!1032 Modify generated ast report file name and fix error msg when requirments are not installed

From: @liuchongming74
Reviewed-by: @lilongfei15,@lilongfei15,@wangyue01
Signed-off-by: @wangyue01

mindinsight/mindconverter/common/exceptions.py

@@ -182,6 +182,7 @@ class BaseConverterError(MindConverterException):
         """Define error code of BaseConverterError."""
         UNKNOWN_ERROR = 0
         UNKNOWN_MODEL = 1
+        PARAM_MISSING = 2
 
     BASE_ERROR_CODE = ConverterErrors.BASE_CONVERTER_FAIL.value
     ERROR_CODE = ErrCode.UNKNOWN_ERROR.value
@@ -193,7 +194,7 @@ class BaseConverterError(MindConverterException):
     @classmethod
     def raise_from(cls):
         """Raise from exceptions below."""
-        except_source = Exception, cls
+        except_source = Exception, UnknownModelError, ParamMissingError, cls
         return except_source
 
 
@@ -209,6 +210,18 @@ class UnknownModelError(BaseConverterError):
         return cls
 
 
+class ParamMissingError(BaseConverterError):
+    """Define cli params missing error."""
+    ERROR_CODE = BaseConverterError.ErrCode.PARAM_MISSING.value
+
+    def __init__(self, msg):
+        super(ParamMissingError, self).__init__(msg=msg)
+
+    @classmethod
+    def raise_from(cls):
+        return cls
+
+
 class GraphInitError(MindConverterException):
     """The graph init fail error."""
 

mindinsight/mindconverter/converter.py

@@ -77,8 +77,7 @@ class Converter:
             raise error
         finally:
             if self._report:
-                dest_report_file = os.path.join(report_dir,
-                                                '_'.join(os.path.basename(infile).split('.')[:-1]) + '_report.txt')
+                dest_report_file = os.path.join(report_dir, f"report_of_{os.path.basename(infile).split('.')[0]}.txt")
                 with os.fdopen(os.open(dest_report_file, self.flags, self.modes), 'a') as file:
                     file.write('\n'.join(self._report))
                 logger.info("Convert report is saved in %s", dest_report_file)
@@ -180,7 +179,7 @@ def _path_split(file):
     """
     file_dir, name = os.path.split(file)
     if file_dir:
-        sep = file[len(file_dir)-1]
+        sep = file[len(file_dir) - 1]
         if file_dir.startswith(sep):
             return file.split(sep)[1:]
 

mindinsight/mindconverter/graph_based_converter/constant.py

@@ -44,7 +44,7 @@ ONNXRUNTIME_MIN_VER = "1.5.2"
 
 BINARY_HEADER_PYTORCH_FILE = \
     b'\x80\x02\x8a\nl\xfc\x9cF\xf9 j\xa8P\x19.\x80\x02M\xe9\x03.\x80\x02}q\x00(X\x10\x00\x00\x00'
-
+TENSORFLOW_MODEL_SUFFIX = "pb"
 BINARY_HEADER_PYTORCH_BITS = 32
 
 ARGUMENT_LENGTH_LIMIT = 512
@@ -82,6 +82,7 @@ class InputType(Enum):
 class FrameworkType(Enum):
     PYTORCH = 0
     TENSORFLOW = 1
+    UNKNOWN = 2
 
 
 def get_imported_module():

mindinsight/mindconverter/graph_based_converter/framework.py

@@ -24,12 +24,11 @@ import mindinsight
 from mindinsight.mindconverter.graph_based_converter.common.utils import lib_version_satisfied, \
     save_code_file_and_report
 from mindinsight.mindconverter.graph_based_converter.constant import BINARY_HEADER_PYTORCH_FILE, FrameworkType, \
-    BINARY_HEADER_PYTORCH_BITS, ONNX_MIN_VER, TF2ONNX_MIN_VER, ONNXRUNTIME_MIN_VER
+    BINARY_HEADER_PYTORCH_BITS, ONNX_MIN_VER, TF2ONNX_MIN_VER, ONNXRUNTIME_MIN_VER, TENSORFLOW_MODEL_SUFFIX
 from mindinsight.mindconverter.graph_based_converter.mapper import ONNXToMindSporeMapper
 from mindinsight.mindconverter.common.log import logger as log, logger_console as log_console
 from mindinsight.mindconverter.common.exceptions import GraphInitError, TreeCreationError, SourceFilesSaveError, \
-    BaseConverterError, UnknownModelError, GeneratorError, TfRuntimeError, RuntimeIntegrityError
-from mindinsight.utils.exceptions import ParamMissError
+    BaseConverterError, UnknownModelError, GeneratorError, TfRuntimeError, RuntimeIntegrityError, ParamMissingError
 
 permissions = os.R_OK | os.W_OK | os.X_OK
 os.umask(permissions << 3 | permissions)
@@ -68,7 +67,7 @@ def torch_installation_validation(func):
         # Check whether pytorch is installed.
         if not find_spec("torch"):
             error = RuntimeIntegrityError("PyTorch is required when using graph based "
-                                          "scripts converter, and PyTorch vision must "
+                                          "scripts converter, and PyTorch version must "
                                           "be consisted with model generation runtime.")
             log.error(error)
             log_console.error("\n")
@@ -242,6 +241,9 @@ def main_graph_base_converter(file_config):
     """
     graph_path = file_config['model_file']
     frame_type = get_framework_type(graph_path)
+    if not file_config.get("shape"):
+        raise ParamMissingError("Param missing, `--shape` is required when using graph mode.")
+
     if frame_type == FrameworkType.PYTORCH.value:
         graph_based_converter_pytorch_to_ms(graph_path=graph_path,
                                             sample_shape=file_config['shape'],
@@ -259,7 +261,6 @@ def main_graph_base_converter(file_config):
     else:
         error_msg = "Get UNSUPPORTED model."
         error = UnknownModelError(error_msg)
-        log.error(str(error))
         raise error
 
 
@@ -269,8 +270,10 @@ def get_framework_type(model_path):
         with open(model_path, 'rb') as f:
             if f.read(BINARY_HEADER_PYTORCH_BITS) == BINARY_HEADER_PYTORCH_FILE:
                 framework_type = FrameworkType.PYTORCH.value
-            else:
+            elif os.path.basename(model_path).split(".")[-1].lower() == TENSORFLOW_MODEL_SUFFIX:
                 framework_type = FrameworkType.TENSORFLOW.value
+            else:
+                framework_type = FrameworkType.UNKNOWN.value
     except IOError:
         error_msg = "Get UNSUPPORTED model."
         error = UnknownModelError(error_msg)
@@ -288,6 +291,4 @@ def check_params_exist(params: list, config):
             miss_param_list = ', '.join((miss_param_list, param)) if miss_param_list else param
 
     if miss_param_list:
-        error = ParamMissError(miss_param_list)
-        log.error(str(error))
-        raise error
+        raise ParamMissingError(f"Param(s) missing, {miss_param_list} is(are) required when using graph mode.")

mindinsight/mindconverter/graph_based_converter/generator/generator.py

@@ -220,9 +220,9 @@ class Generator:
                 path = Scope.path_str_to_list(scope_path)
                 if len(path) < depth_control:
                     continue
-                else:  # depth control within path length
-                    module_num = path[depth_control - 1][0]
-                    repeated_submodules_at_this_depth.add(module_num)
+                # depth control within path length.
+                module_num = path[depth_control - 1][0]
+                repeated_submodules_at_this_depth.add(module_num)
             ret[depth_control] = repeated_submodules_at_this_depth
 
         self._repeated_submodules = ret
@@ -249,9 +249,8 @@ class Generator:
                 compared_value = nd_struct.fragment.actual_args.get(base_parameter)
                 if compared_value == base_value:
                     continue
-                else:
-                    formal_args.add(base_parameter)
-                    break
+                formal_args.add(base_parameter)
+                break
 
         return formal_args
 
@@ -310,8 +309,7 @@ class Generator:
             for module_num in module_nums:
                 if module_num in checked_module:  # module already checked
                     continue
-                else:
-                    checked_module.add(module_num)
+                checked_module.add(module_num)
                 map_filtered = self.module_map_filter(module_num=module_num)
                 formal_args_in_this_module = self._list_formal_parameters_in_a_module(map_filtered)
                 formal_args_in_each_submodule[module_num] = formal_args_in_this_module

mindinsight/mindconverter/graph_based_converter/generator/module_struct.py

@@ -679,12 +679,12 @@ class ModuleStruct:
             if submodule_precursor in self.onnx_names:  # if internal, match with local nodes/submodules return
                 # but do nothing here
                 continue
-            else:  # if external, match with current module construct header x
-                if submodule_precursor in self.construct_header_x.values():
-                    local_x = get_dict_key_by_value(submodule_precursor, self.construct_header_x)
-                    md_struct.set_inputs_in_construct_header(local_x, submodule_precursor)
-                else:  # Extra precursor nodes, raise error
-                    raise ValueError("Found external inputs of the submodule but the module does not have it.")
+            # if external, match with current module construct header x
+            if submodule_precursor in self.construct_header_x.values():
+                local_x = get_dict_key_by_value(submodule_precursor, self.construct_header_x)
+                md_struct.set_inputs_in_construct_header(local_x, submodule_precursor)
+            else:  # Extra precursor nodes, raise error
+                raise ValueError("Found external inputs of the submodule but the module does not have it.")
 
     def register_node_output_to_module(self, nd_struct):
         """Register nodes outputs to this module's return."""