""" Compile Keras Models ===================== **Author**: `Yuwei Hu <https://Huyuwei.github.io/>`_ This article is an introductory tutorial to deploy keras models with NNVM. 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 ``` pip install -U keras --user ``` ``` pip install -U tensorflow --user ``` or please refer to official site https://keras.io/#installation """ import nnvm import tvm import keras import numpy as np def download(url, path, overwrite=False): import os if os.path.isfile(path) and not overwrite: print('File {} exists, skip.'.format(path)) return print('Downloading from url {} to {}'.format(url, path)) try: import urllib.request urllib.request.urlretrieve(url, path) except: import urllib urllib.urlretrieve(url, path) ###################################################################### # 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' download(weights_url, weights_file) keras_resnet50 = keras.applications.resnet50.ResNet50(include_top=True, weights=None, input_shape=(224,224,3), classes=1000) keras_resnet50.load_weights('resnet50_weights.h5') ###################################################################### # 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' download(img_url, 'cat.jpg') img = Image.open('cat.jpg').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 on NNVM # -------------------------- # We should be familiar with the process now. # convert the keras model(NHWC layout) to NNVM format(NCHW layout). sym, params = nnvm.frontend.from_keras(keras_resnet50) # compile the model target = 'cuda' shape_dict = {'input_1': data.shape} with nnvm.compiler.build_config(opt_level=2): graph, lib, params = nnvm.compiler.build(sym, target, shape_dict, params=params) ###################################################################### # Execute on TVM # --------------- # The process is no different from other examples. from tvm.contrib import graph_runtime ctx = tvm.gpu(0) m = graph_runtime.create(graph, lib, ctx) # set inputs m.set_input('input_1', tvm.nd.array(data.astype('float32'))) m.set_input(**params) # execute m.run() # get outputs out_shape = (1000,) tvm_out = m.get_output(0, tvm.nd.empty(out_shape, 'float32')).asnumpy() top1_tvm = np.argmax(tvm_out) ##################################################################### # Look up synset name # ------------------- # Look up prdiction 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 = 'synset.txt' download(synset_url, synset_name) with open(synset_name) as f: synset = eval(f.read()) print('NNVM 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]))