Synchronize r1.0 with master to deeplabv3.

model_zoo/official/cv/deeplabv3/README.md

@@ -15,6 +15,7 @@
 - [Model Description](#model-description)
     - [Performance](#performance)
         - [Evaluation Performance](#evaluation-performance)
+- [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)
 
 
@@ -141,7 +142,7 @@ run_eval_s8_multiscale_flip.sh
     ├── run_standalone_train.sh                   # launch ascend standalone training(1 pc)
   ├── src
     ├── data                          
-        ├── data_generator.py                     # mindrecord data generator
+        ├── dataset.py                            # mindrecord data generator
         ├── build_seg_data.py                     # data preprocessing
     ├── loss
        ├── loss.py                                # loss definition for deeplabv3        
@@ -412,10 +413,9 @@ Note: There OS is output stride, and MS is multiscale.
 | Checkpoint for Fine tuning | 443M (.ckpt file)                                         |
 | Scripts                    | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeplabv3) | 
 
+# [Description of Random Situation](#contents)
+In dataset.py, we set the seed inside "create_dataset" function. We also use random seed in train.py. 
+
 # [ModelZoo Homepage](#contents)
  Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
-
-
-
-
-
+ 

model_zoo/official/cv/deeplabv3/export.py

@@ -0,0 +1,45 @@
+# Copyright 2020 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.
+# ============================================================================
+"""export AIR file."""
+import argparse
+import numpy as np
+
+from mindspore import Tensor
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.serialization import export
+
+from src.nets import net_factory
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='checkpoint export')
+    parser.add_argument('--checkpoint', type=str.lower, default='', help='checkpoint of deeplabv3 (Default: None)')
+    parser.add_argument('--model', type=str.lower, default='deeplab_v3_s8', choices=['deeplab_v3_s16', 'deeplab_v3_s8'],
+                        help='Select model structure (Default: deeplab_v3_s8)')
+    parser.add_argument('--num_classes', type=int, default=21, help='the number of classes (Default: 21)')
+    args = parser.parse_args()
+
+    if args.model == 'deeplab_v3_s16':
+        network = net_factory.nets_map['deeplab_v3_s16']('eval', args.num_classes, 16, True)
+    else:
+        network = net_factory.nets_map['deeplab_v3_s8']('eval', args.num_classes, 8, True)
+    param_dict = load_checkpoint(args.checkpoint)
+
+    # load the parameter into net
+    load_param_into_net(network, param_dict)
+    input_data = np.random.uniform(0.0, 1.0, size=[32, 3, 513, 513]).astype(np.float32)
+    export(network, Tensor(input_data), file_name=args.model+'-300_11.air', file_format='AIR')

model_zoo/official/cv/deeplabv3/mindspore_hub_conf.py

@@ -0,0 +1,27 @@
+# Copyright 2020 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.
+# ============================================================================
+"""hub config."""
+from src.nets import net_factory
+
+def create_network(name, *args, **kwargs):
+    freeze_bn = True
+    num_classes = kwargs["num_classes"]
+    if name == 'deeplab_v3_s16':
+        deeplab_v3_s16_network = net_factory.nets_map["deeplab_v3_s16"]('eval', num_classes, 16, freeze_bn)
+        return deeplab_v3_s16_network
+    if name == 'deeplab_v3_s8':
+        deeplab_v3_s8_network = net_factory.nets_map["deeplab_v3_s8"]('eval', num_classes, 8, freeze_bn)
+        return deeplab_v3_s8_network
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/cv/deeplabv3/train.py

@@ -24,10 +24,13 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.train.callback import LossMonitor, TimeMonitor
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
-from src.data import data_generator
+from mindspore.common import set_seed
+from src.data import dataset as data_generator
 from src.loss import loss
 from src.nets import net_factory
 from src.utils import learning_rates
+
+set_seed(1)
 context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, save_graphs=False,
                     device_target="Ascend", device_id=int(os.getenv('DEVICE_ID')))