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# 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)
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