ConcatOp : Dimensions of inputs should match: shape[0] = [1,40,40,256] vs. shape[1] = [1,38,50,256]

[playground]

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): MacOS M1
• TensorFlow.js installed from (npm or script link): tfjs-node v4.2.0, node v16.17.1

Describe the current behavior
I have a working yolov5 model and exported it to tfjs saved_model. The saved_model loads fine, but throws this error when run predict

let inputTensor = decodedImage.expandDims(0).cast('float32');
let tensor = {};
let tensor['x'] = inputTensor; 
let outputTensor = this.model.predict(tensor);

Error: Session fail to run with error: {{function_node __inference_pruned_8038}} {{function_node __inference_pruned_8038}} ConcatOp : Dimensions of inputs should match: shape[0] = [1,40,40,256] vs. shape[1] = [1,38,50,256]

Other info / logs Include any logs or source code that would be helpful to

Error: Session fail to run with error: {{function_node __inference_pruned_8038}} {{function_node __inference_pruned_8038}} ConcatOp : Dimensions of inputs should match: shape[0] = [1,40,40,256] vs. shape[1] = [1,38,50,256]
         [[{{node model/tf_concat/concat}}]]
         [[PartitionedCall/PartitionedCall/PartitionedCall]]
    at NodeJSKernelBackend.runSavedModel (/myapp/node_modules/@tensorflow/tfjs-node/dist/nodejs_kernel_backend.js:459:43)
    at TFSavedModel.predict (/myapp/node_modules/@tensorflow/tfjs-node/dist/saved_model.js:364:52)
    at Observable._subscribe (/myapp/dist/common/utils.js:257:47)
    at Observable._trySubscribe (/myapp/node_modules/rxjs/dist/cjs/internal/Observable.js:41:25)
    at /myapp/node_modules/rxjs/dist/cjs/internal/Observable.js:35:31
    at Object.errorContext (/myapp/node_modules/rxjs/dist/cjs/internal/util/errorContext.js:22:9)
    at Observable.subscribe (/myapp/node_modules/rxjs/dist/cjs/internal/Observable.js:26:24)
    at Utils.checkImage (/myapp/dist/common/utils.js:196:26)
    at Timeout._onTimeout (/myapp/dist/common/utils.js:388:22)
    at processTicksAndRejections (node:internal/process/task_queues:96:5)

[Linchenn]

@playground Could you provide the tfjs model and the input you are using?

[playground]

Sure, here it's @Linchenn model.zip

Input is x

[playground]

Hi @Linchenn, have you had a chance to try out the model provided above?

[playground]

Hi @Linchenn, just checking in.

[gaikwadrahul8]

Hi, @playground

Apologize for the delayed response and I was trying to replicate your issue on my system but I was getting below error message so Could you please help me with your complete code snippet which you're using or Is it possible for you to create a codepen example for us to reproduce your issue ?

Here is screenshot for your reference and please help me with exact steps to replicate your issue if something I'm missing here ? Thank you!

[playground]

@gaikwadrahul8

Here is the code snippet for loading the model and perform inferencing on an image.

          this.model = await tfnode.node.loadSavedModel(modelPath);

gotImage() {
          const image = readFileSync(imageFile);

          let decodedImage = tfnode.node.decodeImage(image, 3);
          let inputTensor;
          switch(this.version.type) {
            case 'float':
              inputTensor = decodedImage.expandDims(0).cast('float32');
              break;
            default:
              inputTensor = decodedImage.expandDims(0);
              break;
          }
          this.inference(inputTensor)
          .subscribe({
              ....
              ...
           })
}
inference(inputTensor) {
    return new Observable((observer) => {
      try {
        const startTime = Date.now();
        // input_tensor worked for worker safety
        let input = this.version.input && this.version.input.length > 0 ? this.version.input : 'input_tensor';
        let tensor = {};
        tensor[input] = inputTensor; 
        console.log(input, tensor)
        let outputTensor = this.model.predict(tensor);
        const scores = outputTensor['detection_scores'].arraySync();
        const boxes = outputTensor['detection_boxes'].arraySync();
        const classes = outputTensor['detection_classes'].arraySync();
        const num = outputTensor['num_detections'].arraySync();
        const endTime = Date.now();
        outputTensor['detection_scores'].dispose();
        outputTensor['detection_boxes'].dispose();
        outputTensor['detection_classes'].dispose();
        outputTensor['num_detections'].dispose();
        
        let predictions = [];
        const elapsedTime = endTime - startTime;
        for (let i = 0; i < scores[0].length; i++) {
          let score = scores[0][i].toFixed(2);
          if (score >= this.confidentCutoff) {
            predictions.push({
              detectedBox: boxes[0][i].map((el)=>el.toFixed(2)),
              detectedClass: this.labels[classes[0][i]],
              detectedScore: score
            });
          }
        }
        console.log('predictions:', predictions.length, predictions[0]);
        console.log('time took: ', elapsedTime);
        console.log('build json...');
        observer.next({bbox: predictions, elapsedTime: elapsedTime});
        observer.complete();    
      } catch(e) {
        console.log(e)
        observer.error(e)
      }
    });
  }

[playground]

@gaikwadrahul8 @Linchenn any updates on this?