testbed/aws__sagemaker-python-sdk/tests/data/mxnet_mnist/code/inference.py

# coding=utf-8
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

import json
import logging
import os

import mxnet as mx
import numpy as np
from mxnet import gluon

logging.basicConfig(level=logging.DEBUG)


def model_fn(model_dir):
    """Load the gluon model. Called once when hosting service starts.

    :param: model_dir The directory where model files are stored.
    :return: a model (in this case a Gluon network)
    """
    net = gluon.SymbolBlock.imports(
        symbol_file=os.path.join(model_dir, "model-symbol.json"),
        input_names=["data"],
        param_file=os.path.join(model_dir, "model-0000.params"),
    )
    return net


def transform_fn(net, data, input_content_type, output_content_type):
    assert input_content_type == "application/json"
    assert output_content_type == "application/json"

    # parsed should be a 1d array of length 728
    parsed = json.loads(data)
    parsed = parsed["inputs"]

    # convert to numpy array
    arr = np.array(parsed).reshape(-1, 1, 28, 28)

    # convert to mxnet ndarray
    nda = mx.nd.array(arr)

    output = net(nda)

    prediction = mx.nd.argmax(output, axis=1)
    response_body = json.dumps(prediction.asnumpy().tolist())

    return response_body, output_content_type


if __name__ == "__main__":
    model_dir = "/home/ubuntu/models/mxnet-gluon-mnist"
    net = model_fn(model_dir)

    import json
    import random

    data = {"inputs": [random.random() for _ in range(784)]}
    data = json.dumps(data)

    content_type = "application/json"
    a, b = transform_fn(net, data, content_type, content_type)
    print(a, b)