# 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 Keras Models ===================== **Author**: `Yuwei Hu <https://Huyuwei.github.io/>`_ This article is an introductory tutorial to deploy keras models with Relay. For us to begin with, keras should be installed. Tensorflow is also required since it's used as the default backend of keras. A quick solution is to install via pip .. code-block:: bash pip install -U keras --user pip install -U tensorflow --user or please refer to official site https://keras.io/#installation """ import tvm from tvm import te import tvm.relay as relay from tvm.contrib.download import download_testdata import keras import numpy as np ###################################################################### # Load pretrained keras model # ---------------------------- # We load a pretrained resnet-50 classification model provided by keras. weights_url = ''.join(['https://github.com/fchollet/deep-learning-models/releases/', 'download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5']) weights_file = 'resnet50_weights.h5' weights_path = download_testdata(weights_url, weights_file, module='keras') keras_resnet50 = keras.applications.resnet50.ResNet50(include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000) keras_resnet50.load_weights(weights_path) ###################################################################### # Load a test image # ------------------ # A single cat dominates the examples! from PIL import Image from matplotlib import pyplot as plt from keras.applications.resnet50 import preprocess_input 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 data = np.array(img)[np.newaxis, :].astype('float32') data = preprocess_input(data).transpose([0, 3, 1, 2]) print('input_1', data.shape) ###################################################################### # Compile the model with Relay # ---------------------------- # convert the keras model(NHWC layout) to Relay format(NCHW layout). shape_dict = {'input_1': data.shape} mod, params = relay.frontend.from_keras(keras_resnet50, shape_dict) # compile the model target = 'cuda' ctx = tvm.gpu(0) with relay.build_config(opt_level=3): executor = relay.build_module.create_executor('graph', mod, ctx, target) ###################################################################### # Execute on TVM # --------------- dtype = 'float32' tvm_out = executor.evaluate()(tvm.nd.array(data.astype(dtype)), **params) top1_tvm = np.argmax(tvm_out.asnumpy()[0]) ##################################################################### # Look up synset name # ------------------- # Look up prediction top 1 index in 1000 class synset. 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 keras output keras_out = keras_resnet50.predict(data.transpose([0, 2, 3, 1])) top1_keras = np.argmax(keras_out) print('Keras top-1 id: {}, class name: {}'.format(top1_keras, synset[top1_keras]))