iamhankai
4 years ago
parent
ac5371b38f
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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eval.
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"""
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import os
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import argparse
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from mindspore import context
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from mindspore import nn
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.common import dtype as mstype
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from src.pet_dataset import create_dataset
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from src.config import config_ascend, config_gpu
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from src.tnt import tnt_b
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--platform', type=str, default=None, help='run platform')
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args_opt = parser.parse_args()
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if __name__ == '__main__':
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config_platform = None
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if args_opt.platform == "Ascend":
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config_platform = config_ascend
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
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device_id=device_id, save_graphs=False)
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elif args_opt.platform == "GPU":
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config_platform = config_gpu
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context.set_context(mode=context.PYNATIVE_MODE,
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device_target="GPU", save_graphs=False)
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else:
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raise ValueError("Unsupported platform.")
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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net = tnt_b(num_class=config_platform.num_classes)
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if args_opt.checkpoint_path:
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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if args_opt.platform == "Ascend":
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net.to_float(mstype.float16)
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for _, cell in net.cells_and_names():
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if isinstance(cell, nn.Dense):
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cell.to_float(mstype.float32)
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=False,
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config=config_platform,
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platform=args_opt.platform,
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batch_size=config_platform.batch_size)
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step_size = dataset.get_dataset_size()
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model = Model(net, loss_fn=loss, metrics={'acc'})
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res = model.eval(dataset)
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print("result:", res, "ckpt=", args_opt.checkpoint_path)
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""hub config."""
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from src.tnt import tnt_b
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def create_network(name, *args, **kwargs):
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if name == 'TNT-B':
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return tnt_b(*args, **kwargs)
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raise NotImplementedError(f"{name} is not implemented in the repo")
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# Contents
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- [TNT Description](#tnt-description)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Environment Requirements](#environment-requirements)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Training Process](#training-process)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Training Performance](#evaluation-performance)
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- [Inference Performance](#evaluation-performance)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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## [TNT Description](#contents)
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The TNT (Transformer in Transformer) network is a pure transformer model for visual recognition. TNT treats an image as a sequence of patches and treats a patch as a sequence of pixels. TNT block utilizes a outer transformer block to process the sequence of patches and an inner transformer block to process the sequence of pixels.
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[Paper](https://arxiv.org/abs/2103.00112): Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang. Transformer in Transformer. preprint 2021.
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## [Model architecture](#contents)
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The overall network architecture of TNT is show below:
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## [Dataset](#contents)
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Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
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- Dataset size: 7049 colorful images in 1000 classes
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- Train: 3680 images
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- Test: 3369 images
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- Data format: RGB images.
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- Note: Data will be processed in src/dataset.py
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## [Environment Requirements](#contents)
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- Hardware(Ascend/GPU)
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- Prepare hardware environment with Ascend or GPU. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
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- Framework
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- [MindSpore](https://www.mindspore.cn/install/en)
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- For more information, please check the resources below£º
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- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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## [Script description](#contents)
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### [Script and sample code](#contents)
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```python
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TNT
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├── eval.py # inference entry
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├── fig
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│ └── tnt.png # the illustration of TNT network
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├── readme.md # Readme
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└── src
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├── config.py # config of model and data
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├── pet_dataset.py # dataset loader
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└── tnt.py # TNT network
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```
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## [Training process](#contents)
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To Be Done
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## [Eval process](#contents)
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### Usage
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After installing MindSpore via the official website, you can start evaluation as follows:
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### Launch
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```bash
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# infer example
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GPU: python eval.py --model tnt-b --dataset_path ~/Pets/test.mindrecord --platform GPU --checkpoint_path [CHECKPOINT_PATH]
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```
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> checkpoint can be downloaded at https://www.mindspore.cn/resources/hub.
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### Result
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```bash
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result: {'acc': 0.95} ckpt= ./tnt-b-pets.ckpt
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```
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## [Model Description](#contents)
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### [Performance](#contents)
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#### Evaluation Performance
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##### TNT on ImageNet2012
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| Parameters | | |
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| -------------------------- | -------------------------------------- |---------------------------------- |
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| Model Version | TNT-B |TNT-S|
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| uploaded Date | 21/03/2021 (month/day/year) | 21/03/2021 (month/day/year) |
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| MindSpore Version | 1.1 | 1.1 |
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| Dataset | ImageNet2012 | ImageNet2012|
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| Input size | 224x224 | 224x224|
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| Parameters (M) | 86.4 | 23.8 |
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| FLOPs (M) | 14.1 | 5.2 |
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| Accuracy (Top1) | 82.8 | 81.3 |
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###### TNT on Oxford-IIIT Pet
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| Parameters | | |
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| -------------------------- | -------------------------------------- |---------------------------------- |
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| Model Version | TNT-B |TNT-S|
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| uploaded Date | 21/03/2021 (month/day/year) | 21/03/2021 (month/day/year) |
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| MindSpore Version | 1.1 | 1.1 |
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| Dataset | Oxford-IIIT Pet | Oxford-IIIT Pet|
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| Input size | 384x384 | 384x384|
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| Parameters (M) | 86.4 | 23.8 |
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| Accuracy (Top1) | 95.0 | 94.7 |
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## [Description of Random Situation](#contents)
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In dataset.py, we set the seed inside "create_dataset" function. We also use random seed in train.py.
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## [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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network config setting, will be used in train.py and eval.py
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"""
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from easydict import EasyDict as ed
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config_ascend = ed({
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"num_classes": 37,
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"image_height": 384,
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"image_width": 384,
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"batch_size": 50,
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"epoch_size": 300,
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"warmup_epochs": 5,
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"lr": 1e-3,
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"momentum": 0.9,
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"weight_decay": 0.05,
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"label_smooth": 0.1,
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"loss_scale": 1024,
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"save_checkpoint": True,
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"save_checkpoint_epochs": 1,
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"keep_checkpoint_max": 200,
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"save_checkpoint_path": "./checkpoint",
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})
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config_gpu = ed({
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"num_classes": 37,
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"image_height": 384,
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"image_width": 384,
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"batch_size": 50,
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"epoch_size": 300,
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"warmup_epochs": 5,
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"lr": 1e-3,
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"momentum": 0.9,
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"weight_decay": 0.05,
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"label_smooth": 0.1,
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"loss_scale": 1024,
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"save_checkpoint": True,
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"save_checkpoint_epochs": 1,
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"keep_checkpoint_max": 500,
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"save_checkpoint_path": "./checkpoint",
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})
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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create train or eval dataset.
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"""
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import os
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import mindspore.common.dtype as mstype
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import mindspore.dataset.engine as de
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import mindspore.dataset.transforms.py_transforms as py_transforms
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import mindspore.dataset.transforms.c_transforms as c_transforms
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import mindspore.dataset.vision.py_transforms as py_vision
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from mindspore.dataset.vision import Inter
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def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=1):
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"""
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create a train or eval dataset
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Args:
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dataset_path(string): the path of dataset.
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do_train(bool): whether dataset is used for train or eval.
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repeat_num(int): the repeat times of dataset. Default: 1
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batch_size(int): the batch size of dataset. Default: 32
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Returns:
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dataset
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"""
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if platform == "Ascend":
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rank_size = int(os.getenv("RANK_SIZE"))
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rank_id = int(os.getenv("RANK_ID"))
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if rank_size == 1:
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ds = de.MindDataset(dataset_path, num_parallel_workers=8, shuffle=True)
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else:
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ds = de.MindDataset(dataset_path, num_parallel_workers=8, shuffle=True,
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num_shards=rank_size, shard_id=rank_id)
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elif platform == "GPU":
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if do_train:
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from mindspore.communication.management import get_rank, get_group_size
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ds = de.MindDataset(dataset_path, num_parallel_workers=8, shuffle=True,
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num_shards=get_group_size(), shard_id=get_rank())
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else:
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ds = de.MindDataset(dataset_path, num_parallel_workers=8, shuffle=False)
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else:
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raise ValueError("Unsupported platform.")
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resize_height = config.image_height
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resize_width = config.image_width
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buffer_size = 1000
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# define map operations
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random_resize_crop_bicubic = py_vision.RandomResizedCrop(size=(resize_height, resize_width),
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scale=(0.08, 1.0), ratio=(3./4., 4./3.),
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interpolation=Inter.BICUBIC)
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random_horizontal_flip_op = py_vision.RandomHorizontalFlip(0.5)
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color_jitter = 0.4
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adjust_range = (max(0, 1 - color_jitter), 1 + color_jitter)
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random_color_jitter_op = py_vision.RandomColorAdjust(brightness=adjust_range,
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contrast=adjust_range,
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saturation=adjust_range)
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decode_p = py_vision.Decode()
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resize_p = py_vision.Resize(int(resize_height), interpolation=Inter.BICUBIC)
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center_crop_p = py_vision.CenterCrop(resize_height)
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totensor = py_vision.ToTensor()
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normalize_p = py_vision.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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if do_train:
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trans = py_transforms.Compose([decode_p, random_resize_crop_bicubic, random_horizontal_flip_op,
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random_color_jitter_op, totensor, normalize_p])
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else:
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trans = py_transforms.Compose([decode_p, resize_p, center_crop_p, totensor, normalize_p])
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type_cast_op = c_transforms.TypeCast(mstype.int32)
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ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8)
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ds = ds.map(input_columns="label_list", operations=type_cast_op, num_parallel_workers=8)
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# apply shuffle operations
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ds = ds.shuffle(buffer_size=buffer_size)
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# apply batch operations
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ds = ds.batch(batch_size, drop_remainder=True)
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# apply dataset repeat operation
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ds = ds.repeat(repeat_num)
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return ds
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