forked from mindspore-Ecosystem/mindspore
Modify resnet50_adv_pruning network to fix mindspore 1.0.0 version
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zhanghuiyao
parent
e06048bfe0
commit
f4060607df
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@ -4,14 +4,14 @@
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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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- [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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- [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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@ -26,20 +26,20 @@ The Adversarial Pruning method is a reliable neural network pruning algorithm by
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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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- 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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- 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 processor. 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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- Prepare hardware environment with Ascend or GPU processor. 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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- [MindSpore](https://www.mindspore.cn/install/en)
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- For more information, please check the resources below：
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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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- [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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@ -58,6 +58,7 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
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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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@ -66,12 +67,11 @@ To Be Done
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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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```
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# infer example
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```bash
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# infer example
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Ascend: python eval.py --dataset_path ~/Pets/test.mindrecord --platform Ascend --checkpoint_path [CHECKPOINT_PATH]
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GPU: python eval.py --dataset_path ~/Pets/test.mindrecord --platform GPU --checkpoint_path [CHECKPOINT_PATH]
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```
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@ -80,18 +80,18 @@ After installing MindSpore via the official website, you can start evaluation as
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### Result
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```
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```python
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result: {'acc': 0.8023984736985554} ckpt= ./resnet50-imgnet-0.65x-80.24.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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### Evaluation Performance
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#### ResNet50-0.65x on ImageNet2012
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###### ResNet50-0.65x on ImageNet2012
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| Parameters | |
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| -------------------------- | -------------------------------------- |
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| Model Version | ResNet50-0.65x |
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@ -102,7 +102,8 @@ result: {'acc': 0.8023984736985554} ckpt= ./resnet50-imgnet-0.65x-80.24.ckpt
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| FLOPs (G) | 2.1 |
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| Accuracy (Top1) | 75.80 |
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###### ResNet50-0.65x on Oxford-IIIT Pet
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#### ResNet50-0.65x on Oxford-IIIT Pet
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| Parameters | |
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| -------------------------- | -------------------------------------- |
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| Model Version | ResNet50-0.65x |
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@ -26,7 +26,7 @@ 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.config import cfg
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from src.resnet_imgnet import resnet50
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@ -40,14 +40,12 @@ args_opt = parser.parse_args()
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if __name__ == '__main__':
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config_platform = None
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config_platform = cfg
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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.GRAPH_MODE,
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device_target="GPU", save_graphs=False)
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else:
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@ -55,12 +53,6 @@ if __name__ == '__main__':
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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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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@ -76,6 +68,13 @@ if __name__ == '__main__':
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net = resnet50(
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rate=0.65, class_num=config_platform.num_classes, index=index)
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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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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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@ -17,25 +17,7 @@ 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": 438,
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"image_height": 224,
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"image_width": 224,
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"batch_size": 256,
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"epoch_size": 200,
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"warmup_epochs": 1,
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"lr": 0.02,
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"momentum": 0.9,
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"weight_decay": 4e-5,
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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": 5,
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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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cfg = ed({
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"num_classes": 37,
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"image_height": 224,
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"image_width": 224,
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@ -18,10 +18,11 @@ create train or eval dataset.
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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.vision.c_transforms as C
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import mindspore.dataset.transforms.vision.py_transforms as P
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import mindspore.dataset.vision.c_transforms as C
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import mindspore.dataset.vision.py_transforms as P
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import mindspore.dataset.transforms.c_transforms as C2
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from mindspore.dataset.transforms.vision import Inter
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import mindspore.dataset.transforms.py_transforms as P2
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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=100):
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@ -78,13 +79,13 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
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center_crop_p = P.CenterCrop(224)
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totensor = P.ToTensor()
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normalize_p = P.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
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composeop = P.ComposeOp(
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composeop = P2.Compose(
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[decode_p, resize_p, center_crop_p, totensor, normalize_p])
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if do_train:
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trans = [resize_crop_op, horizontal_flip_op, color_op,
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rescale_op, normalize_op, change_swap_op]
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else:
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trans = composeop()
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trans = composeop
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type_cast_op = C2.TypeCast(mstype.int32)
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ds = ds.map(input_columns="image", operations=trans,
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@ -327,21 +327,21 @@ class ResNet(nn.Cell):
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for _, m in self.cells_and_names():
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if isinstance(m, (nn.Conv2d)):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
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m.weight.data.shape).astype("float32")))
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m.weight.set_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
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m.weight.data.shape).astype("float32")))
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if m.bias is not None:
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m.bias.set_parameter_data(
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m.bias.set_data(
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Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
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elif isinstance(m, nn.BatchNorm2d):
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m.gamma.set_parameter_data(
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m.gamma.set_data(
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Tensor(np.ones(m.gamma.data.shape, dtype="float32")))
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m.beta.set_parameter_data(
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m.beta.set_data(
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Tensor(np.zeros(m.beta.data.shape, dtype="float32")))
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elif isinstance(m, nn.Dense):
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m.weight.set_parameter_data(Tensor(np.random.normal(
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m.weight.set_data(Tensor(np.random.normal(
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0, 0.01, m.weight.data.shape).astype("float32")))
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if m.bias is not None:
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m.bias.set_parameter_data(
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m.bias.set_data(
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Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
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