!7610 add export for mobilenetv2, mobilenetv3, resnext50

Merge pull request !7610 from zhaoting/mobilenet

model_zoo/official/cv/mobilenetv2/README.md

@@ -10,6 +10,7 @@
   - [Script and Sample Code](#script-and-sample-code)
     - [Training Process](#training-process)
     - [Evaluation Process](#eval-process)
+    - [Export MindIR](#export-mindir)
 - [Model Description](#model-description)
   - [Performance](#performance)  
     - [Training Performance](#training-performance)
@@ -62,21 +63,22 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 
 ```python
 ├── MobileNetV2
-  ├── README.md     # descriptions about MobileNetV2
+  ├── README.md              # descriptions about MobileNetV2
   ├── scripts
-  │   ├──run_train.sh   # shell script for train, fine_tune or incremental  learn with CPU, GPU or Ascend
-  │   ├──run_eval.sh    # shell script for evaluation with CPU, GPU or Ascend
+  │   ├──run_train.sh        # shell script for train, fine_tune or incremental  learn with CPU, GPU or Ascend
+  │   ├──run_eval.sh         # shell script for evaluation with CPU, GPU or Ascend
   ├── src
-  │   ├──args.py        # parse args
-  │   ├──config.py      # parameter configuration
-  │   ├──dataset.py     # creating dataset
+  │   ├──args.py             # parse args
+  │   ├──config.py           # parameter configuration
+  │   ├──dataset.py          # creating dataset
   │   ├──lr_generator.py     # learning rate config
   │   ├──mobilenetV2.py      # MobileNetV2 architecture
-  │   ├──models.py      # contain define_net and Loss, Monitor
-  │   ├──utils.py       # utils to load ckpt_file for fine tune or incremental learn
-  ├── train.py      # training script
-  ├── eval.py       # evaluation script
-  ├── mindspore_hub_conf.py       #  mindspore hub interface
+  │   ├──models.py           # contain define_net and Loss, Monitor
+  │   ├──utils.py            # utils to load ckpt_file for fine tune or incremental learn
+  ├── train.py               # training script
+  ├── eval.py                # evaluation script
+  ├── export.py              # export mindir script
+  ├── mindspore_hub_conf.py  #  mindspore hub interface
 ```
 
 ## [Training process](#contents)
@@ -172,6 +174,14 @@ Inference result will be stored in the example path, you can find result like th
 result: {'acc': 0.71976314102564111} ckpt=./ckpt_0/mobilenet-200_625.ckpt
 ```
 
+## [Export MindIR](#contents)
+
+Change the export mode and export file in `src/config.py`, and run `export.py`.
+
+```
+python export.py --platform [PLATFORM] --pretrain_ckpt [CKPT_PATH]
+```
+
 # [Model description](#contents)
 
 ## [Performance](#contents)

model_zoo/official/cv/mobilenetv2/export.py

@@ -0,0 +1,35 @@
+# 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.
+# ============================================================================
+"""
+mobilenetv2 export mindir.
+"""
+import numpy as np
+from mindspore import Tensor
+from mindspore.train.serialization import export
+from src.config import set_config
+from src.args import export_parse_args
+from src.models import define_net, load_ckpt
+from src.utils import set_context
+
+if __name__ == '__main__':
+    args_opt = export_parse_args()
+    cfg = set_config(args_opt)
+    set_context(cfg)
+    _, _, net = define_net(cfg, args_opt.is_training)
+
+    load_ckpt(net, args_opt.pretrain_ckpt)
+    input_shp = [1, 3, cfg.image_height, cfg.image_width]
+    input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
+    export(net, input_array, file_name=cfg.export_file, file_format=cfg.export_format)

model_zoo/official/cv/mobilenetv2/scripts/run_train.sh

@@ -31,14 +31,14 @@ run_ascend()
     BASEPATH=$(cd "`dirname $0`" || exit; pwd)
     export PYTHONPATH=${BASEPATH}:$PYTHONPATH
     export RANK_TABLE_FILE=$4
-    DEVICE_NUM=$2
+    export RANK_SIZE=$2
     if [ -d "../train" ];
     then
         rm -rf ../train
     fi
     mkdir ../train
     cd ../train || exit
-    for((i=0; i<${DEVICE_NUM}; i++))
+    for((i=0; i<${RANK_SIZE}; i++))
     do
         export DEVICE_ID=$i
         export RANK_ID=$i

model_zoo/official/cv/mobilenetv2/src/args.py

@@ -61,4 +61,14 @@ def eval_parse_args():
     eval_args = eval_parser.parse_args()
     eval_args.is_training = False
     return eval_args
-    
+
+def export_parse_args():
+    export_parser = argparse.ArgumentParser(description='Image classification export')
+    export_parser.add_argument('--platform', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"), \
+        help='run platform, only support GPU, CPU and Ascend')
+    export_parser.add_argument('--pretrain_ckpt', type=str, required=True, help='Pretrained checkpoint path \
+        for fine tune or incremental learning')
+    export_args = export_parser.parse_args()
+    export_args.is_training = False
+    export_args.run_distribute = False
+    return export_args

model_zoo/official/cv/mobilenetv2/src/config.py

@@ -19,6 +19,8 @@ import os
 from easydict import EasyDict as ed
 
 def set_config(args):
+    if not args.run_distribute:
+        args.run_distribute = False
     config_cpu = ed({
         "num_classes": 26,
         "image_height": 224,
@@ -38,8 +40,9 @@ def set_config(args):
         "keep_checkpoint_max": 20,
         "save_checkpoint_path": "./",
         "platform": args.platform,
-        "run_distribute": False,
-        "activation": "Softmax"
+        "activation": "Softmax",
+        "export_format": "MINDIR",
+        "export_file": "mobilenetv2.mindir"
     })
     config_gpu = ed({
         "num_classes": 1000,
@@ -62,7 +65,9 @@ def set_config(args):
         "platform": args.platform,
         "ccl": "nccl",
         "run_distribute": args.run_distribute,
-        "activation": "Softmax"
+        "activation": "Softmax",
+        "export_format": "MINDIR",
+        "export_file": "mobilenetv2.mindir"
     })
     config_ascend = ed({
         "num_classes": 1000,
@@ -88,7 +93,9 @@ def set_config(args):
         "rank_id": int(os.getenv('RANK_ID', '0')),
         "rank_size": int(os.getenv('RANK_SIZE', '1')),
         "run_distribute": int(os.getenv('RANK_SIZE', '1')) > 1.,
-        "activation": "Softmax"
+        "activation": "Softmax",
+        "export_format": "MINDIR",
+        "export_file": "mobilenetv2.mindir"
     })
     config = ed({"CPU": config_cpu,
                  "GPU": config_gpu,

model_zoo/official/cv/mobilenetv3/Readme.md

@@ -9,6 +9,7 @@
     - [Training Process](#training-process)
     - [Evaluation Process](#evaluation-process)
         - [Evaluation](#evaluation)
+    - [Export MindIR](#export-mindir)
 - [Model Description](#model-description)
     - [Performance](#performance)  
         - [Training Performance](#evaluation-performance)
@@ -57,18 +58,19 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ```python
 ├── MobileNetV3        
-  ├── Readme.md     # descriptions about MobileNetV3                 
+  ├── Readme.md              # descriptions about MobileNetV3                 
   ├── scripts 
-  │   ├──run_train.sh   # shell script for train               
-  │   ├──run_eval.sh    # shell script for evaluation                
+  │   ├──run_train.sh        # shell script for train               
+  │   ├──run_eval.sh         # shell script for evaluation                
   ├── src                              
-  │   ├──config.py      # parameter configuration               
-  │   ├──dataset.py     # creating dataset
+  │   ├──config.py           # parameter configuration               
+  │   ├──dataset.py          # creating dataset
   │   ├──lr_generator.py     # learning rate config                            
   │   ├──mobilenetV3.py      # MobileNetV3 architecture
-  ├── train.py      # training script
-  ├── eval.py       #  evaluation script
-  ├── mindspore_hub_conf.py       #  mindspore hub interface
+  ├── train.py               # training script
+  ├── eval.py                #  evaluation script
+  ├── export.py              # export mindir script
+  ├── mindspore_hub_conf.py  #  mindspore hub interface
 ```
 
 ## [Training process](#contents)
@@ -129,6 +131,14 @@ Inference result will be stored in the example path, you can find result like th
 result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
 ```
 
+## [Export MindIR](#contents)
+
+Change the export mode and export file in `src/config.py`, and run `export.py`.
+
+```
+python export.py --device_target [PLATFORM] --checkpoint_path [CKPT_PATH]
+```
+
 # [Model description](#contents)
 
 ## [Performance](#contents)

model_zoo/official/cv/mobilenetv3/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.
+# ============================================================================
+"""
+mobilenetv3 export mindir.
+"""
+import argparse
+import numpy as np
+from mindspore import context, Tensor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+from src.config import config_gpu
+from src.mobilenetV3 import mobilenet_v3_large
+
+
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--checkpoint_path', type=str, required=True, help='Checkpoint file path')
+parser.add_argument('--device_target', type=str, default="GPU", help='run device_target')
+args_opt = parser.parse_args()
+
+if __name__ == '__main__':
+    cfg = None
+    if args_opt.device_target == "GPU":
+        cfg = config_gpu
+        context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    else:
+        raise ValueError("Unsupported device_target.")
+
+    net = mobilenet_v3_large(num_classes=cfg.num_classes, activation="Softmax")
+
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
+    load_param_into_net(net, param_dict)
+    input_shp = [1, 3, cfg.image_height, cfg.image_width]
+    input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
+    export(net, input_array, file_name=cfg.export_file, file_format=cfg.export_format)

model_zoo/official/cv/mobilenetv3/src/config.py

@@ -33,4 +33,6 @@ config_gpu = ed({
     "save_checkpoint_epochs": 1,
     "keep_checkpoint_max": 500,
     "save_checkpoint_path": "./checkpoint",
+    "export_format": "MINDIR",
+    "export_file": "mobilenetv3.mindir"
 })

model_zoo/official/cv/resnext50/README.md

@@ -12,6 +12,7 @@
     - [Script Parameters](#script-parameters)
     - [Training Process](#training-process)
     - [Evaluation Process](#evaluation-process)
+    - [Export MindIR](#export-mindir)
 - [Model Description](#model-description)
     - [Performance](#performance)  
         - [Training Performance](#evaluation-performance)
@@ -97,6 +98,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
     ├─warmup_step_lr.py               # warmup step learning rate
   ├─eval.py                           # eval net
   ├──train.py                         # train net
+  ├──export.py                        # export mindir script
   ├──mindspore_hub_conf.py            #  mindspore hub interface
   
 ```
@@ -206,6 +208,14 @@ acc=78.16%(TOP1)
 acc=93.88%(TOP5)
 ```
 
+## [Export MindIR](#contents)
+
+Change the export mode and export file in `src/config.py`, and run `export.py`.
+
+```
+python export.py --platform PLATFORM --pretrained CKPT_PATH
+```
+
 # [Model description](#contents)
 
 ## [Performance](#contents)

model_zoo/official/cv/resnext50/export.py

@@ -0,0 +1,56 @@
+# 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.
+# ============================================================================
+"""
+resnext export mindir.
+"""
+import argparse
+import numpy as np
+from mindspore import context, Tensor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+from src.config import config
+from src.image_classification import get_network
+
+
+def parse_args():
+    """parse_args"""
+    parser = argparse.ArgumentParser('mindspore classification test')
+    parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='run platform')
+
+    parser.add_argument('--pretrained', type=str, required=True, help='fully path of pretrained model to load. '
+                        'If it is a direction, it will test all ckpt')
+
+    args, _ = parser.parse_known_args()
+    args.image_size = config.image_size
+    args.num_classes = config.num_classes
+    args.backbone = config.backbone
+
+    args.image_size = list(map(int, config.image_size.split(',')))
+    args.image_height = args.image_size[0]
+    args.image_width = args.image_size[1]
+    args.export_format = config.export_format
+    args.export_file = config.export_file
+    return args
+
+if __name__ == '__main__':
+    args_export = parse_args()
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_export.platform)
+
+    net = get_network(args_export.backbone, num_classes=args_export.num_classes, platform=args_export.platform)
+
+    param_dict = load_checkpoint(args_export.pretrained)
+    load_param_into_net(net, param_dict)
+    input_shp = [1, 3, args_export.image_height, args_export.image_width]
+    input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
+    export(net, input_array, file_name=args_export.export_file, file_format=args_export.export_format)

model_zoo/official/cv/resnext50/src/config.py

@@ -42,5 +42,7 @@ config = ed({
     "is_save_on_master": 1,
 
     "rank": 0,
-    "group_size": 1
+    "group_size": 1,
+    "export_format": "MINDIR",
+    "export_file": "resnext50.mindir"
 })