Remove softmax workaround for NPU

image_classification/.eslintrc.js

@@ -1,6 +1,5 @@
 module.exports = {
   globals: {
     'MLGraphBuilder': 'readonly',
-    'tf': 'readonly',
   },
 };

image_classification/efficientnet_fp16_nchw.js

@@ -147,12 +147,9 @@ export class EfficientNetFP16Nchw {
     const pool1 = this.builder_.averagePool2d(await conv22);
     const reshape = this.builder_.reshape(pool1, [1, 1280]);
     const gemm = this.buildGemm_(reshape, '0');
-    if (contextOptions.deviceType === 'npu') {
-      return this.builder_.cast(await gemm, 'float32');
-    } else {
-      const softmax = this.builder_.softmax(await gemm);
-      return this.builder_.cast(softmax, 'float32');
-    }
+    const softmax = this.builder_.softmax(await gemm);
+
+    return this.builder_.cast(softmax, 'float32');
   }
 
   async build(outputOperand) {

image_classification/index.html

@@ -238,9 +238,6 @@ <h2 class="text-uppercase text-info">No model selected</h2>
   <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/popper.min.js"
     integrity="sha384-9/reFTGAW83EW2RDu2S0VKaIzap3H66lZH81PoYlFhbGU+6BZp6G7niu735Sk7lN"
     crossorigin="anonymous"></script>
-  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"
-    integrity="sha256-28ZvjeNGrGNEIj9/2D8YAPE6Vm5JSvvDs+LI4ED31x8="
-    crossorigin="anonymous"></script>
   <script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"
     integrity="sha384-B4gt1jrGC7Jh4AgTPSdUtOBvfO8shuf57BaghqFfPlYxofvL8/KUEfYiJOMMV+rV"
     crossorigin="anonymous"></script>

image_classification/main.js

@@ -231,18 +231,6 @@ async function renderCamStream() {
 
 // Get top 3 classes of labels from output buffer
 function getTopClasses(buffer, labels) {
-  // Currently we need to fallback softmax to tf.softmax because
-  // NPU dosen't support softmax.
-  // TODO: Remove this workaround once NPU supports softmax.
-  if (deviceType === 'npu') {
-    // Softmax
-    buffer = tf.tidy(() => {
-      const a =
-        tf.tensor(buffer, netInstance.outputDimensions, 'float32');
-      const b = tf.softmax(a);
-      return b.dataSync();
-    });
-  }
   const probs = Array.from(buffer);
   const indexes = probs.map((prob, index) => [prob, index]);
   const sorted = indexes.sort((a, b) => {

image_classification/mobilenet_nchw.js

@@ -153,12 +153,8 @@ export class MobileNetV2Nchw {
           {groups: 1280, strides: [7, 7]});
       const conv5 = this.buildConv_(await conv4, '104', false);
       const reshape = this.builder_.reshape(await conv5, [1, 1000]);
-      if (contextOptions.deviceType === 'npu') {
-        return this.builder_.cast(reshape, 'float32');
-      } else {
-        const softmax = this.builder_.softmax(reshape);
-        return this.builder_.cast(softmax, 'float32');
-      }
+      const softmax = this.builder_.softmax(reshape);
+      return this.builder_.cast(softmax, 'float32');
     }
   }
 

image_classification/resnet50v1_fp16_nchw.js

@@ -118,12 +118,8 @@ export class ResNet50V1FP16Nchw {
     const pool2 = this.builder_.averagePool2d(await bottleneck16);
     const reshape = this.builder_.reshape(pool2, [1, 2048]);
     const gemm = this.buildGemm_(reshape, '0');
-    if (contextOptions.deviceType === 'npu') {
-      return this.builder_.cast(await gemm, 'float32');
-    } else {
-      const softmax = this.builder_.softmax(await gemm);
-      return this.builder_.cast(softmax, 'float32');
-    }
+    const softmax = this.builder_.softmax(await gemm);
+    return this.builder_.cast(softmax, 'float32');
   }
 
   async build(outputOperand) {