leandron commented on a change in pull request #6597: URL: https://github.com/apache/incubator-tvm/pull/6597#discussion_r498389331
########## File path: tutorials/get_started/tvmc_command_line_driver.py ########## @@ -0,0 +1,331 @@ +# 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. +""" +Getting Started with TVM command line driver - TVMC +=================================================== +**Authors**: +`Leandro Nunes <https://github.com/leandron>`_, +`Matthew Barrett <https://github.com/mbaret>`_ + +This tutorial is an introduction to working with TVMC, the TVM command +line driver. TVMC is a tool that exposes TVM features such as +auto-tuning, compiling, profiling and execution of models, via a +command line interface. + +In this tutorial we are going to use TVMC to compile, run and tune a +ResNet-50 on a x86 CPU. + +We are going to start by downloading ResNet 50 V2. Then, we are going +to use TVMC to compile this model into a TVM module, and use the +compiled module to generate predictions. Finally, we are going to experiment +with the auto-tuning options, that can be used to help the compiler to +improve network performance. + +The final goal is to give an overview of TVMC's capabilities and also +some guidance on where to look for more information. +""" + +###################################################################### +# Using TVMC +# ---------- +# +# TVMC is a Python application, part of the TVM Python package. +# When you install TVM using a Python package, you will get TVMC as +# as a command line application called ``tvmc``. +# +# Alternatively, if you have TVM as a Python module on your +# ``$PYTHONPATH``,you can access the command line driver functionality +# via the executable python module, ``python -m tvm.driver.tvmc``. +# +# For simplicity, this tutorial will mention TVMC command line using +# ``tvmc <options>``, but the same results can be obtained with +# ``python -m tvm.driver.tvmc <options>``. +# +# You can check the help page using: +# +# .. code-block:: bash +# +# tvmc --help +# +# +# As you can see in the help page, the main features are +# accessible via the subcommands ``tune``, ``compile`` and ``run``. +# To read about specific options under a given subcommand, use +# ``tvmc <subcommand> --help``. +# +# In the following sections we will use TVMC to tune, compile and +# run a model. But first, we need a model. +# + + +###################################################################### +# Obtaining the model +# ------------------- +# +# We are going to use ResNet-50 V2 as an example to experiment with TVMC. +# The version below is in ONNX format. To download the file, you can use +# the command below: +# +# .. code-block:: bash +# +# wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx +# +# + +###################################################################### +# .. note:: Supported model formats +# +# TVMC supports models created with Keras, ONNX, TensorFlow, TFLite +# and Torch. Use the option``--model-format`` if you need to +# explicitly provide the model format you are using. See ``tvmc +# compile --help`` for more information. +# + + +###################################################################### +# Compiling the model +# ------------------- +# +# The next step once we've downloaded ResNet-50, is to compile it, +# To accomplish that, we are going to use ``tvmc compile``. The +# output we get from the compilation process is a TAR package, +# that can be used to run our model on the target device. +# +# .. code-block:: bash +# +# tvmc compile \ +# --target "llvm" \ +# --output compiled_module.tar \ +# resnet50-v2-7.onnx +# +# Once compilation finishes, the output ``compiled_module.tar`` will be created. This +# can be directly loaded by your application and run via the TVM runtime APIs. +# + + +###################################################################### +# .. note:: Defining the correct target +# +# Specifying the correct target (option ``--target``) can have a huge +# impact on the performance of the compiled module, as it can take +# advantage of hardware features available on the target. For more +# information, please refer to `Auto-tuning a convolutional network +# for x86 CPU <https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_x86.html#define-network>`_. +# + + +###################################################################### +# +# In the next step, we are going to use the compiled module, providing it +# with some inputs, to generate some predictions. +# + + +###################################################################### +# Input pre-processing +# -------------------- +# +# In order to generate predictions, we will need two things: +# +# - the compiled module, which we just produced; +# - a valid input to the model +# +# Each model is particular when it comes to expected tensor shapes, formats and data +# types. For this reason, most models require some pre and +# post processing, to ensure the input(s) is valid and to interpret the output(s). +# +# In TVMC, we adopted NumPy's ``.npz`` format for both input and output data. +# This is a well-supported NumPy format to serialize multiple arrays into a file. +# +# We will use the usual cat image, similar to other TVM tutorials: +# +# .. image:: https://s3.amazonaws.com/model-server/inputs/kitten.jpg +# :height: 224px +# :width: 224px +# :align: center +# +# For our ResNet 50 V2 model, the input is expected to be in ImageNet format. +# Here is an example of a script to pre-process an image for ResNet 50 V2. +# +from tvm.contrib.download import download_testdata +from PIL import Image +import numpy as np + +img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"; +img_path = download_testdata(img_url, "imagenet_cat.png", module="data") + +# Resize it to 224x224 +resized_image = Image.open(img_path).resize((224, 224)) +img_data = np.asarray(resized_image).astype("float32") + +# ONNX expects NCHW input, so convert the array +img_data = np.transpose(img_data, (2, 0, 1)) + +# Normalize according to ImageNet +imagenet_mean = np.array([0.485, 0.456, 0.406]) +imagenet_stddev = np.array([0.229, 0.224, 0.225]) +norm_img_data = np.zeros(img_data.shape).astype("float32") +for i in range(img_data.shape[0]): + norm_img_data[i, :, :] = (img_data[i, :, :] / 255 - imagenet_mean[i]) / imagenet_stddev[i] + +# Add batch dimension +img_data = np.expand_dims(norm_img_data, axis=0) + +# Save to .npz (outputs imagenet_cat.npz) +np.savez("imagenet_cat", data=img_data) + + +###################################################################### +# Running the compiled module +# --------------------------- +# +# With both the compiled module and input file in hand, we can run it by +# invoking ``tvmc run``. +# +# .. code-block:: bash +# +# tvmc run \ +# --inputs imagenet_cat.npz \ +# --output predictions.npz \ +# compiled_module.tar +# +# When running the above command, a new file ``predictions.npz`` should +# be produced. It contains the output tensors. +# +# In this example, we are running the model on the same machine that we used +# for compilation. In some cases we might want to run it remotely via +# an RPC Tracker. To read more about these options please check ``tvmc +# run --help``. +# + +###################################################################### +# Output post-processing +# ---------------------- +# +# As previously mentioned, each model will have its own particular way +# of providing output tensors. +# +# In our case, we need to run some post-processing to render the +# outputs from ResNet 50 V2 into a more human-readable form. +# +# The script below shows an example of the post-processing to extract +# labels from the output of our compiled module. +# +import os.path +import numpy as np + +from scipy.special import softmax + +from tvm.contrib.download import download_testdata + +# Download a list of labels +labels_url = "https://s3.amazonaws.com/onnx-model-zoo/synset.txt"; +labels_path = download_testdata(labels_url, "synset.txt", module="data") + +with open(labels_path, "r") as f: + labels = [l.rstrip() for l in f] + +output_file = "predictions.npz" + +# Open the output and read the output tensor +if os.path.exists(output_file): + with np.load(output_file) as data: + scores = softmax(data["output_0"]) + scores = np.squeeze(scores) + scores = np.argsort(scores)[::-1] + + for i in scores[0:5]: + print("class='%s' with probability=%f" % (labels[i], scores[i])) + + +######################################################################## +# When running the script, a list of predictions should be printed similar +# the the example below. +# +# .. code-block:: bash +# +# $ python post_processing.py +# class=n02123045 tabby, tabby cat ; probability=446.000000 +# class=n02123159 tiger cat ; probability=675.000000 +# class=n02124075 Egyptian cat ; probability=836.000000 +# class=n02129604 tiger, Panthera tigris ; probability=917.000000 +# class=n04040759 radiator ; probability=213.000000 +# + + +###################################################################### +# Tuning the model Review comment: Yeah, agreed it looks a little abrupt. 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![https://s3.amazonaws.com/model-server/inputs/kitten.jpg"](https://s3.amazonaws.com/model-server/inputs/kitten.jpg"){kind=link}