!4258 change binary to mindir

Merge pull request !4258 from leopz/fix_doc

mindspore/ccsrc/pipeline/jit/pipeline.cc

@@ -68,7 +68,7 @@ using mindspore::abstract::AbstractTuplePtr;
 
 const char IR_TYPE_ANF[] = "anf_ir";
 const char IR_TYPE_ONNX[] = "onnx_ir";
-const char IR_TYPE_BINARY[] = "binary_ir";
+const char IR_TYPE_MINDIR[] = "mind_ir";
 
 ExecutorPyPtr ExecutorPy::executor_ = nullptr;
 std::mutex ExecutorPy::instance_lock_;
@@ -222,7 +222,7 @@ py::bytes ExecutorPy::GetFuncGraphProto(const std::string &phase, const std::str
     return proto_str;
   }
 
-  if (ir_type == IR_TYPE_BINARY) {
+  if (ir_type == IR_TYPE_MINDIR) {
     std::string proto_str = GetBinaryProtoString(fg_ptr);
     if (proto_str.empty()) {
       MS_LOG(EXCEPTION) << "Graph proto is empty.";

mindspore/train/quant/quant.py

@@ -445,15 +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 'BINARY' format for exported
+        file_format (str): MindSpore currently supports 'GEIR', 'ONNX' and 'MINDIR' format for exported
             quantization aware model. Default: 'GEIR'.
 
             - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
               Ascend model.
-            - BINARY: Binary format for model. An intermidiate representation format for models.
+            - 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', 'BINARY']
+    supported_formats = ['GEIR', 'MINDIR']
 
     mean = validator.check_type("mean", mean, (int, float))
     std_dev = validator.check_type("std_dev", std_dev, (int, float))

mindspore/train/serialization.py

@@ -453,17 +453,19 @@ 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 'BINARY' format for exported model.
+        file_format (str): MindSpore currently supports 'GEIR', 'ONNX' and 'MINDIR' format for exported model.
 
             - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
               Ascend model.
             - ONNX: Open Neural Network eXchange. An open format built to represent machine learning models.
-            - BINARY: Binary format for model. An intermidiate representation format for models.
+            - MINDIR: MindSpore Native Intermidiate Representation for Anf. An intermidiate representation format
+              for MindSpore models.
+              Recommended suffix for output file is '.mindir'.
     """
     logger.info("exporting model file:%s format:%s.", file_name, file_format)
     check_input_data(*inputs, data_class=Tensor)
 
-    supported_formats = ['GEIR', 'ONNX', 'BINARY']
+    supported_formats = ['GEIR', '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
@@ -485,10 +487,10 @@ def export(net, *inputs, file_name, file_format='GEIR'):
         with open(file_name, 'wb') as f:
             os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
             f.write(onnx_stream)
-    elif file_format == 'BINARY':  # file_format is 'BINARY'
+    elif file_format == 'MINDIR':  # file_format is 'MINDIR'
-        phase_name = 'export.binary'
+        phase_name = 'export.mindir'
         graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
-        onnx_stream = _executor._get_func_graph_proto(graph_id, 'binary_ir')
+        onnx_stream = _executor._get_func_graph_proto(graph_id, 'mind_ir')
         with open(file_name, 'wb') as f:
             os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
             f.write(onnx_stream)

serving/example/export_model/add_model.py

@@ -36,7 +36,7 @@ y = np.ones(4).astype(np.float32)
 def export_net():
     add = Net()
     output = add(Tensor(x), Tensor(y))
-    export(add, Tensor(x), Tensor(y), file_name='tensor_add.pb', file_format='BINARY')
+    export(add, Tensor(x), Tensor(y), file_name='tensor_add.pb', file_format='MINDIR')
     print(x)
     print(y)
     print(output.asnumpy())

tests/st/serving/generate_model.py

@@ -62,14 +62,14 @@ def export_add_model():
     net = AddNet()
     x = np.ones(4).astype(np.float32)
     y = np.ones(4).astype(np.float32)
-    export(net, Tensor(x), Tensor(y), file_name='add.pb', file_format='BINARY')
+    export(net, Tensor(x), Tensor(y), file_name='add.pb', file_format='MINDIR')
 
 def export_bert_model():
     net = BertModel(bert_net_cfg, False)
     input_ids = np.random.randint(0, 1000, size=(2, 32), dtype=np.int32)
     segment_ids = np.zeros((2, 32), dtype=np.int32)
     input_mask = np.zeros((2, 32), dtype=np.int32)
-    export(net, Tensor(input_ids), Tensor(segment_ids), Tensor(input_mask), file_name='bert.pb', file_format='BINARY')
+    export(net, Tensor(input_ids), Tensor(segment_ids), Tensor(input_mask), file_name='bert.pb', file_format='MINDIR')
 
 if __name__ == '__main__':
     export_add_model()

tests/ut/python/utils/test_serialize.py

@@ -322,10 +322,10 @@ def test_export():
 
 
 @non_graph_engine
-def test_binary_export():
+def test_mindir_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_binary_export.pb", file_format="BINARY")
+    export(net, input_data, file_name="./me_binary_export.mindir", file_format="MINDIR")
 
 
 class PrintNet(nn.Cell):