| # 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. |
| |
| import mxnet as mx |
| from mxnet.gluon.data.vision import transforms |
| import time |
| import glob |
| |
| |
| def test_accuracy(net, data_loader, description): |
| count = 0 |
| acc_top1 = mx.gluon.metric.Accuracy() |
| acc_top5 = mx.gluon.metric.TopKAccuracy(5) |
| start = time.time() |
| for x, label in data_loader: |
| output = net(x) |
| acc_top1.update(label, output) |
| acc_top5.update(label, output) |
| count += 1 |
| time_spend = time.time() - start |
| _, top1 = acc_top1.get() |
| _, top5 = acc_top5.get() |
| print('{:21} Top1 Accuracy: {:.4f} Top5 Accuracy: {:.4f} from {:4} batches in {:8.2f}s' |
| .format(description, top1, top5, count, time_spend)) |
| |
| # Preparing input data |
| rgb_mean = (0.485, 0.456, 0.406) |
| rgb_std = (0.229, 0.224, 0.225) |
| batch_size = 64 |
| |
| start = time.time() |
| # Set proper path to ImageNet data set below |
| dataset = mx.gluon.data.vision.ImageRecordDataset('../imagenet/rec/val.rec') |
| transformer = transforms.Compose([transforms.Resize(256), |
| transforms.CenterCrop(224), |
| transforms.ToTensor(), |
| transforms.Normalize(mean=rgb_mean, std=rgb_std)]) |
| # Note: as the input data is used many times it is better to prepare it once. |
| # Therefore, lazy parameter for transform_first is set to False. |
| val_data = mx.gluon.data.DataLoader( |
| dataset.transform_first(transformer, lazy=False), batch_size, shuffle=False) |
| val_data.batch_size = batch_size |
| time_consumed = time.time() - start |
| print("Input data prepared in {:8.2f}s".format(time_consumed)) |
| |
| print("Measure accuracy on the whole data set could take a long time. Please wait...") |
| root_path = '__resnet50_v2_' |
| symbol_part = '-symbol.json' |
| for symbol in glob.glob(root_path + '*' + symbol_part): |
| param = symbol.replace(symbol_part,'-0000.params') |
| net_name = symbol.replace(root_path,'').replace(symbol_part,'').replace('_', ' ') |
| net = mx.gluon.SymbolBlock.imports(symbol, ['data'], param) |
| net.hybridize(static_alloc=True, static_shape=True) |
| test_accuracy(net, val_data, net_name) |
