tqchen commented on a change in pull request #6112: URL: https://github.com/apache/incubator-tvm/pull/6112#discussion_r461721804
########## File path: tvmc/README.md ########## @@ -0,0 +1,122 @@ +<!--- 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. --> + +# TVMC + +```tvmc``` is a tool that provides useful command line invocations to compile, +run and tune models using TVM graph runtime. + +In order to compile and tune, ```tvmc``` takes a model file and parameters as inputs, +and outputs a TAR file that contains the TVM modules that represent the +input model, graph and weights, for the required target. Target can be native or +cross-compiled. + +When running a given model, ```tvmc``` expects a compiled model and input tensor values, so +that it can produce the outputs, when running on the required target, local or remote. + +This document presents an overview and a short tutorial about ```tvmc```. + +## Installation + +```tvmc``` is a Python tool and - provided TVM and dependencies are available - it can be +installed in various ways. + +The recommended way to install ```tvmc``` is via it's ```setuptools``` configuration file, +located at ```tvm/tvmc/setup.py```. To do that, go to the the TVM directory and run the +installation command, as described below: + + cd tvm/tvmc + python setup.py install + +The command above should install everything needed to get started with ```tvmc```, including +all the the supported frontends. + +Once ```tvmc``` is installed, the main entry-point is the ```tvmc``` command line. A set of +sub-commands are available, to run the specific tasks offered by ```tvmc```: ```tune```, +```compile``` and ```run```. + +The simplest way to get more information about a specific sub-command is ```tvmc <subcommand> +-- help```. + + tvmc compile --help + +## Usage + +Now, let's compile a network and generate a few predictions using ```tvmc```. + +As described above, in order to compile a model using ```tvmc```, the first thing we need is +a model file. For the sake of this example, let's use a MobileNet V1 model, in TFLite format. +More information about the model is available on +[this page](https://www.tensorflow.org/lite/guide/hosted_models). + +To download and un-compress the ```.tgz``` file (34Mb), so that we can access the TFLite model, +run the command lines below: + + wget https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz + tar xvzf mobilenet_v1_1.0_224_quant.tgz + +With these commands, we should be able to provide the MobileNet V1 file (```mobilenet_v1_1.0_224_quant.tflite```) +to ```tvmc```, and obtain our TVM compiled model as an output. To do that, run the +following command line: + + tvmc compile -v mobilenet_v1_1.0_224_quant.tflite -o compiled_model.tar + +As an output, you will notice a ```compiled_model.tar```, in the same directory. + +Now it is time to feed the model with some input, that will generate a prediction using TVM. +As models are very diverse in terms of input formats and the source of those inputs (images, streams, +sensors, sound, to name a few), ```tvmc``` supports ```.npz``` (serialized NumPy arrays) as the +main format for ```tvmc run```. To learn more about the ```.npz``` format, please read the +[documentation](https://numpy.org/doc/stable/reference/generated/numpy.savez.html) on NumPy website. + +MobileNet V1 expects a ```(224, 224, 3)``` input tensor. The Python code snippet below, can be used +as an example on how to convert a PNG file into a ```.npz``` file in the expected shape. +The example below uses [PIL](https://pillow.readthedocs.io/en/stable/) and +[NumPy](https://numpy.org) functions to import the image and generate the expected file. + + from tvm.contrib.download import download_testdata + from PIL import Image + import numpy as np + + cat_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true' + image_path = download_testdata(cat_url, 'imagenet_cat.png', module='data') + resized_image = Image.open(image_path).resize((224, 224)) + image_data = np.asarray(resized_image).astype("float32") + image_data = np.expand_dims(image_data, axis=0) + + np.savez("imagenet_cat", input=image_data) Review comment: I agree it makes sense to declare extra set of dependencies, these are the deps we setup the frontend anyway ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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