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[Golang][Doc] improve the samples and doc (#4385)
* [Golang][Doc] improve the samples and doc * [Golang][Doc] add asf header * [Golang][Doc] Improve the end to end example * [Golang][Doc] Improve the end to end example
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# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you 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. | ||
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import os | ||
from tvm import relay | ||
from tvm.contrib.download import download_testdata | ||
import tflite.Model | ||
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################################################ | ||
# Utils for downloading and extracting zip files | ||
# ---------------------------------------------- | ||
def extract(path): | ||
import tarfile | ||
if path.endswith("tgz") or path.endswith("gz"): | ||
dir_path = os.path.dirname(path) | ||
tar = tarfile.open(path) | ||
tar.extractall(path=dir_path) | ||
tar.close() | ||
else: | ||
raise RuntimeError('Could not decompress the file: ' + path) | ||
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################################### | ||
# Download TFLite pre-trained model | ||
# --------------------------------- | ||
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model_url = "https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz" | ||
model_path = download_testdata(model_url, "mobilenet_v2_1.4_224.tgz", module=['tf', 'official']) | ||
model_dir = os.path.dirname(model_path) | ||
extract(model_path) | ||
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# now we have mobilenet_v2_1.4_224.tflite on disk | ||
model_file = os.path.join(model_dir, "mobilenet_v2_1.4_224.tflite") | ||
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# get TFLite model from buffer | ||
tflite_model_buf = open(model_file, "rb").read() | ||
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0) | ||
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############################## | ||
# Load Neural Network in Relay | ||
# ---------------------------- | ||
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# TFLite input tensor name, shape and type | ||
input_tensor = "input" | ||
input_shape = (1, 224, 224, 3) | ||
input_dtype = "float32" | ||
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# parse TFLite model and convert into Relay computation graph | ||
mod, params = relay.frontend.from_tflite(tflite_model, | ||
shape_dict={input_tensor: input_shape}, | ||
dtype_dict={input_tensor: input_dtype}) | ||
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############# | ||
# Compilation | ||
# ----------- | ||
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target = 'llvm' | ||
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# Build with Relay | ||
with relay.build_config(opt_level=3): | ||
graph, lib, params = relay.build_module.build( | ||
mod, target, params=params) | ||
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############################################### | ||
# Save the graph, lib and parameters into files | ||
# --------------------------------------------- | ||
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lib.export_library("./mobilenet.so") | ||
print('lib export succeefully') | ||
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with open("./mobilenet.json", "w") as fo: | ||
fo.write(graph) | ||
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with open("./mobilenet.params", "wb") as fo: | ||
fo.write(relay.save_param_dict(params)) |