# 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. """ 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. 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! 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/master/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., 117., 104.]) 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) ###################################################################### # Compile the model on Relay # -------------------------- # 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 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 relay.build_config(opt_level=3): graph, lib, params = relay.build(mod, target, params=params) ###################################################################### # Execute on TVM # --------------- # The process is no different from other examples. import tvm from tvm import te from tvm.contrib import graph_runtime # context x86 CPU, use tvm.gpu(0) if you run on GPU ctx = tvm.cpu(0) # create a runtime executor module m = graph_runtime.create(graph, lib, ctx) # set inputs m.set_input(input_name, tvm.nd.array(data.astype('float32'))) # set related params m.set_input(**params) # execute m.run() # get outputs tvm_out = m.get_output(0) top1_tvm = np.argmax(tvm_out.asnumpy()[0]) ##################################################################### # Look up synset name # ------------------- # Look up prediction top 1 index in 1000 class synset. 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]))