| """ |
| 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 |
| |
| .. code-block:: bash |
| |
| 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.png') |
| img = Image.open('cat.png').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=3): |
| 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 |
| 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. |
| 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])) |