Drop null for reshape op

face_recognition/facenet_nhwc.js

@@ -109,7 +109,7 @@ export class FaceNetNhwc {
   }
 
   async buildFullyConnected_(input) {
-    input = this.builder_.reshape(input, [1, null]);
+    input = this.builder_.reshape(input, [1, 1792]);
     const weights = await buildConstantByNpy(this.builder_,
         `${this.weightsUrl_}/Bottleneck_kernel_transpose.npy`);
     const bias = await buildConstantByNpy(this.builder_,

facial_landmark_detection/face_landmark_nchw.js

@@ -54,7 +54,7 @@ export class FaceLandmarkNchw {
     if (reshapeSize !== undefined) {
       gemm = this.builder_.gemm(this.builder_.reshape(
           this.builder_.transpose(await input, {permutation: [0, 2, 3, 1]}),
-          [null, reshapeSize]), await weights, options);
+          [1, reshapeSize]), await weights, options);
     } else {
       gemm = this.builder_.gemm(await input, await weights, options);
     }

facial_landmark_detection/face_landmark_nhwc.js

@@ -55,7 +55,7 @@ export class FaceLandmarkNhwc {
     let fc;
     if (reshapeSize !== undefined) {
       fc = this.builder_.gemm(this.builder_.reshape(
-          await input, [null, reshapeSize]), await weights, options);
+          await input, [1, reshapeSize]), await weights, options);
     } else {
       fc = this.builder_.gemm(await input, await weights, options);
     }

image_classification/mobilenet_nchw.js

@@ -123,7 +123,7 @@ export class MobileNetV2Nchw {
 
     const conv3 = await this.buildConv_(bottleneck15, '95', true);
     const pool = this.builder_.averagePool2d(conv3);
-    const reshape = this.builder_.reshape(pool, [1, null]);
+    const reshape = this.builder_.reshape(pool, [1, 1280]);
     const gemm = await this.buildGemm_(reshape, '104');
     return this.builder_.softmax(gemm);
   }

image_classification/mobilenet_nhwc.js

@@ -121,7 +121,7 @@ export class MobileNetV2Nhwc {
         conv3, {windowDimensions: [7, 7], layout: 'nhwc'});
     const conv4 = await this.buildConv_(
         averagePool2d, '222', 'Logits_Conv2d_1c_1x1_Conv2D', false, {autoPad, filterLayout});
-    const reshape = this.builder_.reshape(conv4, [1, null]);
+    const reshape = this.builder_.reshape(conv4, [1, 1001]);
     return this.builder_.softmax(reshape);
   }
 

image_classification/resnet50v2_nchw.js

@@ -63,7 +63,7 @@ export class ResNet50V2Nchw {
     const biasName = prefix + '_bias.npy';
     const bias = await buildConstantByNpy(this.builder_, biasName);
     const options =
-        {c: this.builder_.reshape(bias, [1, null]), bTranspose: true};
+        {c: this.builder_.reshape(bias, [1, 1000]), bTranspose: true};
     return this.builder_.gemm(input, weight, options);
   }
 
@@ -148,7 +148,7 @@ export class ResNet50V2Nchw {
 
     const bn3 = await this.buildBatchNorm_(bottleneck16, '2', '');
     const pool2 = await this.builder_.averagePool2d(bn3);
-    const reshape = this.builder_.reshape(pool2, [1, null]);
+    const reshape = this.builder_.reshape(pool2, [1, 2048]);
     const gemm = await this.buildGemm_(reshape, '0');
     return this.builder_.softmax(gemm);
   }

image_classification/resnet50v2_nhwc.js

@@ -154,7 +154,7 @@ export class ResNet50V2Nhwc {
     const mean = this.builder_.averagePool2d(fusedBn, {layout});
     const conv2 = await this.buildConv_(
         mean, ['', '', 'logits'], {autoPad}, false);
-    const reshape = this.builder_.reshape(conv2, [1, null]);
+    const reshape = this.builder_.reshape(conv2, [1, 1001]);
     return this.builder_.softmax(reshape);
   }
 

image_classification/squeezenet_nchw.js

@@ -65,7 +65,7 @@ export class SqueezeNetNchw {
     const conv25 = await this.buildConv_(fire7, 'conv25');
     const pool3 = this.builder_.averagePool2d(
         conv25, {windowDimensions: [13, 13], strides: [13, 13]});
-    const reshape0 = this.builder_.reshape(pool3, [1, null]);
+    const reshape0 = this.builder_.reshape(pool3, [1, 1000]);
     return this.builder_.softmax(reshape0);
   }
 

image_classification/squeezenet_nhwc.js

@@ -69,7 +69,7 @@ export class SqueezeNetNhwc {
     const conv10 = await this.buildConv_(fire9, 'conv10');
     const averagePool2d = this.builder_.averagePool2d(
         conv10, {windowDimensions: [13, 13], layout});
-    const reshape = this.builder_.reshape(averagePool2d, [1, null]);
+    const reshape = this.builder_.reshape(averagePool2d, [1, 1001]);
     return this.builder_.softmax(reshape);
   }
 

lenet/lenet.js

@@ -77,7 +77,7 @@ export class LeNet {
         this.builder_.maxPool2d(add2, {windowDimensions: pool2WindowShape,
           strides: pool2Strides});
 
-    const reshape1Shape = [1, null];
+    const reshape1Shape = [1, 800];
     const reshape1 = this.builder_.reshape(pool2, reshape1Shape);
 
     // skip the new shape, 2 int64 values
@@ -100,7 +100,7 @@ export class LeNet {
 
     const relu = this.builder_.relu(add3);
 
-    const reshape2Shape = [1, null];
+    const reshape2Shape = [1, 500];
     const reshape2 = this.builder_.reshape(relu, reshape2Shape);
 
     const matmul2Shape = [10, 500];

rnnoise/rnnoise.js

@@ -84,7 +84,7 @@ export class RNNoise {
     const vadGruYTransposed = this.builder_.transpose(
         vadGruY, {permutation: [2, 0, 1, 3]});
     const vadGruTranspose1 = this.builder_.reshape(
-        vadGruYTransposed, [null, this.frames_, this.vadGruHiddenSize]);
+        vadGruYTransposed, [1, this.frames_, this.vadGruHiddenSize]);
     const concatenate1 = this.builder_.concat(
         [inputDenseTanh0, vadGruTranspose1, input], 2);
     const noiseGruX = this.builder_.transpose(
@@ -112,7 +112,7 @@ export class RNNoise {
     const noiseGruYTransposed = this.builder_.transpose(
         noiseGruY, {permutation: [2, 0, 1, 3]});
     const noiseGruTranspose1 = this.builder_.reshape(
-        noiseGruYTransposed, [null, this.frames_, this.noiseGruHiddenSize]);
+        noiseGruYTransposed, [1, this.frames_, this.noiseGruHiddenSize]);
     const concatenate2 = this.builder_.concat(
         [vadGruTranspose1, noiseGruTranspose1, input], 2);
     const denoiseGruX = this.builder_.transpose(
@@ -140,7 +140,7 @@ export class RNNoise {
     const denoiseGruYTransposed = this.builder_.transpose(
         denoiseGruY, {permutation: [2, 0, 1, 3]});
     const denoiseGruTranspose1 = this.builder_.reshape(
-        denoiseGruYTransposed, [null, this.frames_, this.denoiseGruHiddenSize]);
+        denoiseGruYTransposed, [1, this.frames_, this.denoiseGruHiddenSize]);
     const denoiseOutput0 = this.builder_.matmul(
         denoiseGruTranspose1, denoiseOutputKernel0);
     const biasedTensorName = this.builder_.add(