release: v1.4.4

package.json

@@ -16,8 +16,8 @@
     "watch": "concurrently \"npm run watch --prefix packages/web\" \"npm run watch --prefix packages/node\"",
     "build": "npm run build --workspaces --if-present",
     "lint": "npx prettier --write --ignore-unknown .",
-    "publish:latest": "npm run publish:latest --workspaces --if-present",
-    "publish:next": "npm run publish:next --workspaces --if-present",
+    "publish:latest": "npm run publish:latest --workspaces --if-present && npm run deploy-assets",
+    "publish:next": "npm run publish:next --workspaces --if-present && npm run deploy-assets",
     "package:version": "node ./scripts/package-version.mjs",
     "deploy-assets": "node -r dotenv/config ./scripts/deploy-assets.mjs",
     "tag": "git tag $npm_package_version & git push origin $npm_package_version",

packages/node/package.json

@@ -58,27 +58,28 @@
     "package:pack": "npm run build && npm pack . --pack-destination ../../releases"
   },
   "dependencies": {
-    "onnxruntime-node": "^1.15.1",
-    "sharp": "^0.32.4",
-    "@types/lodash": "^4.14.195",
-    "@types/node": "^20.3.1",
-    "lodash": "^4.17.21",
-    "ndarray": "^1.0.19",
-    "zod": "^3.21.4"
+    "onnxruntime-node": "~1.17.0",
+    "sharp": "~0.32.4",
+    "@types/lodash": "~4.14.195",
+    "@types/node": "~20.3.1",
+    "@types/ndarray": "~1.0.14",
+    "lodash": "~4.17.21",
+    "ndarray": "~1.0.19",
+    "zod": "~3.21.4"
   },
   "devDependencies": {
-    "assert": "^2.0.0",
-    "esbuild": "^0.18.18",
-    "npm-dts": "^1.3.12",
-    "os-browserify": "^0.3.0",
-    "path-browserify": "^1.0.1",
-    "process": "^0.11.10",
-    "stream-browserify": "^3.0.0",
-    "ts-loader": "^9.4.3",
-    "tslib": "^2.5.3",
-    "typescript": "^5.1.6",
-    "util": "^0.12.5",
-    "webpack": "^5.85.1",
-    "webpack-cli": "^5.1.4"
+    "assert": "~2.0.0",
+    "esbuild": "~0.18.18",
+    "npm-dts": "~1.3.12",
+    "os-browserify": "~0.3.0",
+    "path-browserify": "~1.0.1",
+    "process": "~0.11.10",
+    "stream-browserify": "~3.0.0",
+    "ts-loader": "~9.4.3",
+    "tslib": "~2.5.3",
+    "typescript": "~5.1.6",
+    "util": "~0.12.5",
+    "webpack": "~5.85.1",
+    "webpack-cli": "~5.1.4"
   }
 }

packages/node/src/codecs.ts

@@ -11,12 +11,20 @@ async function imageDecode(blob: Blob): Promise<NdArray<Uint8Array>> {
     case 'image/x-alpha8': {
       const width = parseInt(mime.params['width']);
       const height = parseInt(mime.params['height']);
-      return ndarray(await blob.arrayBuffer(), [height, width, 1]);
+      return ndarray(new Uint8Array(await blob.arrayBuffer()), [
+        height,
+        width,
+        1
+      ]);
     }
     case 'image/x-rgba8': {
       const width = parseInt(mime.params['width']);
       const height = parseInt(mime.params['height']);
-      return ndarray(await blob.arrayBuffer(), [height, width, 4]);
+      return ndarray(new Uint8Array(await blob.arrayBuffer()), [
+        height,
+        width,
+        4
+      ]);
     }
     case 'application/octet-stream':
     case `image/png`:
@@ -55,7 +63,10 @@ async function imageEncode(
   switch (type) {
     case 'image/x-alpha8':
     case 'image/x-rgba8': {
-      const mime = MimeType.create(type, { width, height });
+      const mime = MimeType.create(type, {
+        width: width.toString(),
+        height: height.toString()
+      });
       return new Blob([imageTensor.data], { type: mime.toString() });
     }
     case `image/png`:

packages/node/src/inference.ts

@@ -39,9 +39,9 @@ async function runInference(
   );
 
   let alphamask = ndarray(predictionsDict[0].data, [resolution, resolution, 1]);
-  alphamask = convertFloat32ToUint8(alphamask);
-  alphamask = tensorResizeBilinear(alphamask, srcWidth, srcHeight);
+  let alphamaskU8 = convertFloat32ToUint8(alphamask);
+  alphamaskU8 = tensorResizeBilinear(alphamaskU8, srcWidth, srcHeight);
 
   if (config.progress) config.progress('compute:inference', 1, 1);
-  return alphamask;
+  return alphamaskU8;
 }

packages/node/src/onnx.ts

@@ -45,7 +45,7 @@ async function runOnnxSession(
 
   for (const key of outputs) {
     const output: ort.Tensor = outputData[key];
-    const shape: Number[] = output.dims as number[];
+    const shape: number[] = output.dims as number[];
     const data: Float32Array = output.data as Float32Array;
     const tensor = ndarray(data, shape);
     outputKVPairs.push(tensor);

packages/node/src/utils.ts

@@ -17,7 +17,13 @@ import { loadFromURI } from './resource';
 import ndarray, { NdArray } from 'ndarray';
 import { imageDecode, imageEncode } from './codecs';
 
-type ImageSource = ArrayBuffer | Uint8Array | Blob | URL | string;
+type ImageSource =
+  | ArrayBuffer
+  | Uint8Array
+  | Blob
+  | URL
+  | string
+  | NdArray<Uint8Array>;
 
 function tensorResizeBilinear(
   imageTensor: NdArray<Uint8Array>,
@@ -69,7 +75,7 @@ function tensorResizeBilinear(
 }
 
 function tensorHWCtoBCHW(
-  imageTensor: NdArray<Uint32Array>,
+  imageTensor: NdArray<Uint8Array>,
   mean: number[] = [128, 128, 128],
   std: number[] = [256, 256, 256]
 ): NdArray<Float32Array> {

packages/web/src/codecs.ts

@@ -9,20 +9,32 @@ async function imageDecode(blob: Blob): Promise<NdArray<Uint8Array>> {
     case 'image/x-alpha8': {
       const width = parseInt(mime.params['width']);
       const height = parseInt(mime.params['height']);
-      return ndarray(await blob.arrayBuffer(), [height, width, 1]);
+      return ndarray(new Uint8Array(await blob.arrayBuffer()), [
+        height,
+        width,
+        1
+      ]);
     }
     case 'image/x-rgba8': {
       const width = parseInt(mime.params['width']);
       const height = parseInt(mime.params['height']);
-      return ndarray(await blob.arrayBuffer(), [height, width, 4]);
+      return ndarray(new Uint8Array(await blob.arrayBuffer()), [
+        height,
+        width,
+        4
+      ]);
     }
     case 'application/octet-stream': // this is an unknwon type
     case `image/png`:
     case `image/jpeg`:
     case `image/webp`: {
       const imageBitmap = await createImageBitmap(blob);
       const imageData = imageBitmapToImageData(imageBitmap);
-      return ndarray(imageData.data, [imageData.height, imageData.width, 4]);
+      return ndarray(new Uint8Array(imageData.data), [
+        imageData.height,
+        imageData.width,
+        4
+      ]);
     }
     default:
       throw new Error(
@@ -41,7 +53,10 @@ async function imageEncode(
   switch (format) {
     case 'image/x-alpha8':
     case 'image/x-rgba8': {
-      const mime = MimeType.create(format, { width, height });
+      const mime = MimeType.create(format, {
+        width: width.toString(),
+        height: height.toString()
+      });
       return new Blob([imageTensor.data], { type: mime.toString() });
     }
     case `image/png`:

packages/web/src/inference.ts

@@ -41,9 +41,9 @@ async function runInference(
   );
 
   let alphamask = ndarray(predictionsDict[0].data, [resolution, resolution, 1]);
-  alphamask = convertFloat32ToUint8(alphamask);
-  alphamask = tensorResizeBilinear(alphamask, srcWidth, srcHeight);
+  let alphamaskU8 = convertFloat32ToUint8(alphamask);
+  alphamaskU8 = tensorResizeBilinear(alphamaskU8, srcWidth, srcHeight);
 
   if (config.progress) config.progress('compute:inference', 1, 1);
-  return alphamask;
+  return alphamaskU8;
 }

packages/web/src/onnx.ts

@@ -85,7 +85,7 @@ async function runOnnxSession(
 
   for (const key of outputs) {
     const output: ort.Tensor = outputData[key];
-    const shape: Number[] = output.dims as number[];
+    const shape: number[] = output.dims as number[];
     const data: Float32Array = output.data as Float32Array;
     const tensor = ndarray(data, shape);
     outputKVPairs.push(tensor);

packages/web/src/utils.ts

@@ -15,7 +15,14 @@ import { imageDecode, imageEncode } from './codecs';
 import { ensureAbsoluteURI } from './url';
 import { Config } from './schema';
 
-type ImageSource = ImageData | ArrayBuffer | Uint8Array | Blob | URL | string;
+type ImageSource =
+  | ImageData
+  | ArrayBuffer
+  | Uint8Array
+  | Blob
+  | URL
+  | string
+  | NdArray<Uint8Array>;
 
 function imageBitmapToImageData(imageBitmap: ImageBitmap): ImageData {
   var canvas = createCanvas(imageBitmap.width, imageBitmap.height);