.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_how_to_compile_models_from_caffe2.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_how_to_compile_models_from_caffe2.py:


Compile Caffe2 Models
=====================
**Author**: `Hiroyuki Makino <https://makihiro.github.io/>`_

This article is an introductory tutorial to deploy Caffe2 models with Relay.

For us to begin with, Caffe2 should be installed.

A quick solution is to install via conda

.. code-block:: bash

    # for cpu
    conda install pytorch-nightly-cpu -c pytorch
    # for gpu with CUDA 8
    conda install pytorch-nightly cuda80 -c pytorch

or please refer to official site
https://caffe2.ai/docs/getting-started.html

Load pretrained Caffe2 model
----------------------------
We load a pretrained resnet50 classification model provided by Caffe2.


.. code-block:: default

    from caffe2.python.models.download import ModelDownloader

    mf = ModelDownloader()


    class Model:
        def __init__(self, model_name):
            self.init_net, self.predict_net, self.value_info = mf.get_c2_model(model_name)


    resnet50 = Model("resnet50")







Load a test image
------------------
A single cat dominates the examples!


.. code-block:: default

    from tvm.contrib.download import download_testdata
    from PIL import Image
    from matplotlib import pyplot as plt
    import numpy as np

    img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
    img_path = download_testdata(img_url, "cat.png", module="data")
    img = Image.open(img_path).resize((224, 224))
    plt.imshow(img)
    plt.show()
    # input preprocess
    def transform_image(image):
        image = np.array(image) - np.array([123.0, 117.0, 104.0])
        image /= np.array([58.395, 57.12, 57.375])
        image = image.transpose((2, 0, 1))
        image = image[np.newaxis, :].astype("float32")
        return image


    data = transform_image(img)




.. image:: /how_to/compile_models/images/sphx_glr_from_caffe2_001.png
    :class: sphx-glr-single-img




Compile the model on Relay
--------------------------


.. code-block:: default


    # Caffe2 input tensor name, shape and type
    input_name = resnet50.predict_net.op[0].input[0]
    shape_dict = {input_name: data.shape}
    dtype_dict = {input_name: data.dtype}

    # parse Caffe2 model and convert into Relay computation graph
    from tvm import relay, transform

    mod, params = relay.frontend.from_caffe2(
        resnet50.init_net, resnet50.predict_net, shape_dict, dtype_dict
    )

    # compile the model
    # target x86 CPU
    target = "llvm"
    with transform.PassContext(opt_level=3):
        lib = relay.build(mod, target, params=params)







Execute on TVM
---------------
The process is no different from other examples.


.. code-block:: default

    import tvm
    from tvm import te
    from tvm.contrib import graph_executor

    # context x86 CPU, use tvm.cuda(0) if you run on GPU
    dev = tvm.cpu(0)
    # create a runtime executor module
    m = graph_executor.GraphModule(lib["default"](dev))
    # set inputs
    m.set_input(input_name, tvm.nd.array(data.astype("float32")))
    # execute
    m.run()
    # get outputs
    tvm_out = m.get_output(0)
    top1_tvm = np.argmax(tvm_out.numpy()[0])







Look up synset name
-------------------
Look up prediction top 1 index in 1000 class synset.


.. code-block:: default

    from caffe2.python import workspace

    synset_url = "".join(
        [
            "https://gist.githubusercontent.com/zhreshold/",
            "4d0b62f3d01426887599d4f7ede23ee5/raw/",
            "596b27d23537e5a1b5751d2b0481ef172f58b539/",
            "imagenet1000_clsid_to_human.txt",
        ]
    )
    synset_name = "imagenet1000_clsid_to_human.txt"
    synset_path = download_testdata(synset_url, synset_name, module="data")
    with open(synset_path) as f:
        synset = eval(f.read())
    print("Relay top-1 id: {}, class name: {}".format(top1_tvm, synset[top1_tvm]))
    # confirm correctness with caffe2 output
    p = workspace.Predictor(resnet50.init_net, resnet50.predict_net)
    caffe2_out = p.run({input_name: data})
    top1_caffe2 = np.argmax(caffe2_out)
    print("Caffe2 top-1 id: {}, class name: {}".format(top1_caffe2, synset[top1_caffe2]))




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Relay top-1 id: 287, class name: lynx, catamount
    Caffe2 top-1 id: 287, class name: lynx, catamount




.. _sphx_glr_download_how_to_compile_models_from_caffe2.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: from_caffe2.py <from_caffe2.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: from_caffe2.ipynb <from_caffe2.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_