `;return}this.userCode=` + ${r} start = ${r}(${s}); + ${r} end = ${r}(${i}); + + void main() { + ${r} outC = getOutputCoords(); + if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { + setOutput(value); + } else { + ${r} coords = outC - start; + setOutput(getX(${o})); + } + } + `}},Aie=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ht(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; + if(${u}) { + `,a===1?"":`} + rc = outputLoc; + ${o[a-2]} += 1; + if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1; + if(${u}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return Ld({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Aie(r.shape,s,i):new _ie(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},Fie={kernelName:wo,backendName:"webgl",kernelFunc:CF},$ie=` + if(a < 0.0 && floor(b) < b){ + return NAN; + } + if (b == 0.0) { + return 1.0; + } + return (round(mod(b, 2.0)) != 1) ? + pow(abs(a), b) : sign(a) * pow(abs(a), b); +`,Die=` + // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. + vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); + vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); + vec4 result = multiplier * pow(abs(a), b); + + // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS + bvec4 isExpZero = equal(b, vec4(0.0)); + result.r = isExpZero.r ? 1.0 : result.r; + result.g = isExpZero.g ? 1.0 : result.g; + result.b = isExpZero.b ? 1.0 : result.b; + result.a = isExpZero.a ? 1.0 : result.a; + + bvec4 isNaN1 = lessThan(a, vec4(0.0)); + bvec4 isNaN2 = lessThan(floor(b), b); + bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); + `+tl+` + return result; +`,Rie=fn({opSnippet:$ie,packedOpSnippet:Die}),Mie={kernelName:ko,backendName:"webgl",kernelFunc:Rie};function Oie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.parseAxisParam(s,r.shape),p=u,d=T.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=T.getInnerMostAxes(p.length,o),l.push(c)),T.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=$Q(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=T.computeOutAndReduceShapes(c.shape,p),g=w.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Xm(r.dtype),x=nl(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=T.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Pie={kernelName:So,backendName:"webgl",kernelFunc:Oie};function Lie(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.readSync(b.dataId)),u=r.map(b=>b.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=DQ(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var zie={kernelName:Gm,backendName:"webgl",kernelFunc:Lie};function Wie(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=RQ(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var Bie={kernelName:Hm,backendName:"webgl",kernelFunc:Wie};function Vie(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=MQ(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var Uie={kernelName:jm,backendName:"webgl",kernelFunc:Vie},EF=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=OQ(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Gie={kernelName:Xc,backendName:"webgl",kernelFunc:EF},Hie="return 1.0 / x;",jie=Ze({opSnippet:Hie}),qie={kernelName:No,backendName:"webgl",kernelFunc:jie},Kie=Ma+` + return (x < 0.0) ? 0.0 : x; +`,Xie=` + vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); + bvec4 isNaN = isnan(x); + + result.r = isNaN.r ? x.r : result.r; + result.g = isNaN.g ? x.g : result.g; + result.b = isNaN.b ? x.b : result.b; + result.a = isNaN.a ? x.a : result.a; + + return result; +`,Yie=Ze({opSnippet:Kie,packedOpSnippet:Xie}),Zie={kernelName:To,backendName:"webgl",kernelFunc:Yie},Jie=Ma+` + return (x < 0.0) ? 0.0 : min(6.0, x); +`,Qie=` + vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); + bvec4 isNaN = isnan(x); + + result.r = isNaN.r ? x.r : result.r; + result.g = isNaN.g ? x.g : result.g; + result.b = isNaN.b ? x.b : result.b; + result.a = isNaN.a ? x.a : result.a; + + return result; +`,eoe=Ze({opSnippet:Jie,packedOpSnippet:Qie}),toe={kernelName:_o,backendName:"webgl",kernelFunc:eoe},noe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + const vec2 effectiveInputOverOutputRatioRC = vec2( + ${u[0]/p[0]}, + ${u[1]/p[1]}); + const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + ivec2 yRC = coords.yz; + + // Fractional source index. + vec2 sourceFracIndexRC = ${d}; + + // Compute the four integer indices. + ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); + ivec2 sourceCeilRC = ivec2( + min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); + + float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d); + float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d); + float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d); + float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d); + + vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC); + + float top = topLeft + (topRight - topLeft) * fracRC.y; + float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; + float newValue = top + (bottom - top) * fracRC.x; + + setOutput(newValue); + } + `}},aoe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + const vec3 effectiveInputOverOutputRatioRC = vec3( + ${u[0]/p[0]}, + ${u[1]/p[1]}, + ${u[1]/p[1]}); + const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, + ${o}.0); + + float getAValue(int b, int r, int c, int d) { + return getChannel(getA(b, r, c, d), vec2(c, d)); + } + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + // Calculate values for next column in yRC.z. + ivec3 yRC = coords.yzz + ivec3(0, 0, 1); + + // Fractional source index. + vec3 sourceFracIndexRC = ${d}; + + // Compute the four integer indices. + ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); + ivec3 sourceCeilRC = ivec3( + min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); + + // Should we calculate next column and row elements in 2x2 packed cell. + bool hasNextCol = d < ${l-1}; + bool hasNextRow = coords.z < ${n-1}; + + // In parallel, construct four corners for all four components in + // packed 2x2 cell. + vec4 topLeft = vec4( + getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d), + hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1) + : 0.0, + hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d) + : 0.0, + (hasNextRow && hasNextCol) ? + getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0); + + vec4 bottomLeft = vec4( + getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d), + hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1) + : 0.0, + hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d) + : 0.0, + (hasNextRow && hasNextCol) ? + getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0); + + vec4 topRight = vec4( + getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d), + hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1) + : 0.0, + hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d) + : 0.0, + (hasNextRow && hasNextCol) ? + getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0); + + vec4 bottomRight = vec4( + getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d), + hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1) + : 0.0, + hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d) + : 0.0, + (hasNextRow && hasNextCol) ? + getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0); + + vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC); + + vec4 top = mix(topLeft, topRight, fracRC.yyzz); + vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz); + vec4 newValue = mix(top, bottom, fracRC.x); + + setOutput(newValue); + } + `}};function roe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aoe(r.shape,l,u,s,i):new noe(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var soe={kernelName:Eo,backendName:"webgl",kernelFunc:roe},ioe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + int r = coords[1]; + int c = coords[2]; + + float accumulator = 0.0; + + const float heightScale = float(${u}); + const float widthScale = float(${p}); + + const float invHeightScale = float(${d}); + const float invWidthScale = float(${c}); + + const int winHeight = int(${h}); + const int winWidth = int(${m}); + + // Compute bounds for where in dy we will look + float startRLerp = floor(float(r) * invHeightScale); + int startDyR = int(startRLerp - float(winHeight / 2)); + + float startCLerp = floor(float(c) * invWidthScale); + int startDyC = int(startCLerp - float(winWidth / 2)); + + // Loop over dy + for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { + int dyR = dyROffset + startDyR; + + // Guard against the window exceeding the bounds of dy + if (dyR < 0 || dyR >= ${s}) { + continue; + } + + for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { + int dyC = dyCOffset + startDyC; + + // Guard against the window exceeding the bounds of dy + if (dyC < 0 || dyC >= ${i}) { + continue; + } + + float dxR = float(dyR) * heightScale; + int topDxRIndex = int(floor(dxR)); + int bottomDxRIndex = int(min(ceil(dxR), ${a-1}.0)); + float dxRLerp = dxR - float(topDxRIndex); + float inverseDxRLerp = 1.0 - dxRLerp; + + float dxC = float(dyC) * widthScale; + int leftDxCIndex = int(floor(dxC)); + int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0)); + float dxCLerp = dxC - float(leftDxCIndex); + float inverseDxCLerp = 1.0 - dxCLerp; + + if (r == topDxRIndex && c == leftDxCIndex) { + // topLeft + accumulator += + getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; + } + + if (r == topDxRIndex && c == rightDxCIndex) { + // topRight + accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; + } + + if (r == bottomDxRIndex && c == leftDxCIndex) { + // bottomLeft + accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; + } + + if (r == bottomDxRIndex && c == rightDxCIndex) { + // bottomRight + accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; + } + } + } + // End loop over dy + + setOutput(accumulator); + } + `}};function ooe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new ioe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var loe={kernelName:Xu,backendName:"webgl",kernelFunc:ooe},uoe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + const vec2 effectiveInputOverOutputRatioRC = vec2( + ${u[0]/p[0]}, + ${u[1]/p[1]}); + const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + ivec2 yRC = coords.yz; + + // Fractional source index. + vec2 sourceFracIndexRC = ${c}; + + // Compute the coordinators of nearest neighbor point. + ivec2 sourceNearestRC = ivec2( + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); + float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); + + setOutput(newValue); + } + `}},poe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + const vec3 effectiveInputOverOutputRatioRC = vec3( + ${u[0]/p[0]}, + ${u[1]/p[1]}, + ${u[1]/p[1]}); + const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, + ${o}.0); + + float getAValue(int b, int r, int c, int d) { + return getChannel(getA(b, r, c, d), vec2(c, d)); + } + + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + // Calculate values for next column in yRC.z. + ivec3 yRC = coords.yzz + ivec3(0, 0, 1); + + // Fractional source index. + vec3 sourceFracIndexRC = ${c}; + + // Compute the coordinators of nearest neighbor point. + ivec3 sourceNearestRC = ivec3( + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); + + // Should we calculate next column and row elements in 2x2 packed cell. + bool hasNextCol = d < ${l-1}; + bool hasNextRow = coords.z < ${n-1}; + + vec4 newValue = vec4( + getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), + hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) + : 0.0, + hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) + : 0.0, + (hasNextRow && hasNextCol) ? + getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); + + setOutput(newValue); + } + `}};function coe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new poe(r.shape,l,u,s,i):new uoe(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var doe={kernelName:Co,backendName:"webgl",kernelFunc:coe},hoe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int b = coords[0]; + int d = coords[3]; + int r = coords[1]; + int c = coords[2]; + + float accumulator = 0.0; + + const float heightScale = float(${u}); + const float widthScale = float(${p}); + + const float invHeightScale = float(${d}); + const float invWidthScale = float(${c}); + + const int winHeight = int(${h}); + const int winWidth = int(${m}); + + // Compute bounds for where in dy we will look + float startRLerp = floor(float(r) * invHeightScale); + int startDyR = int(floor(startRLerp - float(winHeight / 2))); + + float startCLerp = floor(float(c) * invWidthScale); + int startDyC = int(floor(startCLerp - float(winWidth / 2))); + + // Loop over dy + for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { + int dyR = dyROffset + startDyR; + + // Guard against the window exceeding the bounds of dy + if (dyR < 0 || dyR >= ${s}) { + continue; + } + + for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { + int dyC = dyCOffset + startDyC; + + // Guard against the window exceeding the bounds of dy + if (dyC < 0 || dyC >= ${i}) { + continue; + } + + float sourceFracRow = + float(${o[0]}) * + (float(dyR) / float(${l[0]})); + + float sourceFracCol = + float(${o[1]}) * + (float(dyC) / float(${l[1]})); + + int sourceNearestRow = int(min( + float(int(${a}) - 1), + ${n} ? float(round(sourceFracRow)) : + float(floor(sourceFracRow)))); + + int sourceNearestCol = int(min( + float(int(${r}) - 1), + ${n} ? float(round(sourceFracCol)) : + float(floor(sourceFracCol)))); + + if (r == sourceNearestRow && c == sourceNearestCol) { + accumulator += getDy(b, dyR, dyC, d); + } + } + } + // End loop over dy + + setOutput(accumulator); + } + `}};function moe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new hoe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var foe={kernelName:Ku,backendName:"webgl",kernelFunc:moe},goe=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + void main() { + int coord = getOutputCoords(); + setOutput(getX(${e[0]} - coord - 1)); + } + `;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=ht(n);this.userCode=` + void main() { + ${s} coords = getOutputCoords(); + setOutput(getX(${r})); + } + `}},boe=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=In("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ht(n);n===1?this.userCode=` + void main(){ + int rc = getOutputCoords(); + vec4 result = vec4(0.); + result.r = getChannel(getX(${e[0]} - rc - 1), + ${e[0]} - rc - 1); + if(${r}){ + result.g = getChannel(getX(${e[0]} - (rc + 1) - 1), + ${e[0]} - (rc + 1) - 1); + } + setOutput(result); + } + `:this.userCode=` + void main() { + ${i} rc = getOutputCoords(); + vec4 result = vec4(0.); + result.r = ${o(a.slice())}; + if(${r}){ + result.g = ${l(a.slice())}; + } + if(${s}) { + result.b = ${u(a.slice())}; + if(${r}) { + result.a = ${p(a.slice())}; + } + } + setOutput(result); + } + `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function yoe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return aa({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new boe(r.shape,o):new goe(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var xoe={kernelName:Ao,backendName:"webgl",kernelFunc:yoe},voe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + vec3 fill = vec3(${t.join(",")}); + float outputValue = fill[coords[3]];`,this.userCode=` + void main() { + ivec4 coords = getOutputCoords(); + int x = coords[2]; + int y = coords[1]; + float coordXFloat = (float(x) - params[0]) * params[3] - + (float(y) - params[1]) * params[2]; + float coordYFloat = (float(x) - params[0]) * params[2] + + (float(y) - params[1]) * params[3]; + int coordX = int(round(coordXFloat + params[0])); + int coordY = int(round(coordYFloat + params[1])); + ${r} + if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) { + outputValue = getImage(coords[0], coordY, coordX, coords[3]); + } + setOutput(outputValue); + } + `}},woe={kernelName:pp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new voe(a.shape,s),[u,p]=T.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},koe=` + // OpenGL ES does not support round function. + // The algorithm is based on banker's rounding. + float base = floor(x); + if ((x - base) < 0.5) { + return floor(x); + } else if ((x - base) > 0.5) { + return ceil(x); + } else { + if (mod(base, 2.0) == 0.0) { + return base; + } else { + return base + 1.0; + } + } +`,Ioe=Ze({opSnippet:koe}),Soe={kernelName:Fo,backendName:"webgl",kernelFunc:Ioe},Noe="return inversesqrt(x);",Toe=Ze({opSnippet:Noe,cpuKernelImpl:PQ}),Coe={kernelName:$o,backendName:"webgl",kernelFunc:Toe},vk=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` + ${l} strides = ${l}(${r}); + + void main() { + ${u} coords = getOutputCoords(); + float sum = 0.0; + bool found = false; + for (int i = 0; i < ${e}; i++) { + int flattenedIndex = 0; + for (int j = 0; j < ${t}; j++) { + int index = round(${d}); + flattenedIndex += index * ${g}; + } + if (flattenedIndex == coords[0]) { + sum += ${h}; + found = true; + } + } + setOutput(mix(${f}, sum, float(found))); + } + `}},Eoe=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` + ${l} strides = ${l}(${r}); + + void main() { + ${u} coords = getOutputCoords(); + vec4 sum = vec4(0.); + vec4 found = vec4(0.); + for (int i = 0; i < ${e}; i+=2) { + ivec2 flattenedIndex = ivec2(0); + for (int j = 0; j < ${t}; j+=2) { + ivec4 index = round(${d}); + flattenedIndex += index.xz * ${g}; + if (j + 1 < ${t}) { + flattenedIndex += index.yw * ${b}; + } + } + if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] || + flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) { + vec4 updVals = ${h}; + if (flattenedIndex[0] == coords[0]) { + sum.xy += updVals.xy; + found.xy = vec2(1.); + } else if (flattenedIndex[0] == coords[0] + 1) { + sum.zw += updVals.xy; + found.zw = vec2(1.); + } + if (flattenedIndex[1] == coords[0]) { + sum.xy += updVals.zw; + found.xy = vec2(1.); + } else if (flattenedIndex[1] == coords[0] + 1) { + sum.zw += updVals.zw; + found.zw = vec2(1.); + } + } + } + setOutput(mix(${f}, sum, found)); + } + `}};function _oe(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=T.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g;G().getBool("WEBGL_PACK")?g=new Eoe(l,o,h.shape.length,m.shape.length,p,c):g=new vk(l,o,h.shape.length,m.shape.length,p,c);let b=n.runWebGLProgram(g,[m,h,f],m.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(f),y}var Aoe={kernelName:Yu,backendName:"webgl",kernelFunc:_oe},Foe=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=G().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=` + int findBound(int batch, float value) { + int left = 0; + int right = numInputs; + int mid; + ${i} + mid = (left + right) / 2; + if (getSortedSequence(batch, mid) ${o} value) { + left = mid + 1; + } else { + right = mid; + } + } + return right; + } + + void main() { + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + int valueIndex = coords[1]; + + float value = getValues(batch, valueIndex); + + setOutput(float(findBound(batch, value))); + } + `}};function $oe(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a,o=new Foe(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,s],"int32",l)}var Doe={kernelName:Ju,backendName:"webgl",kernelFunc:$oe},Roe=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 1.0) { + setOutput(getA(${r})); + } else { + setOutput(getB(${r})); + } + } + `}};function Moe(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new Roe(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ga(r.dtype,s.dtype))}var Ooe={kernelName:Qu,backendName:"webgl",kernelFunc:Moe},Poe=` + // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. + // see: https://arxiv.org/abs/1706.02515 + float scaleAlpha = ${T.SELU_SCALEALPHA}; + float scale = ${T.SELU_SCALE}; + return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); +`,Loe=Ze({opSnippet:Poe}),zoe={kernelName:Do,backendName:"webgl",kernelFunc:Loe},Woe=Cp+` + return 1.0 / (1.0 + exp(-1.0 * x)); +`,Boe=` + vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); + bvec4 isNaN = isnan(x); + + result.r = isNaN.r ? x.r : result.r; + result.g = isNaN.g ? x.g : result.g; + result.b = isNaN.b ? x.b : result.b; + result.a = isNaN.a ? x.a : result.a; + + return result; +`,Voe=Ze({opSnippet:Woe,packedOpSnippet:Boe,cpuKernelImpl:zQ}),Uoe={kernelName:Po,backendName:"webgl",kernelFunc:Voe},Goe=` + if (isnan(x)) { return 0.0; } + return sign(x); +`,Hoe=Ze({opSnippet:Goe}),joe={kernelName:Oo,backendName:"webgl",kernelFunc:Hoe},qoe=Cp+` + return sin(x); +`,Koe=` + vec4 result = sin(x); + bvec4 isNaN = isnan(x); + ${tl} + return result; +`,Xoe=Ze({opSnippet:qoe,packedOpSnippet:Koe}),Yoe={kernelName:Ro,backendName:"webgl",kernelFunc:Xoe},Zoe=` + float e2x = exp(x); + return (e2x - 1.0 / e2x) / 2.0; +`,Joe=Ze({opSnippet:Zoe}),Qoe={kernelName:Mo,backendName:"webgl",kernelFunc:Joe},ele=` + float epsilon = 1.1920928955078125e-7; + float threshold = log(epsilon) + 2.0; + + bool too_large = x > -threshold; + bool too_small = x < threshold; + + float result; + float exp_x = exp(x); + + if (too_large){ + result = x; + } + else if (too_small){ + result = exp_x; + } + else{ + result = log(exp_x + 1.0); + } + return result; +`,tle=Ze({opSnippet:ele}),nle={kernelName:Lo,backendName:"webgl",kernelFunc:tle},ale=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,y)=>b*y),l=[[0,0]];l.push(...i);for(let b=1+s.length;bn.disposeIntermediateTensorInfo(b)),g},rle={kernelName:tp,backendName:"webgl",kernelFunc:ale};function sle(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: + ${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw: + ${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw: + ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw: + ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=n.readSync(i.dataId)[0],[d,c,h,m,f]=BQ(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var ile={kernelName:Yc,backendName:"webgl",kernelFunc:sle};function ole(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=VQ(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var lle={kernelName:ap,backendName:"webgl",kernelFunc:ole};function ule(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should 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hle(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let b=n.bufferSync(r),y=n.bufferSync(s),x=w.decodeString(n.readSync(i.dataId)[0]),v=LQ(b,y,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,v.dtype,v.values)}let m=new vk(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=ce({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var mle={kernelName:rp,backendName:"webgl",kernelFunc:hle};function fle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=Ep({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var gle={kernelName:np,backendName:"webgl",kernelFunc:fle},HS="return sqrt(x);",ble=Ze({opSnippet:HS,packedOpSnippet:HS,cpuKernelImpl:UQ}),yle={kernelName:zo,backendName:"webgl",kernelFunc:ble},xle="return x * x;",vle=Ze({opSnippet:xle}),wle={kernelName:Qc,backendName:"webgl",kernelFunc:vle},jS="return (a - b) * (a - b);",kle=fn({opSnippet:jS,packedOpSnippet:jS}),Ile={kernelName:Vo,backendName:"webgl",kernelFunc:kle};function Sle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=n.readSync(r.dataId),i=T.fromUint8ToStringArray(s),o=GQ(i,"string",a);return n.makeTensorInfo(r.shape,"string",o)}var Nle={kernelName:ed,backendName:"webgl",kernelFunc:Sle};function Tle({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ma+` + return x > 0.0 ? 1.0 : float(${t.alpha}); + `,s=new ir(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Cle={kernelName:Cs,backendName:"webgl",kernelFunc:Tle},Ele=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ht(n.length),s=ht(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + ${r} begin = ${r}(${e}); + ${r} strides = ${r}(${t}); + + void main() { + ${s} coords = getOutputCoords(); + setOutput(getX(${i})); + } + `}};function _le(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:b,begin:y,end:x,strides:v}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=ce({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let 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Dle(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=qQ(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Rle={kernelName:nd,backendName:"webgl",kernelFunc:Dle};function Mle(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=KQ(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var Ole={kernelName:ad,backendName:"webgl",kernelFunc:Mle},Ple="return tan(x);",Lle=Ze({opSnippet:Ple}),zle={kernelName:Go,backendName:"webgl",kernelFunc:Lle},Wle=` + float e2x = exp(-2.0 * abs(x)); + return sign(x) * (1.0 - e2x) / (1.0 + e2x); +`,Ble=Ze({opSnippet:Wle}),Vle={kernelName:Ho,backendName:"webgl",kernelFunc:Ble};function Ule(e){let{inputs:t,backend:n,attrs:a}=e,{tensor:r,indices:s,updates:i}=t,{}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=T.calculateShapes(i,s,r.shape),c=[d/u,u];if(d===0)return n.makeTensorInfo(r.shape,s.dtype);let h=ce({inputs:{x:s},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:i},backend:n,attrs:{shape:[l,u]}}),f=ce({inputs:{x:r},backend:n,attrs:{shape:c}}),g=new vk(l,o,h.shape.length,m.shape.length,p,c,!1,!0),b=n.runWebGLProgram(g,[m,h,f],f.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),y}var Gle={kernelName:Zu,backendName:"webgl",kernelFunc:Ule},Hle=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>w.decodeString(d)):o,u=Pe(r.shape,r.dtype,l),p=YQ(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Hle(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var qle={kernelName:Ts,backendName:"webgl",kernelFunc:_F},Kle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + void main() { + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + int elemIdx = coords[1]; + + // We compare elements pair-wise within a group of size 2 * inc. + // The comparing rule for each group alternates between ascending + // and descending. Within each group, we compare each pair at + // positions i and i+inc. To decide whether an element at position i + // is x0 or x1, we mod it by 2 * inc, if the result is smaller than + // inc, it is in the first half of the group, we denote it as x0, + // otherwise we denote it as x1. + // For example, as shown in the Bitonic top K paper referenced above, + // Figure5(a) shows that element[1] is in the + // second half of the group when group size is 2, but it is in the + // first half of the group when group size is 4. + + bool isFirstInPair = imod(elemIdx, 2 * inc) < inc; + int i = isFirstInPair ? elemIdx : elemIdx - inc; + + int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); + int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc)); + float x0 = i0 < n ? getX(batch, i0) : negativeInf; + float x1 = i1 < n ? getX(batch, i1) : negativeInf; + + // Denotes which direction indices are in (ascending or descending). + bool reverse = imod(elemIdx, 2 * dir) >= dir; + bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0); + if (reverse == isGreater) { // Elements in opposite order of direction + int iTemp = i0; + i0 = i1; + i1 = iTemp; + } + if (isFirstInPair) { + setOutput(float(i0)); + } else { + setOutput(float(i1)); + } + } + `}},Xle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` + void main() { + // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + int elemIdx = coords[1]; + + // The output size is half of the previous size. + // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4), + // we only need to output the indices at positions |, the indices at + // positions _ can be thrown away, see Figure5(b) After Phase 2 + // (Merge phase) in the Bitonic Top K paper referenced above. + // For example, the paper shows we only need to output the orange bars. + // The output sequence should look like this | | | | | | | |. + // Because the sequence is halved, to map the output index back + // to the previous sequence to find the corresponding value, + // we need to double the index. When we double the index, + // we basically interpolate a position, so 2i looks like + // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position + // of each 2k positions by - elemIdx % k. E.g. for output at + // index 4,5,6,7, we want to get the corresponding element at + // original index 8,9,10,11, for output at index 8,9,10,11, + // we want to get the corresponding element at original index + // 16,17,18,19, so on and so forth. + + int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k)); + int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); + int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k)); + + float x0 = getX(batch, i0); + float x1 = i1 < n ? getX(batch, i1) : x0; + + setOutput(x0 >= x1 ? float(i0) : float(i1)); + } + `}};function Xs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function qS(e){let t=1;for(;tl){let F=n.readSync(r.dataId),[D,$]=ZQ(F,u,r.dtype,s,i);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Ld({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=w.sizeFromShape(u)/p,f=ce({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Xs(n,h);let g=qS(s),b=qS(p),y=null,x=()=>y===null?[f,f]:[f,y],v=(F,D,$)=>{let S=x(),M=new Kle($),B=[[p],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],U=y;y=n.runWebGLProgram(M,S,"int32",B),Xs(n,U)};for(let F=1;F=1;$/=2)v(D,$,[m,b])}for(let F=b;F>g;F/=2){let D=x(),$=new Xle([m,F/2]),S=[[p],[y===null?1:0],[g]],M=y;y=n.runWebGLProgram($,D,"int32",S),Xs(n,M);let B=g/2,U=B*2;for(let H=B;H>=1;H/=2)v(U,H,y.shape)}let I=y;y=Ep({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,s]}}),Xs(n,I);let N=wF({inputs:{x:f,indices:y},backend:n,attrs:{axis:1,batchDims:1}});Xs(n,f);let C=u.slice(0,-1);C.push(s),I=y,y=ce({inputs:{x:y},attrs:{shape:C},backend:n}),Xs(n,I);let _=N;return N=ce({inputs:{x:N},attrs:{shape:C},backend:n}),Xs(n,_),[N,y]}var Zle={kernelName:ip,backendName:"webgl",kernelFunc:Yle},Jle=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` + float mapCoord(float outCoord, float len) { + float inCoord = outCoord; + if(${o} == 2) { + if (inCoord < 0.0) { + if (len <= 1.0) { + inCoord = 0.0; + } else { + float sz2 = 2.0 * len; + if (inCoord < sz2) { + inCoord = sz2 * float(int(float(-inCoord / sz2))) + + inCoord; + } + inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; + } + } else if (inCoord > len - 1.0) { + if (len <= 1.0) { + inCoord = 0.0; + } else { + float sz2 = 2.0 * len; + inCoord -= sz2 * float(int(float(inCoord / sz2))); + if (inCoord >= len) { + inCoord = sz2 - inCoord - 1.0; + } + } + } + return clamp(inCoord, 0.0, len - 1.0); + } else if (${o} == 3) { + if (inCoord < 0.0) { + if (len <= 1.0) { + inCoord = 0.0; + } else { + float sz = len - 1.0; + inCoord += len * (float(int(float(-inCoord / sz))) + 1.0); + } + } else if (inCoord > len - 1.0) { + if (len <= 1.0) { + inCoord = 0.0; + } else { + float sz = len - 1.0; + inCoord -= len * float(int(float(inCoord / sz))); + } + } + return clamp(inCoord, 0.0, len - 1.0); + } else if (${o} == 4) { + return clamp(outCoord, 0.0, len - 1.0); + } else { + return outCoord; + } + } + + float readWithFillValue(int batch, int coordY, int coordX, + int channel) { + float outputValue; + if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) { + outputValue = getImage(batch, coordY, coordX, channel); + } else { + outputValue = float(${r}); + } + return outputValue; + } + + void main() { + ivec4 coords = getOutputCoords(); + float outputValue; + int batch = coords[0]; + int x = coords[2]; + int y = coords[1]; + int channel = coords[3]; + float xf = float(x); + float yf = float(y); + float a1 = getTransforms(batch, 0); + float a2 = getTransforms(batch, 1); + float a3 = getTransforms(batch, 2); + float b1 = getTransforms(batch, 3); + float b2 = getTransforms(batch, 4); + float b3 = getTransforms(batch, 5); + float c1 = getTransforms(batch, 6); + float c2 = getTransforms(batch, 7); + float projection = c1 * xf + c2 * yf + 1.0; + if (projection == 0.0) { + outputValue = float(${r}); + } else { + float inX = (a1 * xf + a2 * yf + a3) / projection; + float inY = (b1 * xf + b2 * yf + b3) / projection; + float mapX = mapCoord(inX, float(${t})); + float mapY = mapCoord(inY, float(${e})); + + if (${i} == 1) { + int coordY = int(round(mapY)); + int coordX = int(round(mapX)); + outputValue = readWithFillValue(batch, coordY, coordX, + channel); + } else { + float yFloor = floor(mapY); + float xFloor = floor(mapX); + float yCeil = yFloor + 1.0; + float xCeil = xFloor + 1.0; + float valueYFloor = (xCeil - mapX) * + readWithFillValue(batch, int(yFloor), int(xFloor), channel) + + (mapX - xFloor) * + readWithFillValue(batch, int(yFloor), int(xCeil), channel); + float valueYCeil = (xCeil - mapX) * + readWithFillValue(batch, int(yCeil), int(xFloor), channel) + + (mapX - xFloor) * + readWithFillValue(batch, int(yCeil), int(xCeil), channel); + outputValue = (yCeil - mapY) * valueYFloor + + (mapY - yFloor) * valueYCeil; + } + } + setOutput(outputValue); + } + `}};function Qle(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],b=new Jle(d,c,i,o,l,g);return n.runWebGLProgram(b,[r,s],"float32")}var eue={kernelName:op,backendName:"webgl",kernelFunc:Qle};function tue(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;wp(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=JQ(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var nue={kernelName:rd,backendName:"webgl",kernelFunc:tue};function aue(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var rue={kernelName:lp,backendName:"webgl",kernelFunc:aue},sue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=` + sumValue += dot(values, segFilter); + `,c="";r%n>0&&(c=` + if (inIdx < 0 || inIdx >= ${r}) { + return initializationValue; + } + `);let h="";r%n>0&&(h=` + if (inIdx < 0 || inIdx >= ${r}) { + return -1.0; + } + `),this.userCode=` + const float initializationValue = ${o}; + + float getValue(int batch, int inIdx) { + ${c} + return getX(batch, inIdx); + } + + float getSegmentIdAtIndex(int inIdx) { + ${h} + return getSegmentIds(inIdx); + } + + void main() { + ivec2 coords = getOutputCoords(); + int batch = coords[0]; + int outIdx = coords[1]; + int inOffset = int(floor(float(outIdx) / float( + ${s})) * float(${n})); + int currentSeg = int(mod(float(outIdx), float(${s}))); + + float sumValue = 0.0; + + for (int i = 0; i < ${u}; i += 4) { + int inIdx = inOffset + i; + vec4 values = vec4( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), + getValue(batch, inIdx + 3) + ); + + vec4 segFilter = vec4( + int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 + ); + + ${d} + } + + int inIdx = inOffset + ${u}; + if (${p===1}) { + vec4 values = vec4( + getValue(batch, inIdx), + initializationValue, + initializationValue, + initializationValue + ); + + int inIdxSeg = int(getSegmentIdAtIndex(inIdx)); + + vec4 segFilter = vec4( + int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, + 0, + 0, + 0 + ); + + ${d} + } else if (${p===2}) { + vec4 values = vec4( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + initializationValue, + initializationValue + ); + + vec4 segFilter = vec4( + int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, + 0, + 0 + ); + + ${d} + } else if (${p===3}) { + vec4 values = vec4( + getValue(batch, inIdx), + getValue(batch, inIdx + 1), + getValue(batch, inIdx + 2), + initializationValue + ); + + vec4 segFilter = vec4( + int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, + int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, + 0 + ); + + ${d} + } + setOutput(${l}); + } + `}};function iue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=T.getAxesPermutation([u],o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=T.getInnerMostAxes(1,o)[0]);let c=T.segment_util.computeOutShape(d.shape,u,i),h=w.sizeFromShape([d.shape[u]]),m=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Xm(r.dtype),g=(v,I,N,C,_)=>{let F=v.shape[0],D=v.shape[1],$=T.segment_util.segOpComputeOptimalWindowSize(D,_),S={windowSize:$,inSize:D,batchSize:F,numSegments:_},M=new sue(S,I),B=n.compileAndRun(M,[v,N],C);if(l.push(B),B.shape[1]===_)return B;let U=EF({backend:n,attrs:{start:0,stop:_,step:1,dtype:"float32"}}),H=_F({inputs:{x:U},backend:n,attrs:{reps:[D/$]}});return l.push(U),l.push(H),g(B,I,H,C,_)},b=g(m,"unsortedSegmentSum",s,f,i),y=ce({inputs:{x:b},backend:n,attrs:{shape:c}}),x=y;if(p!=null){l.push(y);let v=T.getUndoAxesPermutation(p);x=Sn({inputs:{x},backend:n,attrs:{perm:v}})}return 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spe={kernelName:fu,backendName:"wasm",kernelFunc:rpe},BF;function ipe(e){BF=e.wasm.cwrap(gu,null,["number","number","boolean","number","number","number"])}function ope(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i}=a,o=s.shape.reduce((d,c)=>d*c,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(d){return t.dataIdMap.get(d.dataId).id}return BF(p(r),i,o,p(s),Qe[s.dtype],p(u)),u}var lpe={kernelName:gu,backendName:"wasm",setupFunc:ipe,kernelFunc:ope},upe=!0,ppe=Ut(bu,upe);function cpe(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.typedArrayFromHeap(a),i=n.typedArrayFromHeap(r),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var dpe={kernelName:Bc,backendName:"wasm",kernelFunc:cpe};function Ls(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var hpe={kernelName:Pi,backendName:"wasm",kernelFunc:Ls},mpe=Xe(Li),VF;function fpe(e){VF=e.wasm.cwrap(Ns,null,["number","number","number","number"])}function gpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return VF(o,s,i,u),l}var bpe={kernelName:Ns,backendName:"wasm",setupFunc:fpe,kernelFunc:gpe};function UF(e){let{inputs:t,backend:n}=e,a=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);T.assertParamsConsistent(r,a);let s=T.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>w.sizeFromShape(h.shape)>0);if(i.length===1)return rg({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(w.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let v=[-1,w.sizeFromShape(x.shape.slice(a))];return 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Jpe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,I=h.dilationWidth,N=h.strideHeight,C=h.strideWidth,_=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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Got ${r.dtype} and ${s.dtype}`);let u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=n.makeOutput(u.outShape,r.dtype);return n$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,Qe[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var sce={kernelName:ji,backendName:"wasm",setupFunc:ace,kernelFunc:rce},a$;function ice(e){a$=e.wasm.cwrap(ql,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. 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Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return r$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var cce={kernelName:jl,backendName:"wasm",setupFunc:uce,kernelFunc:pce},dce=Xe(Ki),s$;function hce(e){s$=e.wasm.cwrap(Su,null,["number","number","number"])}function mce(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return s$(i(r),i(a),i(s)),s}var fce={kernelName:Su,backendName:"wasm",setupFunc:hce,kernelFunc:mce},gce=!1,bce=Ut(Nu,gce,"bool"),yce=Xe(Xi),xce=Xe(Yi,"float32");function Tv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),zn({inputs:{x:r},backend:a,attrs:{shape:o}})}var vce={kernelName:Tu,backendName:"wasm",kernelFunc:Tv},wce=Xe(Zi,"float32");function i$(e){let{attrs:{shape:t,value:n},backend:a}=e,{attrs:{dtype:r}}=e;r=r||w.inferDtype(n);let s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var kce={kernelName:Hc,backendName:"wasm",kernelFunc:i$},o$;function Ice(e){o$=e.wasm.cwrap(Cu,null,["number","number","number","number","number","number"])}function Sce(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return o$(s,o,l,u,p,i),r}var Nce={kernelName:Cu,backendName:"wasm",kernelFunc:Sce,setupFunc:Ice},Tce=Xe(Ji),Cce=!1,Ece=Ut(Qi,Cce),l$;function _ce(e){l$=e.wasm.cwrap(eo,null,["number","number","number","number","number","number","number"])}function Ace(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return l$(p,d,c,h,m,r,g),f}var Fce={kernelName:eo,backendName:"wasm",setupFunc:_ce,kernelFunc:Ace},u$;function $ce(e){u$=e.wasm.cwrap(ui,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Oc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return u$(b,q,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Rce={kernelName:ui,backendName:"wasm",setupFunc:$ce,kernelFunc:Dce},p$;function Mce(e){p$=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Oce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Oc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return p$(b,q,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Pce={kernelName:pi,backendName:"wasm",setupFunc:Mce,kernelFunc:Oce},c$;function Lce(e){c$=e.wasm.cwrap(_u,null,["number","number","number","number","number","number","array","number"])}function zce(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=r0.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return c$(c,Qe[a.dtype],h,i,d,o,m,f),u}var Wce={kernelName:_u,backendName:"wasm",setupFunc:Lce,kernelFunc:zce},d$;function Bce(e){d$=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Vce(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${_} is not in [0, ${p-1}]`)}let d=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=zn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=w.sizeFromShape(s.shape),m=zn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,v=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),N=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer);return d$(y,Qe[r.dtype],I,b,x,d.batchSize,N,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var Uce={kernelName:Eu,backendName:"wasm",setupFunc:Bce,kernelFunc:Vce},Gce=!1,Hce=Ut(Au,Gce,"bool"),jce=!1,qce=Ut(to,jce,"bool"),Kce=Xe(ao,"bool"),Xce=Xe(ro,"bool"),Yce=Xe(so,"bool"),h$;function Zce(e){h$=e.wasm.cwrap(io,null,["number","number","number","number"])}function Jce(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;h$(r,Qe[t.dtype],n,i)}return s}var Qce={kernelName:io,backendName:"wasm",setupFunc:Zce,kernelFunc:Jce},ede=!1,tde=Ut(Fu,ede,"bool"),nde=!1,ade=Ut($u,nde,"bool"),m$;function rde(e){m$=e.wasm.cwrap(Du,null,["number","number","number","number"])}function sde(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return m$(n.dataIdMap.get(o.dataId).id,a,r,i),o}var ide={kernelName:Du,backendName:"wasm",setupFunc:rde,kernelFunc:sde},ode=Xe(oo),lde=Xe(lo),ude=!1,pde=Ut(Ru,ude,"bool"),cde=Xe(Mu),dde=!1,hde=Ut(Ou,dde,"bool"),mde=!1,fde=Ut(mN,mde,"bool"),f$;function gde(e){f$=e.wasm.cwrap(uo,null,["number","number","number","number","number","number","number"])}function bde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return f$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var yde={kernelName:uo,backendName:"wasm",setupFunc:gde,kernelFunc:bde},g$;function xde(e){g$=e.wasm.cwrap(Pu,null,["number","number","number","number","number","number","number","number","number"])}function vde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let d=n.makeOutput(r.shape,r.dtype);return g$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,i.shape[3],o,l,u,p),d}var wde={kernelName:Pu,backendName:"wasm",setupFunc:xde,kernelFunc:vde},b$;function kde(e){b$=e.wasm.cwrap(po,null,["number","number","number","number"])}function Ide(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ps(i,r,t);if(c){let y=t.dataIdMap.get(u.dataId).id;l=u,o=y}let h=l.shape.length;T.assertAxesAreInnerMostDims("max",p,h);let[m,f]=T.computeOutAndReduceShapes(l.shape,p),g=w.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;b$(o,Qe[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=T.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var Sde={kernelName:po,backendName:"wasm",setupFunc:kde,kernelFunc:Ide},Nde=!1,Tde=Ut(co,Nde),y$;function Cde(e){y$=e.wasm.cwrap(ho,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ede(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=T.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,I=p.inChannels,N=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. 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i={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new $p({...i,...s})}},Bd=class{constructor(t,n={}){this.box=new ln(t),this.options=new lg(n)}draw(t){let n=Gn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new dl([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function nge(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof Tt?a.score:xr(a)?a.detection.score:void 0,s=a instanceof Tt?a.box:xr(a)?a.detection.box:new ln(a),i=r?`${rl(r)}`:void 0;new Bd(s,{label:i}).draw(e)})}function Vd(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function zk(e){return new Promise((t,n)=>{if(e instanceof tt.getEnv().Canvas||Vd(e)){t(null);return}function a(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function 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vr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Wr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ka(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof tt.getEnv().Canvas?a:Ud(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return yr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return Nk({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,O(()=>{let a=yr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Ce){let o=ka(i)?i:Gt(i);return o=Ak(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Qn.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof tt.getEnv().Canvas)return Xo.fromPixels(Vk(i,t,n));throw new 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t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=wge(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Wr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return _n(e,t),{params:s,paramMappings:t}}function Oa(e,t,n){return O(()=>{let a=$t(e,t.filters,n,"same");return a=X(a,t.batch_norm_offset),an(a,0,6)})}var kge=.0010000000474974513;function Ige(e,t,n){return O(()=>{let a=As(e,t.filters,n,"same");return a=_s(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,kge),an(a,0,6)})}function Sge(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function CD(e,t){return O(()=>{let n,a=Oa(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=Sge(o);a=Ige(a,s.depthwise_conv,l),a=Oa(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function Nge(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),u=Math.min(a[n][1],a[n][3]),p=Math.max(a[n][0],a[n][2]),d=Math.max(a[n][1],a[n][3]),c=(i-r)*(o-s),h=(p-l)*(d-u);if(c<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,u),g=Math.min(i,p),b=Math.min(o,d),y=Math.max(g-m,0)*Math.max(b-f,0);return y/(c+h-y)}function ED(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>r).sort((p,d)=>d.score-p.score),l=p=>p<=a?1:0,u=[];return o.forEach(p=>{if(u.length>=i)return;let d=p.score;for(let c=u.length-1;c>=0;--c){let h=Nge(e,p.boxIndex,u[c]);if(h!==0&&(p.score*=l(h),p.score<=r))break}d===p.score&&u.push(p.boxIndex)}),u}function Tge(e){let t=dt(De(e,[1,0])),n=[pe(t[2],t[0]),pe(t[3],t[1])],a=[X(t[0],he(n[0],2)),X(t[1],he(n[1],2))];return{sizes:n,centers:a}}function Cge(e,t){let{sizes:n,centers:a}=Tge(e),r=dt(De(t,[1,0])),s=he(z(mn(he(r[2],5)),n[0]),2),i=X(z(he(r[0],10),n[0]),a[0]),o=he(z(mn(he(r[3],5)),n[1]),2),l=X(z(he(r[1],10),n[1]),a[1]);return De(Ft([pe(i,s),pe(l,o),X(i,s),X(l,o)]),[1,0])}function _D(e,t,n){return O(()=>{let a=e.shape[0],r=Cge(W(On(n.extra_dim,[a,1,1]),[-1,4]),W(e,[-1,4]));r=W(r,[a,r.shape[0]/a,4]);let s=ma(Ve(t,[0,0,1],[-1,-1,-1])),i=Ve(s,[0,0,0],[-1,-1,1]);i=W(i,[a,i.shape[1]]);let o=dt(r),l=dt(i);return{boxes:o,scores:l}})}function xl(e,t){return O(()=>{let n=e.shape[0],a=W(fl(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(fl(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function AD(e,t,n){return O(()=>{let a=Oa(e,n.conv_0,[1,1]),r=Oa(a,n.conv_1,[2,2]),s=Oa(r,n.conv_2,[1,1]),i=Oa(s,n.conv_3,[2,2]),o=Oa(i,n.conv_4,[1,1]),l=Oa(o,n.conv_5,[2,2]),u=Oa(l,n.conv_6,[1,1]),p=Oa(u,n.conv_7,[2,2]),d=xl(t,n.box_predictor_0),c=xl(e,n.box_predictor_1),h=xl(r,n.box_predictor_2),m=xl(i,n.box_predictor_3),f=xl(l,n.box_predictor_4),g=xl(p,n.box_predictor_5),b=et([d.boxPredictionEncoding,c.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),y=et([d.classPrediction,c.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:b,classPredictions:y}})}var Ia=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ws=class extends pn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=re(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=CD(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=AD(s.out,s.conv11,n.prediction_layer);return _D(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Ia(n),s=await vt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(_=>_*g),[N,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(_=>_*f);return new Tt(p[x],new ll(N,v,C-N,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return TD(t)}extractParams(t){return ND(t)}};function FD(e){let t=new Ws;return t.extractWeights(e),t}function Ege(e){return FD(e)}var Jk=class extends Ws{};var $D=.4,DD=[new Ue(.738768,.874946),new Ue(2.42204,2.65704),new Ue(4.30971,7.04493),new Ue(10.246,4.59428),new Ue(12.6868,11.8741)],RD=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],MD=[117.001,114.697,97.404],OD="tiny_yolov2_model",PD="tiny_yolov2_separable_conv_model";var Cg=e=>typeof e=="number";function Qk(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Cg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Cg(t.x)&&Cg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Cg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Vp(e){return O(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Ur(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Vp(n)})}function Gr(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ds(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Vp(n)})}function _ge(e,t){let n=Mp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Op(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function LD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=An(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=_ge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Age(e,t){let n=ia(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Pp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function zD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Age(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return _n(e,n),{params:i,paramMappings:n}}var Ja=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Eg=class Eg extends pn{constructor(t){super("TinyYolov2"),Qk(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Ur(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Ur(a,n.conv6),a=Ur(a,n.conv7),fl(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Vp(fl(t,n.conv0,"valid",!1)):Gr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Gr(a,n.conv6):a,a=n.conv7?Gr(a,n.conv7):a,fl(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Ya(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ja(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return _k(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new ol(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return zD(t,this.config)}extractParams(t){let n=this.config.filterSizes||Eg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return LD(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?qa(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):xe(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+zd(g[b][y][x][0]))/u*o,N=(b+zd(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new il(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};Eg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Up=Eg;var vl=class extends Up{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:$D,classes:["face"],...t?{anchors:RD,meanRgb:MD}:{anchors:DD,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Tt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?PD:OD}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Fge(e,t=!0){let n=new vl(t);return n.extractWeights(e),n}var Zd=class extends Ja{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Sa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function wl(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>gl(l)?r(l):l.detection),i=a||(t instanceof Ce?await Rp(t,s):await Dp(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function Gp(e,t,n,a,r){return wl([e],t,async s=>n(s[0]),a,r)}var WD=.4,BD=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],VD=[117.001,114.697,97.404];var kl=class extends Up{constructor(){let t={withSeparableConvs:!0,iouThreshold:WD,classes:["face"],anchors:BD,meanRgb:VD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Tt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new Ws,tinyFaceDetector:new kl,tinyYolov2:new vl,faceLandmark68Net:new bl,faceLandmark68TinyNet:new Xd,faceRecognitionNet:new yl,faceExpressionNet:new qd,ageGenderNet:new Kd},UD=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),$ge=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Dge=(e,t)=>nt.tinyYolov2.locateFaces(e,t),GD=e=>nt.faceLandmark68Net.detectLandmarks(e),Rge=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Mge=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),Oge=e=>nt.faceExpressionNet.predictExpressions(e),Pge=e=>nt.ageGenderNet.predictAgeAndGender(e),HD=e=>nt.ssdMobilenetv1.load(e),Lge=e=>nt.tinyFaceDetector.load(e),zge=e=>nt.tinyYolov2.load(e),Wge=e=>nt.faceLandmark68Net.load(e),Bge=e=>nt.faceLandmark68TinyNet.load(e),Vge=e=>nt.faceRecognitionNet.load(e),Uge=e=>nt.faceExpressionNet.load(e),Gge=e=>nt.ageGenderNet.load(e),Hge=HD,jge=UD,qge=GD;var _g=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Il=class extends _g{async run(){let t=await this.parentTask,n=await wl(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>bg(a,n[r]))}withAgeAndGender(){return new Nl(this,this.input)}},Sl=class extends _g{async run(){let t=await this.parentTask;if(!t)return;let n=await Gp(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return bg(t,n)}withAgeAndGender(){return new Tl(this,this.input)}},Bs=class extends Il{withAgeAndGender(){return new Us(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},Vs=class extends Sl{withAgeAndGender(){return new Gs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Ag=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Nl=class extends Ag{async run(){let t=await this.parentTask,n=await wl(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Ng(Tg(a,i,o),s)})}withFaceExpressions(){return new Il(this,this.input)}},Tl=class extends Ag{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Gp(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Ng(Tg(t,a,r),n)}withFaceExpressions(){return new Sl(this,this.input)}},Us=class extends Nl{withFaceExpressions(){return new Bs(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},Gs=class extends Tl{withFaceExpressions(){return new Vs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Jd=class extends Sa{constructor(n,a){super();this.parentTask=n;this.input=a}},Hr=class extends Jd{async run(){let t=await this.parentTask;return(await wl(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Sg(t[r],a))}withFaceExpressions(){return new Bs(this,this.input)}withAgeAndGender(){return new Us(this,this.input)}},jr=class extends Jd{async run(){let t=await this.parentTask;if(!t)return;let n=await Gp(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Sg(t,n)}withFaceExpressions(){return new Vs(this,this.input)}withAgeAndGender(){return new Gs(this,this.input)}};var Qd=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},eh=class extends Qd{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ce?await Rp(this.input,n):await Dp(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ce&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Wp(i,r[o]))}withFaceExpressions(){return new Bs(this,this.input)}withAgeAndGender(){return new Us(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},th=class extends Qd{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Rp(this.input,[n]):await Dp(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Wp(t,r)}withFaceExpressions(){return new Vs(this,this.input)}withAgeAndGender(){return new Gs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var nh=class extends Sa{constructor(n,a=new Ia){super();this.input=n;this.options=a}},Hp=class extends nh{async run(){let{input:t,options:n}=this,a;if(n instanceof Zd)a=nt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ia)a=nt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Ja)a=nt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>pl({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new eh(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Il(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Nl(this.runAndExtendWithFaceDetections(),this.input)}},ah=class extends nh{async run(){let t=await new Hp(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?pl({},n):void 0)})}withFaceLandmarks(t=!1){return new th(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Sl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Tl(this.runAndExtendWithFaceDetection(),this.input)}};function Kge(e,t=new Ia){return new ah(e,t)}function Fg(e,t=new Ia){return new Hp(e,t)}async function jD(e,t){return Fg(e,new Ia(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Xge(e,t={}){return Fg(e,new Ja(t)).withFaceLandmarks().withFaceDescriptors()}var Yge=jD;function eI(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s*s,0))}var tI=class e{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof zs)return i;if(i instanceof Float32Array)return new zs(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new zs(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>eI(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Ap(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>zs.fromJSON(a));return new e(n,t.distanceThreshold)}};function Zge(e){let t=new kl;return t.extractWeights(e),t}function qD(e,t){let{width:n,height:a}=new Un(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>qD(r,{width:n,height:a}));if(gl(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Wp(pl(e,r),s)}return xr(e)?pl(e,e.detection.forSize(n,a)):e instanceof sa||e instanceof Tt?e.forSize(n,a):e}var Jge=hD;return $R(Qge);})(); diff --git 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