Modify resnet50_adv_pruning network to fix mindspore 1.0.0 version

4 years ago · f4060607df
parent e06048bfe0
commit f4060607df
5 changed files with 47 additions and 64 deletions
--- a/model_zoo/research/cv/resnet50_adv_pruning/Readme.md
+++ b/model_zoo/research/cv/resnet50_adv_pruning/Readme.md
@ -4,14 +4,14 @@
 - [Dataset](#dataset)
 - [Environment Requirements](#environment-requirements)
 - [Script Description](#script-description)
-  - [Script and Sample Code](#script-and-sample-code)
-    - [Training Process](#training-process)
-    - [Evaluation Process](#evaluation-process)
-      - [Evaluation](#evaluation)
+    - [Script and Sample Code](#script-and-sample-code)
+        - [Training Process](#training-process)
+        - [Evaluation Process](#evaluation-process)
+            - [Evaluation](#evaluation)
 - [Model Description](#model-description)
-  - [Performance](#performance)  
-    - [Training Performance](#evaluation-performance)
-    - [Inference Performance](#evaluation-performance)
+    - [Performance](#performance)  
+        - [Training Performance](#evaluation-performance)
+        - [Inference Performance](#evaluation-performance)
 - [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)

@ -26,20 +26,20 @@ The Adversarial Pruning method is a reliable neural network pruning algorithm by
 Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)

 - Dataset size: 7049 colorful images in 1000 classes
-  - Train:  3680 images
-  - Test: 3369 images
+    - Train:  3680 images
+    - Test: 3369 images
 - Data format: RGB images.
-	- Note: Data will be processed in src/dataset.py
+    - Note: Data will be processed in src/dataset.py

 # [Environment Requirements](#contents)

 - Hardware（Ascend/GPU）
-  - 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.
+    - 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.
 - Framework
-  - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-  - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
-  - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)

 # [Script description](#contents)

@ -58,6 +58,7 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
 ```

 ## [Training process](#contents)
+
 To Be Done

 ## [Eval process](#contents)
@ -66,11 +67,10 @@ To Be Done

 After installing MindSpore via the official website, you can start evaluation as follows:

-
 ### Launch

-```
-# infer example
+```bash
+  # infer example

  Ascend: python eval.py --dataset_path ~/Pets/test.mindrecord --platform Ascend --checkpoint_path [CHECKPOINT_PATH]
  GPU: python eval.py --dataset_path ~/Pets/test.mindrecord --platform GPU --checkpoint_path [CHECKPOINT_PATH]
@ -80,18 +80,18 @@ After installing MindSpore via the official website, you can start evaluation as

 ### Result

-```
+```python
 result: {'acc': 0.8023984736985554} ckpt= ./resnet50-imgnet-0.65x-80.24.ckpt
-
 ```

 # [Model Description](#contents)

 ## [Performance](#contents)

-#### Evaluation Performance
+### Evaluation Performance
+
+#### ResNet50-0.65x on ImageNet2012

-###### ResNet50-0.65x on ImageNet2012
 | Parameters                 |                                        |
 | -------------------------- | -------------------------------------- |
 | Model Version              | ResNet50-0.65x                              |
@ -102,7 +102,8 @@ result: {'acc': 0.8023984736985554} ckpt= ./resnet50-imgnet-0.65x-80.24.ckpt
 | FLOPs (G) | 2.1 |
 | Accuracy (Top1) | 75.80 |

-###### ResNet50-0.65x on Oxford-IIIT Pet
+#### ResNet50-0.65x on Oxford-IIIT Pet
+
 | Parameters                 |                                        |
 | -------------------------- | -------------------------------------- |
 | Model Version              | ResNet50-0.65x                               |
--- a/model_zoo/research/cv/resnet50_adv_pruning/eval.py
+++ b/model_zoo/research/cv/resnet50_adv_pruning/eval.py
@ -26,7 +26,7 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.common import dtype as mstype

 from src.pet_dataset import create_dataset
-from src.config import config_ascend, config_gpu
+from src.config import cfg
 from src.resnet_imgnet import resnet50


@ -40,14 +40,12 @@ args_opt = parser.parse_args()


 if __name__ == '__main__':
-    config_platform = None
+    config_platform = cfg
    if args_opt.platform == "Ascend":
-        config_platform = config_ascend
        device_id = int(os.getenv('DEVICE_ID'))
        context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
                            device_id=device_id, save_graphs=False)
    elif args_opt.platform == "GPU":
-        config_platform = config_gpu
        context.set_context(mode=context.GRAPH_MODE,
                            device_target="GPU", save_graphs=False)
    else:
@ -55,12 +53,6 @@ if __name__ == '__main__':

    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')

-    if args_opt.platform == "Ascend":
-        net.to_float(mstype.float16)
-        for _, cell in net.cells_and_names():
-            if isinstance(cell, nn.Dense):
-                cell.to_float(mstype.float32)
-
    dataset = create_dataset(dataset_path=args_opt.dataset_path,
                             do_train=False,
                             config=config_platform,
@ -76,6 +68,13 @@ if __name__ == '__main__':

    net = resnet50(
        rate=0.65, class_num=config_platform.num_classes, index=index)
+
+    if args_opt.platform == "Ascend":
+        net.to_float(mstype.float16)
+        for _, cell in net.cells_and_names():
+            if isinstance(cell, nn.Dense):
+                cell.to_float(mstype.float32)
+
    if args_opt.checkpoint_path:
        param_dict = load_checkpoint(args_opt.checkpoint_path)
        load_param_into_net(net, param_dict)
--- a/model_zoo/research/cv/resnet50_adv_pruning/src/config.py
+++ b/model_zoo/research/cv/resnet50_adv_pruning/src/config.py
@ -17,25 +17,7 @@ network config setting, will be used in train.py and eval.py
 """
 from easydict import EasyDict as ed

-config_ascend = ed({
-    "num_classes": 438,
-    "image_height": 224,
-    "image_width": 224,
-    "batch_size": 256,
-    "epoch_size": 200,
-    "warmup_epochs": 1,
-    "lr": 0.02,
-    "momentum": 0.9,
-    "weight_decay": 4e-5,
-    "label_smooth": 0.1,
-    "loss_scale": 1024,
-    "save_checkpoint": True,
-    "save_checkpoint_epochs": 5,
-    "keep_checkpoint_max": 200,
-    "save_checkpoint_path": "./checkpoint",
-})
-
-config_gpu = ed({
+cfg = ed({
    "num_classes": 37,
    "image_height": 224,
    "image_width": 224,
--- a/model_zoo/research/cv/resnet50_adv_pruning/src/pet_dataset.py
+++ b/model_zoo/research/cv/resnet50_adv_pruning/src/pet_dataset.py
@ -18,10 +18,11 @@ create train or eval dataset.
 import os
 import mindspore.common.dtype as mstype
 import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.vision.c_transforms as C
-import mindspore.dataset.transforms.vision.py_transforms as P
+import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.vision.py_transforms as P
 import mindspore.dataset.transforms.c_transforms as C2
-from mindspore.dataset.transforms.vision import Inter
+import mindspore.dataset.transforms.py_transforms as P2
+from mindspore.dataset.vision import Inter


 def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=100):
@ -78,13 +79,13 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
    center_crop_p = P.CenterCrop(224)
    totensor = P.ToTensor()
    normalize_p = P.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
-    composeop = P.ComposeOp(
+    composeop = P2.Compose(
        [decode_p, resize_p, center_crop_p, totensor, normalize_p])
    if do_train:
        trans = [resize_crop_op, horizontal_flip_op, color_op,
                 rescale_op, normalize_op, change_swap_op]
    else:
-        trans = composeop()
+        trans = composeop
    type_cast_op = C2.TypeCast(mstype.int32)

    ds = ds.map(input_columns="image", operations=trans,
--- a/model_zoo/research/cv/resnet50_adv_pruning/src/resnet_imgnet.py
+++ b/model_zoo/research/cv/resnet50_adv_pruning/src/resnet_imgnet.py
@ -327,21 +327,21 @@ class ResNet(nn.Cell):
        for _, m in self.cells_and_names():
            if isinstance(m, (nn.Conv2d)):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
-                                                                    m.weight.data.shape).astype("float32")))
+                m.weight.set_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
+                                                          m.weight.data.shape).astype("float32")))
                if m.bias is not None:
-                    m.bias.set_parameter_data(
+                    m.bias.set_data(
                        Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
            elif isinstance(m, nn.BatchNorm2d):
-                m.gamma.set_parameter_data(
+                m.gamma.set_data(
                    Tensor(np.ones(m.gamma.data.shape, dtype="float32")))
-                m.beta.set_parameter_data(
+                m.beta.set_data(
                    Tensor(np.zeros(m.beta.data.shape, dtype="float32")))
            elif isinstance(m, nn.Dense):
-                m.weight.set_parameter_data(Tensor(np.random.normal(
+                m.weight.set_data(Tensor(np.random.normal(
                    0, 0.01, m.weight.data.shape).astype("float32")))
                if m.bias is not None:
-                    m.bias.set_parameter_data(
+                    m.bias.set_data(
                        Tensor(np.zeros(m.bias.data.shape, dtype="float32")))