| # |
| # 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 os |
| import numpy as np |
| |
| from singa import device |
| from singa import tensor |
| from singa import sonnx |
| from singa import autograd |
| import onnx |
| |
| import sys |
| sys.path.append(os.path.dirname(__file__) + '/..') |
| from utils import download_model, check_exist_or_download |
| |
| import logging |
| logging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(message)s') |
| |
| from transformers import RobertaTokenizer |
| |
| tokenizer = RobertaTokenizer.from_pretrained('roberta-base') |
| |
| def preprocess(): |
| text = "This film is so good" |
| tokens = tokenizer.encode(text, add_special_tokens=True) |
| tokens = np.array(tokens) |
| return tokens.reshape([1, -1]).astype(np.float32) |
| |
| class MyModel(sonnx.SONNXModel): |
| |
| def __init__(self, onnx_model): |
| super(MyModel, self).__init__(onnx_model) |
| |
| def forward(self, *x): |
| y = super(MyModel, self).forward(*x) |
| return y[0] |
| |
| def train_one_batch(self, x, y): |
| pass |
| |
| |
| if __name__ == "__main__": |
| url = 'https://media.githubusercontent.com/media/onnx/models/master/text/machine_comprehension/roberta/model/roberta-sequence-classification-9.tar.gz' |
| download_dir = '/tmp/' |
| model_path = os.path.join(download_dir, 'roberta-sequence-classification-9', 'roberta-sequence-classification-9.onnx') |
| |
| logging.info("onnx load model...") |
| download_model(url) |
| onnx_model = onnx.load(model_path) |
| |
| # inference |
| logging.info("preprocessing...") |
| input_ids = preprocess() |
| |
| logging.info("model compling...") |
| dev = device.get_default_device() |
| x = tensor.Tensor(device=dev, data=input_ids) |
| model = MyModel(onnx_model) |
| |
| # verifty the test |
| # from utils import load_dataset |
| # sg_ir = sonnx.prepare(onnx_model) # run without graph |
| # inputs, ref_outputs = load_dataset( |
| # os.path.join('/tmp', 'roberta-sst-9', 'test_data_set_0')) |
| # outputs = sg_ir.run(inputs) |
| # for ref_o, o in zip(ref_outputs, outputs): |
| # np.testing.assert_almost_equal(ref_o, o, 4) |
| |
| logging.info("model running...") |
| y = model.forward(x) |
| y = autograd.reshape(y, y.shape[-2:])[-1, :] |
| y = tensor.softmax(y) |
| y = tensor.to_numpy(y)[0] |
| y = np.argsort(y)[::-1] |
| if(y[0] == 0): |
| print("Prediction: negative") |
| else: |
| print("Prediction: positive") |