# 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. """ .. _tutorial-from-mxnet: Compile MXNet Models ==================== **Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_, \ `Kazutaka Morita <https://github.com/kazum>`_ This article is an introductory tutorial to deploy mxnet models with Relay. For us to begin with, mxnet module is required to be installed. A quick solution is .. code-block:: bash pip install mxnet --user or please refer to offical installation guide. https://mxnet.incubator.apache.org/versions/master/install/index.html """ # some standard imports import mxnet as mx import tvm import tvm.relay as relay import numpy as np ###################################################################### # Download Resnet18 model from Gluon Model Zoo # --------------------------------------------- # In this section, we download a pretrained imagenet model and classify an image. from tvm.contrib.download import download_testdata from mxnet.gluon.model_zoo.vision import get_model from PIL import Image from matplotlib import pyplot as plt block = get_model('resnet18_v1', pretrained=True) img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true' img_name = 'cat.png' synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/', '4d0b62f3d01426887599d4f7ede23ee5/raw/', '596b27d23537e5a1b5751d2b0481ef172f58b539/', 'imagenet1000_clsid_to_human.txt']) synset_name = 'imagenet1000_clsid_to_human.txt' img_path = download_testdata(img_url, 'cat.png', module='data') synset_path = download_testdata(synset_url, synset_name, module='data') with open(synset_path) as f: synset = eval(f.read()) image = Image.open(img_path).resize((224, 224)) plt.imshow(image) plt.show() 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, :] return image x = transform_image(image) print('x', x.shape) ###################################################################### # Compile the Graph # ----------------- # Now we would like to port the Gluon model to a portable computational graph. # It's as easy as several lines. # We support MXNet static graph(symbol) and HybridBlock in mxnet.gluon shape_dict = {'data': x.shape} func, params = relay.frontend.from_mxnet(block, shape_dict) ## we want a probability so add a softmax operator func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_params, func.attrs) ###################################################################### # now compile the graph target = 'cuda' with relay.build_config(opt_level=3): graph, lib, params = relay.build(func, target, params=params) ###################################################################### # Execute the portable graph on TVM # --------------------------------- # Now, we would like to reproduce the same forward computation using TVM. from tvm.contrib import graph_runtime ctx = tvm.gpu(0) dtype = 'float32' m = graph_runtime.create(graph, lib, ctx) # set inputs m.set_input('data', tvm.nd.array(x.astype(dtype))) m.set_input(**params) # execute m.run() # get outputs tvm_output = m.get_output(0) top1 = np.argmax(tvm_output.asnumpy()[0]) print('TVM prediction top-1:', top1, synset[top1]) ###################################################################### # Use MXNet symbol with pretrained weights # ---------------------------------------- # MXNet often use `arg_params` and `aux_params` to store network parameters # separately, here we show how to use these weights with existing API def block2symbol(block): data = mx.sym.Variable('data') sym = block(data) args = {} auxs = {} for k, v in block.collect_params().items(): args[k] = mx.nd.array(v.data().asnumpy()) return sym, args, auxs mx_sym, args, auxs = block2symbol(block) # usually we would save/load it as checkpoint mx.model.save_checkpoint('resnet18_v1', 0, mx_sym, args, auxs) # there are 'resnet18_v1-0000.params' and 'resnet18_v1-symbol.json' on disk ###################################################################### # for a normal mxnet model, we start from here mx_sym, args, auxs = mx.model.load_checkpoint('resnet18_v1', 0) # now we use the same API to get Relay computation graph relay_func, relay_params = relay.frontend.from_mxnet(mx_sym, shape_dict, arg_params=args, aux_params=auxs) # repeat the same steps to run this model using TVM