Correct setup and format for calling REST API for image classification

gibbona1 — Mon, 07 Feb 2022 16:28:46 GMT

I trained a basic image classification model on MNIST using Tensorflow, logging the experiment run with MLflow.

Model: "my_sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 reshape (Reshape)         (None, 28, 28, 1)         0         
                                                                 
 conv2d (Conv2D)           (None, 26, 26, 32)        320       
                                                                 
 max_pooling2d (MaxPooling  (None, 13, 13, 32)       0         
 2D)                                                             
                                                                 
 flatten (Flatten)         (None, 5408)              0         
                                                                 
 dense (Dense)            (None, 100)               540900    
                                                                 
 dense_1 (Dense)            (None, 10)                1010      
                                                                 
=================================================================
Total params: 542,230
Trainable params: 542,230
Non-trainable params: 0
_________________________________________________________________

with mlflow.start_run() as run:
  run_id       = run.info.run_id
  
  mlflow.tensorflow.autolog()
 
  model.fit(trainX, trainY, 
            validation_data = (testX, testY), 
            epochs  = 2, 
            batch_size  = 64)

I then registered the model and enabled model serving.

When trying to send the JSON text through the browser in the form

[{"b64": "AA...AA=="}]

I'm getting errors like the following:

BAD_REQUEST: Encountered an unexpected error while evaluating the model. Verify that the serialized input Dataframe is compatible with the model for inference.
 
Traceback (most recent call last):
  File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/mlflow/pyfunc/scoring_server/__init__.py", line 306, in transformation
    raw_predictions = model.predict(data)
  File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/mlflow/pyfunc/__init__.py", line 605, in predict
    return self._model_impl.predict(data)
  File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/mlflow/keras.py", line 475, in predict
    predicted = _predict(data)
  File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/mlflow/keras.py", line 462, in _predict
    predicted = pd.DataFrame(self.keras_model.predict(data.values))
  File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 1147, in autograph_handler
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
 
    File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/keras/engine/training.py", line 1801, in predict_function  *
        return step_function(self, iterator)
    File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/keras/engine/training.py", line 1790, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/keras/engine/training.py", line 1783, in run_step  **
        outputs = model.predict_step(data)
    File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/keras/engine/training.py", line 1751, in predict_step
        return self(x, training=False)
    File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "/databricks/conda/envs/model-10/lib/python3.8/site-packages/keras/layers/core/reshape.py", line 110, in _fix_unknown_dimension
        raise ValueError(msg)
 
 
ValueError: Exception encountered when calling layer "reshape" (type Reshape).
 
  total size of new array must be unchanged, input_shape = [1], output_shape = [28, 28, 1]
 
  Call arguments received:
 
   • inputs=tf.Tensor(shape=(None, 1), dtype=float32)

This seems to be because I'm passing the image data as an encoded byte string, not a numpy array. According to the TensorFlow documentation, this is how it has to be passed.

If I have an image with shape (28,28,1), called img, I am converting it to the required format like this

image_data = base64.b64encode(img)
json = {"b64": image_data.decode()}

My question has two parts:

How do I adjust my model to handle the b64 encoded string and convert it back to a 28x28 image first?
What is the exact JSON format I need to send the image data to the REST endpoint?

Re: Correct setup and format for calling REST API for image classification

gibbona1 — Fri, 18 Feb 2022 12:57:40 GMT

Hi @Kaniz Fatma ,

Thanks for your answer!

My question is about this backend. You mean putting this line inside the predict() method?

When I'm defining a sequential model in TensorFlow, how do I incorporate what I want it to do to the input from a request?

Re: Correct setup and format for calling REST API for image classification

Atanu — Wed, 16 Mar 2022 04:40:04 GMT

@Anthony Gibbons may be this git should work with your use case - https://github.com/mlflow/mlflow/issues/1661

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Correct setup and format for calling REST API for image classification

Re: Correct setup and format for calling REST API for image classification

Re: Correct setup and format for calling REST API for image classification