TensorFlowJS Mask RCNN - ERROR provided in model.execute(dict) must be int32, but was float32

I have trained a object detection model using transferred learning from Mask R-CNN Inception ResNet V2 1024x1024 and after converting the model to js I get the error: ERROR provided in model.execute(dict) must be int32, but was float32. Here are the steps I took to create the model.

1- Created the training.json, validation.json, testing.json annotation files along with the label_map.txt files from my images. I have also pre-processed the images to fit the 1024 * 1024 size.

2- Used the create_coco_tf_record.py provided by tensorflow to generate tfrecord files. The only alteration I made to the create_coco_tf_record.py file was changing include_mask to True

tf.flags.DEFINE_boolean(
    'include_masks', True **was false**, 'Whether to include instance segmentations masks '

then ran the bottom command using conda

python create_coco_tf_record.py ^
--logtostderr ^
--train_image_dir=C:/model/ai_container/training ^
--val_image_dir=C:/model/ai_container/vidation ^
--test_image_dir=C:/model/ai_container/testing ^
--train_annotations_file=C:/model/ai_container/training/training.json ^
--val_annotations_file=C:/model/ai_container/validation/coco_validation.json ^
--testdev_annotations_file=C:/model/ai_container/testing/coco_testing.json ^
--output_dir=C:/model/ai_container/tfrecord

3- I then train the model. Bellow is the modified portion of my config_file based on base mask-rcnn config file. The batch and num_steps are set to 1 just so I could quickly train the model to test the results.

    train_config: {
  batch_size: 1
  num_steps: 1
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: 0.008
          total_steps: 200000
          warmup_learning_rate: 0.0
          warmup_steps: 5000
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint: "C:/ObjectDetectionAPI/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8/checkpoint/ckpt-0"
  fine_tune_checkpoint_type: "detection"
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
}

train_input_reader: {
  label_map_path: "C:/model/ai_container/label_map.txt"
  tf_record_input_reader {
    input_path: "C:/model/ai_container/tfrecord/coco_train.record*"
  }
  load_instance_masks: true
  mask_type: PNG_MASKS
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  metrics_set: "coco_mask_metrics"
  eval_instance_masks: true
  use_moving_averages: false
  batch_size: 1
  include_metrics_per_category: false
}

eval_input_reader: {
  label_map_path: "C:/model/ai_container/label_map.txt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path: "C:/model/ai_container/tfrecord/coco_val.record*"
  }
  load_instance_masks: true
  mask_type: PNG_MASKS
}

than ran training command:

python object_detection/model_main_tf2.py ^
--pipeline_config_path=C:/ObjectDetectionAPI/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.config ^
--model_dir=C:/TensoFlow/training_process_2 ^
--alsologtostderr

4- Run validation command (might be doing this wrong)

python object_detection/model_main_tf2.py ^
--pipeline_config_path=C:/ObjectDetectionAPI/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.config ^
--model_dir=C:/TensoFlow/training_process_2 ^
--checkpoint_dir=C:/TensoFlow/training_process_2 ^
--sample_1_of_n_eval_examples=1 ^
--alsologtostderr

5- Export Model

python object_detection/exporter_main_v2.py ^
--input_type="image_tensor" ^
--pipeline_config_path=C:/ObjectDetectionAPI/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.config ^
--trained_checkpoint_dir=C:/TensoFlow/training_process_2 ^
--output_directory=C:/TensoFlow/training_process_2/generatedModel

6- Convert Model to tensorflowJs

tensorflowjs_converter ^
--input_format=tf_saved_model ^
--output_format=tfjs_graph_model  ^
--signature_name=serving_default  ^
--saved_model_tags=serve ^
C:/TensoFlow/training_process_2/generatedModel/saved_model C:/TensoFlow/training_process_2/generatedModel/jsmodel

7- Then attempt to load the model into my angular project. I placed the converted model bin and json files in my assets folder.

npm install @tensorflow/tfjs 

---

ngAfterViewInit() {
   tf.loadGraphModel('/assets/tfmodel/model1/model.json').then((model) => {
     this.model = model;
     this.model.executeAsync(tf.zeros([1, 256, 256, 3])).then((result) => {
       this.loadeModel = true;
     });
   });
}

---

I then get the error

    tf.min.js:17 ERROR Error: Uncaught (in promise): Error: The dtype of dict['input_tensor'] provided in model.execute(dict) must be int32, but was float32
    Error: The dtype of dict['input_tensor'] provided in model.execute(dict) must be int32, but was float32
    at F$ (util_base.js:153:11)
    at graph_executor.js:721:9
    at Array.forEach (<anonymous>)
    at e.value (graph_executor.js:705:25)
    at e.<anonymous> (graph_executor.js:467:12)
    at h (tf.min.js:17:2100)
    at Generator.<anonymous> (tf.min.js:17:3441)
    at Generator.next (tf.min.js:17:2463)
    at u (tf.min.js:17:8324)
    at o (tf.min.js:17:8527)
    at resolvePromise (zone.js:1211:31)
    at resolvePromise (zone.js:1165:17)
    at zone.js:1278:17
    at _ZoneDelegate.invokeTask (zone.js:406:31)
    at Object.onInvokeTask (core.mjs:26343:33)
    at _ZoneDelegate.invokeTask (zone.js:405:60)
    at Zone.runTask (zone.js:178:47)
    at drainMicroTaskQueue (zone.js:585:35)

---

Im using angular. I have also tried a few online solutions with no success. If anyone could give me any information on how to possible solve this issue I would be grateful. THANKS.

from TensorFlowJS Mask RCNN - ERROR provided in model.execute(dict) must be int32, but was float32