add yolov3_resnet18&yolov3_darknet53&yolov3_darknet53_quant&deepfm hub conf files

model_zoo/official/cv/yolov3_darknet53/README.md

@@ -112,6 +112,7 @@ sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
 .
 └─yolov3_darknet53      
   ├─README.md
+  ├─mindspore_hub_conf.md             # config for mindspore hub
   ├─scripts      
     ├─run_standalone_train.sh         # launch standalone training(1p) in ascend
     ├─run_distribute_train.sh         # launch distributed training(8p) in ascend
@@ -303,7 +304,7 @@ The above python command will run in the background. You can view the results th
 ### Evaluation Performance 
 
 | Parameters                 | YOLO                                                        |YOLO                                                         |
-| -------------------------- | ----------------------------------------------------------- |----------------------------------------------------------- |
+| -------------------------- | ----------------------------------------------------------- |------------------------------------------------------------ |
 | Model Version              | YOLOv3                                                      |YOLOv3                                                       |
 | Resource                   | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G             | NV SMX2 V100-16G; CPU 2.10GHz, 96cores; Memory, 251G        |
 | uploaded Date              | 06/31/2020 (month/day/year)                                 | 09/02/2020 (month/day/year)                                 |
@@ -315,7 +316,7 @@ The above python command will run in the background. You can view the results th
 | outputs                    | boxes and label                                             | boxes and label                                             |
 | Loss                       | 34                                                          | 34                                                          |
 | Speed                      | 1pc: 350 ms/step;                                           | 1pc: 600 ms/step;                                           |
-| Total time                 | 8pc: 25 hours                                               | 8pc: 18 hours(shape=416)                                               |
+| Total time                 | 8pc: 18.5 hours                                             | 8pc: 18 hours(shape=416)                                    |
 | Parameters (M)             | 62.1                                                        | 62.1                                                        |
 | Checkpoint for Fine tuning | 474M (.ckpt file)                                           | 474M (.ckpt file)                                           |
 | Scripts                    | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53 | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53 |

model_zoo/official/cv/yolov3_darknet53/mindspore_hub_conf.py

@@ -0,0 +1,22 @@
+# 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.yolo import YOLOV3DarkNet53
+
+def create_network(name, *args, **kwargs):
+    if name == "yolov3_darknet53":
+        yolov3_darknet53_net = YOLOV3DarkNet53(is_training=False)
+        return yolov3_darknet53_net
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/cv/yolov3_darknet53_quant/README.md

@@ -111,6 +111,7 @@ sh run_eval.sh dataset/coco2014/ checkpoint/yolov3_quant.ckpt 0
 .
 └─yolov3_darknet53_quant      
   ├─README.md
+  ├─mindspore_hub_conf.md             # config for mindspore hub
   ├─scripts      
     ├─run_standalone_train.sh         # launch standalone training(1p) in ascend
     ├─run_distribute_train.sh         # launch distributed training(8p) in ascend
@@ -284,7 +285,7 @@ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.558
 | outputs                    | boxes and label                                                                                |
 | Loss                       | 34                                                                                             |
 | Speed                      | 1pc: 135 ms/step;                                                                              |
-| Total time                 | 8pc: 24.5 hours                                                                                |
+| Total time                 | 8pc: 23.5 hours                                                                                |
 | Parameters (M)             | 62.1                                                                                           |
 | Checkpoint for Fine tuning | 474M (.ckpt file)                                                                              |
 | Scripts                    | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53_quant |

model_zoo/official/cv/yolov3_darknet53_quant/mindspore_hub_conf.py

@@ -0,0 +1,32 @@
+# 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 mindspore.train.quant import quant
+from src.yolo import YOLOV3DarkNet53
+from src.config import ConfigYOLOV3DarkNet53
+
+
+def create_network(name, *args, **kwargs):
+    if name == "yolov3_darknet53_quant":
+        yolov3_darknet53_quant = YOLOV3DarkNet53(is_training=False)
+        config = ConfigYOLOV3DarkNet53()
+        # convert fusion network to quantization aware network
+        if config.quantization_aware:
+            yolov3_darknet53_quant = quant.convert_quant_network(yolov3_darknet53_quant,
+                                                                 bn_fold=True,
+                                                                 per_channel=[True, False],
+                                                                 symmetric=[True, False])
+        return yolov3_darknet53_quant
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/cv/yolov3_resnet18/README.md

@@ -98,6 +98,7 @@ After installing MindSpore via the official website, you can start training and
 ```
 └── cv
     ├── README.md                           // descriptions about all the models
+    ├── mindspore_hub_conf.md               // config for mindspore hub
     └── yolov3_resnet18        
         ├── README.md                       // descriptions about yolov3_resnet18
         ├── scripts 

model_zoo/official/cv/yolov3_resnet18/mindspore_hub_conf.py

@@ -0,0 +1,23 @@
+# 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.yolov3 import yolov3_resnet18
+from src.config import ConfigYOLOV3ResNet18
+
+def create_network(name, *args, **kwargs):
+    if name == "yolov3_resnet18":
+        yolov3_resnet18_net = yolov3_resnet18(ConfigYOLOV3ResNet18())
+        return yolov3_resnet18_net
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/recommend/deepfm/README.md

@@ -117,6 +117,7 @@ After installing MindSpore via the official website, you can start training and
 .
 └─deepfm      
   ├─README.md
+  ├─mindspore_hub_conf.md             # config for mindspore hub
   ├─scripts      
     ├─run_standalone_train.sh         # launch standalone training(1p) in Ascend or GPU
     ├─run_distribute_train.sh         # launch distributed training(8p) in Ascend

model_zoo/official/recommend/deepfm/mindspore_hub_conf.py

@@ -0,0 +1,26 @@
+# 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.deepfm import ModelBuilder
+from src.config import ModelConfig, TrainConfig
+
+def create_network(name, *args, **kwargs):
+    if name == 'deepfm':
+        model_config = ModelConfig()
+        train_config = TrainConfig()
+        model_builder = ModelBuilder(model_config, train_config)
+        _, deepfm_eval_net = model_builder.get_train_eval_net()
+        return deepfm_eval_net
+    raise NotImplementedError(f"{name} is not implemented in the repo")