Change export interface

mindspore/ccsrc/pipeline/jit/pipeline.cc

@@ -396,13 +396,13 @@ void ExecutorPy::GetGeBackendPolicy() const {
   }
 }
 
-bool IsPhaseExportGeir(const std::string &phase_s) {
-  auto phase_to_export = "export.geir";
+bool IsPhaseExportAir(const std::string &phase_s) {
+  auto phase_to_export = "export.air";
   return phase_s.rfind(phase_to_export) != std::string::npos;
 }
 
 std::vector<ActionItem> GetPipline(const ResourcePtr &resource, const std::string &phase_s, bool use_vm) {
-  bool is_geir = IsPhaseExportGeir(phase_s);
+  bool is_air = IsPhaseExportAir(phase_s);
 
   std::string backend = MsContext::GetInstance()->backend_policy();
 
@@ -419,7 +419,7 @@ std::vector<ActionItem> GetPipline(const ResourcePtr &resource, const std::strin
   }
 #endif
 
-  if (use_vm && backend != "ge" && !is_geir) {
+  if (use_vm && backend != "ge" && !is_air) {
     // Create backend and session
     auto backend_ptr = compile::CreateBackend();
     // Connect session to debugger
@@ -938,8 +938,9 @@ void FinalizeHccl() {
 void ExportGraph(const std::string &file_name, const std::string &, const std::string &phase) {
 #if (ENABLE_GE || ENABLE_D)
   ExportDFGraph(file_name, phase);
+#else
+  MS_EXCEPTION(ValueError) << "Only MindSpore with Ascend backend support exporting file in 'AIR' format.";
 #endif
-  MS_LOG(WARNING) << "In ut test no export_graph";
 }
 
 void ReleaseGeTsd() {

mindspore/common/api.py

@@ -515,7 +515,7 @@ class _Executor:
             graph_id (str): id of graph to be exported
         """
         from .._c_expression import export_graph
-        export_graph(file_name, 'GEIR', graph_id)
+        export_graph(file_name, 'AIR', graph_id)
 
     def fetch_info_for_quant_export(self, exec_id):
         """Get graph proto from pipeline."""

mindspore/train/quant/quant.py

@@ -435,9 +435,9 @@ class ExportToQuantInferNetwork:
         return network
 
 
-def export(network, *inputs, file_name, mean=127.5, std_dev=127.5, file_format='GEIR'):
+def export(network, *inputs, file_name, mean=127.5, std_dev=127.5, file_format='AIR'):
     """
-    Exports MindSpore quantization predict model to deploy with GEIR.
+    Exports MindSpore quantization predict model to deploy with AIR.
 
     Args:
         network (Cell): MindSpore network produced by `convert_quant_network`.
@@ -445,17 +445,17 @@ def export(network, *inputs, file_name, mean=127.5, std_dev=127.5, file_format='
         file_name (str): File name of model to export.
         mean (int): Input data mean. Default: 127.5.
         std_dev (int, float): Input data variance. Default: 127.5.
-        file_format (str): MindSpore currently supports 'GEIR', 'ONNX' and 'MINDIR' format for exported
-            quantization aware model. Default: 'GEIR'.
+        file_format (str): MindSpore currently supports 'AIR', 'ONNX' and 'MINDIR' format for exported
+            quantization aware model. Default: 'AIR'.
 
-            - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
+            - AIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
               Ascend model.
             - MINDIR: MindSpore Native Intermidiate Representation for Anf. An intermidiate representation format
               for MindSpore models.
               Recommended suffix for output file is '.mindir'.
     """
     supported_device = ["Ascend", "GPU"]
-    supported_formats = ['GEIR', 'MINDIR']
+    supported_formats = ['AIR', 'MINDIR']
 
     mean = validator.check_type("mean", mean, (int, float))
     std_dev = validator.check_type("std_dev", std_dev, (int, float))

mindspore/train/serialization.py

@@ -445,7 +445,7 @@ def _fill_param_into_net(net, parameter_list):
     load_param_into_net(net, parameter_dict)
 
 
-def export(net, *inputs, file_name, file_format='GEIR'):
+def export(net, *inputs, file_name, file_format='AIR'):
     """
     Exports MindSpore predict model to file in specified format.
 
@@ -453,11 +453,12 @@ def export(net, *inputs, file_name, file_format='GEIR'):
         net (Cell): MindSpore network.
         inputs (Tensor): Inputs of the `net`.
         file_name (str): File name of model to export.
-        file_format (str): MindSpore currently supports 'GEIR', 'ONNX' and 'MINDIR' format for exported model.
+        file_format (str): MindSpore currently supports 'AIR', 'ONNX' and 'MINDIR' format for exported model.
 
-            - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
-              Ascend model.
+            - AIR: Ascend Intermidiate Representation. An intermidiate representation format of Ascend model.
+              Recommended suffix for output file is '.air'.
             - ONNX: Open Neural Network eXchange. An open format built to represent machine learning models.
+              Recommended suffix for output file is '.onnx'.
             - MINDIR: MindSpore Native Intermidiate Representation for Anf. An intermidiate representation format
               for MindSpore models.
               Recommended suffix for output file is '.mindir'.
@@ -465,7 +466,11 @@ def export(net, *inputs, file_name, file_format='GEIR'):
     logger.info("exporting model file:%s format:%s.", file_name, file_format)
     check_input_data(*inputs, data_class=Tensor)
 
-    supported_formats = ['GEIR', 'ONNX', 'MINDIR']
+    if file_format == 'GEIR':
+        logger.warning(f"Format 'GEIR' is deprecated, it would be removed in future release, use 'AIR' instead.")
+        file_format = 'AIR'
+
+    supported_formats = ['AIR', 'ONNX', 'MINDIR']
     if file_format not in supported_formats:
         raise ValueError(f'Illegal file format {file_format}, it must be one of {supported_formats}')
     # switch network mode to infer when it is training
@@ -474,13 +479,11 @@ def export(net, *inputs, file_name, file_format='GEIR'):
         net.set_train(mode=False)
     # export model
     net.init_parameters_data()
-    if file_format == 'GEIR':
-        phase_name = 'export.geir'
+    if file_format == 'AIR':
+        phase_name = 'export.air'
         graph_id, _ = _executor.compile(net, *inputs, phase=phase_name)
         _executor.export(file_name, graph_id)
     elif file_format == 'ONNX':  # file_format is 'ONNX'
-        # NOTICE: the pahse name `export_onnx` is used for judging whether is exporting onnx in the compile pipeline,
-        #         do not change it to other values.
         phase_name = 'export.onnx'
         graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
         onnx_stream = _executor._get_func_graph_proto(graph_id)

model_zoo/official/cv/googlenet/README.md

@@ -108,7 +108,7 @@ python eval.py > eval.log 2>&1 &  OR  sh run_eval.sh
         │   ├──config.py            // parameter configuration 
         ├── train.py               // training script 
         ├── eval.py               //  evaluation script 
-        ├── export.py            // export checkpoint files into geir/onnx 
+        ├── export.py            // export checkpoint files into air/onnx 
 ```
 
 ## [Script Parameters](#contents)
@@ -133,7 +133,7 @@ Major parameters in train.py and config.py are:
 --checkpoint_path: The absolute full path to the checkpoint file saved
                    after training.
 --onnx_filename: File name of the onnx model used in export.py. 
---geir_filename: File name of the geir model used in export.py.    
+--air_filename: File name of the air model used in export.py.    
 ```
 
 
@@ -226,7 +226,7 @@ accuracy: {'acc': 0.9217}
 | Total time                 | 1pc: 63.85 mins;  8pcs: 11.28 mins                          |
 | Parameters (M)             | 13.0                                                         |
 | Checkpoint for Fine tuning | 43.07M (.ckpt file)                                         |
-| Model for inference        | 21.50M (.onnx file),  21.60M(.geir file)                    |
+| Model for inference        | 21.50M (.onnx file),  21.60M(.air file)                    |
 | Scripts                    | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet |
 
 

model_zoo/official/cv/googlenet/export.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """
-##############export checkpoint file into geir and onnx models#################
+##############export checkpoint file into air and onnx models#################
 python export.py
 """
 import numpy as np
@@ -33,4 +33,4 @@ if __name__ == '__main__':
 
     input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, 224, 224]), ms.float32)
     export(net, input_arr, file_name=cfg.onnx_filename, file_format="ONNX")
-    export(net, input_arr, file_name=cfg.geir_filename, file_format="GEIR")
+    export(net, input_arr, file_name=cfg.air_filename, file_format="AIR")

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

@@ -34,5 +34,5 @@ cifar_cfg = edict({
     'keep_checkpoint_max': 10,
     'checkpoint_path': './train_googlenet_cifar10-125_390.ckpt',
     'onnx_filename': 'googlenet.onnx',
-    'geir_filename': 'googlenet.geir'
+    'air_filename': 'googlenet.air'
 })

model_zoo/official/cv/inceptionv3/export.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """
-##############export checkpoint file into geir and onnx models#################
+##############export checkpoint file into air and onnx models#################
 """
 import argparse
 import numpy as np
@@ -37,4 +37,4 @@ if __name__ == '__main__':
 
     input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, 299, 299]), ms.float32)
     export(net, input_arr, file_name=cfg.onnx_filename, file_format="ONNX")
-    export(net, input_arr, file_name=cfg.geir_filename, file_format="GEIR")
+    export(net, input_arr, file_name=cfg.air_filename, file_format="AIR")

model_zoo/official/cv/lenet_quant/export.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """
-export quantization aware training network to infer `GEIR` backend.
+export quantization aware training network to infer `AIR` backend.
 """
 
 import argparse
@@ -53,4 +53,4 @@ if __name__ == "__main__":
 
     # export network
     inputs = Tensor(np.ones([1, 1, cfg.image_height, cfg.image_width]), mindspore.float32)
-    quant.export(network, inputs, file_name="lenet_quant", file_format='GEIR')
+    quant.export(network, inputs, file_name="lenet_quant", file_format='AIR')

model_zoo/official/cv/mobilenetv2_quant/export.py

@@ -50,5 +50,5 @@ if __name__ == '__main__':
     # export network
     print("============== Starting export ==============")
     inputs = Tensor(np.ones([1, 3, cfg.image_height, cfg.image_width]), mindspore.float32)
-    quant.export(network, inputs, file_name="mobilenet_quant", file_format='GEIR')
+    quant.export(network, inputs, file_name="mobilenet_quant", file_format='AIR')
     print("============== End export ==============")

tests/st/tbe_networks/export_geir.py

@@ -24,4 +24,4 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 def test_resnet50_export(batch_size=1, num_classes=5):
     input_np = np.random.uniform(0.0, 1.0, size=[batch_size, 3, 224, 224]).astype(np.float32)
     net = resnet50(batch_size, num_classes)
-    export(net, Tensor(input_np), file_name="./me_resnet50.pb", file_format="GEIR")
+    export(net, Tensor(input_np), file_name="./me_resnet50.pb", file_format="AIR")

tests/ut/python/utils/test_serialize.py

@@ -87,8 +87,12 @@ def test_save_graph():
     x = Tensor(np.random.rand(2, 1, 2, 3).astype(np.float32))
     y = Tensor(np.array([1.2]).astype(np.float32))
     out_put = net(x, y)
-    _save_graph(network=net, file_name="net-graph.meta")
+    output_file = "net-graph.meta"
+    _save_graph(network=net, file_name=output_file)
     out_me_list.append(out_put)
+    assert os.path.exists(output_file)
+    os.chmod(output_file, stat.S_IWRITE)
+    os.remove(output_file)
 
 
 def test_save_checkpoint():
@@ -318,7 +322,8 @@ class MYNET(nn.Cell):
 def test_export():
     net = MYNET()
     input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32))
-    export(net, input_data, file_name="./me_export.pb", file_format="GEIR")
+    with pytest.raises(ValueError):
+        export(net, input_data, file_name="./me_export.pb", file_format="AIR")
 
 
 @non_graph_engine