amend yolov4 filter

model_zoo/official/cv/deeplabv3/README.md

@@ -94,6 +94,16 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 
 After installing MindSpore via the official website, you can start training and evaluation as follows:
 
+- Prepare backbone
+
+Download resnet101 for here(https://download.pytorch.org/models/resnet101-5d3b4d8f.pth).
+
+Use convert_resnet101.py to convert as backbone.
+
+```shell
+python convert_resnet101.py
+```
+
 - Running on Ascend
 
 Based on original DeepLabV3 paper, we reproduce two training experiments on vocaug (also as trainaug) dataset and evaluate on voc val dataset.
@@ -416,6 +426,7 @@ run_standalone_train.sh
        ├── get_multicards_json.py                 # get rank table file
     └── utils
        └── learning_rates.py                      # generate learning rate
+  ├── convert_resnet101.py                        # convert resnet101 as backbone
   ├── eval.py                                     # eval net
   ├── train.py                                    # train net
   └── requirements.txt                            # requirements file

model_zoo/official/cv/deeplabv3/README_CN.md

@@ -55,6 +55,16 @@ DeepLab是一系列图像语义分割模型，DeepLabV3版本相比以前的版
 
 Pascal VOC数据集和语义边界数据集（Semantic Boundaries Dataset，SBD）
 
+- 准备Backbone模型
+
+准备resnet101模型，点此下载(https://download.pytorch.org/models/resnet101-5d3b4d8f.pth).
+
+使用convert_resnet101.py脚本转换Backbone模型.
+
+```shell
+python convert_resnet101.py
+```
+
 - 下载分段数据集。
 
 - 准备训练数据清单文件。清单文件用于保存图片和标注对的相对路径。如下：
@@ -431,6 +441,7 @@ run_standalone_train.sh
        ├── get_multicards_json.py                 # 获取rank table文件
     └── utils
        └── learning_rates.py                      # 生成学习率
+  ├── convert_resnet101.py                        # 转换resnet101模型
   ├── eval.py                                     # 评估网络
   ├── train.py                                    # 训练网络
   └── requirements.txt                            # requirements文件

model_zoo/official/cv/deeplabv3/convert_resnet101.py

@@ -0,0 +1,39 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed 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.
+# ============================================================================
+"""convert backbone resnet101"""
+import torch
+from mindspore import Tensor
+from mindspore.train.serialization import save_checkpoint
+
+
+def torch2ms():
+    pretrained_dict = torch.load('./resnet101-5d3b4d8f.pth')
+    new_params = []
+
+    for key, value in pretrained_dict.items():
+        if not key.__contains__('fc'):
+            if key.__contains__('bn'):
+                key = key.replace('running_mean', 'moving_mean')
+                key = key.replace('running_var', 'moving_variance')
+                key = key.replace('weight', 'gamma')
+                key = key.replace('bias', 'beta')
+            param_dict = {'name': key, 'data': Tensor(value.detach().numpy())}
+            new_params.append(param_dict)
+    save_checkpoint(new_params, './resnet101-5d3b4d8f.ckpt')
+    print("Convert resnet-101 completed!")
+
+
+if __name__ == '__main__':
+    torch2ms()

model_zoo/official/cv/yolov4/src/initializer.py

@@ -207,7 +207,9 @@ def load_yolov4_params(args, network):
         if args.pretrained_checkpoint:
             param_dict = load_checkpoint(args.pretrained_checkpoint)
             for key in list(param_dict.keys()):
-                if key in args.checkpoint_filter_list:
+                for filter_key in args.checkpoint_filter_list:
+                    if filter_key not in key:
+                        continue
                     args.logger.info('filter {}'.format(key))
                     del param_dict[key]
             load_param_into_net(network, param_dict)